From 48c463680b51fa767b4cd7bd62865f192d0354ac Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 6 Feb 2021 18:30:32 +0100 Subject: Reintroduce interface to saemix Also after the upgrade from buster to bullseye of my local system, some test results for saemix have changed. --- .travis.yml | 2 - DESCRIPTION | 5 +- NAMESPACE | 8 + NEWS.md | 10 + R/endpoints.R | 8 +- R/plot.mixed.mmkin.R | 23 +- R/saem.R | 512 +++ R/summary.saem.mmkin.R | 268 ++ _pkgdown.yml | 7 +- build.log | 2 +- check.log | 15 +- docs/dev/404.html | 2 +- docs/dev/articles/index.html | 2 +- docs/dev/authors.html | 8 +- docs/dev/index.html | 4 +- docs/dev/news/index.html | 211 +- docs/dev/pkgdown.yml | 4 +- docs/dev/reference/Rplot001.png | Bin 19395 -> 1011 bytes docs/dev/reference/Rplot003.png | Bin 28735 -> 28745 bytes docs/dev/reference/Rplot005.png | Bin 57095 -> 59143 bytes docs/dev/reference/Rplot006.png | Bin 24624 -> 22103 bytes docs/dev/reference/confint.mkinfit.html | 127 +- docs/dev/reference/endpoints.html | 4 +- docs/dev/reference/index.html | 10 +- docs/dev/reference/logLik.mkinfit.html | 12 +- docs/dev/reference/mkinresplot.html | 6 +- docs/dev/reference/mmkin-1.png | Bin 114048 -> 110459 bytes docs/dev/reference/mmkin-2.png | Bin 110392 -> 107057 bytes docs/dev/reference/mmkin-3.png | Bin 97556 -> 96062 bytes docs/dev/reference/mmkin-4.png | Bin 70005 -> 67191 bytes docs/dev/reference/mmkin-5.png | Bin 66093 -> 64880 bytes docs/dev/reference/mmkin.html | 31 +- docs/dev/reference/nlme.mmkin-1.png | Bin 119655 -> 124677 bytes docs/dev/reference/nlme.mmkin-2.png | Bin 159253 -> 169523 bytes docs/dev/reference/nlme.mmkin-3.png | Bin 161800 -> 172692 bytes docs/dev/reference/nlme.mmkin.html | 76 +- docs/dev/reference/plot.mixed.mmkin-1.png | Bin 86076 -> 84734 bytes docs/dev/reference/plot.mixed.mmkin-2.png | Bin 164488 -> 173916 bytes docs/dev/reference/plot.mixed.mmkin-3.png | Bin 163536 -> 172396 bytes docs/dev/reference/plot.mixed.mmkin-4.png | Bin 166687 -> 175502 bytes docs/dev/reference/plot.mixed.mmkin.html | 8 +- docs/dev/reference/saem-3.png | Bin 82102 -> 82107 bytes docs/dev/reference/saem-5.png | Bin 164443 -> 173288 bytes docs/dev/reference/saem.html | 56 +- docs/dev/reference/summary.saem.mmkin.html | 426 +- docs/dev/reference/transform_odeparms.html | 80 +- docs/dev/sitemap.xml | 3 - man/endpoints.Rd | 8 +- man/plot.mixed.mmkin.Rd | 11 +- man/saem.Rd | 155 + man/summary.saem.mmkin.Rd | 100 + test.log | 27 +- ...t-for-saem-object-with-mkin-transformations.svg | 4782 ++++++++++---------- ...for-saem-object-with-saemix-transformations.svg | 1234 ++--- tests/testthat/setup_script.R | 18 + tests/testthat/summary_saem_biphasic_s.txt | 77 + tests/testthat/test_mixed.R | 135 +- tests/testthat/test_plot.R | 11 + 58 files changed, 4938 insertions(+), 3550 deletions(-) create mode 100644 R/saem.R create mode 100644 R/summary.saem.mmkin.R create mode 100644 man/saem.Rd create mode 100644 man/summary.saem.mmkin.Rd create mode 100644 tests/testthat/summary_saem_biphasic_s.txt diff --git a/.travis.yml b/.travis.yml index 282f1302..6c03b451 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,8 +11,6 @@ addons: cache: packages repos: CRAN: https://cloud.r-project.org -r_github_packages: - - saemixdevelopment/saemixextension@master script: - R CMD build . - R CMD check --no-tests mkin_*.tar.gz diff --git a/DESCRIPTION b/DESCRIPTION index dd57aefa..15871070 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data -Version: 1.0.1 +Version: 1.0.1.9000 Date: 2021-02-06 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), @@ -19,9 +19,10 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006, particular purpose. Depends: R (>= 2.15.1), parallel Imports: stats, graphics, methods, deSolve, R6, inline (>= 0.3.17), numDeriv, - lmtest, pkgbuild, nlme (>= 3.1-151), purrr + lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix (>= 3.1.9000) Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr, benchmarkme, tibble, stats4 +Remotes: github::saemixdevelopment/saemixextension License: GPL LazyLoad: yes LazyData: yes diff --git a/NAMESPACE b/NAMESPACE index 9776d2f3..f2497283 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -30,11 +30,15 @@ S3method(print,mkinmod) S3method(print,mmkin) S3method(print,nafta) S3method(print,nlme.mmkin) +S3method(print,saem.mmkin) S3method(print,summary.mkinfit) S3method(print,summary.nlme.mmkin) +S3method(print,summary.saem.mmkin) S3method(residuals,mkinfit) +S3method(saem,mmkin) S3method(summary,mkinfit) S3method(summary,nlme.mmkin) +S3method(summary,saem.mmkin) S3method(update,mkinfit) S3method(update,mmkin) S3method(update,nlme.mmkin) @@ -86,6 +90,9 @@ export(parms) export(plot_err) export(plot_res) export(plot_sep) +export(saem) +export(saemix_data) +export(saemix_model) export(sigma_twocomp) export(transform_odeparms) import(deSolve) @@ -126,5 +133,6 @@ importFrom(stats,residuals) importFrom(stats,rnorm) importFrom(stats,shapiro.test) importFrom(stats,update) +importFrom(stats,vcov) importFrom(utils,getFromNamespace) importFrom(utils,write.table) diff --git a/NEWS.md b/NEWS.md index 124111d6..08745e9f 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,13 @@ +# mkin 1.0.1.9000 + +- Switch to a versioning scheme where the fourth version component indicates development versions + +- Reintroduce the interface to the current development version of saemix, in particular: + +- 'saemix_model' and 'saemix_data': Helper functions to set up nonlinear mixed-effects models for mmkin row objects + +- 'saem': generic function to fit saemix models using 'saemix_model' and 'saemix_data', with a generator 'saem.mmkin', summary and plot methods + # mkin 1.0.1 - 'confint.mmkin', 'nlme.mmkin', 'transform_odeparms': Fix example code in dontrun sections that failed with current defaults diff --git a/R/endpoints.R b/R/endpoints.R index b5872e68..f1f47581 100644 --- a/R/endpoints.R +++ b/R/endpoints.R @@ -10,8 +10,8 @@ #' Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from #' HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models #' -#' @param fit An object of class [mkinfit] or [nlme.mmkin] -#' or another object that has list components +#' @param fit An object of class [mkinfit], [nlme.mmkin] or +#' [saem.mmkin]. Or another object that has list components #' mkinmod containing an [mkinmod] degradation model, and two numeric vectors, #' bparms.optim and bparms.fixed, that contain parameter values #' for that model. @@ -20,8 +20,8 @@ #' and, if applicable, a vector of formation fractions named ff #' and, if the SFORB model was in use, a vector of eigenvalues #' of these SFORB models, equivalent to DFOP rate constants -#' @note The function is used internally by [summary.mkinfit] -#' and [summary.nlme.mmkin] +#' @note The function is used internally by [summary.mkinfit], +#' [summary.nlme.mmkin] and [summary.saem.mmkin]. #' @author Johannes Ranke #' @examples #' diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R index 5a0b7412..1674d855 100644 --- a/R/plot.mixed.mmkin.R +++ b/R/plot.mixed.mmkin.R @@ -2,7 +2,7 @@ utils::globalVariables("ds") #' Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object #' -#' @param x An object of class [mixed.mmkin], [nlme.mmkin] +#' @param x An object of class [mixed.mmkin], [saem.mmkin] or [nlme.mmkin] #' @param i A numeric index to select datasets for which to plot the individual predictions, #' in case plots get too large #' @inheritParams plot.mkinfit @@ -39,6 +39,15 @@ utils::globalVariables("ds") #' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) #' plot(f_nlme) #' +#' f_saem <- saem(f, transformations = "saemix") +#' plot(f_saem) +#' +#' # We can overlay the two variants if we generate predictions +#' pred_nlme <- mkinpredict(dfop_sfo, +#' f_nlme$bparms.optim[-1], +#' c(parent = f_nlme$bparms.optim[[1]], A1 = 0), +#' seq(0, 180, by = 0.2)) +#' plot(f_saem, pred_over = list(nlme = pred_nlme)) #' } #' @export plot.mixed.mmkin <- function(x, @@ -82,6 +91,18 @@ plot.mixed.mmkin <- function(x, type = ifelse(standardized, "pearson", "response")) } + if (inherits(x, "saem.mmkin")) { + if (x$transformations == "saemix") backtransform = FALSE + degparms_i <- saemix::psi(x$so) + rownames(degparms_i) <- ds_names + degparms_i_names <- setdiff(x$so@results@name.fixed, names(fit_1$errparms)) + colnames(degparms_i) <- degparms_i_names + residual_type = ifelse(standardized, "standardized", "residual") + residuals <- x$data[[residual_type]] + degparms_pop <- x$so@results@fixed.effects + names(degparms_pop) <- degparms_i_names + } + degparms_fixed <- fit_1$fixed$value names(degparms_fixed) <- rownames(fit_1$fixed) degparms_all <- cbind(as.matrix(degparms_i), diff --git a/R/saem.R b/R/saem.R new file mode 100644 index 00000000..fd2a77b4 --- /dev/null +++ b/R/saem.R @@ -0,0 +1,512 @@ +utils::globalVariables(c("predicted", "std")) + +#' Fit nonlinear mixed models with SAEM +#' +#' This function uses [saemix::saemix()] as a backend for fitting nonlinear mixed +#' effects models created from [mmkin] row objects using the Stochastic Approximation +#' Expectation Maximisation algorithm (SAEM). +#' +#' An mmkin row object is essentially a list of mkinfit objects that have been +#' obtained by fitting the same model to a list of datasets using [mkinfit]. +#' +#' Starting values for the fixed effects (population mean parameters, argument +#' psi0 of [saemix::saemixModel()] are the mean values of the parameters found +#' using [mmkin]. +#' +#' @param object An [mmkin] row object containing several fits of the same +#' [mkinmod] model to different datasets +#' @param verbose Should we print information about created objects of +#' type [saemix::SaemixModel] and [saemix::SaemixData]? +#' @param transformations Per default, all parameter transformations are done +#' in mkin. If this argument is set to 'saemix', parameter transformations +#' are done in 'saemix' for the supported cases. Currently this is only +#' supported in cases where the initial concentration of the parent is not fixed, +#' SFO or DFOP is used for the parent and there is either no metabolite or one. +#' @param degparms_start Parameter values given as a named numeric vector will +#' be used to override the starting values obtained from the 'mmkin' object. +#' @param solution_type Possibility to specify the solution type in case the +#' automatic choice is not desired +#' @param quiet Should we suppress the messages saemix prints at the beginning +#' and the end of the optimisation process? +#' @param control Passed to [saemix::saemix] +#' @param \dots Further parameters passed to [saemix::saemixModel]. +#' @return An S3 object of class 'saem.mmkin', containing the fitted +#' [saemix::SaemixObject] as a list component named 'so'. The +#' object also inherits from 'mixed.mmkin'. +#' @seealso [summary.saem.mmkin] [plot.mixed.mmkin] +#' @examples +#' \dontrun{ +#' ds <- lapply(experimental_data_for_UBA_2019[6:10], +#' function(x) subset(x$data[c("name", "time", "value")])) +#' names(ds) <- paste("Dataset", 6:10) +#' f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, +#' state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) +#' f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed) +#' +#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) +#' f_saem_sfo <- saem(f_mmkin_parent["SFO", ]) +#' f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#' f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#' +#' # The returned saem.mmkin object contains an SaemixObject, therefore we can use +#' # functions from saemix +#' library(saemix) +#' compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)) +#' plot(f_saem_fomc$so, plot.type = "convergence") +#' plot(f_saem_fomc$so, plot.type = "individual.fit") +#' plot(f_saem_fomc$so, plot.type = "npde") +#' plot(f_saem_fomc$so, plot.type = "vpc") +#' +#' f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") +#' f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ]) +#' compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +#' +#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), +#' A1 = mkinsub("SFO")) +#' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), +#' A1 = mkinsub("SFO")) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), +#' A1 = mkinsub("SFO")) +#' # The following fit uses analytical solutions for SFO-SFO and DFOP-SFO, +#' # and compiled ODEs for FOMC that are much slower +#' f_mmkin <- mmkin(list( +#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), +#' ds, quiet = TRUE) +#' # saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds +#' # each on this system, as we use analytical solutions written for saemix. +#' # When using the analytical solutions written for mkin this took around +#' # four minutes +#' f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ]) +#' f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#' # We can use print, plot and summary methods to check the results +#' print(f_saem_dfop_sfo) +#' plot(f_saem_dfop_sfo) +#' summary(f_saem_dfop_sfo, data = TRUE) +#' +#' # The following takes about 6 minutes +#' #f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", +#' # control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) +#' +#' #saemix::compare.saemix(list( +#' # f_saem_dfop_sfo$so, +#' # f_saem_dfop_sfo_deSolve$so)) +#' +#' # If the model supports it, we can also use eigenvalue based solutions, which +#' # take a similar amount of time +#' #f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen", +#' # control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) +#' } +#' @export +saem <- function(object, ...) UseMethod("saem") + +#' @rdname saem +#' @export +saem.mmkin <- function(object, + transformations = c("mkin", "saemix"), + degparms_start = numeric(), + solution_type = "auto", + control = list(displayProgress = FALSE, print = FALSE, + save = FALSE, save.graphs = FALSE), + verbose = FALSE, quiet = FALSE, ...) +{ + transformations <- match.arg(transformations) + m_saemix <- saemix_model(object, verbose = verbose, + degparms_start = degparms_start, solution_type = solution_type, + transformations = transformations, ...) + d_saemix <- saemix_data(object, verbose = verbose) + + fit_time <- system.time({ + utils::capture.output(f_saemix <- saemix::saemix(m_saemix, d_saemix, control), split = !quiet) + }) + + transparms_optim <- f_saemix@results@fixed.effects + names(transparms_optim) <- f_saemix@results@name.fixed + + if (transformations == "mkin") { + bparms_optim <- backtransform_odeparms(transparms_optim, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions) + } else { + bparms_optim <- transparms_optim + } + + return_data <- nlme_data(object) + + return_data$predicted <- f_saemix@model@model( + psi = saemix::psi(f_saemix), + id = as.numeric(return_data$ds), + xidep = return_data[c("time", "name")]) + + return_data <- transform(return_data, + residual = predicted - value, + std = sigma_twocomp(predicted, + f_saemix@results@respar[1], f_saemix@results@respar[2])) + return_data <- transform(return_data, + standardized = residual / std) + + result <- list( + mkinmod = object[[1]]$mkinmod, + mmkin = object, + solution_type = object[[1]]$solution_type, + transformations = transformations, + transform_rates = object[[1]]$transform_rates, + transform_fractions = object[[1]]$transform_fractions, + so = f_saemix, + time = fit_time, + mean_dp_start = attr(m_saemix, "mean_dp_start"), + bparms.optim = bparms_optim, + bparms.fixed = object[[1]]$bparms.fixed, + data = return_data, + err_mod = object[[1]]$err_mod, + date.fit = date(), + saemixversion = as.character(utils::packageVersion("saemix")), + mkinversion = as.character(utils::packageVersion("mkin")), + Rversion = paste(R.version$major, R.version$minor, sep=".") + ) + + class(result) <- c("saem.mmkin", "mixed.mmkin") + return(result) +} + +#' @export +#' @rdname saem +#' @param x An saem.mmkin object to print +#' @param digits Number of digits to use for printing +print.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) { + cat( "Kinetic nonlinear mixed-effects model fit by SAEM" ) + cat("\nStructural model:\n") + diffs <- x$mmkin[[1]]$mkinmod$diffs + nice_diffs <- gsub("^(d.*) =", "\\1/dt =", diffs) + writeLines(strwrap(nice_diffs, exdent = 11)) + cat("\nData:\n") + cat(nrow(x$data), "observations of", + length(unique(x$data$name)), "variable(s) grouped in", + length(unique(x$data$ds)), "datasets\n") + + cat("\nLikelihood computed by importance sampling\n") + print(data.frame( + AIC = AIC(x$so, type = "is"), + BIC = BIC(x$so, type = "is"), + logLik = logLik(x$so, type = "is"), + row.names = " "), digits = digits) + + cat("\nFitted parameters:\n") + conf.int <- x$so@results@conf.int[c("estimate", "lower", "upper")] + rownames(conf.int) <- x$so@results@conf.int[["name"]] + print(conf.int, digits = digits) + + invisible(x) +} + +#' @rdname saem +#' @return An [saemix::SaemixModel] object. +#' @export +saemix_model <- function(object, solution_type = "auto", transformations = c("mkin", "saemix"), + degparms_start = numeric(), verbose = FALSE, ...) +{ + if (nrow(object) > 1) stop("Only row objects allowed") + + mkin_model <- object[[1]]$mkinmod + + degparms_optim <- mean_degparms(object) + if (transformations == "saemix") { + degparms_optim <- backtransform_odeparms(degparms_optim, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions) + } + degparms_fixed <- object[[1]]$bparms.fixed + + # Transformations are done in the degradation function + transform.par = rep(0, length(degparms_optim)) + + odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE) + odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE) + + odeparms_fixed_names <- setdiff(names(degparms_fixed), odeini_fixed_parm_names) + odeparms_fixed <- degparms_fixed[odeparms_fixed_names] + + odeini_fixed <- degparms_fixed[odeini_fixed_parm_names] + names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names) + + model_function <- FALSE + + # Model functions with analytical solutions + # Fixed parameters, use_of_ff = "min" and turning off sinks currently not supported here + # In general, we need to consider exactly how the parameters in mkinfit were specified, + # as the parameters are currently mapped by position in these solutions + sinks <- sapply(mkin_model$spec, function(x) x$sink) + if (length(odeparms_fixed) == 0 & mkin_model$use_of_ff == "max" & all(sinks)) { + # Parent only + if (length(mkin_model$spec) == 1) { + parent_type <- mkin_model$spec[[1]]$type + if (length(odeini_fixed) == 1) { + if (parent_type == "SFO") { + stop("saemix needs at least two parameters to work on.") + } + if (parent_type == "FOMC") { + model_function <- function(psi, id, xidep) { + odeini_fixed / (xidep[, "time"]/exp(psi[id, 2]) + 1)^exp(psi[id, 1]) + } + } + if (parent_type == "DFOP") { + model_function <- function(psi, id, xidep) { + g <- plogis(psi[id, 3]) + t <- xidep[, "time"] + odeini_fixed * (g * exp(- exp(psi[id, 1]) * t) + + (1 - g) * exp(- exp(psi[id, 2]) * t)) + } + } + if (parent_type == "HS") { + model_function <- function(psi, id, xidep) { + tb <- exp(psi[id, 3]) + t <- xidep[, "time"] + k1 = exp(psi[id, 1]) + odeini_fixed * ifelse(t <= tb, + exp(- k1 * t), + exp(- k1 * tb) * exp(- exp(psi[id, 2]) * (t - tb))) + } + } + } else { + if (parent_type == "SFO") { + if (transformations == "mkin") { + model_function <- function(psi, id, xidep) { + psi[id, 1] * exp( - exp(psi[id, 2]) * xidep[, "time"]) + } + } else { + model_function <- function(psi, id, xidep) { + psi[id, 1] * exp( - psi[id, 2] * xidep[, "time"]) + } + transform.par = c(0, 1) + } + } + if (parent_type == "FOMC") { + model_function <- function(psi, id, xidep) { + psi[id, 1] / (xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 2]) + } + } + if (parent_type == "DFOP") { + if (transformations == "mkin") { + model_function <- function(psi, id, xidep) { + g <- plogis(psi[id, 4]) + t <- xidep[, "time"] + psi[id, 1] * (g * exp(- exp(psi[id, 2]) * t) + + (1 - g) * exp(- exp(psi[id, 3]) * t)) + } + } else { + model_function <- function(psi, id, xidep) { + g <- psi[id, 4] + t <- xidep[, "time"] + psi[id, 1] * (g * exp(- psi[id, 2] * t) + + (1 - g) * exp(- psi[id, 3] * t)) + } + transform.par = c(0, 1, 1, 3) + } + } + if (parent_type == "HS") { + model_function <- function(psi, id, xidep) { + tb <- exp(psi[id, 4]) + t <- xidep[, "time"] + k1 = exp(psi[id, 2]) + psi[id, 1] * ifelse(t <= tb, + exp(- k1 * t), + exp(- k1 * tb) * exp(- exp(psi[id, 3]) * (t - tb))) + } + } + } + } + + # Parent with one metabolite + # Parameter names used in the model functions are as in + # https://nbviewer.jupyter.org/urls/jrwb.de/nb/Symbolic%20ODE%20solutions%20for%20mkin.ipynb + types <- unname(sapply(mkin_model$spec, function(x) x$type)) + if (length(mkin_model$spec) == 2 &! "SFORB" %in% types ) { + # Initial value for the metabolite (n20) must be fixed + if (names(odeini_fixed) == names(mkin_model$spec)[2]) { + n20 <- odeini_fixed + parent_name <- names(mkin_model$spec)[1] + if (identical(types, c("SFO", "SFO"))) { + if (transformations == "mkin") { + model_function <- function(psi, id, xidep) { + t <- xidep[, "time"] + n10 <- psi[id, 1] + k1 <- exp(psi[id, 2]) + k2 <- exp(psi[id, 3]) + f12 <- plogis(psi[id, 4]) + ifelse(xidep[, "name"] == parent_name, + n10 * exp(- k1 * t), + (((k2 - k1) * n20 - f12 * k1 * n10) * exp(- k2 * t)) / (k2 - k1) + + (f12 * k1 * n10 * exp(- k1 * t)) / (k2 - k1) + ) + } + } else { + model_function <- function(psi, id, xidep) { + t <- xidep[, "time"] + n10 <- psi[id, 1] + k1 <- psi[id, 2] + k2 <- psi[id, 3] + f12 <- psi[id, 4] + ifelse(xidep[, "name"] == parent_name, + n10 * exp(- k1 * t), + (((k2 - k1) * n20 - f12 * k1 * n10) * exp(- k2 * t)) / (k2 - k1) + + (f12 * k1 * n10 * exp(- k1 * t)) / (k2 - k1) + ) + } + transform.par = c(0, 1, 1, 3) + } + } + if (identical(types, c("DFOP", "SFO"))) { + if (transformations == "mkin") { + model_function <- function(psi, id, xidep) { + t <- xidep[, "time"] + n10 <- psi[id, 1] + k2 <- exp(psi[id, 2]) + f12 <- plogis(psi[id, 3]) + l1 <- exp(psi[id, 4]) + l2 <- exp(psi[id, 5]) + g <- plogis(psi[id, 6]) + ifelse(xidep[, "name"] == parent_name, + n10 * (g * exp(- l1 * t) + (1 - g) * exp(- l2 * t)), + ((f12 * g - f12) * l2 * n10 * exp(- l2 * t)) / (l2 - k2) - + (f12 * g * l1 * n10 * exp(- l1 * t)) / (l1 - k2) + + ((((l1 - k2) * l2 - k2 * l1 + k2^2) * n20 + + ((f12 * l1 + (f12 * g - f12) * k2) * l2 - + f12 * g * k2 * l1) * n10) * exp( - k2 * t)) / + ((l1 - k2) * l2 - k2 * l1 + k2^2) + ) + } + } else { + model_function <- function(psi, id, xidep) { + t <- xidep[, "time"] + n10 <- psi[id, 1] + k2 <- psi[id, 2] + f12 <- psi[id, 3] + l1 <- psi[id, 4] + l2 <- psi[id, 5] + g <- psi[id, 6] + ifelse(xidep[, "name"] == parent_name, + n10 * (g * exp(- l1 * t) + (1 - g) * exp(- l2 * t)), + ((f12 * g - f12) * l2 * n10 * exp(- l2 * t)) / (l2 - k2) - + (f12 * g * l1 * n10 * exp(- l1 * t)) / (l1 - k2) + + ((((l1 - k2) * l2 - k2 * l1 + k2^2) * n20 + + ((f12 * l1 + (f12 * g - f12) * k2) * l2 - + f12 * g * k2 * l1) * n10) * exp( - k2 * t)) / + ((l1 - k2) * l2 - k2 * l1 + k2^2) + ) + } + transform.par = c(0, 1, 3, 1, 1, 3) + } + } + } + } + } + + if (is.function(model_function) & solution_type == "auto") { + solution_type = "analytical saemix" + } else { + + if (solution_type == "auto") + solution_type <- object[[1]]$solution_type + + model_function <- function(psi, id, xidep) { + + uid <- unique(id) + + res_list <- lapply(uid, function(i) { + + transparms_optim <- as.numeric(psi[i, ]) # psi[i, ] is a dataframe when called in saemix.predict + names(transparms_optim) <- names(degparms_optim) + + odeini_optim <- transparms_optim[odeini_optim_parm_names] + names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names) + + odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)] + + ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names) + odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model, + transform_rates = object[[1]]$transform_rates, + transform_fractions = object[[1]]$transform_fractions) + odeparms <- c(odeparms_optim, odeparms_fixed) + + xidep_i <- subset(xidep, id == i) + + if (solution_type == "analytical") { + out_values <- mkin_model$deg_func(xidep_i, odeini, odeparms) + } else { + + i_time <- xidep_i$time + i_name <- xidep_i$name + + out_wide <- mkinpredict(mkin_model, + odeparms = odeparms, odeini = odeini, + solution_type = solution_type, + outtimes = sort(unique(i_time)), + na_stop = FALSE + ) + + out_index <- cbind(as.character(i_time), as.character(i_name)) + out_values <- out_wide[out_index] + } + return(out_values) + }) + res <- unlist(res_list) + return(res) + } + } + + error.model <- switch(object[[1]]$err_mod, + const = "constant", + tc = "combined", + obs = "constant") + + if (object[[1]]$err_mod == "obs") { + warning("The error model 'obs' (variance by variable) can currently not be transferred to an saemix model") + } + + error.init <- switch(object[[1]]$err_mod, + const = c(a = mean(sapply(object, function(x) x$errparms)), b = 1), + tc = c(a = mean(sapply(object, function(x) x$errparms[1])), + b = mean(sapply(object, function(x) x$errparms[2]))), + obs = c(a = mean(sapply(object, function(x) x$errparms)), b = 1)) + + degparms_psi0 <- degparms_optim + degparms_psi0[names(degparms_start)] <- degparms_start + psi0_matrix <- matrix(degparms_psi0, nrow = 1) + colnames(psi0_matrix) <- names(degparms_psi0) + + res <- saemix::saemixModel(model_function, + psi0 = psi0_matrix, + "Mixed model generated from mmkin object", + transform.par = transform.par, + error.model = error.model, + verbose = verbose + ) + attr(res, "mean_dp_start") <- degparms_optim + return(res) +} + +#' @rdname saem +#' @return An [saemix::SaemixData] object. +#' @export +saemix_data <- function(object, verbose = FALSE, ...) { + if (nrow(object) > 1) stop("Only row objects allowed") + ds_names <- colnames(object) + + ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")]) + names(ds_list) <- ds_names + ds_saemix_all <- purrr::map_dfr(ds_list, function(x) x, .id = "ds") + ds_saemix <- data.frame(ds = ds_saemix_all$ds, + name = as.character(ds_saemix_all$variable), + time = ds_saemix_all$time, + value = ds_saemix_all$observed, + stringsAsFactors = FALSE) + + res <- saemix::saemixData(ds_saemix, + name.group = "ds", + name.predictors = c("time", "name"), + name.response = "value", + verbose = verbose, + ...) + return(res) +} diff --git a/R/summary.saem.mmkin.R b/R/summary.saem.mmkin.R new file mode 100644 index 00000000..e92c561c --- /dev/null +++ b/R/summary.saem.mmkin.R @@ -0,0 +1,268 @@ +#' Summary method for class "saem.mmkin" +#' +#' Lists model equations, initial parameter values, optimised parameters +#' for fixed effects (population), random effects (deviations from the +#' population mean) and residual error model, as well as the resulting +#' endpoints such as formation fractions and DT50 values. Optionally +#' (default is FALSE), the data are listed in full. +#' +#' @param object an object of class [saem.mmkin] +#' @param x an object of class [summary.saem.mmkin] +#' @param data logical, indicating whether the full data should be included in +#' the summary. +#' @param verbose Should the summary be verbose? +#' @param distimes logical, indicating whether DT50 and DT90 values should be +#' included. +#' @param digits Number of digits to use for printing +#' @param \dots optional arguments passed to methods like \code{print}. +#' @return The summary function returns a list based on the [saemix::SaemixObject] +#' obtained in the fit, with at least the following additional components +#' \item{saemixversion, mkinversion, Rversion}{The saemix, mkin and R versions used} +#' \item{date.fit, date.summary}{The dates where the fit and the summary were +#' produced} +#' \item{diffs}{The differential equations used in the degradation model} +#' \item{use_of_ff}{Was maximum or minimum use made of formation fractions} +#' \item{data}{The data} +#' \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals} +#' \item{confint_back}{Backtransformed parameters, with confidence intervals if available} +#' \item{confint_errmod}{Error model parameters with confidence intervals} +#' \item{ff}{The estimated formation fractions derived from the fitted +#' model.} +#' \item{distimes}{The DT50 and DT90 values for each observed variable.} +#' \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} +#' The print method is called for its side effect, i.e. printing the summary. +#' @importFrom stats predict vcov +#' @author Johannes Ranke for the mkin specific parts +#' saemix authors for the parts inherited from saemix. +#' @examples +#' # Generate five datasets following DFOP-SFO kinetics +#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), +#' m1 = mkinsub("SFO"), quiet = TRUE) +#' set.seed(1234) +#' k1_in <- rlnorm(5, log(0.1), 0.3) +#' k2_in <- rlnorm(5, log(0.02), 0.3) +#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +#' k_m1_in <- rlnorm(5, log(0.02), 0.3) +#' +#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' } +#' +#' ds_mean_dfop_sfo <- lapply(1:5, function(i) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], +#' f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' }) +#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5) +#' +#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { +#' add_err(ds, +#' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), +#' n = 1)[[1]] +#' }) +#' +#' \dontrun{ +#' # Evaluate using mmkin and saem +#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, +#' quiet = TRUE, error_model = "tc", cores = 5) +#' f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) +#' summary(f_saem_dfop_sfo, data = TRUE) +#' } +#' +#' @export +summary.saem.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) { + + mod_vars <- names(object$mkinmod$diffs) + + pnames <- names(object$mean_dp_start) + np <- length(pnames) + + conf.int <- object$so@results@conf.int + rownames(conf.int) <- conf.int$name + confint_trans <- as.matrix(conf.int[pnames, c("estimate", "lower", "upper")]) + colnames(confint_trans)[1] <- "est." + + # In case objects were produced by earlier versions of saem + if (is.null(object$transformations)) object$transformations <- "mkin" + + if (object$transformations == "mkin") { + bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, + object$transform_rates, object$transform_fractions) + bpnames <- names(bp) + + # Transform boundaries of CI for one parameter at a time, + # with the exception of sets of formation fractions (single fractions are OK). + f_names_skip <- character(0) + for (box in mod_vars) { # Figure out sets of fractions to skip + f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE) + n_paths <- length(f_names) + if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) + } + + confint_back <- matrix(NA, nrow = length(bp), ncol = 3, + dimnames = list(bpnames, colnames(confint_trans))) + confint_back[, "est."] <- bp + + for (pname in pnames) { + if (!pname %in% f_names_skip) { + par.lower <- confint_trans[pname, "lower"] + par.upper <- confint_trans[pname, "upper"] + names(par.lower) <- names(par.upper) <- pname + bpl <- backtransform_odeparms(par.lower, object$mkinmod, + object$transform_rates, + object$transform_fractions) + bpu <- backtransform_odeparms(par.upper, object$mkinmod, + object$transform_rates, + object$transform_fractions) + confint_back[names(bpl), "lower"] <- bpl + confint_back[names(bpu), "upper"] <- bpu + } + } + } else { + confint_back <- confint_trans + } + + # Correlation of fixed effects (inspired by summary.nlme) + varFix <- vcov(object$so)[1:np, 1:np] + stdFix <- sqrt(diag(varFix)) + object$corFixed <- array( + t(varFix/stdFix)/stdFix, + dim(varFix), + list(pnames, pnames)) + + # Random effects + rnames <- paste0("SD.", pnames) + confint_ranef <- as.matrix(conf.int[rnames, c("estimate", "lower", "upper")]) + colnames(confint_ranef)[1] <- "est." + + # Error model + enames <- if (object$err_mod == "const") "a.1" else c("a.1", "b.1") + confint_errmod <- as.matrix(conf.int[enames, c("estimate", "lower", "upper")]) + colnames(confint_errmod)[1] <- "est." + + + object$confint_trans <- confint_trans + object$confint_ranef <- confint_ranef + object$confint_errmod <- confint_errmod + object$confint_back <- confint_back + + object$date.summary = date() + object$use_of_ff = object$mkinmod$use_of_ff + object$error_model_algorithm = object$mmkin_orig[[1]]$error_model_algorithm + err_mod = object$mmkin_orig[[1]]$err_mod + + object$diffs <- object$mkinmod$diffs + object$print_data <- data # boolean: Should we print the data? + so_pred <- object$so@results@predictions + + names(object$data)[4] <- "observed" # rename value to observed + + object$verbose <- verbose + + object$fixed <- object$mmkin_orig[[1]]$fixed + object$AIC = AIC(object$so) + object$BIC = BIC(object$so) + object$logLik = logLik(object$so, method = "is") + + ep <- endpoints(object) + if (length(ep$ff) != 0) + object$ff <- ep$ff + if (distimes) object$distimes <- ep$distimes + if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB + class(object) <- c("summary.saem.mmkin") + return(object) +} + +#' @rdname summary.saem.mmkin +#' @export +print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) { + cat("saemix version used for fitting: ", x$saemixversion, "\n") + cat("mkin version used for pre-fitting: ", x$mkinversion, "\n") + cat("R version used for fitting: ", x$Rversion, "\n") + + cat("Date of fit: ", x$date.fit, "\n") + cat("Date of summary:", x$date.summary, "\n") + + cat("\nEquations:\n") + nice_diffs <- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]]) + writeLines(strwrap(nice_diffs, exdent = 11)) + + cat("\nData:\n") + cat(nrow(x$data), "observations of", + length(unique(x$data$name)), "variable(s) grouped in", + length(unique(x$data$ds)), "datasets\n") + + cat("\nModel predictions using solution type", x$solution_type, "\n") + + cat("\nFitted in", x$time[["elapsed"]], "s using", paste(x$so@options$nbiter.saemix, collapse = ", "), "iterations\n") + + cat("\nVariance model: ") + cat(switch(x$err_mod, + const = "Constant variance", + obs = "Variance unique to each observed variable", + tc = "Two-component variance function"), "\n") + + cat("\nMean of starting values for individual parameters:\n") + print(x$mean_dp_start, digits = digits) + + cat("\nFixed degradation parameter values:\n") + if(length(x$fixed$value) == 0) cat("None\n") + else print(x$fixed, digits = digits) + + cat("\nResults:\n\n") + cat("Likelihood computed by importance sampling\n") + print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik, + row.names = " "), digits = digits) + + cat("\nOptimised parameters:\n") + print(x$confint_trans, digits = digits) + + if (nrow(x$confint_trans) > 1) { + corr <- x$corFixed + class(corr) <- "correlation" + print(corr, title = "\nCorrelation:", ...) + } + + cat("\nRandom effects:\n") + print(x$confint_ranef, digits = digits) + + cat("\nVariance model:\n") + print(x$confint_errmod, digits = digits) + + if (x$transformations == "mkin") { + cat("\nBacktransformed parameters:\n") + print(x$confint_back, digits = digits) + } + + printSFORB <- !is.null(x$SFORB) + if(printSFORB){ + cat("\nEstimated Eigenvalues of SFORB model(s):\n") + print(x$SFORB, digits = digits,...) + } + + printff <- !is.null(x$ff) + if(printff){ + cat("\nResulting formation fractions:\n") + print(data.frame(ff = x$ff), digits = digits,...) + } + + printdistimes <- !is.null(x$distimes) + if(printdistimes){ + cat("\nEstimated disappearance times:\n") + print(x$distimes, digits = digits,...) + } + + if (x$print_data){ + cat("\nData:\n") + print(format(x$data, digits = digits, ...), row.names = FALSE) + } + + invisible(x) +} diff --git a/_pkgdown.yml b/_pkgdown.yml index f632ddb0..5cfaeedf 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,8 +1,8 @@ url: https://pkgdown.jrwb.de/mkin development: - mode: release - version_label: default + mode: devel + version_label: info template: bootswatch: spacelab @@ -39,10 +39,13 @@ reference: desc: Create and work with nonlinear mixed effects models contents: - nlme.mmkin + - saem.mmkin - plot.mixed.mmkin - summary.nlme.mmkin + - summary.saem.mmkin - nlme_function - get_deg_func + - saemix_model - mixed - title: Datasets and known results contents: diff --git a/build.log b/build.log index 09d94134..7a3b024e 100644 --- a/build.log +++ b/build.log @@ -6,5 +6,5 @@ * creating vignettes ... OK * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories -* building ‘mkin_1.0.1.tar.gz’ +* building ‘mkin_1.0.1.9000.tar.gz’ diff --git a/check.log b/check.log index d0aeb08a..58d4d6c7 100644 --- a/check.log +++ b/check.log @@ -5,10 +5,15 @@ * using options ‘--no-tests --as-cran’ * checking for file ‘mkin/DESCRIPTION’ ... OK * checking extension type ... Package -* this is package ‘mkin’ version ‘1.0.1’ +* this is package ‘mkin’ version ‘1.0.1.9000’ * package encoding: UTF-8 -* checking CRAN incoming feasibility ... Note_to_CRAN_maintainers +* checking CRAN incoming feasibility ... NOTE Maintainer: ‘Johannes Ranke ’ + +Version contains large components (1.0.1.9000) + +Unknown, possibly mis-spelled, fields in DESCRIPTION: + ‘Remotes’ * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK @@ -67,5 +72,9 @@ Maintainer: ‘Johannes Ranke ’ * checking for detritus in the temp directory ... OK * DONE -Status: OK +Status: 1 NOTE +See + ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ +for details. + diff --git a/docs/dev/404.html b/docs/dev/404.html index bea38406..5f29faf2 100644 --- a/docs/dev/404.html +++ b/docs/dev/404.html @@ -71,7 +71,7 @@ mkin - 0.9.50.4 + 1.0.1.9000 diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 6daa6960..441d49c0 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -71,7 +71,7 @@ mkin - 0.9.50.4 + 1.0.1.9000 diff --git a/docs/dev/authors.html b/docs/dev/authors.html index d592b39f..9641eec0 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -71,7 +71,7 @@ mkin - 0.9.50.4 + 1.0.1.9000 @@ -147,15 +147,15 @@

  • Katrin Lindenberger. Contributor. -

    +
    contributed to mkinresplot()

  • René Lehmann. Contributor. -

    +
    ilr() and invilr()

  • Eurofins Regulatory AG. Copyright holder. -

    +
    copyright for some of the contributions of JR 2012-2014

    • diff --git a/docs/dev/index.html b/docs/dev/index.html index a4399963..8888633d 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -38,7 +38,7 @@ mkin - 0.9.50.4 + 1.0.1.9000 @@ -147,7 +147,7 @@
  • Three different error models can be selected using the argument error_model to the mkinfit function.
  • The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) should now be specified as error_model = "obs".
  • A two-component error model similar to the one proposed by Rocke and Lorenzato can be selected using the argument error_model = "tc".
    • -
  • Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the nlme.mmkin method.
    • +
  • Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the nlme.mmkin method. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils).
    • diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index c3597efe..998917f2 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -71,7 +71,7 @@ mkin - 0.9.50.4 + 1.0.1.9000
    @@ -141,13 +141,36 @@ Source: NEWS.md -
    -

    -mkin 0.9.50.4 (unreleased) Unreleased +
    +

    +mkin 1.0.1.9000 Unreleased

    -
    +
      +

    • Switch to a versioning scheme where the fourth version component indicates development versions

      • +

    • Reintroduce the interface to the current development version of saemix, in particular:

      • +

    • ‘saemix_model’ and ‘saemix_data’: Helper functions to set up nonlinear mixed-effects models for mmkin row objects

      • +

    • ‘saem’: generic function to fit saemix models using ‘saemix_model’ and ‘saemix_data’, with a generator ‘saem.mmkin’, summary and plot methods

      • +
    +
    +
    +

    +mkin 1.0.1 Unreleased +

    +
      +

    • ‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults

      • +

    • ‘logLik.mkinfit’: Improve example code to avoid warnings and show convenient syntax

      • +

    • ‘mkinresplot’: Re-add Katrin Lindenberger as coauthor who was accidentally removed long ago

      • +

    • Remove tests relying on non-convergence of the FOMC fit to the FOCUS A dataset as this is platform dependent (revealed by the new additional tests on CRAN, thanks!)

      • +

    • Increase test tolerance for some parameter comparisons that also proved to be platform dependent

      • +
    +
    +
    +

    +mkin 1.0.0 2021-02-03 +

    +

    -General new features

    +General

    • ‘mkinmod’ models gain arguments ‘name’ and ‘dll_dir’ which, in conjunction with a current version of the ‘inline’ package, make it possible to still use the DLL used for fast ODE solutions with ‘deSolve’ after saving and restoring the ‘mkinmod’ object.

    • ‘mkindsg’ R6 class for groups of ‘mkinds’ datasets with metadata

      • @@ -156,6 +179,8 @@

    • ‘focus_soil_moisture’ FOCUS default soil moisture data

    • ‘update’ method for ‘mmkin’ objects

    • ‘transform_odeparms’, ‘backtransform_odeparms’: Use logit transformation for solitary fractions like the g parameter of the DFOP model, or formation fractions for a pathway to only one target variable

      • +

    • ‘plot.mmkin’: Add a ylab argument, making it possible to customize the y axis label of the panels on the left without affecting the residual plots. Reduce legend size and vertical distance between panels

      • +

    • ‘plot.mkinfit’: Change default ylab from “Observed” to “Residue”. Pass xlab to residual plot if show_residuals is TRUE.

    @@ -163,18 +188,14 @@ Mixed-effects models

    • ‘mixed.mmkin’ New container for mmkin objects for plotting with the ‘plot.mixed.mmkin’ method

      • -

    • ‘plot.mixed.mmkin’ method used for ‘nlme.mmkin’ and ‘saem.mmkin’, both inheriting from ‘mixed.mmkin’ (currently virtual)

      • +

    • ‘plot.mixed.mmkin’ method used for ‘nlme.mmkin’ inheriting from ‘mixed.mmkin’ (currently virtual)

    • ‘plot’, ‘summary’ and ‘print’ methods for ‘nlme.mmkin’ objects

      • -

    • Add the current development version of the saemix package as a second, optional backend for mixed-effects models

      • -

    • DESCRIPTION: Additional_repositories entry pointing to my drat repository on github for a suitable saemix version

      • -

    • ‘saemix_model’, ‘saemix_data’: Helper functions to fit nonlinear mixed-effects models for mmkin row objects.

      • -

    • ‘saem’ generic function to fit saemix models using ‘saemix_model’ and ‘saemix_data’, with a generator ‘saem.mmkin’, summary and plot methods

    -
    +

    -mkin 0.9.50.3 (2020-10-08) 2020-10-08 +mkin 0.9.50.3 (2020-10-08) 2020-10-08

    • ‘parms’: Add a method for mmkin objects

      • @@ -188,18 +209,18 @@

    • ‘endpoints’: Back-calculate DT50 value from DT90 also for the biphasic models DFOP, HS and SFORB

    -
    +

    -mkin 0.9.50.2 (2020-05-12) 2020-05-12 +mkin 0.9.50.2 (2020-05-12) 2020-05-12

    • Increase tolerance for a platform specific test results on the Solaris test machine on CRAN

    • Updates and corrections (using the spelling package) to the documentation

    -
    +

    -mkin 0.9.50.1 (2020-05-11) 2020-05-11 +mkin 0.9.50.1 (2020-05-11) 2020-05-11

    • Support SFORB with formation fractions

      • @@ -207,17 +228,17 @@

    • Improve performance by a) avoiding expensive calls in the cost function like merge() and data.frame(), and b) by implementing analytical solutions for SFO-SFO and DFOP-SFO

    -
    +

    -mkin 0.9.49.11 (2020-04-20) 2020-04-20 +mkin 0.9.49.11 (2020-04-20) 2020-04-20

    • Increase a test tolerance to make it pass on all CRAN check machines
    -
    +

    -mkin 0.9.49.10 (2020-04-18) 2020-04-18 +mkin 0.9.49.10 (2020-04-18) 2020-04-18

    • ‘nlme.mmkin’: An nlme method for mmkin row objects and an associated S3 class with print, plot, anova and endpoint methods

      • @@ -228,18 +249,18 @@

    • ‘summary.mkinfit’: Add AIC, BIC and log likelihood to the summary

    -
    +

    -mkin 0.9.49.9 (2020-03-31) 2020-03-31 +mkin 0.9.49.9 (2020-03-31) 2020-03-31

    • ‘mkinmod’: Use pkgbuild::has_compiler instead of Sys.which(‘gcc’), as the latter will often fail even if Rtools are installed

    • ‘mkinds’: Use roxygen for documenting fields and methods of this R6 class

    -
    +

    -mkin 0.9.49.8 (2020-01-09) 2020-01-09 +mkin 0.9.49.8 (2020-01-09) 2020-01-09

    • ‘aw’: Generic function for calculating Akaike weights, methods for mkinfit objects and mmkin columns

      • @@ -249,18 +270,18 @@

    • ‘confint.mkinfit’: Make the quadratic approximation the default, as the likelihood profiling takes a lot of time, especially if the fit has more than three parameters

    -
    +

    -mkin 0.9.49.7 (2019-11-01) 2019-11-02 +mkin 0.9.49.7 (2019-11-01) 2019-11-02

    • Fix a bug introduced in 0.9.49.6 that occurred if the direct optimisation yielded a higher likelihood than the three-step optimisation in the d_3 algorithm, which caused the fitted parameters of the three-step optimisation to be returned instead of the parameters of the direct optimisation

    • Add a ‘nobs’ method for mkinfit objects, enabling the default ‘BIC’ method from the stats package. Also, add a ‘BIC’ method for mmkin column objects.

    -
    +

    -mkin 0.9.49.6 (2019-10-31) 2019-10-31 +mkin 0.9.49.6 (2019-10-31) 2019-10-31

    • Implement a likelihood ratio test as a method for ‘lrtest’ from the lmtest package

      • @@ -278,9 +299,9 @@

    • Support summarizing ‘mkinfit’ objects generated with versions < 0.9.49.5

    -
    +

    -mkin 0.9.49.5 (2019-07-04) 2019-07-04 +mkin 0.9.49.5 (2019-07-04) 2019-07-04

    • Several algorithms for minimization of the negative log-likelihood for non-constant error models (two-component and variance by variable). In the case the error model is constant variance, least squares is used as this is more stable. The default algorithm ‘d_3’ tries direct minimization and a three-step procedure, and returns the model with the highest likelihood.

      • @@ -297,9 +318,9 @@

    • Add example datasets obtained from risk assessment reports published by the European Food Safety Agency.

    -
    +

    -mkin 0.9.48.1 (2019-03-04) 2019-03-04 +mkin 0.9.48.1 (2019-03-04) 2019-03-04

    • Add the function ‘logLik.mkinfit’ which makes it possible to calculate an AIC for mkinfit objects

      • @@ -314,9 +335,9 @@

    • ‘nafta’: Add evaluations according to the NAFTA guidance

    -
    +

    -mkin 0.9.47.5 (2018-09-14) 2018-09-14 +mkin 0.9.47.5 (2018-09-14) 2018-09-14

    • Make the two-component error model stop in cases where it is inadequate to avoid nls crashes on windows

      • @@ -324,27 +345,27 @@

    • Exclude more example code from testing on CRAN to avoid NOTES from long execution times

    -
    +

    -mkin 0.9.47.3 Unreleased +mkin 0.9.47.3 Unreleased

    • ‘mkinfit’: Improve fitting the error model for reweight.method = ‘tc’. Add ‘manual’ to possible arguments for ‘weight’

    • Test that FOCUS_2006_C can be evaluated with DFOP and reweight.method = ‘tc’

    -
    +

    -mkin 0.9.47.2 (2018-07-19) 2018-07-19 +mkin 0.9.47.2 (2018-07-19) 2018-07-19

    • ‘sigma_twocomp’: Rename ‘sigma_rl’ to ‘sigma_twocomp’ as the Rocke and Lorenzato model assumes lognormal distribution for large y. Correct references to the Rocke and Lorenzato model accordingly.

    • ‘mkinfit’: Use 1.1 as starting value for N parameter of IORE models to obtain convergence in more difficult cases. Show parameter names when ‘trace_parms’ is ‘TRUE’.

    -
    +

    -mkin 0.9.47.1 (2018-02-06) 2018-02-06 +mkin 0.9.47.1 (2018-02-06) 2018-02-06

    • Skip some tests on CRAN and winbuilder to avoid timeouts

      • @@ -355,27 +376,27 @@

    • ‘summary.mkinfit’: Show versions of mkin and R used for fitting (not the ones used for the summary) if the fit was generated with mkin >= 0.9.47.1

    -
    +

    -mkin 0.9.46.3 (2017-11-16) 2017-11-16 +mkin 0.9.46.3 (2017-11-16) 2017-11-16

    • README.md, vignettes/mkin.Rmd: URLs were updated

    • synthetic_data_for_UBA: Add the code used to generate the data in the interest of reproducibility

    -
    +

    -mkin 0.9.46.2 (2017-10-10) 2017-10-10 +mkin 0.9.46.2 (2017-10-10) 2017-10-10

    • Converted the vignette FOCUS_Z from tex/pdf to markdown/html

    • DESCRIPTION: Add ORCID

    -
    +

    -mkin 0.9.46.1 (2017-09-14) 2017-09-14 +mkin 0.9.46.1 (2017-09-14) 2017-09-14

    • plot.mkinfit: Fix scaling of residual plots for the case of separate plots for each observed variable

      • @@ -383,17 +404,17 @@

    • Documentation updates

    -
    +

    -mkin 0.9.46 (2017-07-24) 2017-07-29 +mkin 0.9.46 (2017-07-24) 2017-07-29

    • Remove test_FOMC_ill-defined.R as it is too platform dependent
    -
    +

    -mkin 0.9.45.2 (2017-07-24) 2017-07-22 +mkin 0.9.45.2 (2017-07-24) 2017-07-22

    • Rename twa to max_twa_parent to avoid conflict with twa from my pfm package

      • @@ -402,9 +423,9 @@

    • Switch from microbenchmark to rbenchmark as the former is not supported on all platforms

    -
    +

    -mkin 0.9.45.1 (2016-12-20) Unreleased +mkin 0.9.45.1 (2016-12-20) Unreleased

    @@ -414,9 +435,9 @@

    -
    +

    -mkin 0.9.45 (2016-12-08) 2016-12-08 +mkin 0.9.45 (2016-12-08) 2016-12-08

    @@ -428,9 +449,9 @@

    -
    +

    -mkin 0.9.44 (2016-06-29) 2016-06-29 +mkin 0.9.44 (2016-06-29) 2016-06-29

    @@ -440,9 +461,9 @@

    -
    +

    -mkin 0.9.43 (2016-06-28) 2016-06-28 +mkin 0.9.43 (2016-06-28) 2016-06-28

    @@ -479,9 +500,9 @@

    -
    +

    -mkin 0.9.42 (2016-03-25) 2016-03-25 +mkin 0.9.42 (2016-03-25) 2016-03-25

    @@ -500,9 +521,9 @@

    -
    +

    -mkin 0.9-41 (2015-11-09) 2015-11-09 +mkin 0.9-41 (2015-11-09) 2015-11-09

    @@ -523,9 +544,9 @@

    -
    +

    -mkin 0.9-40 (2015-07-21) 2015-07-21 +mkin 0.9-40 (2015-07-21) 2015-07-21

    @@ -544,9 +565,9 @@

    -
    +

    -mkin 0.9-39 (2015-06-26) 2015-06-26 +mkin 0.9-39 (2015-06-26) 2015-06-26

    @@ -565,9 +586,9 @@

    -
    +

    -mkin 0.9-38 (2015-06-24) 2015-06-23 +mkin 0.9-38 (2015-06-24) 2015-06-23

    @@ -586,9 +607,9 @@

    -
    +

    -mkin 0.9-36 (2015-06-21) 2015-06-21 +mkin 0.9-36 (2015-06-21) 2015-06-21

    @@ -608,9 +629,9 @@

    -
    +

    -mkin 0.9-35 (2015-05-15) 2015-05-15 +mkin 0.9-35 (2015-05-15) 2015-05-15

    @@ -640,9 +661,9 @@

    -
    +

    -mkin 0.9-34 (2014-11-22) 2014-11-22 +mkin 0.9-34 (2014-11-22) 2014-11-22

    @@ -662,9 +683,9 @@

    -
    +

    -mkin 0.9-33 (2014-10-22) 2014-10-12 +mkin 0.9-33 (2014-10-22) 2014-10-12

    @@ -695,9 +716,9 @@

    -
    +

    -mkin 0.9-32 (2014-07-24) 2014-07-24 +mkin 0.9-32 (2014-07-24) 2014-07-24

    @@ -705,7 +726,7 @@

    • The number of degrees of freedom is difficult to define in the case of ilr transformation of formation fractions. Now for each source compartment the number of ilr parameters (=number of optimised parameters) is divided by the number of pathways to metabolites (=number of affected data series) which leads to fractional degrees of freedom in some cases.

    • The default for the initial value for the first state value is now taken from the mean of the observations at time zero, if available.

      • -

    • The kinetic model can alternatively be specified with a shorthand name for parent only degradation models, e.g. SFO, or DFOP.

      • +

    • The kinetic model can alternatively be specified with a shorthand name for parent only degradation models, e.g. SFO, or DFOP.

    • Optimisation method, number of model evaluations and time elapsed during optimisation are given in the summary of mkinfit objects.

    • The maximum number of iterations in the optimisation algorithm can be specified using the argument maxit.modFit to the mkinfit function.

    • mkinfit gives a warning when the fit does not converge (does not apply to SANN method). This warning is included in the summary.

      • @@ -732,9 +753,9 @@

    -
    +

    -mkin 0.9-31 (2014-07-14) 2014-07-14 +mkin 0.9-31 (2014-07-14) 2014-07-14

    @@ -744,9 +765,9 @@

    -
    +

    -mkin 0.9-30 (2014-07-11) 2014-07-11 +mkin 0.9-30 (2014-07-11) 2014-07-11

    @@ -759,7 +780,7 @@

    Major changes

      -

    • The original and the transformed parameters now have different names (e.g. k_parent and log_k_parent. They also differ in how many they are when we have formation fractions but no pathway to sink.

      • +

    • The original and the transformed parameters now have different names (e.g. k_parent and log_k_parent. They also differ in how many they are when we have formation fractions but no pathway to sink.

    • The order of some of the information blocks in print.summary.mkinfit.R() has been ordered in a more logical way.

    @@ -776,9 +797,9 @@
    -
    +

    -mkin 0.9-29 (2014-06-27) 2014-06-27 +mkin 0.9-29 (2014-06-27) 2014-06-27

    • R/mkinresplot.R: Make it possible to specify xlim

      • @@ -786,9 +807,9 @@

    • R/endpoints.R, man/endpoints.Rd: Calculate additional (pseudo)-DT50 values for FOMC, DFOP, HS and SFORB. Avoid calculation of formation fractions from rate constants when they are directly fitted

    -
    +

    -mkin 0.9-28 (2014-05-20) 2014-05-20 +mkin 0.9-28 (2014-05-20) 2014-05-20

    • Do not backtransform confidence intervals for formation fractions if more than one compound is formed, as such parameters only define the pathways as a set

      • @@ -796,9 +817,9 @@

    • Correct ‘isotropic’ into ‘isometric’ for the ilr transformation

    -
    +

    -mkin 0.9-27 (2014-05-10) 2014-05-10 +mkin 0.9-27 (2014-05-10) 2014-05-10

    • Fork the GUI into a separate package gmkin

      • @@ -820,9 +841,9 @@

    • Add gmkin workspace datasets FOCUS_2006_gmkin and FOCUS_2006_Z_gmkin

    -
    +

    -mkin 0.9-24 (2013-11-06) 2013-11-06 +mkin 0.9-24 (2013-11-06) 2013-11-06

    • Bugfix re-enabling the fixing of any combination of initial values for state variables

      • @@ -830,9 +851,9 @@

    • Backtransform fixed ODE parameters for the summary

    -
    +

    -mkin 0.9-22 (2013-10-26) 2013-10-26 +mkin 0.9-22 (2013-10-26) 2013-10-26

    • Get rid of the optimisation step in mkinerrmin - this was unnecessary. Thanks to KinGUII for the inspiration - actually this is equation 6-2 in FOCUS kinetics p. 91 that I had overlooked originally

      • diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 2963e810..f9b16e29 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -1,4 +1,4 @@ -pandoc: 2.2.1 +pandoc: 2.9.2.1 pkgdown: 1.6.1 pkgdown_sha: ~ articles: @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-01-25T13:41Z +last_built: 2021-02-06T17:26Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png index bca41e2c..17a35806 100644 Binary files a/docs/dev/reference/Rplot001.png and b/docs/dev/reference/Rplot001.png differ diff --git a/docs/dev/reference/Rplot003.png b/docs/dev/reference/Rplot003.png index ff6bc722..2b011ec1 100644 Binary files a/docs/dev/reference/Rplot003.png and b/docs/dev/reference/Rplot003.png differ diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png index 5e675828..8c91d61e 100644 Binary files a/docs/dev/reference/Rplot005.png and b/docs/dev/reference/Rplot005.png differ diff --git a/docs/dev/reference/Rplot006.png b/docs/dev/reference/Rplot006.png index da52f580..730a7481 100644 Binary files a/docs/dev/reference/Rplot006.png and b/docs/dev/reference/Rplot006.png differ diff --git a/docs/dev/reference/confint.mkinfit.html b/docs/dev/reference/confint.mkinfit.html index 515a7c9e..89bb3d89 100644 --- a/docs/dev/reference/confint.mkinfit.html +++ b/docs/dev/reference/confint.mkinfit.html @@ -79,7 +79,7 @@ method of Venzon and Moolgavkar (1988)." /> mkin - 0.9.50.4 + 1.0.1.9000
    @@ -128,7 +128,7 @@ method of Venzon and Moolgavkar (1988)." />
    • - +
      • @@ -273,68 +273,69 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, if (identical(Sys.getenv("NOT_CRAN"), "true")) { n_cores <- parallel::detectCores() - 1 } else { - n_cores <- 1 + n_cores <- 1 } if (Sys.getenv("TRAVIS") != "") n_cores = 1 if (Sys.info()["sysname"] == "Windows") n_cores = 1 -SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), quiet = TRUE) +SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), + use_of_ff = "min", quiet = TRUE) SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE) f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE) system.time(ci_profile <- confint(f_d_1, method = "profile", cores = 1, quiet = TRUE))
    #> user system elapsed -#> 3.900 0.929 3.548
    # Using more cores does not save much time here, as parent_0 takes up most of the time +#> 4.258 0.916 3.889
    # Using more cores does not save much time here, as parent_0 takes up most of the time # If we additionally exclude parent_0 (the confidence of which is often of -# minor interest), we get a nice performance improvement from about 50 -# seconds to about 12 seconds if we use at least four cores +# minor interest), we get a nice performance improvement if we use at least 4 cores system.time(ci_profile_no_parent_0 <- confint(f_d_1, method = "profile", c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = n_cores)) -
    #> Profiling the likelihood
    #> Warning: scheduled cores 3, 2, 1 encountered errors in user code, all values of the jobs will be affected
    #> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
    #> Timing stopped at: 0.009 0.034 0.257
    ci_profile -
    #> 2.5% 97.5% -#> parent_0 96.456003640 1.027703e+02 -#> k_parent 0.090911032 1.071578e-01 -#> k_m1 0.003892606 6.702775e-03 -#> f_parent_to_m1 0.471328495 5.611550e-01 -#> sigma 2.535612399 3.985263e+00
    ci_quadratic_transformed <- confint(f_d_1, method = "quadratic") +
    #> Profiling the likelihood
    #> user system elapsed +#> 1.459 0.088 0.907
    ci_profile +
    #> 2.5% 97.5% +#> parent_0 96.456003640 1.027703e+02 +#> k_parent_sink 0.040762501 5.549764e-02 +#> k_parent_m1 0.046786482 5.500879e-02 +#> k_m1_sink 0.003892605 6.702778e-03 +#> sigma 2.535612399 3.985263e+00
    ci_quadratic_transformed <- confint(f_d_1, method = "quadratic") ci_quadratic_transformed -
    #> 2.5% 97.5% -#> parent_0 96.403833585 102.79311650 -#> k_parent 0.090823771 0.10725430 -#> k_m1 0.004012219 0.00689755 -#> f_parent_to_m1 0.469118824 0.55959615 -#> sigma 2.396089689 3.85491806
    ci_quadratic_untransformed <- confint(f_d_1, method = "quadratic", transformed = FALSE) +
    #> 2.5% 97.5% +#> parent_0 96.403841640 1.027931e+02 +#> k_parent_sink 0.041033378 5.596269e-02 +#> k_parent_m1 0.046777902 5.511931e-02 +#> k_m1_sink 0.004012217 6.897547e-03 +#> sigma 2.396089689 3.854918e+00
    ci_quadratic_untransformed <- confint(f_d_1, method = "quadratic", transformed = FALSE) ci_quadratic_untransformed -
    #> 2.5% 97.5% -#> parent_0 96.403833589 1.027931e+02 -#> k_parent 0.090491913 1.069035e-01 -#> k_m1 0.003835485 6.685823e-03 -#> f_parent_to_m1 0.469113477 5.598387e-01 -#> sigma 2.396089689 3.854918e+00
    # Against the expectation based on Bates and Watts (1988), the confidence +
    #> 2.5% 97.5% +#> parent_0 96.403841645 102.79312449 +#> k_parent_sink 0.040485331 0.05535491 +#> k_parent_m1 0.046611582 0.05494364 +#> k_m1_sink 0.003835483 0.00668582 +#> sigma 2.396089689 3.85491806
    # Against the expectation based on Bates and Watts (1988), the confidence # intervals based on the internal parameter transformation are less # congruent with the likelihood based intervals. Note the superiority of the # interval based on the untransformed fit for k_m1_sink rel_diffs_transformed <- abs((ci_quadratic_transformed - ci_profile)/ci_profile) rel_diffs_untransformed <- abs((ci_quadratic_untransformed - ci_profile)/ci_profile) rel_diffs_transformed < rel_diffs_untransformed -
    #> 2.5% 97.5% -#> parent_0 FALSE FALSE -#> k_parent TRUE TRUE -#> k_m1 FALSE FALSE -#> f_parent_to_m1 TRUE FALSE -#> sigma TRUE FALSE
    signif(rel_diffs_transformed, 3) -
    #> 2.5% 97.5% -#> parent_0 0.000541 0.000222 -#> k_parent 0.000960 0.000900 -#> k_m1 0.030700 0.029100 -#> f_parent_to_m1 0.004690 0.002780 -#> sigma 0.055000 0.032700
    signif(rel_diffs_untransformed, 3) -
    #> 2.5% 97.5% -#> parent_0 0.000541 0.000222 -#> k_parent 0.004610 0.002370 -#> k_m1 0.014700 0.002530 -#> f_parent_to_m1 0.004700 0.002350 -#> sigma 0.055000 0.032700
    +
    #> 2.5% 97.5% +#> parent_0 FALSE FALSE +#> k_parent_sink TRUE FALSE +#> k_parent_m1 TRUE FALSE +#> k_m1_sink FALSE FALSE +#> sigma FALSE FALSE
    signif(rel_diffs_transformed, 3) +
    #> 2.5% 97.5% +#> parent_0 0.000541 0.000222 +#> k_parent_sink 0.006650 0.008380 +#> k_parent_m1 0.000183 0.002010 +#> k_m1_sink 0.030700 0.029100 +#> sigma 0.055000 0.032700
    signif(rel_diffs_untransformed, 3) +
    #> 2.5% 97.5% +#> parent_0 0.000541 0.000222 +#> k_parent_sink 0.006800 0.002570 +#> k_parent_m1 0.003740 0.001180 +#> k_m1_sink 0.014700 0.002530 +#> sigma 0.055000 0.032700
    # Investigate a case with formation fractions f_d_2 <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE) @@ -348,14 +349,14 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> sigma 2.535612399 3.985263e+00
    ci_quadratic_transformed_ff <- confint(f_d_2, method = "quadratic") ci_quadratic_transformed_ff
    #> 2.5% 97.5% -#> parent_0 96.403833585 102.79311650 +#> parent_0 96.403833578 102.79311649 #> k_parent 0.090823771 0.10725430 #> k_m1 0.004012219 0.00689755 #> f_parent_to_m1 0.469118824 0.55959615 #> sigma 2.396089689 3.85491806
    ci_quadratic_untransformed_ff <- confint(f_d_2, method = "quadratic", transformed = FALSE) ci_quadratic_untransformed_ff
    #> 2.5% 97.5% -#> parent_0 96.403833589 1.027931e+02 +#> parent_0 96.403833583 1.027931e+02 #> k_parent 0.090491913 1.069035e-01 #> k_m1 0.003835485 6.685823e-03 #> f_parent_to_m1 0.469113477 5.598387e-01 @@ -373,15 +374,15 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> f_parent_to_m1 TRUE FALSE #> sigma TRUE FALSE
    rel_diffs_transformed_ff
    #> 2.5% 97.5% -#> parent_0 0.0005408689 0.0002217234 +#> parent_0 0.0005408690 0.0002217233 #> k_parent 0.0009598532 0.0009001864 -#> k_m1 0.0307283044 0.0290588365 -#> f_parent_to_m1 0.0046881768 0.0027780063 +#> k_m1 0.0307283041 0.0290588361 +#> f_parent_to_m1 0.0046881769 0.0027780063 #> sigma 0.0550252516 0.0327066836
    rel_diffs_untransformed_ff
    #> 2.5% 97.5% -#> parent_0 0.0005408689 0.0002217233 -#> k_parent 0.0046102155 0.0023732281 -#> k_m1 0.0146740688 0.0025291817 +#> parent_0 0.0005408689 0.0002217232 +#> k_parent 0.0046102156 0.0023732281 +#> k_m1 0.0146740690 0.0025291820 #> f_parent_to_m1 0.0046995211 0.0023457712 #> sigma 0.0550252516 0.0327066836
    # The profiling for the following fit does not finish in a reasonable time, @@ -395,18 +396,18 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, error_model_algorithm = "direct", quiet = TRUE) confint(f_tc_2, method = "quadratic")
    #> 2.5% 97.5% -#> parent_0 94.596126334 106.19944007 -#> k_M1 0.037605408 0.04490759 -#> k_M2 0.008568739 0.01087675 -#> f_parent_to_M1 0.021463787 0.62023881 -#> f_parent_to_M2 0.015166531 0.37975349 -#> k1 0.273897467 0.33388084 -#> k2 0.018614555 0.02250379 -#> g 0.671943606 0.73583278 -#> sigma_low 0.251283766 0.83992113 -#> rsd_high 0.040411014 0.07662005
    confint(f_tc_2, "parent_0", method = "quadratic") +#> parent_0 94.596039609 106.19954892 +#> k_M1 0.037605368 0.04490762 +#> k_M2 0.008568731 0.01087676 +#> f_parent_to_M1 0.021462489 0.62023882 +#> f_parent_to_M2 0.015165617 0.37975348 +#> k1 0.273897348 0.33388101 +#> k2 0.018614554 0.02250378 +#> g 0.671943411 0.73583305 +#> sigma_low 0.251283495 0.83992077 +#> rsd_high 0.040411024 0.07662008
    confint(f_tc_2, "parent_0", method = "quadratic")
    #> 2.5% 97.5% -#> parent_0 94.59613 106.1994
    # } +#> parent_0 94.59604 106.1995
    # }
    @@ -127,7 +127,7 @@ advantage that the SFORB model can also be used for metabolites." />
    • - +
      • diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index e038ef5c..7e98aa50 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ mkin - 0.9.50.4 + 1.0.1.9000
    @@ -175,7 +175,7 @@ -

    mmkin()

    +

    mmkin() print(<mmkin>)

    Fit one or more kinetic models with one or more state variables to one or more datasets

    @@ -297,12 +297,6 @@ of an mmkin object

    AIC(<mmkin>) BIC(<mmkin>)

    Calculate the AIC for a column of an mmkin object

    - - - -

    print(<mmkin>)

    - -

    Print method for mmkin objects

    diff --git a/docs/dev/reference/logLik.mkinfit.html b/docs/dev/reference/logLik.mkinfit.html index 66539dbd..82c0654f 100644 --- a/docs/dev/reference/logLik.mkinfit.html +++ b/docs/dev/reference/logLik.mkinfit.html @@ -76,7 +76,7 @@ the error model." /> mkin - 0.9.50.4 + 1.0.1.9000
    @@ -125,7 +125,7 @@ the error model." />
    • - +
      • @@ -196,11 +196,11 @@ and the fitted error model parameters.

      parent = mkinsub("SFO", to = "m1"), m1 = mkinsub("SFO") ) -
    #> Temporary DLL for differentials generated and loaded
    d_t <- FOCUS_2006_D +
    #> Temporary DLL for differentials generated and loaded
    d_t <- subset(FOCUS_2006_D, value != 0) f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity) -
    #> Warning: Observations with value of zero were removed from the data
    f_obs <- mkinfit(sfo_sfo, d_t, error_model = "obs", quiet = TRUE) -
    #> Warning: Observations with value of zero were removed from the data
    f_tc <- mkinfit(sfo_sfo, d_t, error_model = "tc", quiet = TRUE) -
    #> Warning: Observations with value of zero were removed from the data
    AIC(f_nw, f_obs, f_tc) + f_obs <- update(f_nw, error_model = "obs") + f_tc <- update(f_nw, error_model = "tc") + AIC(f_nw, f_obs, f_tc)
    #> df AIC #> f_nw 5 204.4486 #> f_obs 6 205.8727 diff --git a/docs/dev/reference/mkinresplot.html b/docs/dev/reference/mkinresplot.html index 4b2f6bea..2e10d5f6 100644 --- a/docs/dev/reference/mkinresplot.html +++ b/docs/dev/reference/mkinresplot.html @@ -75,7 +75,7 @@ argument show_residuals = TRUE." /> mkin - 0.9.50.4 + 1.0.1.9000
    @@ -124,7 +124,7 @@ argument show_residuals = TRUE." />
    • - +
      • @@ -242,7 +242,7 @@ lines of the mkinfit object, and plot_res< combining the plot of the fit and the residual plot.

    Author

    -

    Johannes Ranke

    +

    Johannes Ranke and Katrin Lindenberger

    Examples

    
diff --git a/docs/dev/reference/mmkin-1.png b/docs/dev/reference/mmkin-1.png
index 7b7da90a..0db3379f 100644
Binary files a/docs/dev/reference/mmkin-1.png and b/docs/dev/reference/mmkin-1.png differ
diff --git a/docs/dev/reference/mmkin-2.png b/docs/dev/reference/mmkin-2.png
index ce2b2af4..024a9892 100644
Binary files a/docs/dev/reference/mmkin-2.png and b/docs/dev/reference/mmkin-2.png differ
diff --git a/docs/dev/reference/mmkin-3.png b/docs/dev/reference/mmkin-3.png
index bb96f1b2..a23d7cb9 100644
Binary files a/docs/dev/reference/mmkin-3.png and b/docs/dev/reference/mmkin-3.png differ
diff --git a/docs/dev/reference/mmkin-4.png b/docs/dev/reference/mmkin-4.png
index 351b21aa..89975db5 100644
Binary files a/docs/dev/reference/mmkin-4.png and b/docs/dev/reference/mmkin-4.png differ
diff --git a/docs/dev/reference/mmkin-5.png b/docs/dev/reference/mmkin-5.png
index c1c05eea..a2f34983 100644
Binary files a/docs/dev/reference/mmkin-5.png and b/docs/dev/reference/mmkin-5.png differ
diff --git a/docs/dev/reference/mmkin.html b/docs/dev/reference/mmkin.html
index 651eb9a6..65c91adf 100644
--- a/docs/dev/reference/mmkin.html
+++ b/docs/dev/reference/mmkin.html
@@ -75,7 +75,7 @@ datasets specified in its first two arguments." />
       
       
         mkin
-        0.9.50.4
+        1.0.1.9000
       
     
    
 
@@ -124,7 +124,7 @@ datasets specified in its first two arguments." />
       
      
         
    • 
   
-    
+    
      
   
 
      • 
@@ -158,7 +158,10 @@ datasets specified in its first two arguments.
      
   cores = parallel::detectCores(),
   cluster = NULL,
   ...
-)
    +) + +# S3 method for mmkin +print(x, ...)

    Arguments

    @@ -189,7 +192,11 @@ for parallel execution.

    - + + + + +
    ...

    Further arguments that will be passed to mkinfit.

    Not used.

    x

    An mmkin object.
    @@ -227,19 +234,19 @@ plotting.

    time_default
    #> user system elapsed -#> 4.968 0.427 1.342
    time_1 +#> 4.438 0.334 1.640
    time_1
    #> user system elapsed -#> 5.365 0.000 5.368
    +#> 5.535 0.004 5.539
    endpoints(fits.0[["SFO_lin", 2]])
    #> $ff #> parent_M1 parent_sink M1_M2 M1_sink -#> 0.7340478 0.2659522 0.7505687 0.2494313 +#> 0.7340481 0.2659519 0.7505683 0.2494317 #> #> $distimes #> DT50 DT90 #> parent 0.877769 2.915885 -#> M1 2.325746 7.725960 -#> M2 33.720083 112.015691 +#> M1 2.325744 7.725956 +#> M2 33.720100 112.015749 #>
    # plot.mkinfit handles rows or columns of mmkin result objects plot(fits.0[1, ]) @@ -266,12 +273,10 @@ plotting.

    #> dataset #> model A B C D #> SFO OK OK OK OK -#> FOMC C OK OK OK +#> FOMC OK OK OK OK #> DFOP OK OK OK OK #> -#> OK: No warnings -#> C: Optimisation did not converge: -#> false convergence (8)
    # We get false convergence for the FOMC fit to FOCUS_2006_A because this +#> OK: No warnings
    # We get false convergence for the FOMC fit to FOCUS_2006_A because this # dataset is really SFO, and the FOMC fit is overparameterised stopCluster(cl) # } diff --git a/docs/dev/reference/nlme.mmkin-1.png b/docs/dev/reference/nlme.mmkin-1.png index 25bebeca..9186c135 100644 Binary files a/docs/dev/reference/nlme.mmkin-1.png and b/docs/dev/reference/nlme.mmkin-1.png differ diff --git a/docs/dev/reference/nlme.mmkin-2.png b/docs/dev/reference/nlme.mmkin-2.png index c314c149..d395fe02 100644 Binary files a/docs/dev/reference/nlme.mmkin-2.png and b/docs/dev/reference/nlme.mmkin-2.png differ diff --git a/docs/dev/reference/nlme.mmkin-3.png b/docs/dev/reference/nlme.mmkin-3.png index a40b7cad..40518a59 100644 Binary files a/docs/dev/reference/nlme.mmkin-3.png and b/docs/dev/reference/nlme.mmkin-3.png differ diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index a4d7070a..2649c111 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> mkin - 0.9.50.4 + 1.0.1.9000
    @@ -123,7 +123,7 @@ have been obtained by fitting the same model to a list of datasets." />
    • - +
      • @@ -262,6 +262,12 @@ parameters taken from the mmkin object are used

      Upon success, a fitted 'nlme.mmkin' object, which is an nlme object with additional elements. It also inherits from 'mixed.mmkin'.

      +

      Details

      + +

      Note that the convergence of the nlme algorithms depends on the quality +of the data. In degradation kinetics, we often only have few datasets +(e.g. data for few soils) and complicated degradation models, which may +make it impossible to obtain convergence with nlme.

      Note

      As the object inherits from nlme::nlme, there is a wealth of @@ -284,7 +290,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as anova(f_nlme_sfo, f_nlme_dfop)

    #> Model df AIC BIC logLik Test L.Ratio p-value #> f_nlme_sfo 1 5 625.0539 637.5529 -307.5269 -#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9268 <.0001
    print(f_nlme_dfop) +#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9269 <.0001
    print(f_nlme_dfop)
    #> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: @@ -312,7 +318,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
    endpoints(f_nlme_dfop)
    #> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 10.79857 100.7937 30.34192 4.193937 43.85442 +#> parent 10.79857 100.7937 30.34193 4.193938 43.85443 #>
    ds_2 <- lapply(experimental_data_for_UBA_2019[6:10], function(x) x$data[c("name", "time", "value")]) @@ -335,16 +341,17 @@ methods that will automatically work on 'nlme.mmkin' objects, such as # f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ]) #plot(f_nlme_sfo_sfo_ff) - # With the log-Cholesky parameterization, this converges in 11 - # iterations and around 100 seconds, but without tweaking control - # parameters (with pdDiag, increasing the tolerance and pnlsMaxIter was - # necessary) - f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ]) -
    #> Error in nlme.formula(model = value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_A1, f_parent_qlogis, log_k1, log_k2, g_qlogis), data = structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L ), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 4.3, 4.6, 7, 7.2, 8.2, 8, 11, 13.7, 11.5, 12.7, 14.9, 14.5, 12.1, 12.3, 9.9, 10.2, 8.8, 7.8, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 3.9, 3.1, 6.9, 6.6, 10.4, 8.3, 14.4, 13.7, 22.1, 22.3, 27.5, 25.4, 28, 26.6, 25.8, 25.3, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 9.6, 7.7, 15, 15.1, 21.2, 21.1, 19.7, 18.9, 17.5, 15.9, 9.5, 9.8, 6.2, 6.1, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 4.2, 3.9, 7.4, 7.9, 14.5, 13.7, 14.2, 12.2, 13.7, 13.2, 13.6, 15.4, 10.4, 11.6, 10, 9.5, 9.1, 9, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3, 3.3, 3.4, 3.9, 2.9, 6.4, 7.2, 9.1, 8.5, 11.7, 12, 13.3, 13.2, 14.3, 12.1)), row.names = c(NA, -170L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE), start = list( fixed = c(parent_0 = 93.8101519326534, log_k_A1 = -9.76474551635931, f_parent_qlogis = -0.971114801595408, log_k1 = -1.87993711571859, log_k2 = -4.27081421366622, g_qlogis = 0.135644115277507 ), random = list(ds = structure(c(2.56569977430371, -3.49441920289139, -3.32614443321494, 4.35347873814922, -0.0986148763466161, 4.65850590018027, 1.8618544764481, 6.12693257601545, 4.91792724701579, -17.5652201996596, -0.466203822618637, 0.746660653597927, 0.282193987271096, -0.42053488943072, -0.142115928819667, 0.369240076779088, -1.38985563501659, 1.02592753494098, 0.73090914081534, -0.736221117518819, 0.768170629350299, -1.89347658079869, 1.72168783460352, 0.844607177798114, -1.44098906095325, -0.377731855445672, 0.168180098477565, 0.469683412912104, 0.500717664434525, -0.760849320378522), .Dim = 5:6, .Dimnames = list(c("1", "2", "3", "4", "5"), c("parent_0", "log_k_A1", "f_parent_qlogis", "log_k1", "log_k2", "g_qlogis"))))), fixed = list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), random = structure(numeric(0), class = c("pdDiag", "pdMat"), formula = structure(list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), class = "listForm"), Dimnames = list(NULL, NULL))): maximum number of iterations (maxIter = 50) reached without convergence
    #> Timing stopped at: 49.95 16.5 44.08
    + # For the following, we need to increase pnlsMaxIter and the tolerance + # to get convergence + f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ], + control = list(pnlsMaxIter = 120, tolerance = 5e-4)) + plot(f_nlme_dfop_sfo) -
    #> Error in plot(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found
    +
    anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) -
    #> Error in anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo): object 'f_nlme_dfop_sfo' not found
    +
    #> Model df AIC BIC logLik Test L.Ratio p-value +#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274 +#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3273 <.0001
    endpoints(f_nlme_sfo_sfo)
    #> $ff #> parent_sink parent_A1 A1_sink @@ -355,7 +362,15 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> parent 19.13518 63.5657 #> A1 66.02155 219.3189 #>
    endpoints(f_nlme_dfop_sfo) -
    #> Error in endpoints(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found
    +
    #> $ff +#> parent_A1 parent_sink +#> 0.2768575 0.7231425 +#> +#> $distimes +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> parent 11.07091 104.6320 31.49737 4.462384 46.20825 +#> A1 162.30492 539.1653 NA NA NA +#>
    if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available # Attempts to fit metabolite kinetics with the tc error model are possible, # but need tweeking of control values and sometimes do not converge @@ -381,7 +396,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis -#> 94.04775 -1.82340 -4.16715 0.05685 +#> 94.04774 -1.82340 -4.16716 0.05686 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) @@ -395,10 +410,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Formula: ~fitted(.) #> Parameter estimates: #> const prop -#> 2.23224114 0.01262341
    - f_2_obs <- mmkin(list("SFO-SFO" = m_sfo_sfo, - "DFOP-SFO" = m_dfop_sfo), - ds_2, quiet = TRUE, error_model = "obs") +#> 2.23223147 0.01262395
    + f_2_obs <- update(f_2, error_model = "obs") f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ]) print(f_nlme_sfo_sfo_obs)
    #> Kinetic nonlinear mixed-effects model fit by maximum likelihood @@ -429,18 +442,21 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Formula: ~1 | name #> Parameter estimates: #> parent A1 -#> 1.0000000 0.2050003
    f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ]) -
    #> Error in nlme.formula(model = value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_A1, f_parent_qlogis, log_k1, log_k2, g_qlogis), data = structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L ), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 4.3, 4.6, 7, 7.2, 8.2, 8, 11, 13.7, 11.5, 12.7, 14.9, 14.5, 12.1, 12.3, 9.9, 10.2, 8.8, 7.8, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 3.9, 3.1, 6.9, 6.6, 10.4, 8.3, 14.4, 13.7, 22.1, 22.3, 27.5, 25.4, 28, 26.6, 25.8, 25.3, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 9.6, 7.7, 15, 15.1, 21.2, 21.1, 19.7, 18.9, 17.5, 15.9, 9.5, 9.8, 6.2, 6.1, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 4.2, 3.9, 7.4, 7.9, 14.5, 13.7, 14.2, 12.2, 13.7, 13.2, 13.6, 15.4, 10.4, 11.6, 10, 9.5, 9.1, 9, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3, 3.3, 3.4, 3.9, 2.9, 6.4, 7.2, 9.1, 8.5, 11.7, 12, 13.3, 13.2, 14.3, 12.1)), row.names = c(NA, -170L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE), start = list( fixed = c(parent_0 = 93.4272167134207, log_k_A1 = -9.71590717106959, f_parent_qlogis = -0.953712099744438, log_k1 = -1.95256957646888, log_k2 = -4.42919226610318, g_qlogis = 0.193023137298073 ), random = list(ds = structure(c(2.85557330683041, -3.87630303729395, -2.78062140212751, 4.82042042600536, -1.01906929341432, 4.613992019697, 2.05871276943309, 6.0766404049189, 4.86471337131288, -17.6140585653619, -0.480721175257541, 0.773079218835614, 0.260464433006093, -0.440615012802434, -0.112207463781733, 0.445812953745225, -1.49588630006094, 1.13602040717272, 0.801850880762046, -0.887797941619048, 0.936480292463262, -2.43093808171905, 1.91256225793793, 0.984827519864443, -1.40293198854659, -0.455176326336681, 0.376355651864385, 0.343919720700401, 0.46329187713133, -0.728390923359434 ), .Dim = 5:6, .Dimnames = list(c("1", "2", "3", "4", "5"), c("parent_0", "log_k_A1", "f_parent_qlogis", "log_k1", "log_k2", "g_qlogis"))))), fixed = list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), random = structure(numeric(0), class = c("pdDiag", "pdMat"), formula = structure(list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), class = "listForm"), Dimnames = list(NULL, NULL)), weights = structure(numeric(0), formula = ~1 | name, class = c("varIdent", "varFunc"))): maximum number of iterations (maxIter = 50) reached without convergence
    #> Timing stopped at: 59.38 16.5 53.5
    - f_2_tc <- mmkin(list("SFO-SFO" = m_sfo_sfo, - "DFOP-SFO" = m_dfop_sfo), - ds_2, quiet = TRUE, error_model = "tc") - # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # stops with error message - f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ]) -
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Warning: longer object length is not a multiple of shorter object length
    #> Error in X[, fmap[[nm]]] <- gradnm: number of items to replace is not a multiple of replacement length
    #> Timing stopped at: 6.363 2.688 5.469
    # We get warnings about false convergence in the LME step in several iterations - # but as the last such warning occurs in iteration 25 and we have 28 iterations - # we can ignore these - anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs, f_nlme_dfop_sfo_tc) -
    #> Error in anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs, f_nlme_dfop_sfo_tc): object 'f_nlme_dfop_sfo' not found
    +#> 1.0000000 0.2049995
    f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], + control = list(pnlsMaxIter = 120, tolerance = 5e-4)) + + f_2_tc <- update(f_2, error_model = "tc") + # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations + # f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ], + # control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm + + anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs) +
    #> Model df AIC BIC logLik Test L.Ratio +#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274 +#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32093 +#> p-value +#> f_nlme_dfop_sfo +#> f_nlme_dfop_sfo_obs <.0001
    # }
    diff --git a/docs/dev/reference/plot.mixed.mmkin-1.png b/docs/dev/reference/plot.mixed.mmkin-1.png index 5cb33214..9c9a2211 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-1.png and b/docs/dev/reference/plot.mixed.mmkin-1.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-2.png b/docs/dev/reference/plot.mixed.mmkin-2.png index c0d67204..0f66ff0f 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-2.png and b/docs/dev/reference/plot.mixed.mmkin-2.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-3.png b/docs/dev/reference/plot.mixed.mmkin-3.png index 5e00afe6..34212f1c 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-3.png and b/docs/dev/reference/plot.mixed.mmkin-3.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-4.png b/docs/dev/reference/plot.mixed.mmkin-4.png index 6a5f3b9c..c1450d24 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-4.png and b/docs/dev/reference/plot.mixed.mmkin-4.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index 55c411e7..601e1554 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.1.9000
    @@ -161,7 +161,7 @@ maxabs = "auto", ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), nrow.legend = ceiling((length(i) + 1)/ncol.legend), - rel.height.legend = 0.03 + 0.08 * nrow.legend, + rel.height.legend = 0.02 + 0.07 * nrow.legend, rel.height.bottom = 1.1, pch_ds = 1:length(i), col_ds = pch_ds + 1, @@ -283,10 +283,10 @@ corresponding model prediction lines for the different datasets.

    f_saem <- saem(f, transformations = "saemix")
    #> Running main SAEM algorithm -#> [1] "Mon Dec 21 05:58:23 2020" +#> [1] "Sat Feb 6 18:29:17 2021" #> .... #> Minimisation finished -#> [1] "Mon Dec 21 05:58:30 2020"
    plot(f_saem) +#> [1] "Sat Feb 6 18:29:23 2021"
    plot(f_saem)
    # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo, diff --git a/docs/dev/reference/saem-3.png b/docs/dev/reference/saem-3.png index 6a32cda1..4474b1f1 100644 Binary files a/docs/dev/reference/saem-3.png and b/docs/dev/reference/saem-3.png differ diff --git a/docs/dev/reference/saem-5.png b/docs/dev/reference/saem-5.png index 6e6e0f91..27ed3f8f 100644 Binary files a/docs/dev/reference/saem-5.png and b/docs/dev/reference/saem-5.png differ diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 59589378..4578db2a 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 0.9.50.4 + 1.0.1.9000
    @@ -261,27 +261,27 @@ using mmkin.

    state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
    #> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:42 2021" +#> [1] "Sat Feb 6 18:29:26 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:43 2021"
    +#> [1] "Sat Feb 6 18:29:27 2021"
    f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
    #> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:45 2021" +#> [1] "Sat Feb 6 18:29:28 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:46 2021"
    f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Sat Feb 6 18:29:30 2021"
    f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
    #> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:47 2021" +#> [1] "Sat Feb 6 18:29:30 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:49 2021"
    f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Sat Feb 6 18:29:32 2021"
    f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
    #> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:49 2021" +#> [1] "Sat Feb 6 18:29:32 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:52 2021"
    +#> [1] "Sat Feb 6 18:29:35 2021"
    # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -324,10 +324,10 @@ using mmkin.

    f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
    #> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:55 2021" +#> [1] "Sat Feb 6 18:29:37 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:00 2021"
    compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +#> [1] "Sat Feb 6 18:29:42 2021"
    compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
    #> Error in compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)): 'compare.saemix' requires at least two models.
    sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) @@ -346,15 +346,15 @@ using mmkin.

    # four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
    #> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:42:02 2021" +#> [1] "Sat Feb 6 18:29:44 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:07 2021"
    f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Sat Feb 6 18:29:48 2021"
    f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
    #> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:42:08 2021" +#> [1] "Sat Feb 6 18:29:49 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:17 2021"
    # We can use print, plot and summary methods to check the results +#> [1] "Sat Feb 6 18:29:57 2021"
    # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo)
    #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -395,10 +395,10 @@ using mmkin.

    #> SD.g_qlogis 0.44771 -0.86417 1.7596
    plot(f_saem_dfop_sfo)
    summary(f_saem_dfop_sfo, data = TRUE)
    #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 0.9.50.4 +#> mkin version used for pre-fitting: 1.0.1.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Mon Jan 25 14:42:18 2021 -#> Date of summary: Mon Jan 25 14:42:18 2021 +#> Date of fit: Sat Feb 6 18:29:57 2021 +#> Date of summary: Sat Feb 6 18:29:58 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -413,7 +413,7 @@ using mmkin.

    #> #> Model predictions using solution type analytical #> -#> Fitted in 9.954 s using 300, 100 iterations +#> Fitted in 8.539 s using 300, 100 iterations #> #> Variance model: Constant variance #> @@ -489,12 +489,12 @@ using mmkin.

    #> Dataset 6 parent 3 69.2 71.32042 2.12042 1.883 1.125873 #> Dataset 6 parent 6 58.1 56.45256 -1.64744 1.883 -0.874739 #> Dataset 6 parent 6 56.6 56.45256 -0.14744 1.883 -0.078288 -#> Dataset 6 parent 10 44.4 44.48523 0.08523 1.883 0.045256 +#> Dataset 6 parent 10 44.4 44.48523 0.08523 1.883 0.045257 #> Dataset 6 parent 10 43.4 44.48523 1.08523 1.883 0.576224 #> Dataset 6 parent 20 33.3 29.75774 -3.54226 1.883 -1.880826 #> Dataset 6 parent 20 29.2 29.75774 0.55774 1.883 0.296141 #> Dataset 6 parent 34 17.6 19.35710 1.75710 1.883 0.932966 -#> Dataset 6 parent 34 18.0 19.35710 1.35710 1.883 0.720578 +#> Dataset 6 parent 34 18.0 19.35710 1.35710 1.883 0.720579 #> Dataset 6 parent 55 10.5 10.48443 -0.01557 1.883 -0.008266 #> Dataset 6 parent 55 9.3 10.48443 1.18443 1.883 0.628895 #> Dataset 6 parent 90 4.5 3.78622 -0.71378 1.883 -0.378995 @@ -560,9 +560,9 @@ using mmkin.

    #> Dataset 8 parent 1 64.9 67.73197 2.83197 1.883 1.503686 #> Dataset 8 parent 1 66.2 67.73197 1.53197 1.883 0.813428 #> Dataset 8 parent 3 43.5 41.58448 -1.91552 1.883 -1.017081 -#> Dataset 8 parent 3 44.1 41.58448 -2.51552 1.883 -1.335661 +#> Dataset 8 parent 3 44.1 41.58448 -2.51552 1.883 -1.335662 #> Dataset 8 parent 8 18.3 19.62286 1.32286 1.883 0.702395 -#> Dataset 8 parent 8 18.1 19.62286 1.52286 1.883 0.808589 +#> Dataset 8 parent 8 18.1 19.62286 1.52286 1.883 0.808588 #> Dataset 8 parent 14 10.2 10.77819 0.57819 1.883 0.306999 #> Dataset 8 parent 14 10.8 10.77819 -0.02181 1.883 -0.011582 #> Dataset 8 parent 27 4.9 3.26977 -1.63023 1.883 -0.865599 @@ -575,13 +575,13 @@ using mmkin.

    #> Dataset 8 A1 1 7.7 7.61539 -0.08461 1.883 -0.044923 #> Dataset 8 A1 3 15.0 15.47954 0.47954 1.883 0.254622 #> Dataset 8 A1 3 15.1 15.47954 0.37954 1.883 0.201525 -#> Dataset 8 A1 8 21.2 20.22616 -0.97384 1.883 -0.517076 +#> Dataset 8 A1 8 21.2 20.22616 -0.97384 1.883 -0.517075 #> Dataset 8 A1 8 21.1 20.22616 -0.87384 1.883 -0.463979 #> Dataset 8 A1 14 19.7 20.00067 0.30067 1.883 0.159645 #> Dataset 8 A1 14 18.9 20.00067 1.10067 1.883 0.584419 -#> Dataset 8 A1 27 17.5 16.38142 -1.11858 1.883 -0.593929 -#> Dataset 8 A1 27 15.9 16.38142 0.48142 1.883 0.255619 -#> Dataset 8 A1 48 9.5 10.25357 0.75357 1.883 0.400123 +#> Dataset 8 A1 27 17.5 16.38142 -1.11858 1.883 -0.593928 +#> Dataset 8 A1 27 15.9 16.38142 0.48142 1.883 0.255620 +#> Dataset 8 A1 48 9.5 10.25357 0.75357 1.883 0.400124 #> Dataset 8 A1 48 9.8 10.25357 0.45357 1.883 0.240833 #> Dataset 8 A1 70 6.2 5.95728 -0.24272 1.883 -0.128878 #> Dataset 8 A1 70 6.1 5.95728 -0.14272 1.883 -0.075781 @@ -622,7 +622,7 @@ using mmkin.

    #> Dataset 9 A1 91 10.0 10.09177 0.09177 1.883 0.048727 #> Dataset 9 A1 91 9.5 10.09177 0.59177 1.883 0.314211 #> Dataset 9 A1 120 9.1 7.91379 -1.18621 1.883 -0.629841 -#> Dataset 9 A1 120 9.0 7.91379 -1.08621 1.883 -0.576745 +#> Dataset 9 A1 120 9.0 7.91379 -1.08621 1.883 -0.576744 #> Dataset 10 parent 0 96.1 93.65257 -2.44743 1.883 -1.299505 #> Dataset 10 parent 0 94.3 93.65257 -0.64743 1.883 -0.343763 #> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html index 722415fb..93e1365d 100644 --- a/docs/dev/reference/summary.saem.mmkin.html +++ b/docs/dev/reference/summary.saem.mmkin.html @@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally mkin - 0.9.50.4 + 1.0.1.9000
    @@ -260,15 +260,15 @@ saemix authors for the parts inherited from saemix.

    quiet = TRUE, error_model = "tc", cores = 5) f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
    #> Running main SAEM algorithm -#> [1] "Mon Jan 11 12:42:40 2021" +#> [1] "Sat Feb 6 18:30:00 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 11 12:42:53 2021"
    summary(f_saem_dfop_sfo, data = TRUE) +#> [1] "Sat Feb 6 18:30:11 2021"
    summary(f_saem_dfop_sfo, data = TRUE)
    #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 0.9.50.4 +#> mkin version used for pre-fitting: 1.0.1.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Mon Jan 11 12:42:54 2021 -#> Date of summary: Mon Jan 11 12:42:54 2021 +#> Date of fit: Sat Feb 6 18:30:12 2021 +#> Date of summary: Sat Feb 6 18:30:12 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -283,7 +283,7 @@ saemix authors for the parts inherited from saemix.

    #> #> Model predictions using solution type analytical #> -#> Fitted in 13.298 s using 300, 100 iterations +#> Fitted in 11.769 s using 300, 100 iterations #> #> Variance model: Two-component variance function #> @@ -291,7 +291,7 @@ saemix authors for the parts inherited from saemix.

    #> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 #> 101.65645 -4.05368 -0.94311 -2.35943 -4.07006 #> g_qlogis -#> -0.01133 +#> -0.01132 #> #> Fixed degradation parameter values: #> None @@ -299,232 +299,232 @@ saemix authors for the parts inherited from saemix.

    #> Results: #> #> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 830 824.5 -401 +#> AIC BIC logLik +#> 829.3 823.9 -400.7 #> #> Optimised parameters: -#> est. lower upper -#> parent_0 101.4423 97.862 105.0224 -#> log_k_m1 -4.0703 -4.191 -3.9495 -#> f_parent_qlogis -0.9539 -1.313 -0.5949 -#> log_k1 -2.9724 -3.811 -2.1342 -#> log_k2 -3.4977 -4.206 -2.7895 -#> g_qlogis -0.0449 -1.116 1.0262 +#> est. lower upper +#> parent_0 101.29457 97.855 104.7344 +#> log_k_m1 -4.06337 -4.182 -3.9445 +#> f_parent_qlogis -0.94546 -1.307 -0.5841 +#> log_k1 -2.98794 -3.844 -2.1321 +#> log_k2 -3.47891 -4.253 -2.7050 +#> g_qlogis -0.03211 -1.157 1.0931 #> #> Correlation: #> prnt_0 lg_k_1 f_prn_ log_k1 log_k2 -#> log_k_m1 -0.207 -#> f_parent_qlogis -0.148 0.202 -#> log_k1 0.040 -0.038 -0.022 -#> log_k2 0.022 -0.015 -0.009 0.001 -#> g_qlogis -0.012 0.005 0.011 -0.173 -0.130 +#> log_k_m1 -0.202 +#> f_parent_qlogis -0.145 0.195 +#> log_k1 0.094 -0.099 -0.049 +#> log_k2 -0.042 0.056 0.024 -0.097 +#> g_qlogis -0.005 0.000 0.007 -0.160 -0.113 #> #> Random effects: -#> est. lower upper -#> SD.parent_0 2.88564 -0.5163 6.2876 -#> SD.log_k_m1 0.08502 -0.0427 0.2127 -#> SD.f_parent_qlogis 0.38857 0.1350 0.6421 -#> SD.log_k1 0.92338 0.3296 1.5172 -#> SD.log_k2 0.78644 0.2817 1.2912 -#> SD.g_qlogis 0.34614 -0.8727 1.5650 +#> est. lower upper +#> SD.parent_0 2.70085 -0.64980 6.0515 +#> SD.log_k_m1 0.08408 -0.04023 0.2084 +#> SD.f_parent_qlogis 0.39215 0.13695 0.6473 +#> SD.log_k1 0.89280 0.27466 1.5109 +#> SD.log_k2 0.82387 0.26388 1.3838 +#> SD.g_qlogis 0.36468 -0.86978 1.5991 #> #> Variance model: #> est. lower upper -#> a.1 0.65859 0.49250 0.82469 -#> b.1 0.06411 0.05006 0.07817 +#> a.1 0.65724 0.49361 0.82086 +#> b.1 0.06434 0.05034 0.07835 #> #> Backtransformed parameters: #> est. lower upper -#> parent_0 101.44231 97.86220 105.02241 -#> k_m1 0.01707 0.01513 0.01926 -#> f_parent_to_m1 0.27811 0.21201 0.35551 -#> k1 0.05118 0.02213 0.11834 -#> k2 0.03027 0.01491 0.06145 -#> g 0.48878 0.24675 0.73618 +#> parent_0 101.29457 97.85477 104.73437 +#> k_m1 0.01719 0.01526 0.01936 +#> f_parent_to_m1 0.27980 0.21302 0.35798 +#> k1 0.05039 0.02141 0.11859 +#> k2 0.03084 0.01422 0.06687 +#> g 0.49197 0.23916 0.74896 #> #> Resulting formation fractions: #> ff -#> parent_m1 0.2781 -#> parent_sink 0.7219 +#> parent_m1 0.2798 +#> parent_sink 0.7202 #> #> Estimated disappearance times: #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 17.53 61.64 18.55 13.54 22.9 -#> m1 40.60 134.88 NA NA NA +#> parent 17.49 61.05 18.38 13.76 22.47 +#> m1 40.32 133.94 NA NA NA #> #> Data: -#> ds name time observed predicted residual std standardized -#> ds 1 parent 0 89.8 9.869e+01 8.894553 6.3618 1.398124 -#> ds 1 parent 0 104.1 9.869e+01 -5.405447 6.3618 -0.849676 -#> ds 1 parent 1 88.7 9.413e+01 5.426448 6.0706 0.893897 -#> ds 1 parent 1 95.5 9.413e+01 -1.373552 6.0706 -0.226265 -#> ds 1 parent 3 81.8 8.576e+01 3.961821 5.5377 0.715422 -#> ds 1 parent 3 94.5 8.576e+01 -8.738179 5.5377 -1.577932 -#> ds 1 parent 7 71.5 7.168e+01 0.184828 4.6429 0.039809 -#> ds 1 parent 7 70.3 7.168e+01 1.384828 4.6429 0.298270 -#> ds 1 parent 14 54.2 5.351e+01 -0.688235 3.4934 -0.197008 -#> ds 1 parent 14 49.6 5.351e+01 3.911765 3.4934 1.119747 -#> ds 1 parent 28 31.5 3.209e+01 0.590445 2.1603 0.273322 -#> ds 1 parent 28 28.8 3.209e+01 3.290445 2.1603 1.523177 -#> ds 1 parent 60 12.1 1.272e+01 0.618158 1.0481 0.589761 -#> ds 1 parent 60 13.6 1.272e+01 -0.881842 1.0481 -0.841332 -#> ds 1 parent 90 6.2 6.085e+00 -0.115212 0.7655 -0.150512 -#> ds 1 parent 90 8.3 6.085e+00 -2.215212 0.7655 -2.893953 -#> ds 1 parent 120 2.2 3.009e+00 0.809439 0.6863 1.179470 -#> ds 1 parent 120 2.4 3.009e+00 0.609439 0.6863 0.888041 -#> ds 1 m1 1 0.3 1.129e+00 0.828817 0.6626 1.250938 -#> ds 1 m1 1 0.2 1.129e+00 0.928817 0.6626 1.401869 -#> ds 1 m1 3 2.2 3.141e+00 0.940880 0.6887 1.366187 -#> ds 1 m1 3 3.0 3.141e+00 0.140880 0.6887 0.204562 -#> ds 1 m1 7 6.5 6.326e+00 -0.174162 0.7735 -0.225175 -#> ds 1 m1 7 5.0 6.326e+00 1.325838 0.7735 1.714181 -#> ds 1 m1 14 10.2 9.883e+00 -0.317417 0.9139 -0.347326 -#> ds 1 m1 14 9.5 9.883e+00 0.382583 0.9139 0.418631 -#> ds 1 m1 28 12.2 1.251e+01 0.309856 1.0378 0.298572 -#> ds 1 m1 28 13.4 1.251e+01 -0.890144 1.0378 -0.857726 -#> ds 1 m1 60 11.8 1.086e+01 -0.940009 0.9584 -0.980812 -#> ds 1 m1 60 13.2 1.086e+01 -2.340009 0.9584 -2.441581 -#> ds 1 m1 90 6.6 7.823e+00 1.222977 0.8278 1.477332 -#> ds 1 m1 90 9.3 7.823e+00 -1.477023 0.8278 -1.784214 -#> ds 1 m1 120 3.5 5.315e+00 1.815201 0.7415 2.447906 -#> ds 1 m1 120 5.4 5.315e+00 -0.084799 0.7415 -0.114356 -#> ds 2 parent 0 118.0 1.031e+02 -14.876736 6.6443 -2.239038 -#> ds 2 parent 0 99.8 1.031e+02 3.323264 6.6443 0.500171 -#> ds 2 parent 1 90.2 9.757e+01 7.371379 6.2902 1.171891 -#> ds 2 parent 1 94.6 9.757e+01 2.971379 6.2902 0.472386 -#> ds 2 parent 3 96.1 8.788e+01 -8.222746 5.6724 -1.449599 -#> ds 2 parent 3 78.4 8.788e+01 9.477254 5.6724 1.670758 -#> ds 2 parent 7 77.9 7.293e+01 -4.972272 4.7218 -1.053054 -#> ds 2 parent 7 77.7 7.293e+01 -4.772272 4.7218 -1.010697 -#> ds 2 parent 14 56.0 5.602e+01 0.016773 3.6513 0.004594 -#> ds 2 parent 14 54.7 5.602e+01 1.316773 3.6513 0.360633 -#> ds 2 parent 28 36.6 3.855e+01 1.945779 2.5575 0.760803 -#> ds 2 parent 28 36.8 3.855e+01 1.745779 2.5575 0.682603 -#> ds 2 parent 60 22.1 2.101e+01 -1.086693 1.4996 -0.724663 -#> ds 2 parent 60 24.7 2.101e+01 -3.686693 1.4996 -2.458475 -#> ds 2 parent 90 12.4 1.246e+01 0.058759 1.0353 0.056757 -#> ds 2 parent 90 10.8 1.246e+01 1.658759 1.0353 1.602256 -#> ds 2 parent 120 6.8 7.406e+00 0.606226 0.8119 0.746659 -#> ds 2 parent 120 7.9 7.406e+00 -0.493774 0.8119 -0.608157 -#> ds 2 m1 1 1.3 1.438e+00 0.138236 0.6650 0.207869 -#> ds 2 m1 3 3.7 3.879e+00 0.178617 0.7040 0.253726 -#> ds 2 m1 3 4.7 3.879e+00 -0.821383 0.7040 -1.166780 -#> ds 2 m1 7 8.1 7.389e+00 -0.710951 0.8113 -0.876337 -#> ds 2 m1 7 7.9 7.389e+00 -0.510951 0.8113 -0.629812 -#> ds 2 m1 14 10.1 1.069e+01 0.593533 0.9507 0.624328 -#> ds 2 m1 14 10.3 1.069e+01 0.393533 0.9507 0.413951 -#> ds 2 m1 28 10.7 1.240e+01 1.703647 1.0325 1.649956 -#> ds 2 m1 28 12.2 1.240e+01 0.203647 1.0325 0.197229 -#> ds 2 m1 60 10.7 1.055e+01 -0.147672 0.9442 -0.156405 -#> ds 2 m1 60 12.5 1.055e+01 -1.947672 0.9442 -2.062848 -#> ds 2 m1 90 9.1 8.010e+00 -1.090041 0.8351 -1.305210 -#> ds 2 m1 90 7.4 8.010e+00 0.609959 0.8351 0.730362 -#> ds 2 m1 120 6.1 5.793e+00 -0.306797 0.7561 -0.405759 -#> ds 2 m1 120 4.5 5.793e+00 1.293203 0.7561 1.710347 -#> ds 3 parent 0 106.2 1.035e+02 -2.712344 6.6675 -0.406801 -#> ds 3 parent 0 106.9 1.035e+02 -3.412344 6.6675 -0.511788 -#> ds 3 parent 1 107.4 9.548e+01 -11.924044 6.1566 -1.936801 -#> ds 3 parent 1 96.1 9.548e+01 -0.624044 6.1566 -0.101362 -#> ds 3 parent 3 79.4 8.246e+01 3.056105 5.3274 0.573662 -#> ds 3 parent 3 82.6 8.246e+01 -0.143895 5.3274 -0.027010 -#> ds 3 parent 7 63.9 6.489e+01 0.991141 4.2122 0.235304 -#> ds 3 parent 7 62.4 6.489e+01 2.491141 4.2122 0.591416 -#> ds 3 parent 14 51.0 4.869e+01 -2.306824 3.1906 -0.723013 -#> ds 3 parent 14 47.1 4.869e+01 1.593176 3.1906 0.499338 -#> ds 3 parent 28 36.1 3.480e+01 -1.304261 2.3260 -0.560722 -#> ds 3 parent 28 36.6 3.480e+01 -1.804261 2.3260 -0.775679 -#> ds 3 parent 60 20.1 1.988e+01 -0.221952 1.4346 -0.154719 -#> ds 3 parent 60 19.8 1.988e+01 0.078048 1.4346 0.054406 -#> ds 3 parent 90 11.3 1.194e+01 0.642458 1.0099 0.636132 -#> ds 3 parent 90 10.7 1.194e+01 1.242458 1.0099 1.230224 -#> ds 3 parent 120 8.2 7.176e+00 -1.023847 0.8034 -1.274423 -#> ds 3 parent 120 7.3 7.176e+00 -0.123847 0.8034 -0.154158 -#> ds 3 m1 0 0.8 8.527e-13 -0.800000 0.6586 -1.214712 -#> ds 3 m1 1 1.8 1.856e+00 0.055925 0.6693 0.083562 -#> ds 3 m1 1 2.3 1.856e+00 -0.444075 0.6693 -0.663537 -#> ds 3 m1 3 4.2 4.780e+00 0.580164 0.7264 0.798676 -#> ds 3 m1 3 4.1 4.780e+00 0.680164 0.7264 0.936340 -#> ds 3 m1 7 6.8 8.410e+00 1.609920 0.8512 1.891455 -#> ds 3 m1 7 10.1 8.410e+00 -1.690080 0.8512 -1.985633 -#> ds 3 m1 14 11.4 1.098e+01 -0.424444 0.9638 -0.440389 -#> ds 3 m1 14 12.8 1.098e+01 -1.824444 0.9638 -1.892979 -#> ds 3 m1 28 11.5 1.142e+01 -0.079336 0.9848 -0.080558 -#> ds 3 m1 28 10.6 1.142e+01 0.820664 0.9848 0.833311 -#> ds 3 m1 60 7.5 9.110e+00 1.610231 0.8803 1.829222 -#> ds 3 m1 60 8.6 9.110e+00 0.510231 0.8803 0.579622 -#> ds 3 m1 90 7.3 6.799e+00 -0.501085 0.7898 -0.634463 -#> ds 3 m1 90 8.1 6.799e+00 -1.301085 0.7898 -1.647404 -#> ds 3 m1 120 5.3 4.868e+00 -0.431505 0.7288 -0.592064 -#> ds 3 m1 120 3.8 4.868e+00 1.068495 0.7288 1.466073 -#> ds 4 parent 0 104.7 9.926e+01 -5.444622 6.3975 -0.851049 -#> ds 4 parent 0 88.3 9.926e+01 10.955378 6.3975 1.712436 -#> ds 4 parent 1 94.2 9.618e+01 1.978413 6.2013 0.319030 -#> ds 4 parent 1 94.6 9.618e+01 1.578413 6.2013 0.254527 -#> ds 4 parent 3 78.1 9.037e+01 12.268550 5.8311 2.103985 -#> ds 4 parent 3 96.5 9.037e+01 -6.131450 5.8311 -1.051508 -#> ds 4 parent 7 76.2 7.999e+01 3.794958 5.1708 0.733918 -#> ds 4 parent 7 77.8 7.999e+01 2.194958 5.1708 0.424489 -#> ds 4 parent 14 70.8 6.518e+01 -5.624996 4.2301 -1.329742 -#> ds 4 parent 14 67.3 6.518e+01 -2.124996 4.2301 -0.502346 -#> ds 4 parent 28 43.1 4.462e+01 1.517860 2.9354 0.517085 -#> ds 4 parent 28 45.1 4.462e+01 -0.482140 2.9354 -0.164249 -#> ds 4 parent 60 21.3 2.130e+01 -0.003305 1.5159 -0.002180 -#> ds 4 parent 60 23.5 2.130e+01 -2.203305 1.5159 -1.453435 -#> ds 4 parent 90 11.8 1.180e+01 0.002834 1.0032 0.002825 -#> ds 4 parent 90 12.1 1.180e+01 -0.297166 1.0032 -0.296226 -#> ds 4 parent 120 7.0 6.868e+00 -0.132251 0.7922 -0.166937 -#> ds 4 parent 120 6.2 6.868e+00 0.667749 0.7922 0.842879 -#> ds 4 m1 0 1.6 0.000e+00 -1.600000 0.6586 -2.429424 -#> ds 4 m1 1 0.9 6.826e-01 -0.217363 0.6600 -0.329315 -#> ds 4 m1 3 3.7 1.935e+00 -1.765082 0.6702 -2.633768 -#> ds 4 m1 3 2.0 1.935e+00 -0.065082 0.6702 -0.097112 -#> ds 4 m1 7 3.6 4.035e+00 0.434805 0.7076 0.614501 -#> ds 4 m1 7 3.8 4.035e+00 0.234805 0.7076 0.331845 -#> ds 4 m1 14 7.1 6.652e+00 -0.448187 0.7846 -0.571220 -#> ds 4 m1 14 6.6 6.652e+00 0.051813 0.7846 0.066036 -#> ds 4 m1 28 9.5 9.156e+00 -0.343805 0.8822 -0.389696 -#> ds 4 m1 28 9.3 9.156e+00 -0.143805 0.8822 -0.163000 -#> ds 4 m1 60 8.3 8.848e+00 0.547762 0.8692 0.630185 -#> ds 4 m1 60 9.0 8.848e+00 -0.152238 0.8692 -0.175146 -#> ds 4 m1 90 6.6 6.674e+00 0.073979 0.7854 0.094194 -#> ds 4 m1 90 7.7 6.674e+00 -1.026021 0.7854 -1.306390 -#> ds 4 m1 120 3.7 4.668e+00 0.967537 0.7234 1.337503 -#> ds 4 m1 120 3.5 4.668e+00 1.167537 0.7234 1.613979 -#> ds 5 parent 0 110.4 1.022e+02 -8.170986 6.5872 -1.240433 -#> ds 5 parent 0 112.1 1.022e+02 -9.870986 6.5872 -1.498509 -#> ds 5 parent 1 93.5 9.513e+01 1.630764 6.1346 0.265832 -#> ds 5 parent 1 91.0 9.513e+01 4.130764 6.1346 0.673359 -#> ds 5 parent 3 71.0 8.296e+01 11.964279 5.3597 2.232268 -#> ds 5 parent 3 89.7 8.296e+01 -6.735721 5.3597 -1.256735 -#> ds 5 parent 7 60.4 6.495e+01 4.547441 4.2157 1.078684 -#> ds 5 parent 7 59.1 6.495e+01 5.847441 4.2157 1.387053 -#> ds 5 parent 14 56.5 4.626e+01 -10.241319 3.0380 -3.371047 -#> ds 5 parent 14 47.0 4.626e+01 -0.741319 3.0380 -0.244014 -#> ds 5 parent 28 30.2 3.026e+01 0.058478 2.0487 0.028544 -#> ds 5 parent 28 23.9 3.026e+01 6.358478 2.0487 3.103661 -#> ds 5 parent 60 17.0 1.792e+01 0.919046 1.3242 0.694024 -#> ds 5 parent 60 18.7 1.792e+01 -0.780954 1.3242 -0.589742 -#> ds 5 parent 90 11.3 1.187e+01 0.573917 1.0066 0.570144 -#> ds 5 parent 90 11.9 1.187e+01 -0.026083 1.0066 -0.025912 -#> ds 5 parent 120 9.0 7.898e+00 -1.102089 0.8307 -1.326622 -#> ds 5 parent 120 8.1 7.898e+00 -0.202089 0.8307 -0.243261 -#> ds 5 m1 0 0.7 -1.421e-14 -0.700000 0.6586 -1.062873 -#> ds 5 m1 1 3.0 3.144e+00 0.143526 0.6887 0.208390 -#> ds 5 m1 1 2.6 3.144e+00 0.543526 0.6887 0.789161 -#> ds 5 m1 3 5.1 8.390e+00 3.290265 0.8504 3.869277 -#> ds 5 m1 3 7.5 8.390e+00 0.890265 0.8504 1.046932 -#> ds 5 m1 7 16.5 1.566e+01 -0.841368 1.2007 -0.700751 -#> ds 5 m1 7 19.0 1.566e+01 -3.341368 1.2007 -2.782928 -#> ds 5 m1 14 22.9 2.188e+01 -1.017753 1.5498 -0.656687 -#> ds 5 m1 14 23.2 2.188e+01 -1.317753 1.5498 -0.850257 -#> ds 5 m1 28 22.2 2.386e+01 1.655914 1.6652 0.994399 -#> ds 5 m1 28 24.4 2.386e+01 -0.544086 1.6652 -0.326731 -#> ds 5 m1 60 15.5 1.859e+01 3.091124 1.3618 2.269915 -#> ds 5 m1 60 19.8 1.859e+01 -1.208876 1.3618 -0.887718 -#> ds 5 m1 90 14.9 1.372e+01 -1.176815 1.0990 -1.070784 -#> ds 5 m1 90 14.2 1.372e+01 -0.476815 1.0990 -0.433854 -#> ds 5 m1 120 10.9 9.961e+00 -0.938796 0.9174 -1.023332 -#> ds 5 m1 120 10.4 9.961e+00 -0.438796 0.9174 -0.478308
    # } +#> ds name time observed predicted residual std standardized +#> ds 1 parent 0 89.8 9.878e+01 8.98039 6.3899 1.40541 +#> ds 1 parent 0 104.1 9.878e+01 -5.31961 6.3899 -0.83251 +#> ds 1 parent 1 88.7 9.422e+01 5.52084 6.0981 0.90533 +#> ds 1 parent 1 95.5 9.422e+01 -1.27916 6.0981 -0.20976 +#> ds 1 parent 3 81.8 8.587e+01 4.06752 5.5641 0.73103 +#> ds 1 parent 3 94.5 8.587e+01 -8.63248 5.5641 -1.55147 +#> ds 1 parent 7 71.5 7.180e+01 0.29615 4.6662 0.06347 +#> ds 1 parent 7 70.3 7.180e+01 1.49615 4.6662 0.32063 +#> ds 1 parent 14 54.2 5.360e+01 -0.59602 3.5112 -0.16975 +#> ds 1 parent 14 49.6 5.360e+01 4.00398 3.5112 1.14035 +#> ds 1 parent 28 31.5 3.213e+01 0.62529 2.1691 0.28828 +#> ds 1 parent 28 28.8 3.213e+01 3.32529 2.1691 1.53306 +#> ds 1 parent 60 12.1 1.271e+01 0.60718 1.0490 0.57879 +#> ds 1 parent 60 13.6 1.271e+01 -0.89282 1.0490 -0.85108 +#> ds 1 parent 90 6.2 6.080e+00 -0.12020 0.7649 -0.15716 +#> ds 1 parent 90 8.3 6.080e+00 -2.22020 0.7649 -2.90279 +#> ds 1 parent 120 2.2 3.011e+00 0.81059 0.6852 1.18302 +#> ds 1 parent 120 2.4 3.011e+00 0.61059 0.6852 0.89113 +#> ds 1 m1 1 0.3 1.131e+00 0.83071 0.6613 1.25628 +#> ds 1 m1 1 0.2 1.131e+00 0.93071 0.6613 1.40750 +#> ds 1 m1 3 2.2 3.147e+00 0.94691 0.6877 1.37688 +#> ds 1 m1 3 3.0 3.147e+00 0.14691 0.6877 0.21361 +#> ds 1 m1 7 6.5 6.341e+00 -0.15949 0.7736 -0.20618 +#> ds 1 m1 7 5.0 6.341e+00 1.34051 0.7736 1.73290 +#> ds 1 m1 14 10.2 9.910e+00 -0.28991 0.9157 -0.31659 +#> ds 1 m1 14 9.5 9.910e+00 0.41009 0.9157 0.44783 +#> ds 1 m1 28 12.2 1.255e+01 0.34690 1.0410 0.33323 +#> ds 1 m1 28 13.4 1.255e+01 -0.85310 1.0410 -0.81949 +#> ds 1 m1 60 11.8 1.087e+01 -0.92713 0.9599 -0.96586 +#> ds 1 m1 60 13.2 1.087e+01 -2.32713 0.9599 -2.42434 +#> ds 1 m1 90 6.6 7.813e+00 1.21254 0.8274 1.46541 +#> ds 1 m1 90 9.3 7.813e+00 -1.48746 0.8274 -1.79766 +#> ds 1 m1 120 3.5 5.295e+00 1.79489 0.7403 2.42457 +#> ds 1 m1 120 5.4 5.295e+00 -0.10511 0.7403 -0.14198 +#> ds 2 parent 0 118.0 1.074e+02 -10.63436 6.9396 -1.53242 +#> ds 2 parent 0 99.8 1.074e+02 7.56564 6.9396 1.09021 +#> ds 2 parent 1 90.2 1.012e+02 10.96486 6.5425 1.67594 +#> ds 2 parent 1 94.6 1.012e+02 6.56486 6.5425 1.00342 +#> ds 2 parent 3 96.1 9.054e+01 -5.56014 5.8627 -0.94839 +#> ds 2 parent 3 78.4 9.054e+01 12.13986 5.8627 2.07069 +#> ds 2 parent 7 77.9 7.468e+01 -3.21805 4.8501 -0.66350 +#> ds 2 parent 7 77.7 7.468e+01 -3.01805 4.8501 -0.62226 +#> ds 2 parent 14 56.0 5.748e+01 1.47774 3.7563 0.39340 +#> ds 2 parent 14 54.7 5.748e+01 2.77774 3.7563 0.73948 +#> ds 2 parent 28 36.6 3.996e+01 3.36317 2.6541 1.26717 +#> ds 2 parent 28 36.8 3.996e+01 3.16317 2.6541 1.19182 +#> ds 2 parent 60 22.1 2.132e+01 -0.78225 1.5210 -0.51430 +#> ds 2 parent 60 24.7 2.132e+01 -3.38225 1.5210 -2.22369 +#> ds 2 parent 90 12.4 1.215e+01 -0.25010 1.0213 -0.24487 +#> ds 2 parent 90 10.8 1.215e+01 1.34990 1.0213 1.32169 +#> ds 2 parent 120 6.8 6.931e+00 0.13105 0.7943 0.16500 +#> ds 2 parent 120 7.9 6.931e+00 -0.96895 0.7943 -1.21994 +#> ds 2 m1 1 1.3 1.519e+00 0.21924 0.6645 0.32995 +#> ds 2 m1 3 3.7 4.049e+00 0.34891 0.7070 0.49351 +#> ds 2 m1 3 4.7 4.049e+00 -0.65109 0.7070 -0.92094 +#> ds 2 m1 7 8.1 7.565e+00 -0.53526 0.8179 -0.65448 +#> ds 2 m1 7 7.9 7.565e+00 -0.33526 0.8179 -0.40993 +#> ds 2 m1 14 10.1 1.071e+01 0.60614 0.9521 0.63663 +#> ds 2 m1 14 10.3 1.071e+01 0.40614 0.9521 0.42657 +#> ds 2 m1 28 10.7 1.224e+01 1.54440 1.0260 1.50526 +#> ds 2 m1 28 12.2 1.224e+01 0.04440 1.0260 0.04327 +#> ds 2 m1 60 10.7 1.056e+01 -0.14005 0.9453 -0.14815 +#> ds 2 m1 60 12.5 1.056e+01 -1.94005 0.9453 -2.05226 +#> ds 2 m1 90 9.1 8.089e+00 -1.01088 0.8384 -1.20577 +#> ds 2 m1 90 7.4 8.089e+00 0.68912 0.8384 0.82197 +#> ds 2 m1 120 6.1 5.855e+00 -0.24463 0.7576 -0.32292 +#> ds 2 m1 120 4.5 5.855e+00 1.35537 0.7576 1.78911 +#> ds 3 parent 0 106.2 1.095e+02 3.30335 7.0765 0.46680 +#> ds 3 parent 0 106.9 1.095e+02 2.60335 7.0765 0.36788 +#> ds 3 parent 1 107.4 9.939e+01 -8.01282 6.4287 -1.24641 +#> ds 3 parent 1 96.1 9.939e+01 3.28718 6.4287 0.51133 +#> ds 3 parent 3 79.4 8.365e+01 4.24698 5.4222 0.78326 +#> ds 3 parent 3 82.6 8.365e+01 1.04698 5.4222 0.19309 +#> ds 3 parent 7 63.9 6.405e+01 0.14704 4.1732 0.03523 +#> ds 3 parent 7 62.4 6.405e+01 1.64704 4.1732 0.39467 +#> ds 3 parent 14 51.0 4.795e+01 -3.04985 3.1546 -0.96681 +#> ds 3 parent 14 47.1 4.795e+01 0.85015 3.1546 0.26950 +#> ds 3 parent 28 36.1 3.501e+01 -1.09227 2.3465 -0.46549 +#> ds 3 parent 28 36.6 3.501e+01 -1.59227 2.3465 -0.67858 +#> ds 3 parent 60 20.1 2.012e+01 0.02116 1.4520 0.01457 +#> ds 3 parent 60 19.8 2.012e+01 0.32116 1.4520 0.22119 +#> ds 3 parent 90 11.3 1.206e+01 0.76096 1.0170 0.74826 +#> ds 3 parent 90 10.7 1.206e+01 1.36096 1.0170 1.33825 +#> ds 3 parent 120 8.2 7.230e+00 -0.97022 0.8052 -1.20493 +#> ds 3 parent 120 7.3 7.230e+00 -0.07022 0.8052 -0.08721 +#> ds 3 m1 0 0.8 -5.684e-13 -0.80000 0.6572 -1.21722 +#> ds 3 m1 1 1.8 2.045e+00 0.24538 0.6703 0.36608 +#> ds 3 m1 1 2.3 2.045e+00 -0.25462 0.6703 -0.37987 +#> ds 3 m1 3 4.2 5.136e+00 0.93594 0.7356 1.27228 +#> ds 3 m1 3 4.1 5.136e+00 1.03594 0.7356 1.40822 +#> ds 3 m1 7 6.8 8.674e+00 1.87438 0.8623 2.17381 +#> ds 3 m1 7 10.1 8.674e+00 -1.42562 0.8623 -1.65335 +#> ds 3 m1 14 11.4 1.083e+01 -0.56746 0.9580 -0.59233 +#> ds 3 m1 14 12.8 1.083e+01 -1.96746 0.9580 -2.05369 +#> ds 3 m1 28 11.5 1.098e+01 -0.51762 0.9651 -0.53637 +#> ds 3 m1 28 10.6 1.098e+01 0.38238 0.9651 0.39623 +#> ds 3 m1 60 7.5 8.889e+00 1.38911 0.8713 1.59436 +#> ds 3 m1 60 8.6 8.889e+00 0.28911 0.8713 0.33183 +#> ds 3 m1 90 7.3 6.774e+00 -0.52608 0.7886 -0.66708 +#> ds 3 m1 90 8.1 6.774e+00 -1.32608 0.7886 -1.68150 +#> ds 3 m1 120 5.3 4.954e+00 -0.34584 0.7305 -0.47345 +#> ds 3 m1 120 3.8 4.954e+00 1.15416 0.7305 1.58004 +#> ds 4 parent 0 104.7 9.957e+01 -5.13169 6.4403 -0.79681 +#> ds 4 parent 0 88.3 9.957e+01 11.26831 6.4403 1.74966 +#> ds 4 parent 1 94.2 9.644e+01 2.23888 6.2400 0.35879 +#> ds 4 parent 1 94.6 9.644e+01 1.83888 6.2400 0.29469 +#> ds 4 parent 3 78.1 9.054e+01 12.43946 5.8627 2.12180 +#> ds 4 parent 3 96.5 9.054e+01 -5.96054 5.8627 -1.01669 +#> ds 4 parent 7 76.2 8.004e+01 3.83771 5.1918 0.73919 +#> ds 4 parent 7 77.8 8.004e+01 2.23771 5.1918 0.43101 +#> ds 4 parent 14 70.8 6.511e+01 -5.69246 4.2406 -1.34238 +#> ds 4 parent 14 67.3 6.511e+01 -2.19246 4.2406 -0.51702 +#> ds 4 parent 28 43.1 4.454e+01 1.43744 2.9401 0.48890 +#> ds 4 parent 28 45.1 4.454e+01 -0.56256 2.9401 -0.19134 +#> ds 4 parent 60 21.3 2.132e+01 0.02005 1.5211 0.01318 +#> ds 4 parent 60 23.5 2.132e+01 -2.17995 1.5211 -1.43310 +#> ds 4 parent 90 11.8 1.182e+01 0.02167 1.0053 0.02156 +#> ds 4 parent 90 12.1 1.182e+01 -0.27833 1.0053 -0.27687 +#> ds 4 parent 120 7.0 6.852e+00 -0.14780 0.7914 -0.18675 +#> ds 4 parent 120 6.2 6.852e+00 0.65220 0.7914 0.82408 +#> ds 4 m1 0 1.6 -5.684e-14 -1.60000 0.6572 -2.43444 +#> ds 4 m1 1 0.9 6.918e-01 -0.20821 0.6587 -0.31607 +#> ds 4 m1 3 3.7 1.959e+00 -1.74131 0.6692 -2.60204 +#> ds 4 m1 3 2.0 1.959e+00 -0.04131 0.6692 -0.06173 +#> ds 4 m1 7 3.6 4.076e+00 0.47590 0.7076 0.67252 +#> ds 4 m1 7 3.8 4.076e+00 0.27590 0.7076 0.38989 +#> ds 4 m1 14 7.1 6.698e+00 -0.40189 0.7859 -0.51135 +#> ds 4 m1 14 6.6 6.698e+00 0.09811 0.7859 0.12483 +#> ds 4 m1 28 9.5 9.175e+00 -0.32492 0.8835 -0.36779 +#> ds 4 m1 28 9.3 9.175e+00 -0.12492 0.8835 -0.14141 +#> ds 4 m1 60 8.3 8.818e+00 0.51810 0.8683 0.59671 +#> ds 4 m1 60 9.0 8.818e+00 -0.18190 0.8683 -0.20949 +#> ds 4 m1 90 6.6 6.645e+00 0.04480 0.7841 0.05713 +#> ds 4 m1 90 7.7 6.645e+00 -1.05520 0.7841 -1.34581 +#> ds 4 m1 120 3.7 4.648e+00 0.94805 0.7221 1.31293 +#> ds 4 m1 120 3.5 4.648e+00 1.14805 0.7221 1.58991 +#> ds 5 parent 0 110.4 1.026e+02 -7.81752 6.6333 -1.17853 +#> ds 5 parent 0 112.1 1.026e+02 -9.51752 6.6333 -1.43482 +#> ds 5 parent 1 93.5 9.560e+01 2.10274 6.1865 0.33989 +#> ds 5 parent 1 91.0 9.560e+01 4.60274 6.1865 0.74399 +#> ds 5 parent 3 71.0 8.356e+01 12.55799 5.4165 2.31846 +#> ds 5 parent 3 89.7 8.356e+01 -6.14201 5.4165 -1.13394 +#> ds 5 parent 7 60.4 6.550e+01 5.09732 4.2653 1.19506 +#> ds 5 parent 7 59.1 6.550e+01 6.39732 4.2653 1.49984 +#> ds 5 parent 14 56.5 4.641e+01 -10.09145 3.0576 -3.30044 +#> ds 5 parent 14 47.0 4.641e+01 -0.59145 3.0576 -0.19344 +#> ds 5 parent 28 30.2 2.982e+01 -0.37647 2.0284 -0.18560 +#> ds 5 parent 28 23.9 2.982e+01 5.92353 2.0284 2.92028 +#> ds 5 parent 60 17.0 1.754e+01 0.53981 1.3060 0.41332 +#> ds 5 parent 60 18.7 1.754e+01 -1.16019 1.3060 -0.88834 +#> ds 5 parent 90 11.3 1.175e+01 0.45050 1.0018 0.44969 +#> ds 5 parent 90 11.9 1.175e+01 -0.14950 1.0018 -0.14923 +#> ds 5 parent 120 9.0 7.915e+00 -1.08476 0.8315 -1.30462 +#> ds 5 parent 120 8.1 7.915e+00 -0.18476 0.8315 -0.22220 +#> ds 5 m1 0 0.7 0.000e+00 -0.70000 0.6572 -1.06507 +#> ds 5 m1 1 3.0 3.062e+00 0.06170 0.6861 0.08992 +#> ds 5 m1 1 2.6 3.062e+00 0.46170 0.6861 0.67290 +#> ds 5 m1 3 5.1 8.209e+00 3.10938 0.8432 3.68760 +#> ds 5 m1 3 7.5 8.209e+00 0.70938 0.8432 0.84130 +#> ds 5 m1 7 16.5 1.544e+01 -1.05567 1.1914 -0.88605 +#> ds 5 m1 7 19.0 1.544e+01 -3.55567 1.1914 -2.98436 +#> ds 5 m1 14 22.9 2.181e+01 -1.08765 1.5498 -0.70181 +#> ds 5 m1 14 23.2 2.181e+01 -1.38765 1.5498 -0.89539 +#> ds 5 m1 28 22.2 2.404e+01 1.83624 1.6805 1.09270 +#> ds 5 m1 28 24.4 2.404e+01 -0.36376 1.6805 -0.21647 +#> ds 5 m1 60 15.5 1.875e+01 3.25390 1.3741 2.36805 +#> ds 5 m1 60 19.8 1.875e+01 -1.04610 1.3741 -0.76131 +#> ds 5 m1 90 14.9 1.380e+01 -1.09507 1.1050 -0.99102 +#> ds 5 m1 90 14.2 1.380e+01 -0.39507 1.1050 -0.35753 +#> ds 5 m1 120 10.9 1.002e+01 -0.88429 0.9205 -0.96069 +#> ds 5 m1 120 10.4 1.002e+01 -0.38429 0.9205 -0.41749
    # }
    diff --git a/docs/dev/reference/transform_odeparms.html b/docs/dev/reference/transform_odeparms.html index 46b66073..6e19505f 100644 --- a/docs/dev/reference/transform_odeparms.html +++ b/docs/dev/reference/transform_odeparms.html @@ -77,7 +77,7 @@ the ilr transformation is used." /> mkin - 0.9.50.4 + 1.0.1.9000
    @@ -126,7 +126,7 @@ the ilr transformation is used." />
    • - +
      • @@ -231,50 +231,64 @@ This is no problem for the internal use in mkinfit.
      SFO_SFO <- mkinmod( parent = list(type = "SFO", to = "m1", sink = TRUE), - m1 = list(type = "SFO")) -
      #> Temporary DLL for differentials generated and loaded
      # Fit the model to the FOCUS example dataset D using defaults -fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) -
      #> Warning: Observations with value of zero were removed from the data
      fit.s <- summary(fit) + m1 = list(type = "SFO"), use_of_ff = "min") +
      #> Temporary DLL for differentials generated and loaded
      +# Fit the model to the FOCUS example dataset D using defaults +FOCUS_D <- subset(FOCUS_2006_D, value != 0) # remove zero values to avoid warning +fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) +fit.s <- summary(fit) # Transformed and backtransformed parameters print(fit.s$par, 3) -
      #> Estimate Std. Error Lower Upper -#> parent_0 99.5985 1.5702 96.404 102.79 -#> log_k_parent -2.3157 0.0409 -2.399 -2.23 -#> log_k_m1 -5.2475 0.1332 -5.518 -4.98 -#> f_parent_qlogis 0.0579 0.0893 -0.124 0.24 -#> sigma 3.1255 0.3585 2.396 3.85
      print(fit.s$bpar, 3) -
      #> Estimate se_notrans t value Pr(>t) Lower Upper -#> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40383 102.7931 -#> k_parent 0.09870 0.00403 24.47 4.96e-23 0.09082 0.1073 -#> k_m1 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069 -#> f_parent_to_m1 0.51448 0.02230 23.07 3.10e-22 0.46912 0.5596 -#> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549
      +
      #> Estimate Std. Error Lower Upper +#> parent_0 99.60 1.5702 96.40 102.79 +#> log_k_parent_sink -3.04 0.0763 -3.19 -2.88 +#> log_k_parent_m1 -2.98 0.0403 -3.06 -2.90 +#> log_k_m1_sink -5.25 0.1332 -5.52 -4.98 +#> sigma 3.13 0.3585 2.40 3.85
      print(fit.s$bpar, 3) +
      #> Estimate se_notrans t value Pr(>t) Lower Upper +#> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 102.7931 +#> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04103 0.0560 +#> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04678 0.0551 +#> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069 +#> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549
      # \dontrun{ -# Compare to the version without transforming rate parameters -fit.2 <- mkinfit(SFO_SFO, FOCUS_2006_D, transform_rates = FALSE, quiet = TRUE) -
      #> Warning: Observations with value of zero were removed from the data
      #> Error in if (cost < cost.current) { assign("cost.current", cost, inherits = TRUE) if (!quiet) cat(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"), " at call ", calls, ": ", signif(cost.current, 6), "\n", sep = "")}: missing value where TRUE/FALSE needed
      #> Timing stopped at: 0.006 0 0.005
      fit.2.s <- summary(fit.2) -
      #> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'fit.2' not found
      print(fit.2.s$par, 3) -
      #> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': object 'fit.2.s' not found
      print(fit.2.s$bpar, 3) -
      #> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': object 'fit.2.s' not found
      # } +# Compare to the version without transforming rate parameters (does not work +# with analytical solution, we get NA values for m1 in predictions) +fit.2 <- mkinfit(SFO_SFO, FOCUS_D, transform_rates = FALSE, + solution_type = "deSolve", quiet = TRUE) +fit.2.s <- summary(fit.2) +print(fit.2.s$par, 3) +
      #> Estimate Std. Error Lower Upper +#> parent_0 99.59848 1.57022 96.40384 1.03e+02 +#> k_parent_sink 0.04792 0.00365 0.04049 5.54e-02 +#> k_parent_m1 0.05078 0.00205 0.04661 5.49e-02 +#> k_m1_sink 0.00526 0.00070 0.00384 6.69e-03 +#> sigma 3.12550 0.35852 2.39609 3.85e+00
      print(fit.2.s$bpar, 3) +
      #> Estimate se_notrans t value Pr(>t) Lower Upper +#> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 1.03e+02 +#> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04049 5.54e-02 +#> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04661 5.49e-02 +#> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00384 6.69e-03 +#> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.85e+00
      # } initials <- fit$start$value names(initials) <- rownames(fit$start) transformed <- fit$start_transformed$value names(transformed) <- rownames(fit$start_transformed) transform_odeparms(initials, SFO_SFO) -
      #> parent_0 log_k_parent log_k_m1 f_parent_qlogis -#> 100.750000 -2.302585 -2.301586 0.000000
      backtransform_odeparms(transformed, SFO_SFO) -
      #> parent_0 k_parent k_m1 f_parent_to_m1 -#> 100.7500 0.1000 0.1001 0.5000
      +
      #> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink +#> 100.750000 -2.302585 -2.301586 -2.300587
      backtransform_odeparms(transformed, SFO_SFO) +
      #> parent_0 k_parent_sink k_parent_m1 k_m1_sink +#> 100.7500 0.1000 0.1001 0.1002
      # \dontrun{ -# The case of formation fractions +# The case of formation fractions (this is now the default) SFO_SFO.ff <- mkinmod( parent = list(type = "SFO", to = "m1", sink = TRUE), m1 = list(type = "SFO"), use_of_ff = "max")
      #> Temporary DLL for differentials generated and loaded
      -fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE) -
      #> Warning: Observations with value of zero were removed from the data
      fit.ff.s <- summary(fit.ff) +fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_D, quiet = TRUE) +fit.ff.s <- summary(fit.ff) print(fit.ff.s$par, 3)
      #> Estimate Std. Error Lower Upper #> parent_0 99.5985 1.5702 96.404 102.79 @@ -299,8 +313,8 @@ This is no problem for the internal use in mkinfit.= "max")
      #> Temporary DLL for differentials generated and loaded
      -fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_2006_D, quiet = TRUE) -
      #> Warning: Observations with value of zero were removed from the data
      fit.ff.2.s <- summary(fit.ff.2) +fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_D, quiet = TRUE) +fit.ff.2.s <- summary(fit.ff.2) print(fit.ff.2.s$par, 3)
      #> Estimate Std. Error Lower Upper #> parent_0 84.79 3.012 78.67 90.91 diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index ecf68e50..27f0e392 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -174,9 +174,6 @@ https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html - - https://pkgdown.jrwb.de/mkin/reference/print.mmkin.html - https://pkgdown.jrwb.de/mkin/reference/reexports.html diff --git a/man/endpoints.Rd b/man/endpoints.Rd index 0b225e62..72487717 100644 --- a/man/endpoints.Rd +++ b/man/endpoints.Rd @@ -8,8 +8,8 @@ with mkinfit} endpoints(fit) } \arguments{ -\item{fit}{An object of class \link{mkinfit} or \link{nlme.mmkin} -or another object that has list components +\item{fit}{An object of class \link{mkinfit}, \link{nlme.mmkin} or +\link{saem.mmkin}. Or another object that has list components mkinmod containing an \link{mkinmod} degradation model, and two numeric vectors, bparms.optim and bparms.fixed, that contain parameter values for that model.} @@ -32,8 +32,8 @@ Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models } \note{ -The function is used internally by \link{summary.mkinfit} -and \link{summary.nlme.mmkin} +The function is used internally by \link{summary.mkinfit}, +\link{summary.nlme.mmkin} and \link{summary.saem.mmkin}. } \examples{ diff --git a/man/plot.mixed.mmkin.Rd b/man/plot.mixed.mmkin.Rd index 87a82286..b1200729 100644 --- a/man/plot.mixed.mmkin.Rd +++ b/man/plot.mixed.mmkin.Rd @@ -27,7 +27,7 @@ ) } \arguments{ -\item{x}{An object of class \link{mixed.mmkin}, \link{nlme.mmkin}} +\item{x}{An object of class \link{mixed.mmkin}, \link{saem.mmkin} or \link{nlme.mmkin}} \item{i}{A numeric index to select datasets for which to plot the individual predictions, in case plots get too large} @@ -94,6 +94,15 @@ plot(f[, 3:4], standardized = TRUE) f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) plot(f_nlme) +f_saem <- saem(f, transformations = "saemix") +plot(f_saem) + +# We can overlay the two variants if we generate predictions +pred_nlme <- mkinpredict(dfop_sfo, + f_nlme$bparms.optim[-1], + c(parent = f_nlme$bparms.optim[[1]], A1 = 0), + seq(0, 180, by = 0.2)) +plot(f_saem, pred_over = list(nlme = pred_nlme)) } } \author{ diff --git a/man/saem.Rd b/man/saem.Rd new file mode 100644 index 00000000..d5a8f17e --- /dev/null +++ b/man/saem.Rd @@ -0,0 +1,155 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/saem.R +\name{saem} +\alias{saem} +\alias{saem.mmkin} +\alias{print.saem.mmkin} +\alias{saemix_model} +\alias{saemix_data} +\title{Fit nonlinear mixed models with SAEM} +\usage{ +saem(object, ...) + +\method{saem}{mmkin}( + object, + transformations = c("mkin", "saemix"), + degparms_start = numeric(), + solution_type = "auto", + control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = + FALSE), + verbose = FALSE, + quiet = FALSE, + ... +) + +\method{print}{saem.mmkin}(x, digits = max(3, getOption("digits") - 3), ...) + +saemix_model( + object, + solution_type = "auto", + transformations = c("mkin", "saemix"), + degparms_start = numeric(), + verbose = FALSE, + ... +) + +saemix_data(object, verbose = FALSE, ...) +} +\arguments{ +\item{object}{An \link{mmkin} row object containing several fits of the same +\link{mkinmod} model to different datasets} + +\item{\dots}{Further parameters passed to \link[saemix:saemixModel]{saemix::saemixModel}.} + +\item{transformations}{Per default, all parameter transformations are done +in mkin. If this argument is set to 'saemix', parameter transformations +are done in 'saemix' for the supported cases. Currently this is only +supported in cases where the initial concentration of the parent is not fixed, +SFO or DFOP is used for the parent and there is either no metabolite or one.} + +\item{degparms_start}{Parameter values given as a named numeric vector will +be used to override the starting values obtained from the 'mmkin' object.} + +\item{solution_type}{Possibility to specify the solution type in case the +automatic choice is not desired} + +\item{control}{Passed to \link[saemix:saemix]{saemix::saemix}} + +\item{verbose}{Should we print information about created objects of +type \link[saemix:SaemixModel-class]{saemix::SaemixModel} and \link[saemix:SaemixData-class]{saemix::SaemixData}?} + +\item{quiet}{Should we suppress the messages saemix prints at the beginning +and the end of the optimisation process?} + +\item{x}{An saem.mmkin object to print} + +\item{digits}{Number of digits to use for printing} +} +\value{ +An S3 object of class 'saem.mmkin', containing the fitted +\link[saemix:SaemixObject-class]{saemix::SaemixObject} as a list component named 'so'. The +object also inherits from 'mixed.mmkin'. + +An \link[saemix:SaemixModel-class]{saemix::SaemixModel} object. + +An \link[saemix:SaemixData-class]{saemix::SaemixData} object. +} +\description{ +This function uses \code{\link[saemix:saemix]{saemix::saemix()}} as a backend for fitting nonlinear mixed +effects models created from \link{mmkin} row objects using the Stochastic Approximation +Expectation Maximisation algorithm (SAEM). +} +\details{ +An mmkin row object is essentially a list of mkinfit objects that have been +obtained by fitting the same model to a list of datasets using \link{mkinfit}. + +Starting values for the fixed effects (population mean parameters, argument +psi0 of \code{\link[saemix:saemixModel]{saemix::saemixModel()}} are the mean values of the parameters found +using \link{mmkin}. +} +\examples{ +\dontrun{ +ds <- lapply(experimental_data_for_UBA_2019[6:10], + function(x) subset(x$data[c("name", "time", "value")])) +names(ds) <- paste("Dataset", 6:10) +f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, + state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) +f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed) + +f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) +f_saem_sfo <- saem(f_mmkin_parent["SFO", ]) +f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) + +# The returned saem.mmkin object contains an SaemixObject, therefore we can use +# functions from saemix +library(saemix) +compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)) +plot(f_saem_fomc$so, plot.type = "convergence") +plot(f_saem_fomc$so, plot.type = "individual.fit") +plot(f_saem_fomc$so, plot.type = "npde") +plot(f_saem_fomc$so, plot.type = "vpc") + +f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") +f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ]) +compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) + +sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), + A1 = mkinsub("SFO")) +fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), + A1 = mkinsub("SFO")) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), + A1 = mkinsub("SFO")) +# The following fit uses analytical solutions for SFO-SFO and DFOP-SFO, +# and compiled ODEs for FOMC that are much slower +f_mmkin <- mmkin(list( + "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), + ds, quiet = TRUE) +# saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds +# each on this system, as we use analytical solutions written for saemix. +# When using the analytical solutions written for mkin this took around +# four minutes +f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ]) +f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +# We can use print, plot and summary methods to check the results +print(f_saem_dfop_sfo) +plot(f_saem_dfop_sfo) +summary(f_saem_dfop_sfo, data = TRUE) + +# The following takes about 6 minutes +#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", +# control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) + +#saemix::compare.saemix(list( +# f_saem_dfop_sfo$so, +# f_saem_dfop_sfo_deSolve$so)) + +# If the model supports it, we can also use eigenvalue based solutions, which +# take a similar amount of time +#f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen", +# control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) +} +} +\seealso{ +\link{summary.saem.mmkin} \link{plot.mixed.mmkin} +} diff --git a/man/summary.saem.mmkin.Rd b/man/summary.saem.mmkin.Rd new file mode 100644 index 00000000..67cb3cbb --- /dev/null +++ b/man/summary.saem.mmkin.Rd @@ -0,0 +1,100 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/summary.saem.mmkin.R +\name{summary.saem.mmkin} +\alias{summary.saem.mmkin} +\alias{print.summary.saem.mmkin} +\title{Summary method for class "saem.mmkin"} +\usage{ +\method{summary}{saem.mmkin}(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) + +\method{print}{summary.saem.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) +} +\arguments{ +\item{object}{an object of class \link{saem.mmkin}} + +\item{data}{logical, indicating whether the full data should be included in +the summary.} + +\item{verbose}{Should the summary be verbose?} + +\item{distimes}{logical, indicating whether DT50 and DT90 values should be +included.} + +\item{\dots}{optional arguments passed to methods like \code{print}.} + +\item{x}{an object of class \link{summary.saem.mmkin}} + +\item{digits}{Number of digits to use for printing} +} +\value{ +The summary function returns a list based on the \link[saemix:SaemixObject-class]{saemix::SaemixObject} +obtained in the fit, with at least the following additional components +\item{saemixversion, mkinversion, Rversion}{The saemix, mkin and R versions used} +\item{date.fit, date.summary}{The dates where the fit and the summary were +produced} +\item{diffs}{The differential equations used in the degradation model} +\item{use_of_ff}{Was maximum or minimum use made of formation fractions} +\item{data}{The data} +\item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals} +\item{confint_back}{Backtransformed parameters, with confidence intervals if available} +\item{confint_errmod}{Error model parameters with confidence intervals} +\item{ff}{The estimated formation fractions derived from the fitted +model.} +\item{distimes}{The DT50 and DT90 values for each observed variable.} +\item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} +The print method is called for its side effect, i.e. printing the summary. +} +\description{ +Lists model equations, initial parameter values, optimised parameters +for fixed effects (population), random effects (deviations from the +population mean) and residual error model, as well as the resulting +endpoints such as formation fractions and DT50 values. Optionally +(default is FALSE), the data are listed in full. +} +\examples{ +# Generate five datasets following DFOP-SFO kinetics +sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), + m1 = mkinsub("SFO"), quiet = TRUE) +set.seed(1234) +k1_in <- rlnorm(5, log(0.1), 0.3) +k2_in <- rlnorm(5, log(0.02), 0.3) +g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +k_m1_in <- rlnorm(5, log(0.02), 0.3) + +pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { + mkinpredict(dfop_sfo, + c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), + c(parent = 100, m1 = 0), + sampling_times) +} + +ds_mean_dfop_sfo <- lapply(1:5, function(i) { + mkinpredict(dfop_sfo, + c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], + f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), + c(parent = 100, m1 = 0), + sampling_times) +}) +names(ds_mean_dfop_sfo) <- paste("ds", 1:5) + +ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { + add_err(ds, + sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), + n = 1)[[1]] +}) + +\dontrun{ +# Evaluate using mmkin and saem +f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, + quiet = TRUE, error_model = "tc", cores = 5) +f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) +summary(f_saem_dfop_sfo, data = TRUE) +} + +} +\author{ +Johannes Ranke for the mkin specific parts +saemix authors for the parts inherited from saemix. +} diff --git a/test.log b/test.log index d609022b..7e1fe957 100644 --- a/test.log +++ b/test.log @@ -6,32 +6,39 @@ Testing mkin ✔ | 2 | Export dataset for reading into CAKE ✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [1.0 s] ✔ | 4 | Calculation of FOCUS chi2 error levels [0.5 s] -✔ | 7 | Fitting the SFORB model [3.4 s] -✔ | 5 | Analytical solutions for coupled models [3.3 s] +✔ | 7 | Fitting the SFORB model [3.5 s] +✔ | 5 | Analytical solutions for coupled models [3.2 s] ✔ | 5 | Calculation of Akaike weights ✔ | 12 | Confidence intervals and p-values [1.0 s] -✔ | 14 | Error model fitting [4.2 s] +✔ | 14 | Error model fitting [4.4 s] ✔ | 5 | Time step normalisation ✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s] ✔ | 1 | Fitting the logistic model [0.2 s] -✔ | 5 | Nonlinear mixed-effects models [0.1 s] +✔ | 34 1 | Nonlinear mixed-effects models [25.9 s] +──────────────────────────────────────────────────────────────────────────────── +Skip (test_mixed.R:150:3): saem results are reproducible for biphasic fits +Reason: Fitting with saemix takes around 10 minutes when using deSolve +──────────────────────────────────────────────────────────────────────────────── ✔ | 2 | Test dataset classes mkinds and mkindsg ✔ | 1 | mkinfit features [0.3 s] ✔ | 10 | Special cases of mkinfit calls [0.3 s] ✔ | 8 | mkinmod model generation and printing [0.2 s] ✔ | 3 | Model predictions with mkinpredict [0.3 s] ✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.6 s] -✔ | 9 | Nonlinear mixed-effects models [8.0 s] -✔ | 14 | Plotting [1.7 s] +✔ | 9 | Nonlinear mixed-effects models [8.1 s] +✔ | 16 | Plotting [1.9 s] ✔ | 4 | Residuals extracted from mkinfit models ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s] ✔ | 4 | Summary [0.1 s] ✔ | 1 | Summaries of old mkinfit objects ✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2 s] -✔ | 9 | Hypothesis tests [8.1 s] -✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.6 s] +✔ | 9 | Hypothesis tests [8.3 s] +✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.5 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 41.3 s +Duration: 67.9 s -[ FAIL 0 | WARN 0 | SKIP 0 | PASS 174 ] +── Skipped tests ────────────────────────────────────────────────────────────── +● Fitting with saemix takes around 10 minutes when using deSolve (1) + +[ FAIL 0 | WARN 0 | SKIP 1 | PASS 205 ] diff --git a/tests/figs/plotting/mixed-model-fit-for-saem-object-with-mkin-transformations.svg b/tests/figs/plotting/mixed-model-fit-for-saem-object-with-mkin-transformations.svg index ce93625d..0c2992d5 100644 --- a/tests/figs/plotting/mixed-model-fit-for-saem-object-with-mkin-transformations.svg +++ b/tests/figs/plotting/mixed-model-fit-for-saem-object-with-mkin-transformations.svg @@ -13,720 +13,720 @@ - 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+ + + + + + + +0 +20 +40 +60 +80 +100 + + + + + + +-4 +-2 +0 +2 +4 + - - + + -Predicted -Standardized residual +Predicted +Standardized residual - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + - + @@ -407,152 +407,152 @@ 50 100 150 - - - - - - - -0 -5 -10 -15 -20 -25 - + + + + + + + +0 +5 +10 +15 +20 +25 + - - + + -Time -A1 +Time +Residues A1 - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + @@ -573,141 +573,141 @@ 15 20 25 - - - - - - --4 --2 -0 -2 -4 - + + + + + + +-4 +-2 +0 +2 +4 + - - + + -Predicted -Standardized residual +Predicted +Standardized residual - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R index 547b2d6c..9229c198 100644 --- a/tests/testthat/setup_script.R +++ b/tests/testthat/setup_script.R @@ -178,6 +178,10 @@ ds_biphasic <- lapply(ds_biphasic_mean, function(ds) { }) # Mixed model fits +saemix_available <- FALSE +if (requireNamespace("saemix", quietly = TRUE)) { + if(packageVersion("saemix") >= "3.1.9000") saemix_available <- TRUE +} mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = n_cores) mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cores = n_cores) mmkin_biphasic <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, quiet = TRUE, cores = n_cores) @@ -186,6 +190,16 @@ mmkin_biphasic_mixed <- mixed(mmkin_biphasic) dfop_nlme_1 <- nlme(mmkin_dfop_1) nlme_biphasic <- nlme(mmkin_biphasic) +if (saemix_available) { + sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix") + + dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin") + dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix") + + saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE) + saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE) +} + ds_uba <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")])) names(ds_uba) <- paste("Dataset", 6:10) @@ -197,3 +211,7 @@ f_uba_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo_uba, "DFOP-SFO" = dfop_sfo_uba), ds_uba, quiet = TRUE, cores = n_cores) f_uba_dfop_sfo_mixed <- mixed(f_uba_mmkin[2, ]) +if (saemix_available) { + f_uba_sfo_sfo_saem <- saem(f_uba_mmkin["SFO-SFO", ], quiet = TRUE, transformations = "saemix") + f_uba_dfop_sfo_saem <- saem(f_uba_mmkin["DFOP-SFO", ], quiet = TRUE, transformations = "saemix") +} diff --git a/tests/testthat/summary_saem_biphasic_s.txt b/tests/testthat/summary_saem_biphasic_s.txt new file mode 100644 index 00000000..1e0f1ccc --- /dev/null +++ b/tests/testthat/summary_saem_biphasic_s.txt @@ -0,0 +1,77 @@ +saemix version used for fitting: Dummy 0.0 for testing +mkin version used for pre-fitting: Dummy 0.0 for testing +R version used for fitting: Dummy R version for testing +Date of fit: Dummy date for testing +Date of summary: Dummy date for testing + +Equations: +d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * parent +d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) + * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * parent - k_m1 * m1 + +Data: +509 observations of 2 variable(s) grouped in 15 datasets + +Model predictions using solution type analytical + +Fitted in test time 0 s using 300, 100 iterations + +Variance model: Constant variance + +Mean of starting values for individual parameters: + parent_0 k_m1 f_parent_to_m1 k1 k2 + 1.0e+02 4.8e-03 4.8e-01 6.8e-02 1.3e-02 + g + 4.2e-01 + +Fixed degradation parameter values: +None + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2645 2654 -1310 + +Optimised parameters: + est. lower upper +parent_0 1.0e+02 99.627 1.0e+02 +k_m1 4.8e-03 0.004 5.6e-03 +f_parent_to_m1 4.8e-01 0.437 5.2e-01 +k1 6.5e-02 0.051 8.0e-02 +k2 1.2e-02 0.010 1.4e-02 +g 4.3e-01 0.362 5.0e-01 + +Correlation: + prnt_0 k_m1 f_p__1 k1 k2 +k_m1 -0.156 +f_parent_to_m1 -0.157 0.372 +k1 0.159 0.000 -0.029 +k2 0.074 0.145 0.032 0.332 +g -0.072 -0.142 -0.044 -0.422 -0.570 + +Random effects: + est. lower upper +SD.parent_0 1.14 0.251 2.03 +SD.k_m1 0.14 -0.073 0.35 +SD.f_parent_to_m1 0.29 0.176 0.41 +SD.k1 0.36 0.211 0.52 +SD.k2 0.18 0.089 0.27 +SD.g 0.32 0.098 0.53 + +Variance model: + est. lower upper +a.1 2.7 2.5 2.9 + +Resulting formation fractions: + ff +parent_m1 0.48 +parent_sink 0.52 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +parent 25 145 44 11 58 +m1 145 481 NA NA NA diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R index 6f28d0c3..0eb1f0d5 100644 --- a/tests/testthat/test_mixed.R +++ b/tests/testthat/test_mixed.R @@ -1,9 +1,98 @@ context("Nonlinear mixed-effects models") +test_that("Parent fits using saemix are correctly implemented", { + skip_if(!saemix_available) + + expect_error(saem(fits), "Only row objects") + # Some fits were done in the setup script + mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100)) + expect_error(update(mmkin_sfo_1, models = c("SFOOO")), "Please supply models.*") + + sfo_saem_2 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "mkin") + sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters") + s_sfo_s1 <- summary(sfo_saem_1) + s_sfo_s2 <- summary(sfo_saem_2) + + sfo_nlme_1 <- expect_warning(nlme(mmkin_sfo_1), "not converge") + s_sfo_n <- summary(sfo_nlme_1) + + # Compare with input + expect_equal(round(s_sfo_s2$confint_ranef["SD.log_k_parent", "est."], 1), 0.3) + # k_parent is a bit different from input 0.03 here + expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), 0.035) + expect_equal(round(s_sfo_s2$confint_back["k_parent", "est."], 3), 0.035) + + # But the result is pretty unanimous between methods + expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), + round(s_sfo_s2$confint_back["k_parent", "est."], 3)) + expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), + round(s_sfo_n$confint_back["k_parent", "est."], 3)) + + mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores) + fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE) + ci_fomc_s1 <- summary(fomc_saem_1)$confint_back + + fomc_pop <- as.numeric(fomc_pop) + expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop)) + expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop)) + + mmkin_fomc_2 <- update(mmkin_fomc_1, state.ini = 100, fixed_initials = "parent") + fomc_saem_2 <- saem(mmkin_fomc_2, quiet = TRUE, transformations = "mkin") + ci_fomc_s2 <- summary(fomc_saem_2)$confint_back + + expect_true(all(ci_fomc_s2[, "lower"] < fomc_pop[2:3])) + expect_true(all(ci_fomc_s2[, "upper"] > fomc_pop[2:3])) + + s_dfop_s1 <- summary(dfop_saemix_1) + s_dfop_s2 <- summary(dfop_saemix_2) + s_dfop_n <- summary(dfop_nlme_1) + + dfop_pop <- as.numeric(dfop_pop) + expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop)) + expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop)) + expect_true(all(s_dfop_s2$confint_back[, "lower"] < dfop_pop)) + expect_true(all(s_dfop_s2$confint_back[, "upper"] > dfop_pop)) + + dfop_mmkin_means_trans <- apply(parms(mmkin_dfop_1, transformed = TRUE), 1, mean) + dfop_mmkin_means <- backtransform_odeparms(dfop_mmkin_means_trans, mmkin_dfop_1$mkinmod) + + # We get < 22% deviations by averaging the transformed parameters + rel_diff_mmkin <- (dfop_mmkin_means - dfop_pop) / dfop_pop + expect_true(all(rel_diff_mmkin < 0.22)) + + # We get < 50% deviations with transformations made in mkin + rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop + expect_true(all(rel_diff_1 < 0.5)) + + # We get < 12% deviations with transformations made in saemix + rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop + expect_true(all(rel_diff_2 < 0.12)) + + mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores) + hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE) + ci_hs_s1 <- summary(hs_saem_1)$confint_back + + hs_pop <- as.numeric(hs_pop) + # expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated + expect_true(all(ci_hs_s1[, "upper"] > hs_pop)) + + mmkin_hs_2 <- update(mmkin_hs_1, state.ini = 100, fixed_initials = "parent") + hs_saem_2 <- saem(mmkin_hs_2, quiet = TRUE) + ci_hs_s2 <- summary(hs_saem_2)$confint_back + + #expect_true(all(ci_hs_s2[, "lower"] < hs_pop[2:4])) # k1 again overestimated + expect_true(all(ci_hs_s2[, "upper"] > hs_pop[2:4])) + + # HS would likely benefit from implemenation of transformations = "saemix" +}) + test_that("Print methods work", { expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt") expect_known_output(print(mmkin_biphasic_mixed, digits = 2), "print_mmkin_biphasic_mixed.txt") expect_known_output(print(nlme_biphasic, digits = 1), "print_nlme_biphasic.txt") + + skip_if(!saemix_available) + expect_known_output(print(sfo_saem_1, digits = 1), "print_sfo_saem_1.txt") }) test_that("nlme results are reproducible to some degree", { @@ -20,6 +109,50 @@ test_that("nlme results are reproducible to some degree", { dfop_sfo_pop <- as.numeric(dfop_sfo_pop) ci_dfop_sfo_n <- summary(nlme_biphasic)$confint_back - # expect_true(all(ci_dfop_sfo_n[, "lower"] < dfop_sfo_pop)) # k2 is overestimated + expect_true(all(ci_dfop_sfo_n[, "lower"] < dfop_sfo_pop)) expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop)) }) + +test_that("saem results are reproducible for biphasic fits", { + + skip_if(!saemix_available) + test_summary <- summary(saem_biphasic_s) + test_summary$saemixversion <- "Dummy 0.0 for testing" + test_summary$mkinversion <- "Dummy 0.0 for testing" + test_summary$Rversion <- "Dummy R version for testing" + test_summary$date.fit <- "Dummy date for testing" + test_summary$date.summary <- "Dummy date for testing" + test_summary$time <- c(elapsed = "test time 0") + + expect_known_output(print(test_summary, digits = 2), "summary_saem_biphasic_s.txt") + + dfop_sfo_pop <- as.numeric(dfop_sfo_pop) + no_k1 <- c(1, 2, 3, 5, 6) + no_k2 <- c(1, 2, 3, 4, 6) + no_k1_k2 <- c(1, 2, 3, 6) + + ci_dfop_sfo_s_s <- summary(saem_biphasic_s)$confint_back + # k1 and k2 are overestimated + expect_true(all(ci_dfop_sfo_s_s[no_k1_k2, "lower"] < dfop_sfo_pop[no_k1_k2])) + expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop)) + + # k1 and k2 are not fitted well + ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$confint_back + expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2])) + expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1])) + + # I tried to only do few iterations in routine tests as this is so slow + # but then deSolve fails at some point (presumably at the switch between + # the two types of iterations) + #saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", + # control = list(nbiter.saemix = c(10, 5), nbiter.burn = 5), quiet = TRUE) + + skip("Fitting with saemix takes around 10 minutes when using deSolve") + saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", quiet = TRUE) + + # As with the analytical solution, k1 and k2 are not fitted well + ci_dfop_sfo_s_d <- summary(saem_biphasic_2)$confint_back + expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2])) + expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1])) +}) + diff --git a/tests/testthat/test_plot.R b/tests/testthat/test_plot.R index 0bf3ee66..1c95d069 100644 --- a/tests/testthat/test_plot.R +++ b/tests/testthat/test_plot.R @@ -35,6 +35,11 @@ test_that("Plotting mkinfit, mmkin and mixed model objects is reproducible", { plot_biphasic_mmkin <- function() plot(f_uba_dfop_sfo_mixed) vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_biphasic_mmkin) + if (saemix_available) { + plot_biphasic_saem_s <- function() plot(f_uba_dfop_sfo_saem) + vdiffr::expect_doppelganger("mixed model fit for saem object with saemix transformations", plot_biphasic_saem_s) + } + skip_on_travis() plot_biphasic_nlme <- function() plot(dfop_nlme_1) @@ -43,6 +48,12 @@ test_that("Plotting mkinfit, mmkin and mixed model objects is reproducible", { #plot_biphasic_mmkin <- function() plot(mixed(mmkin_biphasic)) # Biphasic fits with lots of data and fits have lots of potential for differences plot_biphasic_nlme <- function() plot(nlme_biphasic) + if (saemix_available) { + #plot_biphasic_saem_s <- function() plot(saem_biphasic_s) + plot_biphasic_saem_m <- function() plot(saem_biphasic_m) + + vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_biphasic_saem_m) + } # different results when working with eigenvalues plot_errmod_fit_D_obs_eigen <- function() plot_err(fit_D_obs_eigen, sep_obs = FALSE) -- cgit v1.2.1 From 3dde3b95f1db925c89cd04d19f95c6fc9f68f473 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 13 Feb 2021 12:40:44 +0100 Subject: Update docs --- docs/dev/404.html | 2 +- docs/dev/articles/index.html | 2 +- docs/dev/authors.html | 2 +- docs/dev/index.html | 2 +- docs/dev/news/index.html | 18 ++++++++++---- docs/dev/pkgdown.yml | 2 +- docs/dev/reference/endpoints.html | 2 +- docs/dev/reference/index.html | 2 +- docs/dev/reference/plot.mixed.mmkin.html | 6 ++--- docs/dev/reference/saem.html | 38 +++++++++++++++--------------- docs/dev/reference/summary.saem.mmkin.html | 14 +++++------ 11 files changed, 49 insertions(+), 41 deletions(-) diff --git a/docs/dev/404.html b/docs/dev/404.html index 5f29faf2..ea547a2a 100644 --- a/docs/dev/404.html +++ b/docs/dev/404.html @@ -71,7 +71,7 @@ mkin - 1.0.1.9000 + 1.0.2.9000
      diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 441d49c0..57d0c0eb 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -71,7 +71,7 @@ mkin - 1.0.1.9000 + 1.0.2.9000
    diff --git a/docs/dev/authors.html b/docs/dev/authors.html index 9641eec0..8ffb9f97 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -71,7 +71,7 @@ mkin - 1.0.1.9000 + 1.0.2.9000
    diff --git a/docs/dev/index.html b/docs/dev/index.html index 8888633d..ff9b201f 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -38,7 +38,7 @@ mkin - 1.0.1.9000 + 1.0.2.9000
    diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index 998917f2..eab75984 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -71,7 +71,7 @@ mkin - 1.0.1.9000 + 1.0.2.9000
    @@ -141,9 +141,9 @@ Source: NEWS.md
    -
    -

    -mkin 1.0.1.9000 Unreleased +
    +

    +mkin 1.0.2.9000 Unreleased

    • Switch to a versioning scheme where the fourth version component indicates development versions

      • @@ -151,10 +151,18 @@

    • ‘saemix_model’ and ‘saemix_data’: Helper functions to set up nonlinear mixed-effects models for mmkin row objects

    • ‘saem’: generic function to fit saemix models using ‘saemix_model’ and ‘saemix_data’, with a generator ‘saem.mmkin’, summary and plot methods

    +
    +
    +

    +mkin 1.0.2 Unreleased +

    +
      +
    • ‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’
      • +

    -mkin 1.0.1 Unreleased +mkin 1.0.1 2021-02-10

    • ‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults

      • diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index f9b16e29..8921a1e4 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-02-06T17:26Z +last_built: 2021-02-13T11:32Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index a13e11a7..301b454f 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> mkin - 1.0.1.9000 + 1.0.2.9000
    diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index 7e98aa50..b52ecb22 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ mkin - 1.0.1.9000 + 1.0.2.9000

    diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index 601e1554..ef169074 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ mkin - 1.0.1.9000 + 1.0.2.9000
    @@ -283,10 +283,10 @@ corresponding model prediction lines for the different datasets.

    f_saem <- saem(f, transformations = "saemix")
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:17 2021" +#> [1] "Sat Feb 13 12:33:07 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:23 2021"
    plot(f_saem) +#> [1] "Sat Feb 13 12:33:13 2021"
    plot(f_saem)
    # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo, diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 4578db2a..02483bec 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.1.9000 + 1.0.2.9000
    @@ -261,27 +261,27 @@ using mmkin.

    state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:26 2021" +#> [1] "Sat Feb 13 12:33:16 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:27 2021"
    +#> [1] "Sat Feb 13 12:33:18 2021"
    f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:28 2021" +#> [1] "Sat Feb 13 12:33:19 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:30 2021"
    f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Sat Feb 13 12:33:20 2021"
    f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:30 2021" +#> [1] "Sat Feb 13 12:33:20 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:32 2021"
    f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Sat Feb 13 12:33:22 2021"
    f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:32 2021" +#> [1] "Sat Feb 13 12:33:23 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:35 2021"
    +#> [1] "Sat Feb 13 12:33:25 2021"
    # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -324,10 +324,10 @@ using mmkin.

    f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:37 2021" +#> [1] "Sat Feb 13 12:33:28 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:42 2021"
    compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +#> [1] "Sat Feb 13 12:33:32 2021"
    compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
    #> Error in compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)): 'compare.saemix' requires at least two models.
    sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) @@ -346,15 +346,15 @@ using mmkin.

    # four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:44 2021" +#> [1] "Sat Feb 13 12:33:35 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:48 2021"
    f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Sat Feb 13 12:33:39 2021"
    f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:29:49 2021" +#> [1] "Sat Feb 13 12:33:40 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:29:57 2021"
    # We can use print, plot and summary methods to check the results +#> [1] "Sat Feb 13 12:33:48 2021"
    # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo)
    #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -395,10 +395,10 @@ using mmkin.

    #> SD.g_qlogis 0.44771 -0.86417 1.7596
    plot(f_saem_dfop_sfo)
    summary(f_saem_dfop_sfo, data = TRUE)
    #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.1.9000 +#> mkin version used for pre-fitting: 1.0.2.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Sat Feb 6 18:29:57 2021 -#> Date of summary: Sat Feb 6 18:29:58 2021 +#> Date of fit: Sat Feb 13 12:33:48 2021 +#> Date of summary: Sat Feb 13 12:33:48 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -413,7 +413,7 @@ using mmkin.

    #> #> Model predictions using solution type analytical #> -#> Fitted in 8.539 s using 300, 100 iterations +#> Fitted in 8.875 s using 300, 100 iterations #> #> Variance model: Constant variance #> diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html index 93e1365d..81054813 100644 --- a/docs/dev/reference/summary.saem.mmkin.html +++ b/docs/dev/reference/summary.saem.mmkin.html @@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally mkin - 1.0.1.9000 + 1.0.2.9000
    @@ -260,15 +260,15 @@ saemix authors for the parts inherited from saemix.

    quiet = TRUE, error_model = "tc", cores = 5) f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
    #> Running main SAEM algorithm -#> [1] "Sat Feb 6 18:30:00 2021" +#> [1] "Sat Feb 13 12:33:52 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 6 18:30:11 2021"
    summary(f_saem_dfop_sfo, data = TRUE) +#> [1] "Sat Feb 13 12:34:03 2021"
    summary(f_saem_dfop_sfo, data = TRUE)
    #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.1.9000 +#> mkin version used for pre-fitting: 1.0.2.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Sat Feb 6 18:30:12 2021 -#> Date of summary: Sat Feb 6 18:30:12 2021 +#> Date of fit: Sat Feb 13 12:34:04 2021 +#> Date of summary: Sat Feb 13 12:34:04 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -283,7 +283,7 @@ saemix authors for the parts inherited from saemix.

    #> #> Model predictions using solution type analytical #> -#> Fitted in 11.769 s using 300, 100 iterations +#> Fitted in 11.899 s using 300, 100 iterations #> #> Variance model: Two-component variance function #> -- cgit v1.2.1 From 99f04e6a05acde950f26c55f468f1f184e9dbfd9 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 13 Feb 2021 13:17:20 +0100 Subject: Rebuild docs --- docs/dev/pkgdown.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 8921a1e4..2e984557 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-02-13T11:32Z +last_built: 2021-02-13T12:16Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles -- cgit v1.2.1 From f2c57bd3c183238fa796f9bee5ce52b62f3f75d6 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 13 Feb 2021 13:25:12 +0100 Subject: Structure NEWS entry --- NEWS.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/NEWS.md b/NEWS.md index ad8ae80b..3e599283 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,7 +1,11 @@ # mkin 1.0.2.9000 +## General + - Switch to a versioning scheme where the fourth version component indicates development versions +## Mixed-effects models + - Reintroduce the interface to the current development version of saemix, in particular: - 'saemix_model' and 'saemix_data': Helper functions to set up nonlinear mixed-effects models for mmkin row objects -- cgit v1.2.1 From c4f327e62f19c0a3fc77a538f7cf0c2c619019d8 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 13 Feb 2021 13:27:09 +0100 Subject: Update static docs --- docs/dev/news/index.html | 20 +++++++++++++++----- docs/dev/pkgdown.yml | 2 +- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index eab75984..1421edf4 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -145,12 +145,22 @@

    mkin 1.0.2.9000 Unreleased

    +
    +

    +General

    +
      +
    • Switch to a versioning scheme where the fourth version component indicates development versions
      • +
    +
    +
    +

    +Mixed-effects models

      -

    • Switch to a versioning scheme where the fourth version component indicates development versions

    • Reintroduce the interface to the current development version of saemix, in particular:

    • ‘saemix_model’ and ‘saemix_data’: Helper functions to set up nonlinear mixed-effects models for mmkin row objects

    • ‘saem’: generic function to fit saemix models using ‘saemix_model’ and ‘saemix_data’, with a generator ‘saem.mmkin’, summary and plot methods

    +

    @@ -176,9 +186,9 @@

    mkin 1.0.0 2021-02-03

    -
    +

    -General

    +General

    • ‘mkinmod’ models gain arguments ‘name’ and ‘dll_dir’ which, in conjunction with a current version of the ‘inline’ package, make it possible to still use the DLL used for fast ODE solutions with ‘deSolve’ after saving and restoring the ‘mkinmod’ object.

    • ‘mkindsg’ R6 class for groups of ‘mkinds’ datasets with metadata

      • @@ -191,9 +201,9 @@

    • ‘plot.mkinfit’: Change default ylab from “Observed” to “Residue”. Pass xlab to residual plot if show_residuals is TRUE.

    -
    +

    -Mixed-effects models

    +Mixed-effects models

    • ‘mixed.mmkin’ New container for mmkin objects for plotting with the ‘plot.mixed.mmkin’ method

    • ‘plot.mixed.mmkin’ method used for ‘nlme.mmkin’ inheriting from ‘mixed.mmkin’ (currently virtual)

      • diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 2e984557..c74efaf7 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-02-13T12:16Z +last_built: 2021-02-13T12:27Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles -- cgit v1.2.1 From b9be19af5e3085216d0cd5af439332f631fa8b92 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 15 Feb 2021 17:36:12 +0100 Subject: Fully rebuild docs, rerun tests and check --- GNUmakefile | 2 +- build.log | 3 +- check.log | 4 +- docs/dev/404.html | 2 +- docs/dev/articles/FOCUS_D.html | 23 +-- .../accessible-code-block-0.0.1/empty-anchor.js | 15 ++ .../articles/FOCUS_D_files/figure-html/plot-1.png | Bin 81432 -> 79176 bytes .../FOCUS_D_files/header-attrs-2.6/header-attrs.js | 12 ++ 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create mode 100644 docs/dev/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js create mode 100644 docs/dev/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js create mode 100644 docs/dev/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js create mode 100644 docs/dev/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js diff --git a/GNUmakefile b/GNUmakefile index 8604d3b3..d7c68ebf 100644 --- a/GNUmakefile +++ b/GNUmakefile @@ -112,7 +112,7 @@ pd: roxygen "$(RBIN)/Rscript" -e "pkgdown::build_site(run_dont_run = TRUE, lazy = TRUE)" git add -A -pd_release: roxygen +pd_all: roxygen "$(RBIN)/Rscript" -e "pkgdown::build_site(run_dont_run = TRUE)" git add -A diff --git a/build.log b/build.log index 293831aa..d50a4860 100644 --- a/build.log +++ b/build.log @@ -6,4 +6,5 @@ * creating vignettes ... OK * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories -* building ‘mkin_1.0.2.9000.tar.gz’ +* building ‘mkin_1.0.3.9000.tar.gz’ + diff --git a/check.log b/check.log index 2a4d87ad..ac59f6af 100644 --- a/check.log +++ b/check.log @@ -5,12 +5,12 @@ * using options ‘--no-tests --as-cran’ * checking for file ‘mkin/DESCRIPTION’ ... OK * checking extension type ... Package -* this is package ‘mkin’ version ‘1.0.2.9000’ +* this is package ‘mkin’ version ‘1.0.3.9000’ * package encoding: UTF-8 * checking CRAN incoming feasibility ... NOTE Maintainer: ‘Johannes Ranke ’ -Version contains large components (1.0.2.9000) +Version contains large components (1.0.3.9000) Unknown, possibly mis-spelled, fields in DESCRIPTION: ‘Remotes’ diff --git a/docs/dev/404.html b/docs/dev/404.html index ea547a2a..f9e51aa3 100644 --- a/docs/dev/404.html +++ b/docs/dev/404.html @@ -71,7 +71,7 @@ mkin - 1.0.2.9000 + 1.0.3.9000
    diff --git a/docs/dev/articles/FOCUS_D.html b/docs/dev/articles/FOCUS_D.html index dd86f677..a35a255a 100644 --- a/docs/dev/articles/FOCUS_D.html +++ b/docs/dev/articles/FOCUS_D.html @@ -32,7 +32,7 @@ mkin - 0.9.50.4 + 1.0.3.9000
    @@ -81,7 +81,7 @@
    diff --git a/docs/dev/reference/DFOP.solution.html b/docs/dev/reference/DFOP.solution.html index 22b28732..3ee660f2 100644 --- a/docs/dev/reference/DFOP.solution.html +++ b/docs/dev/reference/DFOP.solution.html @@ -73,7 +73,7 @@ two exponential decline functions." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -122,7 +122,7 @@ two exponential decline functions." />
    • - +
      • diff --git a/docs/dev/reference/Extract.mmkin.html b/docs/dev/reference/Extract.mmkin.html index 0c02355f..8381337a 100644 --- a/docs/dev/reference/Extract.mmkin.html +++ b/docs/dev/reference/Extract.mmkin.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000
    @@ -121,7 +121,7 @@
    • - +
      • @@ -220,7 +220,7 @@ either a list of mkinfit objects or a single mkinfit object.

      )
    #> $par #> parent_0 log_alpha log_beta sigma -#> 99.666193 2.549849 5.050586 1.890202 +#> 99.666192 2.549850 5.050586 1.890202 #> #> $objective #> [1] 28.58291 diff --git a/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html index 16d12378..a188430d 100644 --- a/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html +++ b/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html @@ -76,7 +76,7 @@ in this fit." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -125,7 +125,7 @@ in this fit." />
    • - +
      • diff --git a/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html index 6b8a6119..0bee1c16 100644 --- a/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html @@ -76,7 +76,7 @@ in this fit." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -125,7 +125,7 @@ in this fit." />
    • - +
      • diff --git a/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html index 076a66c6..460fdf0d 100644 --- a/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html +++ b/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html @@ -76,7 +76,7 @@ in this fit." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -125,7 +125,7 @@ in this fit." />
    • - +
      • diff --git a/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html index 08f1d416..c1a5fdff 100644 --- a/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html +++ b/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html @@ -76,7 +76,7 @@ in this fit." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -125,7 +125,7 @@ in this fit." />
    • - +
      • diff --git a/docs/dev/reference/FOCUS_2006_datasets.html b/docs/dev/reference/FOCUS_2006_datasets.html index df4651f8..fb3a8f17 100644 --- a/docs/dev/reference/FOCUS_2006_datasets.html +++ b/docs/dev/reference/FOCUS_2006_datasets.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000
    @@ -121,7 +121,7 @@
    • - +
      • diff --git a/docs/dev/reference/FOMC.solution.html b/docs/dev/reference/FOMC.solution.html index ed5c4d21..1a5124e0 100644 --- a/docs/dev/reference/FOMC.solution.html +++ b/docs/dev/reference/FOMC.solution.html @@ -73,7 +73,7 @@ a decreasing rate constant." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -122,7 +122,7 @@ a decreasing rate constant." />
    • - +
      • diff --git a/docs/dev/reference/HS.solution.html b/docs/dev/reference/HS.solution.html index 8cf5c7f9..1b79e8b6 100644 --- a/docs/dev/reference/HS.solution.html +++ b/docs/dev/reference/HS.solution.html @@ -73,7 +73,7 @@ between them." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -122,7 +122,7 @@ between them." />
    • - +
      • diff --git a/docs/dev/reference/IORE.solution.html b/docs/dev/reference/IORE.solution.html index 29d615dc..bc17319e 100644 --- a/docs/dev/reference/IORE.solution.html +++ b/docs/dev/reference/IORE.solution.html @@ -73,7 +73,7 @@ a concentration dependent rate constant." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -122,7 +122,7 @@ a concentration dependent rate constant." />
    • - +
      • diff --git a/docs/dev/reference/NAFTA_SOP_2015-1.png b/docs/dev/reference/NAFTA_SOP_2015-1.png index 4d823d77..4f0d7833 100644 Binary files a/docs/dev/reference/NAFTA_SOP_2015-1.png and b/docs/dev/reference/NAFTA_SOP_2015-1.png differ diff --git a/docs/dev/reference/NAFTA_SOP_2015.html b/docs/dev/reference/NAFTA_SOP_2015.html index 4243faba..fb65fec8 100644 --- a/docs/dev/reference/NAFTA_SOP_2015.html +++ b/docs/dev/reference/NAFTA_SOP_2015.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000
    @@ -121,7 +121,7 @@
    • - +
      • diff --git a/docs/dev/reference/NAFTA_SOP_Attachment-1.png b/docs/dev/reference/NAFTA_SOP_Attachment-1.png index 6eb10cde..9417685e 100644 Binary files a/docs/dev/reference/NAFTA_SOP_Attachment-1.png and b/docs/dev/reference/NAFTA_SOP_Attachment-1.png differ diff --git a/docs/dev/reference/NAFTA_SOP_Attachment.html b/docs/dev/reference/NAFTA_SOP_Attachment.html index de984984..311a7c61 100644 --- a/docs/dev/reference/NAFTA_SOP_Attachment.html +++ b/docs/dev/reference/NAFTA_SOP_Attachment.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000
    @@ -121,7 +121,7 @@
    • - +
      • @@ -193,7 +193,7 @@ #> Estimate Pr(>t) Lower Upper #> parent_0 9.99e+01 1.41e-26 98.8116 101.0810 #> k1 2.67e-02 5.05e-06 0.0243 0.0295 -#> k2 2.42e-12 5.00e-01 0.0000 Inf +#> k2 2.26e-12 5.00e-01 0.0000 Inf #> g 6.47e-01 3.67e-06 0.6248 0.6677 #> sigma 1.27e+00 8.91e-06 0.8395 1.6929 #> @@ -202,7 +202,7 @@ #> DT50 DT90 DT50_rep #> SFO 67.7 2.25e+02 6.77e+01 #> IORE 58.2 1.07e+03 3.22e+02 -#> DFOP 55.5 5.22e+11 2.86e+11 +#> DFOP 55.5 5.59e+11 3.07e+11 #> #> Representative half-life: #> [1] 321.51
    plot(nafta_att_p5a) diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png index 17a35806..7f498242 100644 Binary files a/docs/dev/reference/Rplot001.png and b/docs/dev/reference/Rplot001.png differ diff --git a/docs/dev/reference/Rplot002.png b/docs/dev/reference/Rplot002.png index 9b97a634..54c31a3f 100644 Binary files a/docs/dev/reference/Rplot002.png and b/docs/dev/reference/Rplot002.png differ diff --git a/docs/dev/reference/Rplot006.png b/docs/dev/reference/Rplot006.png index 730a7481..81525882 100644 Binary files a/docs/dev/reference/Rplot006.png and b/docs/dev/reference/Rplot006.png differ diff --git a/docs/dev/reference/Rplot007.png b/docs/dev/reference/Rplot007.png index fce3b6ee..10b7455a 100644 Binary files a/docs/dev/reference/Rplot007.png and b/docs/dev/reference/Rplot007.png differ diff --git a/docs/dev/reference/SFO.solution.html b/docs/dev/reference/SFO.solution.html index b3e7ef9a..43c434c6 100644 --- a/docs/dev/reference/SFO.solution.html +++ b/docs/dev/reference/SFO.solution.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000
    @@ -121,7 +121,7 @@
    • - +
      • diff --git a/docs/dev/reference/SFORB.solution.html b/docs/dev/reference/SFORB.solution.html index 9310212f..807fbe5c 100644 --- a/docs/dev/reference/SFORB.solution.html +++ b/docs/dev/reference/SFORB.solution.html @@ -76,7 +76,7 @@ and no substance in the bound fraction." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -125,7 +125,7 @@ and no substance in the bound fraction." />
    • - +
      • diff --git a/docs/dev/reference/add_err-1.png b/docs/dev/reference/add_err-1.png index d2ce797f..9ba106db 100644 Binary files a/docs/dev/reference/add_err-1.png and b/docs/dev/reference/add_err-1.png differ diff --git a/docs/dev/reference/add_err-2.png b/docs/dev/reference/add_err-2.png index ac220c9e..3088c40e 100644 Binary files a/docs/dev/reference/add_err-2.png and b/docs/dev/reference/add_err-2.png differ diff --git a/docs/dev/reference/add_err-3.png b/docs/dev/reference/add_err-3.png index 40465b71..493a761a 100644 Binary files a/docs/dev/reference/add_err-3.png and b/docs/dev/reference/add_err-3.png differ diff --git a/docs/dev/reference/add_err.html b/docs/dev/reference/add_err.html index 6ea30515..b94cef29 100644 --- a/docs/dev/reference/add_err.html +++ b/docs/dev/reference/add_err.html @@ -74,7 +74,7 @@ may depend on the predicted value and is specified as a standard deviation." /> mkin - 0.9.50.4 + 1.0.3.9000
    @@ -123,7 +123,7 @@ may depend on the predicted value and is specified as a standard deviation." />
    • - +
      • diff --git a/docs/dev/reference/aw.html b/docs/dev/reference/aw.html index ef808dd8..3b06f2a6 100644 --- a/docs/dev/reference/aw.html +++ b/docs/dev/reference/aw.html @@ -74,7 +74,7 @@ by Burnham and Anderson (2004)." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -123,7 +123,7 @@ by Burnham and Anderson (2004)." />
      • - +
        • diff --git a/docs/dev/reference/confint.mkinfit.html b/docs/dev/reference/confint.mkinfit.html index 89bb3d89..2237a539 100644 --- a/docs/dev/reference/confint.mkinfit.html +++ b/docs/dev/reference/confint.mkinfit.html @@ -79,7 +79,7 @@ method of Venzon and Moolgavkar (1988)." /> mkin - 1.0.1.9000 + 1.0.3.9000 @@ -285,13 +285,13 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE) system.time(ci_profile <- confint(f_d_1, method = "profile", cores = 1, quiet = TRUE))
        #> user system elapsed -#> 4.258 0.916 3.889
        # Using more cores does not save much time here, as parent_0 takes up most of the time +#> 4.295 1.008 3.959
        # Using more cores does not save much time here, as parent_0 takes up most of the time # If we additionally exclude parent_0 (the confidence of which is often of # minor interest), we get a nice performance improvement if we use at least 4 cores system.time(ci_profile_no_parent_0 <- confint(f_d_1, method = "profile", c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = n_cores))
        #> Profiling the likelihood
        #> user system elapsed -#> 1.459 0.088 0.907
        ci_profile +#> 1.451 0.126 0.923
        ci_profile
        #> 2.5% 97.5% #> parent_0 96.456003640 1.027703e+02 #> k_parent_sink 0.040762501 5.549764e-02 diff --git a/docs/dev/reference/create_deg_func.html b/docs/dev/reference/create_deg_func.html index 4945d157..65a682bb 100644 --- a/docs/dev/reference/create_deg_func.html +++ b/docs/dev/reference/create_deg_func.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000
        @@ -121,7 +121,7 @@
        • - +
          • @@ -181,8 +181,8 @@ deSolve = mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE), replications = 2)
          #> Loading required package: rbenchmark
          #> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.396 1.000 0.395 0 0 -#> 2 deSolve 2 0.694 1.753 0.693 0 0 +#> 1 analytical 2 0.396 1.00 0.396 0 0 +#> 2 deSolve 2 0.709 1.79 0.707 0 0 #> sys.child #> 1 0 #> 2 0
          DFOP_SFO <- mkinmod( @@ -193,8 +193,8 @@ deSolve = mkinfit(DFOP_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE), replications = 2)
          #> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.838 1.000 0.838 0.001 0 -#> 2 deSolve 2 1.573 1.877 1.572 0.000 0 +#> 1 analytical 2 0.844 1.000 0.844 0 0 +#> 2 deSolve 2 1.624 1.924 1.624 0 0 #> sys.child #> 1 0 #> 2 0
          # } diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index 21dea623..a06599df 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> mkin - 0.9.50.4 + 1.0.3.9000
          diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index 301b454f..c9912f9c 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> mkin - 1.0.2.9000 + 1.0.3.9000 diff --git a/docs/dev/reference/experimental_data_for_UBA-1.png b/docs/dev/reference/experimental_data_for_UBA-1.png index 24cb54c5..33946ded 100644 Binary files a/docs/dev/reference/experimental_data_for_UBA-1.png and b/docs/dev/reference/experimental_data_for_UBA-1.png differ diff --git a/docs/dev/reference/experimental_data_for_UBA.html b/docs/dev/reference/experimental_data_for_UBA.html index 78e57fb0..9904370f 100644 --- a/docs/dev/reference/experimental_data_for_UBA.html +++ b/docs/dev/reference/experimental_data_for_UBA.html @@ -100,7 +100,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl mkin - 0.9.50.4 + 1.0.3.9000 @@ -149,7 +149,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
          • - +
            • diff --git a/docs/dev/reference/f_time_norm_focus.html b/docs/dev/reference/f_time_norm_focus.html index 3421043d..852e00a0 100644 --- a/docs/dev/reference/f_time_norm_focus.html +++ b/docs/dev/reference/f_time_norm_focus.html @@ -73,7 +73,7 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)." /> mkin - 0.9.50.4 + 1.0.3.9000 diff --git a/docs/dev/reference/focus_soil_moisture.html b/docs/dev/reference/focus_soil_moisture.html index c46fd69a..0e6fea28 100644 --- a/docs/dev/reference/focus_soil_moisture.html +++ b/docs/dev/reference/focus_soil_moisture.html @@ -73,7 +73,7 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -122,7 +122,7 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5." />
            • - +
              • diff --git a/docs/dev/reference/get_deg_func.html b/docs/dev/reference/get_deg_func.html index a266bf5f..fb661085 100644 --- a/docs/dev/reference/get_deg_func.html +++ b/docs/dev/reference/get_deg_func.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000 @@ -121,7 +121,7 @@
              • - +
                • diff --git a/docs/dev/reference/ilr.html b/docs/dev/reference/ilr.html index 98e51211..452647d6 100644 --- a/docs/dev/reference/ilr.html +++ b/docs/dev/reference/ilr.html @@ -73,7 +73,7 @@ transformations." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -122,7 +122,7 @@ transformations." />
                • - +
                  • diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index b52ecb22..03a21517 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ mkin - 1.0.2.9000 + 1.0.3.9000 diff --git a/docs/dev/reference/loftest-1.png b/docs/dev/reference/loftest-1.png index 6b918fec..d6006ecc 100644 Binary files a/docs/dev/reference/loftest-1.png and b/docs/dev/reference/loftest-1.png differ diff --git a/docs/dev/reference/loftest-2.png b/docs/dev/reference/loftest-2.png index 60874bd3..4d0dc551 100644 Binary files a/docs/dev/reference/loftest-2.png and b/docs/dev/reference/loftest-2.png differ diff --git a/docs/dev/reference/loftest-3.png b/docs/dev/reference/loftest-3.png index 4837e7f2..6afd084b 100644 Binary files a/docs/dev/reference/loftest-3.png and b/docs/dev/reference/loftest-3.png differ diff --git a/docs/dev/reference/loftest-4.png b/docs/dev/reference/loftest-4.png index 9c18ac30..f94eede1 100644 Binary files a/docs/dev/reference/loftest-4.png and b/docs/dev/reference/loftest-4.png differ diff --git a/docs/dev/reference/loftest-5.png b/docs/dev/reference/loftest-5.png index 11f2bda7..43460a65 100644 Binary files a/docs/dev/reference/loftest-5.png and b/docs/dev/reference/loftest-5.png differ diff --git a/docs/dev/reference/loftest.html b/docs/dev/reference/loftest.html index abbbd3b9..9dbd547d 100644 --- a/docs/dev/reference/loftest.html +++ b/docs/dev/reference/loftest.html @@ -75,7 +75,7 @@ lrtest.default from the lmtest package." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -124,7 +124,7 @@ lrtest.default from the lmtest package." />
                  • - +
                    • diff --git a/docs/dev/reference/logLik.mkinfit.html b/docs/dev/reference/logLik.mkinfit.html index 82c0654f..3e9452c6 100644 --- a/docs/dev/reference/logLik.mkinfit.html +++ b/docs/dev/reference/logLik.mkinfit.html @@ -76,7 +76,7 @@ the error model." /> mkin - 1.0.1.9000 + 1.0.3.9000 diff --git a/docs/dev/reference/logistic.solution-2.png b/docs/dev/reference/logistic.solution-2.png index 764996df..73e6436d 100644 Binary files a/docs/dev/reference/logistic.solution-2.png and b/docs/dev/reference/logistic.solution-2.png differ diff --git a/docs/dev/reference/logistic.solution.html b/docs/dev/reference/logistic.solution.html index 950e8a8e..ab68c99e 100644 --- a/docs/dev/reference/logistic.solution.html +++ b/docs/dev/reference/logistic.solution.html @@ -73,7 +73,7 @@ an increasing rate constant, supposedly caused by microbial growth" /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -122,7 +122,7 @@ an increasing rate constant, supposedly caused by microbial growth" />
                    • - +
                      • @@ -240,10 +240,10 @@ Version 1.1, 18 December 2014 plot_sep(m)
                      summary(m)$bpar
                      #> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 1.057896e+02 1.9023449703 55.610119 3.768361e-16 1.016451e+02 -#> kmax 6.398190e-02 0.0143201031 4.467978 3.841829e-04 3.929235e-02 +#> parent_0 1.057896e+02 1.9023449590 55.610120 3.768360e-16 1.016451e+02 +#> kmax 6.398190e-02 0.0143201029 4.467978 3.841828e-04 3.929235e-02 #> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846688e-08 -#> r 2.263946e-01 0.1718110715 1.317695 1.061044e-01 4.335843e-02 +#> r 2.263946e-01 0.1718110662 1.317695 1.061043e-01 4.335843e-02 #> sigma 5.332935e+00 0.9145907310 5.830952 4.036926e-05 3.340213e+00 #> Upper #> parent_0 109.9341588 diff --git a/docs/dev/reference/lrtest.mkinfit.html b/docs/dev/reference/lrtest.mkinfit.html index b76ebc87..f2d8472e 100644 --- a/docs/dev/reference/lrtest.mkinfit.html +++ b/docs/dev/reference/lrtest.mkinfit.html @@ -76,7 +76,7 @@ and can be expressed by fixing the parameters of the other." /> mkin - 0.9.50.4 + 1.0.3.9000
                      @@ -125,7 +125,7 @@ and can be expressed by fixing the parameters of the other." />
                      • - +
                        • diff --git a/docs/dev/reference/max_twa_parent.html b/docs/dev/reference/max_twa_parent.html index 25f745e9..a358568a 100644 --- a/docs/dev/reference/max_twa_parent.html +++ b/docs/dev/reference/max_twa_parent.html @@ -78,7 +78,7 @@ soil section of the FOCUS guidance." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -127,7 +127,7 @@ soil section of the FOCUS guidance." />
                        • - +
                          • diff --git a/docs/dev/reference/mccall81_245T-1.png b/docs/dev/reference/mccall81_245T-1.png index 5daa5f18..91fe060e 100644 Binary files a/docs/dev/reference/mccall81_245T-1.png and b/docs/dev/reference/mccall81_245T-1.png differ diff --git a/docs/dev/reference/mccall81_245T.html b/docs/dev/reference/mccall81_245T.html index 7179533d..f79137be 100644 --- a/docs/dev/reference/mccall81_245T.html +++ b/docs/dev/reference/mccall81_245T.html @@ -74,7 +74,7 @@ mkin - 0.9.50.4 + 1.0.3.9000 @@ -181,30 +181,30 @@ fit.1 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE)
                          #> Warning: Observations with value of zero were removed from the data
                          summary(fit.1)$bpar
                          #> Estimate se_notrans t value Pr(>t) -#> T245_0 1.038550e+02 2.1847074888 47.537272 4.472189e-18 +#> T245_0 1.038550e+02 2.1847074945 47.537272 4.472189e-18 #> k_T245 4.337042e-02 0.0018983965 22.845818 2.276911e-13 -#> k_phenol 4.050581e-01 0.2986993400 1.356073 9.756988e-02 -#> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623176e-07 -#> f_T245_to_phenol 6.227599e-01 0.3985340295 1.562627 6.949412e-02 -#> f_phenol_to_anisole 1.000000e+00 0.6718439378 1.488441 7.867787e-02 -#> sigma 2.514628e+00 0.4907558750 5.123989 6.233156e-05 +#> k_phenol 4.050581e-01 0.2986993563 1.356073 9.756989e-02 +#> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623177e-07 +#> f_T245_to_phenol 6.227599e-01 0.3985340558 1.562627 6.949413e-02 +#> f_phenol_to_anisole 1.000000e+00 0.6718439825 1.488441 7.867789e-02 +#> sigma 2.514628e+00 0.4907558883 5.123989 6.233157e-05 #> Lower Upper -#> T245_0 99.246061370 1.084640e+02 +#> T245_0 99.246061385 1.084640e+02 #> k_T245 0.039631621 4.746194e-02 -#> k_phenol 0.218013878 7.525762e-01 +#> k_phenol 0.218013879 7.525762e-01 #> k_anisole 0.005370739 8.305299e-03 -#> f_T245_to_phenol 0.547559083 6.924813e-01 +#> f_T245_to_phenol 0.547559081 6.924813e-01 #> f_phenol_to_anisole 0.000000000 1.000000e+00 #> sigma 1.706607296 3.322649e+00
                          endpoints(fit.1)
                          #> $ff #> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 6.894640e-11 +#> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 #> #> $distimes #> DT50 DT90 #> T245 15.982025 53.09114 #> phenol 1.711229 5.68458 -#> anisole 103.784092 344.76329 +#> anisole 103.784093 344.76329 #>
                          # formation fraction from phenol to anisol is practically 1. As we cannot # fix formation fractions when using the ilr transformation, we can turn of # the sink in the model generation @@ -215,28 +215,28 @@ quiet = TRUE)
                          #> Warning: Observations with value of zero were removed from the data
                          summary(fit.2)$bpar
                          #> Estimate se_notrans t value Pr(>t) Lower -#> T245_0 1.038550e+02 2.1623653027 48.028439 4.993108e-19 99.271020526 -#> k_T245 4.337042e-02 0.0018343666 23.643268 3.573555e-14 0.039650977 -#> k_phenol 4.050582e-01 0.1177237248 3.440752 1.679252e-03 0.218746585 -#> k_anisole 6.678741e-03 0.0006829745 9.778903 1.872894e-08 0.005377083 -#> f_T245_to_phenol 6.227599e-01 0.0342197865 18.198825 2.039410e-12 0.547975628 +#> T245_0 1.038550e+02 2.1623653066 48.028439 4.993108e-19 99.271020284 +#> k_T245 4.337042e-02 0.0018343666 23.643268 3.573556e-14 0.039650976 +#> k_phenol 4.050582e-01 0.1177237473 3.440752 1.679254e-03 0.218746587 +#> k_anisole 6.678742e-03 0.0006829745 9.778903 1.872894e-08 0.005377083 +#> f_T245_to_phenol 6.227599e-01 0.0342197875 18.198824 2.039411e-12 0.547975637 #> sigma 2.514628e+00 0.3790944250 6.633250 2.875782e-06 1.710983655 #> Upper -#> T245_0 108.43904097 +#> T245_0 108.43904074 #> k_T245 0.04743877 -#> k_phenol 0.75005577 +#> k_phenol 0.75005585 #> k_anisole 0.00829550 -#> f_T245_to_phenol 0.69212306 +#> f_T245_to_phenol 0.69212308 #> sigma 3.31827222
                          endpoints(fit.1)
                          #> $ff #> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 6.894640e-11 +#> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 #> #> $distimes #> DT50 DT90 #> T245 15.982025 53.09114 #> phenol 1.711229 5.68458 -#> anisole 103.784092 344.76329 +#> anisole 103.784093 344.76329 #>
                          plot_sep(fit.2)
                          # }
                          diff --git a/docs/dev/reference/mixed-1.png b/docs/dev/reference/mixed-1.png index 3400c4aa..28a376f4 100644 Binary files a/docs/dev/reference/mixed-1.png and b/docs/dev/reference/mixed-1.png differ diff --git a/docs/dev/reference/mixed.html b/docs/dev/reference/mixed.html index 18a67af8..7bf8dd56 100644 --- a/docs/dev/reference/mixed.html +++ b/docs/dev/reference/mixed.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000 @@ -121,7 +121,7 @@
                          • - +
                            • @@ -235,18 +235,16 @@ #> Status of individual fits: #> #> dataset -#> model 1 2 3 4 5 6 7 8 -#> DFOP-SFO OK OK OK OK OK C OK OK +#> model 1 2 3 4 5 6 7 8 +#> DFOP-SFO OK OK OK OK OK OK OK OK #> #> OK: No warnings -#> C: Optimisation did not converge: -#> iteration limit reached without convergence (10) #> #> Mean fitted parameters: #> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 -#> 100.606304 -8.759216 -0.002001 -3.350539 -3.989549 +#> 100.674757 -8.761916 -0.004347 -3.348812 -3.986853 #> g_qlogis -#> -0.090353
                            plot(f_mixed) +#> -0.087392
                            plot(f_mixed)
                            # }
                            diff --git a/docs/dev/reference/mkin_long_to_wide.html b/docs/dev/reference/mkin_long_to_wide.html index 28a37800..6246fbe2 100644 --- a/docs/dev/reference/mkin_long_to_wide.html +++ b/docs/dev/reference/mkin_long_to_wide.html @@ -74,7 +74,7 @@ variable and several dependent variables as columns." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -123,7 +123,7 @@ variable and several dependent variables as columns." />
                            • - +
                              • diff --git a/docs/dev/reference/mkin_wide_to_long.html b/docs/dev/reference/mkin_wide_to_long.html index f085d162..f2bf00c1 100644 --- a/docs/dev/reference/mkin_wide_to_long.html +++ b/docs/dev/reference/mkin_wide_to_long.html @@ -74,7 +74,7 @@ mkinfit." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -123,7 +123,7 @@ mkinfit." />
                              • - +
                                • diff --git a/docs/dev/reference/mkinds.html b/docs/dev/reference/mkinds.html index 907f8ad3..0d1de46d 100644 --- a/docs/dev/reference/mkinds.html +++ b/docs/dev/reference/mkinds.html @@ -75,7 +75,7 @@ provided by this package come as mkinds objects nevertheless." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -124,7 +124,7 @@ provided by this package come as mkinds objects nevertheless." />
                                • - +
                                  • diff --git a/docs/dev/reference/mkindsg.html b/docs/dev/reference/mkindsg.html index 3e4dfb39..67c6e5df 100644 --- a/docs/dev/reference/mkindsg.html +++ b/docs/dev/reference/mkindsg.html @@ -75,7 +75,7 @@ dataset if no data are supplied." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -124,7 +124,7 @@ dataset if no data are supplied." />
                                  • - +
                                    • @@ -239,12 +239,12 @@ or covariates like soil pH).

                                    print(mdsg)
                                    #> <mkindsg> holding 5 mkinds objects #> Title $title: Experimental X -#> Occurrene of observed compounds $observed_n: +#> Occurrence of observed compounds $observed_n: #> parent A1 #> 5 5
                                    print(mdsg, verbose = TRUE)
                                    #> <mkindsg> holding 5 mkinds objects #> Title $title: Experimental X -#> Occurrene of observed compounds $observed_n: +#> Occurrence of observed compounds $observed_n: #> parent A1 #> 5 5 #> @@ -290,7 +290,7 @@ or covariates like soil pH).

                                    #> Observation unit: \%AR
                                    print(mdsg, verbose = TRUE, data = TRUE)
                                    #> <mkindsg> holding 5 mkinds objects #> Title $title: Experimental X -#> Occurrene of observed compounds $observed_n: +#> Occurrence of observed compounds $observed_n: #> parent A1 #> 5 5 #> diff --git a/docs/dev/reference/mkinerrmin.html b/docs/dev/reference/mkinerrmin.html index 1cff040d..94c575cb 100644 --- a/docs/dev/reference/mkinerrmin.html +++ b/docs/dev/reference/mkinerrmin.html @@ -73,7 +73,7 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006." /> mkin - 0.9.50.4 + 1.0.3.9000
                                    @@ -122,7 +122,7 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006." />
                                    • - +
                                      • diff --git a/docs/dev/reference/mkinerrplot.html b/docs/dev/reference/mkinerrplot.html index 2324b968..7f1fd048 100644 --- a/docs/dev/reference/mkinerrplot.html +++ b/docs/dev/reference/mkinerrplot.html @@ -76,7 +76,7 @@ using the argument show_errplot = TRUE." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -125,7 +125,7 @@ using the argument show_errplot = TRUE." />
                                      • - +
                                        • diff --git a/docs/dev/reference/mkinfit-1.png b/docs/dev/reference/mkinfit-1.png index eed9064f..de2a90a9 100644 Binary files a/docs/dev/reference/mkinfit-1.png and b/docs/dev/reference/mkinfit-1.png differ diff --git a/docs/dev/reference/mkinfit.html b/docs/dev/reference/mkinfit.html index 6ae7b343..5910038f 100644 --- a/docs/dev/reference/mkinfit.html +++ b/docs/dev/reference/mkinfit.html @@ -80,7 +80,7 @@ likelihood function." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -431,17 +431,17 @@ doi: 10.3390/environments6 # Use shorthand notation for parent only degradation fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE) summary(fit) -
                                        #> mkin version used for fitting: 0.9.50.4 +
                                        #> mkin version used for fitting: 1.0.3.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Mon Jan 11 12:41:45 2021 -#> Date of summary: Mon Jan 11 12:41:45 2021 +#> Date of fit: Mon Feb 15 17:09:39 2021 +#> Date of summary: Mon Feb 15 17:09:39 2021 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> #> Model predictions using solution type analytical #> -#> Fitted using 222 model solutions performed in 0.046 s +#> Fitted using 222 model solutions performed in 0.045 s #> #> Error model: Constant variance #> @@ -476,10 +476,10 @@ doi: 10.3390/environments6 #> #> Parameter correlation: #> parent_0 log_alpha log_beta sigma -#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.758e-08 -#> log_alpha -1.565e-01 1.000e+00 9.564e-01 1.007e-07 -#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.568e-08 -#> sigma 4.758e-08 1.007e-07 8.568e-08 1.000e+00 +#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.772e-08 +#> log_alpha -1.565e-01 1.000e+00 9.564e-01 1.005e-07 +#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.541e-08 +#> sigma 4.772e-08 1.005e-07 8.541e-08 1.000e+00 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. @@ -548,7 +548,7 @@ doi: 10.3390/environments6 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
                                        parms(fit.tc)
                                        #> parent_0 k_parent k_m1 f_parent_to_m1 sigma_low -#> 1.007343e+02 1.005562e-01 5.166712e-03 5.083933e-01 3.049884e-03 +#> 1.007343e+02 1.005562e-01 5.166712e-03 5.083933e-01 3.049883e-03 #> rsd_high #> 7.928118e-02
                                        endpoints(fit.tc)
                                        #> $ff @@ -574,10 +574,10 @@ doi: 10.3390/environments6 analytical = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc", solution_type = "analytical")) } -
                                        #> Loading required package: rbenchmark
                                        #> test relative elapsed -#> 3 analytical 1.000 0.526 -#> 1 deSolve_compiled 1.903 1.001 -#> 2 eigen 2.308 1.214
                                        # } +
                                        #> test relative elapsed +#> 3 analytical 1.000 0.563 +#> 1 deSolve_compiled 1.702 0.958 +#> 2 eigen 2.597 1.462
                                        # } # Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO # \dontrun{ @@ -587,22 +587,21 @@ doi: 10.3390/environments6
                                        #> Temporary DLL for differentials generated and loaded
                                        fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE) # Again, we get a warning and try a more sophisticated error model fit.FOMC_SFO.tc <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, error_model = "tc") -
                                        #> Warning: Optimisation did not converge: -#> iteration limit reached without convergence (10)
                                        # This model has a higher likelihood, but not significantly so +# This model has a higher likelihood, but not significantly so lrtest(fit.tc, fit.FOMC_SFO.tc)
                                        #> Likelihood ratio test #> #> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 7 -64.870 -#> 2 6 -64.983 -1 0.2259 0.6346
                                        # Also, the missing standard error for log_beta and the t-tests for alpha +#> 1 7 -64.829 +#> 2 6 -64.983 -1 0.3075 0.5792
                                        # Also, the missing standard error for log_beta and the t-tests for alpha # and beta indicate overparameterisation summary(fit.FOMC_SFO.tc, data = FALSE) -
                                        #> Warning: NaNs produced
                                        #> Warning: NaNs produced
                                        #> Warning: NaNs produced
                                        #> Warning: diag(.) had 0 or NA entries; non-finite result is doubtful
                                        #> mkin version used for fitting: 0.9.50.4 +
                                        #> Warning: NaNs produced
                                        #> Warning: NaNs produced
                                        #> Warning: diag(.) had 0 or NA entries; non-finite result is doubtful
                                        #> mkin version used for fitting: 1.0.3.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Mon Jan 11 12:41:56 2021 -#> Date of summary: Mon Jan 11 12:41:56 2021 +#> Date of fit: Mon Feb 15 17:09:50 2021 +#> Date of summary: Mon Feb 15 17:09:50 2021 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -611,12 +610,12 @@ doi: 10.3390/environments6 #> #> Model predictions using solution type deSolve #> -#> Fitted using 4273 model solutions performed in 3.456 s +#> Fitted using 3729 model solutions performed in 2.815 s #> #> Error model: Two-component variance function #> #> Error model algorithm: d_3 -#> Three-step fitting yielded a higher likelihood than direct fitting +#> Direct fitting and three-step fitting yield approximately the same likelihood #> #> Starting values for parameters to be optimised: #> value type @@ -642,72 +641,67 @@ doi: 10.3390/environments6 #> value type #> m1_0 0 state #> -#> -#> Warning(s): -#> Optimisation did not converge: -#> iteration limit reached without convergence (10) -#> #> Results: #> -#> AIC BIC logLik -#> 143.7396 155.2027 -64.86982 +#> AIC BIC logLik +#> 143.658 155.1211 -64.82902 #> #> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 1.016e+02 1.90600 97.7400 105.5000 -#> log_k_m1 -5.285e+00 0.09286 -5.4740 -5.0950 -#> f_parent_qlogis 6.482e-04 0.06164 -0.1251 0.1264 -#> log_alpha 5.467e+00 NaN NaN NaN -#> log_beta 7.750e+00 NaN NaN NaN -#> sigma_low 0.000e+00 NaN NaN NaN -#> rsd_high 7.989e-02 NaN NaN NaN +#> Estimate Std. Error Lower Upper +#> parent_0 101.600000 2.6400000 96.240000 107.000000 +#> log_k_m1 -5.284000 0.0929100 -5.474000 -5.095000 +#> f_parent_qlogis 0.001426 0.0767000 -0.155000 0.157800 +#> log_alpha 5.522000 0.0077320 5.506000 5.538000 +#> log_beta 7.806000 NaN NaN NaN +#> sigma_low 0.002488 0.0002431 0.001992 0.002984 +#> rsd_high 0.079210 0.0093280 0.060180 0.098230 #> #> Parameter correlation: -#> parent_0 log_k_m1 f_parent_qlogis log_alpha log_beta -#> parent_0 1.0000000 -0.0002167 -0.6060 NaN NaN -#> log_k_m1 -0.0002167 1.0000000 0.5474 NaN NaN -#> f_parent_qlogis -0.6060320 0.5474423 1.0000 NaN NaN -#> log_alpha NaN NaN NaN 1 NaN -#> log_beta NaN NaN NaN NaN 1 -#> sigma_low NaN NaN NaN NaN NaN -#> rsd_high NaN NaN NaN NaN NaN -#> sigma_low rsd_high -#> parent_0 NaN NaN -#> log_k_m1 NaN NaN -#> f_parent_qlogis NaN NaN -#> log_alpha NaN NaN -#> log_beta NaN NaN -#> sigma_low 1 NaN -#> rsd_high NaN 1 +#> parent_0 log_k_m1 f_parent_qlogis log_alpha log_beta +#> parent_0 1.000000 -0.095226 -0.76678 0.70544 NaN +#> log_k_m1 -0.095226 1.000000 0.51432 -0.14387 NaN +#> f_parent_qlogis -0.766780 0.514321 1.00000 -0.61396 NaN +#> log_alpha 0.705444 -0.143872 -0.61396 1.00000 NaN +#> log_beta NaN NaN NaN NaN 1 +#> sigma_low 0.016073 0.001586 0.01548 5.87007 NaN +#> rsd_high 0.006626 -0.011700 -0.05357 0.04849 NaN +#> sigma_low rsd_high +#> parent_0 0.016073 0.006626 +#> log_k_m1 0.001586 -0.011700 +#> f_parent_qlogis 0.015476 -0.053566 +#> log_alpha 5.870075 0.048487 +#> log_beta NaN NaN +#> sigma_low 1.000000 -0.652558 +#> rsd_high -0.652558 1.000000 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. #> t-test (unrealistically) based on the assumption of normal distribution #> for estimators of untransformed parameters. #> Estimate t value Pr(>t) Lower Upper -#> parent_0 1.016e+02 32.5400 7.812e-26 97.740000 1.055e+02 -#> k_m1 5.069e-03 10.0400 1.448e-11 0.004194 6.126e-03 -#> f_parent_to_m1 5.002e-01 20.7300 5.001e-20 0.468800 5.315e-01 -#> alpha 2.367e+02 0.6205 2.697e-01 NA NA -#> beta 2.322e+03 0.6114 2.727e-01 NA NA -#> sigma_low 0.000e+00 NaN NaN NaN NaN -#> rsd_high 7.989e-02 8.6630 4.393e-10 NaN NaN +#> parent_0 1.016e+02 32.7800 6.311e-26 9.624e+01 1.070e+02 +#> k_m1 5.072e-03 10.1200 1.216e-11 4.196e-03 6.130e-03 +#> f_parent_to_m1 5.004e-01 20.8300 4.317e-20 4.613e-01 5.394e-01 +#> alpha 2.502e+02 0.5624 2.889e-01 2.463e+02 2.542e+02 +#> beta 2.455e+03 0.5549 2.915e-01 NA NA +#> sigma_low 2.488e-03 0.4843 3.158e-01 1.992e-03 2.984e-03 +#> rsd_high 7.921e-02 8.4300 8.001e-10 6.018e-02 9.823e-02 #> #> FOCUS Chi2 error levels in percent: #> err.min n.optim df -#> All data 6.782 5 14 -#> parent 7.142 3 6 -#> m1 4.639 2 8 +#> All data 6.781 5 14 +#> parent 7.141 3 6 +#> m1 4.640 2 8 #> #> Resulting formation fractions: #> ff -#> parent_m1 0.5002 -#> parent_sink 0.4998 +#> parent_m1 0.5004 +#> parent_sink 0.4996 #> #> Estimated disappearance times: -#> DT50 DT90 DT50back -#> parent 6.81 22.7 6.833 -#> m1 136.74 454.2 NA
                                        +#> DT50 DT90 DT50back +#> parent 6.812 22.7 6.834 +#> m1 136.661 454.0 NA
                                        # We can easily use starting parameters from the parent only fit (only for illustration) fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE, error_model = "tc") fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html index 9a7dac6f..413f0ae1 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -78,7 +78,7 @@ mkinmod." /> mkin - 0.9.50.4 + 1.0.3.9000
                                        @@ -127,7 +127,7 @@ mkinmod." />
                                        • - +
                                          • @@ -348,7 +348,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media

                                          parent = mkinsub("SFO", "m1", full_name = "Test compound"), m1 = mkinsub("SFO", full_name = "Metabolite M1"), name = "SFO_SFO", dll_dir = DLL_dir, unload = TRUE, overwrite = TRUE) -
                                          #> Copied DLL from /tmp/Rtmpy4eiQb/file554e573761a7.so to /home/jranke/.local/share/mkin/SFO_SFO.so
                                          # Now we can save the model and restore it in a new session +
                                          #> Copied DLL from /tmp/Rtmp92fCb2/file133ad522561845.so to /home/jranke/.local/share/mkin/SFO_SFO.so
                                          # Now we can save the model and restore it in a new session saveRDS(SFO_SFO.2, file = "~/SFO_SFO.rds") # Terminate the R session here if you would like to check, and then do library(mkin) @@ -397,7 +397,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media

                                          #> }) #> return(predicted) #> } -#> <environment: 0x55555cac8d00>
                                          +#> <environment: 0x5555572517f8>
                                          # If we have several parallel metabolites # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R) m_synth_DFOP_par <- mkinmod( diff --git a/docs/dev/reference/mkinparplot-1.png b/docs/dev/reference/mkinparplot-1.png index dcf3e4b5..c9ed49eb 100644 Binary files a/docs/dev/reference/mkinparplot-1.png and b/docs/dev/reference/mkinparplot-1.png differ diff --git a/docs/dev/reference/mkinparplot.html b/docs/dev/reference/mkinparplot.html index 0a989ef9..bac6e71c 100644 --- a/docs/dev/reference/mkinparplot.html +++ b/docs/dev/reference/mkinparplot.html @@ -73,7 +73,7 @@ mkinfit." /> mkin - 0.9.50.4 + 1.0.3.9000
                                          @@ -122,7 +122,7 @@ mkinfit." />
                                          • - +
                                            • @@ -176,7 +176,8 @@ effect, namely to produce a plot.

                                            phenol = mkinsub("SFO", to = c("anisole")), anisole = mkinsub("SFO"), use_of_ff = "max")
                                            #> Temporary DLL for differentials generated and loaded
                                            fit <- mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE) -
                                            #> Warning: Observations with value of zero were removed from the data
                                            mkinparplot(fit) +
                                            #> Warning: Observations with value of zero were removed from the data
                                            #> Warning: Optimisation did not converge: +#> false convergence (8)
                                            mkinparplot(fit)
                                            # }
                                            diff --git a/docs/dev/reference/mkinplot.html b/docs/dev/reference/mkinplot.html index 417c8c73..120bddb3 100644 --- a/docs/dev/reference/mkinplot.html +++ b/docs/dev/reference/mkinplot.html @@ -73,7 +73,7 @@ plot.mkinfit." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -122,7 +122,7 @@ plot.mkinfit." />
                                            • - +
                                              • diff --git a/docs/dev/reference/mkinpredict.html b/docs/dev/reference/mkinpredict.html index 3221fc23..1ebaecb5 100644 --- a/docs/dev/reference/mkinpredict.html +++ b/docs/dev/reference/mkinpredict.html @@ -74,7 +74,7 @@ kinetic parameters and initial values for the state variables." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -123,7 +123,7 @@ kinetic parameters and initial values for the state variables." />
                                              • - +
                                                • @@ -408,10 +408,10 @@ as these always return mapped output.

                                                solution_type = "analytical", use_compiled = FALSE)[201,]) }
                                                #> test relative elapsed +#> 2 deSolve_compiled 1.0 0.005 #> 4 analytical 1.0 0.005 -#> 2 deSolve_compiled 1.2 0.006 -#> 1 eigen 4.0 0.020 -#> 3 deSolve 44.2 0.221
                                                +#> 1 eigen 4.4 0.022 +#> 3 deSolve 46.8 0.234
                                                # \dontrun{ # Predict from a fitted model f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE) diff --git a/docs/dev/reference/mkinresplot.html b/docs/dev/reference/mkinresplot.html index 2e10d5f6..30377f2c 100644 --- a/docs/dev/reference/mkinresplot.html +++ b/docs/dev/reference/mkinresplot.html @@ -75,7 +75,7 @@ argument show_residuals = TRUE." /> mkin - 1.0.1.9000 + 1.0.3.9000
                                                diff --git a/docs/dev/reference/mmkin.html b/docs/dev/reference/mmkin.html index 65c91adf..5da1b1de 100644 --- a/docs/dev/reference/mmkin.html +++ b/docs/dev/reference/mmkin.html @@ -75,7 +75,7 @@ datasets specified in its first two arguments." /> mkin - 1.0.1.9000 + 1.0.3.9000 @@ -234,9 +234,9 @@ plotting.

                                                time_default
                                                #> user system elapsed -#> 4.438 0.334 1.640
                                                time_1 +#> 4.921 0.475 1.707
                                                time_1
                                                #> user system elapsed -#> 5.535 0.004 5.539
                                                +#> 5.680 0.003 5.684
                                                endpoints(fits.0[["SFO_lin", 2]])
                                                #> $ff #> parent_M1 parent_sink M1_M2 M1_sink diff --git a/docs/dev/reference/nafta-1.png b/docs/dev/reference/nafta-1.png index 4d823d77..4f0d7833 100644 Binary files a/docs/dev/reference/nafta-1.png and b/docs/dev/reference/nafta-1.png differ diff --git a/docs/dev/reference/nafta.html b/docs/dev/reference/nafta.html index bbc8797d..6fb797a5 100644 --- a/docs/dev/reference/nafta.html +++ b/docs/dev/reference/nafta.html @@ -76,7 +76,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> mkin - 0.9.50.4 + 1.0.3.9000
                                                @@ -125,7 +125,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />
                                                • - +
                                                  • diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png index 193722c7..728cc557 100644 Binary files a/docs/dev/reference/nlme-1.png and b/docs/dev/reference/nlme-1.png differ diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png index c0fb6dcf..e8167455 100644 Binary files a/docs/dev/reference/nlme-2.png and b/docs/dev/reference/nlme-2.png differ diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index b3e88428..b850eb3d 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -124,7 +124,7 @@ datasets. They are used internally by the nlme.mmkin() method." />
                                                  • - +
                                                    • @@ -224,37 +224,37 @@ nlme for the case of a single grouping variable ds.

                                                    summary(m_nlme)
                                                    #> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) -#> Data: grouped_data +#> Data: grouped_data #> AIC BIC logLik -#> 252.7798 262.1358 -121.3899 +#> 300.6824 310.2426 -145.3412 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal -#> parent_0 log_k_parent_sink Residual -#> StdDev: 0.0006768135 0.6800777 2.489397 +#> parent_0 log_k_parent_sink Residual +#> StdDev: 1.697361 0.6801209 3.666073 #> -#> Fixed effects: parent_0 + log_k_parent_sink ~ 1 -#> Value Std.Error DF t-value p-value -#> parent_0 101.74884 0.6456014 44 157.60321 0 -#> log_k_parent_sink -3.05575 0.4015811 44 -7.60929 0 +#> Fixed effects: parent_0 + log_k_parent_sink ~ 1 +#> Value Std.Error DF t-value p-value +#> parent_0 100.99378 1.3890416 46 72.70753 0 +#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.026 +#> log_k_parent_sink 0.027 #> #> Standardized Within-Group Residuals: #> Min Q1 Med Q3 Max -#> -2.1317488 -0.6878121 0.0828385 0.8592270 2.9529864 +#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 #> -#> Number of Observations: 48 +#> Number of Observations: 50 #> Number of Groups: 3
                                                    plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
                                                    # augPred does not work on fits with more than one state # variable # # The procedure is greatly simplified by the nlme.mmkin function f_nlme <- nlme(f) -
                                                    #> Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!
                                                    plot(f_nlme) +plot(f_nlme)
                                                    @@ -157,7 +157,7 @@ have been obtained by fitting the same model to a list of datasets.

                                                    data = "auto", fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text = paste(el, 1, sep = "~")))), - random = pdDiag(fixed), + random = pdDiag(fixed), groups, start = mean_degparms(model, random = TRUE), correlation = NULL, diff --git a/docs/dev/reference/nobs.mkinfit.html b/docs/dev/reference/nobs.mkinfit.html index 621eb622..0b6c963c 100644 --- a/docs/dev/reference/nobs.mkinfit.html +++ b/docs/dev/reference/nobs.mkinfit.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000 @@ -121,7 +121,7 @@
                                                    • - +
                                                      • diff --git a/docs/dev/reference/parms.html b/docs/dev/reference/parms.html index ba0e89bb..9f6f4225 100644 --- a/docs/dev/reference/parms.html +++ b/docs/dev/reference/parms.html @@ -74,7 +74,7 @@ considering the error structure that was assumed for the fit." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -123,7 +123,7 @@ considering the error structure that was assumed for the fit." />
                                                      • - +
                                                        • @@ -219,10 +219,10 @@ such matrices is returned.

                                                        #> #> $DFOP #> Dataset 7 -#> parent_0 91.058971597 +#> parent_0 91.058971589 #> k1 0.044946770 #> k2 0.002868336 -#> g 0.526942414 +#> g 0.526942415 #> sigma 2.221302196 #>
                                                        parms(fits)
                                                        #> $SFO @@ -233,17 +233,17 @@ such matrices is returned.

                                                        #> #> $FOMC #> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481458 +#> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481459 #> alpha 1.338667 0.4967832 1.639099 1.074460 0.2805272 #> beta 13.033315 14.1451255 5.007077 4.397126 6.9052224 #> sigma 1.847671 1.9167519 1.066063 3.146056 1.6222778 #> #> $DFOP #> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 96.55213663 91.058971597 90.34509493 98.14858820 94.311323733 +#> parent_0 96.55213663 91.058971589 90.34509493 98.14858820 94.311323734 #> k1 0.21954588 0.044946770 0.41232288 0.31697588 0.080663857 #> k2 0.02957934 0.002868336 0.07581766 0.03260384 0.003425417 -#> g 0.44845068 0.526942414 0.66091967 0.65322767 0.342652880 +#> g 0.44845068 0.526942415 0.66091967 0.65322767 0.342652880 #> sigma 1.35690468 2.221302196 1.34169076 2.87159846 1.942067831 #>
                                                        parms(fits, transformed = TRUE)
                                                        #> $SFO diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index ef169074..630e95a3 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ mkin - 1.0.2.9000 + 1.0.3.9000
                                                        @@ -283,10 +283,10 @@ corresponding model prediction lines for the different datasets.

                                                        f_saem <- saem(f, transformations = "saemix")
                                                        #> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:07 2021" +#> [1] "Mon Feb 15 17:12:17 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:13 2021"
                                                        plot(f_saem) +#> [1] "Mon Feb 15 17:12:24 2021"
                                                        plot(f_saem)
                                                        # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo, diff --git a/docs/dev/reference/plot.mkinfit-2.png b/docs/dev/reference/plot.mkinfit-2.png index 376c812f..a11d1680 100644 Binary files a/docs/dev/reference/plot.mkinfit-2.png and b/docs/dev/reference/plot.mkinfit-2.png differ diff --git a/docs/dev/reference/plot.mkinfit-5.png b/docs/dev/reference/plot.mkinfit-5.png index bc44de88..6631aa68 100644 Binary files a/docs/dev/reference/plot.mkinfit-5.png and b/docs/dev/reference/plot.mkinfit-5.png differ diff --git a/docs/dev/reference/plot.mkinfit-6.png b/docs/dev/reference/plot.mkinfit-6.png index eb8cbd92..946b20c5 100644 Binary files a/docs/dev/reference/plot.mkinfit-6.png and b/docs/dev/reference/plot.mkinfit-6.png differ diff --git a/docs/dev/reference/plot.mkinfit-7.png b/docs/dev/reference/plot.mkinfit-7.png index 92a664f4..10807ea8 100644 Binary files a/docs/dev/reference/plot.mkinfit-7.png and b/docs/dev/reference/plot.mkinfit-7.png differ diff --git a/docs/dev/reference/plot.mkinfit.html b/docs/dev/reference/plot.mkinfit.html index c5bfb528..c5249ecd 100644 --- a/docs/dev/reference/plot.mkinfit.html +++ b/docs/dev/reference/plot.mkinfit.html @@ -74,7 +74,7 @@ observed data together with the solution of the fitted model." /> mkin - 0.9.50.4 + 1.0.3.9000
                                                        diff --git a/docs/dev/reference/plot.mmkin-1.png b/docs/dev/reference/plot.mmkin-1.png index f12b7907..647dfb8a 100644 Binary files a/docs/dev/reference/plot.mmkin-1.png and b/docs/dev/reference/plot.mmkin-1.png differ diff --git a/docs/dev/reference/plot.mmkin-2.png b/docs/dev/reference/plot.mmkin-2.png index e3127554..1bc1c9db 100644 Binary files a/docs/dev/reference/plot.mmkin-2.png and b/docs/dev/reference/plot.mmkin-2.png differ diff --git a/docs/dev/reference/plot.mmkin-3.png b/docs/dev/reference/plot.mmkin-3.png index 5448976e..50d6ffac 100644 Binary files a/docs/dev/reference/plot.mmkin-3.png and b/docs/dev/reference/plot.mmkin-3.png differ diff --git a/docs/dev/reference/plot.mmkin-4.png b/docs/dev/reference/plot.mmkin-4.png index 9a25fc50..e049fa16 100644 Binary files a/docs/dev/reference/plot.mmkin-4.png and b/docs/dev/reference/plot.mmkin-4.png differ diff --git a/docs/dev/reference/plot.mmkin-5.png b/docs/dev/reference/plot.mmkin-5.png index 82b422b5..2421995b 100644 Binary files a/docs/dev/reference/plot.mmkin-5.png and b/docs/dev/reference/plot.mmkin-5.png differ diff --git a/docs/dev/reference/plot.mmkin.html b/docs/dev/reference/plot.mmkin.html index ee80c6e4..9ca0df94 100644 --- a/docs/dev/reference/plot.mmkin.html +++ b/docs/dev/reference/plot.mmkin.html @@ -76,7 +76,7 @@ the fit of at least one model to the same dataset is shown." /> mkin - 0.9.50.4 + 1.0.3.9000 diff --git a/docs/dev/reference/plot.nafta.html b/docs/dev/reference/plot.nafta.html index 9fc59f94..c24fba99 100644 --- a/docs/dev/reference/plot.nafta.html +++ b/docs/dev/reference/plot.nafta.html @@ -73,7 +73,7 @@ function (SFO, then IORE, then DFOP)." /> mkin - 0.9.50.4 + 1.0.3.9000 @@ -122,7 +122,7 @@ function (SFO, then IORE, then DFOP)." />
                                                        • - +
                                                          • diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index f12a2690..371567d8 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -79,7 +79,7 @@ below to see their documentation. mkin - 0.9.50.4 + 1.0.3.9000 @@ -128,7 +128,7 @@ below to see their documentation.
                                                          • - +
                                                            • diff --git a/docs/dev/reference/residuals.mkinfit.html b/docs/dev/reference/residuals.mkinfit.html index 95114dae..3f518ab7 100644 --- a/docs/dev/reference/residuals.mkinfit.html +++ b/docs/dev/reference/residuals.mkinfit.html @@ -72,7 +72,7 @@ mkin - 0.9.50.4 + 1.0.3.9000 @@ -121,7 +121,7 @@
                                                            • - +
                                                              • @@ -175,7 +175,7 @@ standard deviation obtained from the fitted error model?

                                                              #> [1] 0.09726374 -0.13912142 -0.15351210 0.73388322 -0.08657004 -0.93204702 #> [7] -0.03269080 1.45347823 -0.88423697
                                                              residuals(f, standardized = TRUE)
                                                              #> [1] 0.13969917 -0.19981904 -0.22048826 1.05407091 -0.12433989 -1.33869208 -#> [7] -0.04695354 2.08761977 -1.27002287
                                                              +#> [7] -0.04695355 2.08761977 -1.27002287 @@ -261,27 +261,27 @@ using mmkin.

                                                              state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
                                                              #> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:16 2021" +#> [1] "Mon Feb 15 17:12:32 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:18 2021"
                                                              +#> [1] "Mon Feb 15 17:12:34 2021"
                                                              f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
                                                              #> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:19 2021" +#> [1] "Mon Feb 15 17:12:35 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:20 2021"
                                                              f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Mon Feb 15 17:12:36 2021"
                                                              f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
                                                              #> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:20 2021" +#> [1] "Mon Feb 15 17:12:36 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:22 2021"
                                                              f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Mon Feb 15 17:12:38 2021"
                                                              f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
                                                              #> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:23 2021" +#> [1] "Mon Feb 15 17:12:39 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:25 2021"
                                                              +#> [1] "Mon Feb 15 17:12:42 2021"
                                                              # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -324,10 +324,10 @@ using mmkin.

                                                              f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
                                                              #> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:28 2021" +#> [1] "Mon Feb 15 17:12:44 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:32 2021"
                                                              compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +#> [1] "Mon Feb 15 17:12:49 2021"
                                                              compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
                                                              #> Error in compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)): 'compare.saemix' requires at least two models.
                                                              sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) @@ -346,15 +346,15 @@ using mmkin.

                                                              # four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
                                                              #> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:35 2021" +#> [1] "Mon Feb 15 17:12:51 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:39 2021"
                                                              f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Mon Feb 15 17:12:56 2021"
                                                              f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
                                                              #> Running main SAEM algorithm -#> [1] "Sat Feb 13 12:33:40 2021" +#> [1] "Mon Feb 15 17:12:56 2021" #> .... #> Minimisation finished -#> [1] "Sat Feb 13 12:33:48 2021"
                                                              # We can use print, plot and summary methods to check the results +#> [1] "Mon Feb 15 17:13:05 2021"
                                                              # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo)
                                                              #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -395,10 +395,10 @@ using mmkin.

                                                              #> SD.g_qlogis 0.44771 -0.86417 1.7596
                                                              plot(f_saem_dfop_sfo)
                                                              summary(f_saem_dfop_sfo, data = TRUE)
                                                              #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.2.9000 +#> mkin version used for pre-fitting: 1.0.3.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Sat Feb 13 12:33:48 2021 -#> Date of summary: Sat Feb 13 12:33:48 2021 +#> Date of fit: Mon Feb 15 17:13:05 2021 +#> Date of summary: Mon Feb 15 17:13:06 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -413,7 +413,7 @@ using mmkin.

                                                              #> #> Model predictions using solution type analytical #> -#> Fitted in 8.875 s using 300, 100 iterations +#> Fitted in 8.985 s using 300, 100 iterations #> #> Variance model: Constant variance #> diff --git a/docs/dev/reference/schaefer07_complex_case-1.png b/docs/dev/reference/schaefer07_complex_case-1.png index 16e657dd..96aab2dc 100644 Binary files a/docs/dev/reference/schaefer07_complex_case-1.png and b/docs/dev/reference/schaefer07_complex_case-1.png differ diff --git a/docs/dev/reference/schaefer07_complex_case.html b/docs/dev/reference/schaefer07_complex_case.html index 83555b99..4ccad5c4 100644 --- a/docs/dev/reference/schaefer07_complex_case.html +++ b/docs/dev/reference/schaefer07_complex_case.html @@ -74,7 +74,7 @@ mkin - 0.9.50.4 + 1.0.3.9000
                                                              @@ -123,7 +123,7 @@
                                                              • - +
                                                                • @@ -186,15 +186,15 @@
                                                                endpoints(fit)
                                                                #> $ff #> parent_A1 parent_B1 parent_C1 parent_sink A1_A2 A1_sink -#> 0.3809620 0.1954665 0.4235715 0.0000000 0.4479662 0.5520338 +#> 0.3809620 0.1954667 0.4235713 0.0000000 0.4479619 0.5520381 #> #> $distimes #> DT50 DT90 #> parent 13.95078 46.34350 -#> A1 49.75343 165.27731 -#> B1 37.26912 123.80533 -#> C1 11.23131 37.30959 -#> A2 28.50569 94.69386 +#> A1 49.75342 165.27728 +#> B1 37.26908 123.80520 +#> C1 11.23131 37.30961 +#> A2 28.50624 94.69567 #>
                                                                # } # Compare with the results obtained in the original publication print(schaefer07_complex_results) diff --git a/docs/dev/reference/sigma_twocomp-1.png b/docs/dev/reference/sigma_twocomp-1.png index 21db3145..6e61684e 100644 Binary files a/docs/dev/reference/sigma_twocomp-1.png and b/docs/dev/reference/sigma_twocomp-1.png differ diff --git a/docs/dev/reference/sigma_twocomp.html b/docs/dev/reference/sigma_twocomp.html index 8212e480..b7d295b2 100644 --- a/docs/dev/reference/sigma_twocomp.html +++ b/docs/dev/reference/sigma_twocomp.html @@ -73,7 +73,7 @@ dependence of the measured value \(y\):" /> mkin - 0.9.50.4 + 1.0.3.9000
                                                                @@ -122,7 +122,7 @@ dependence of the measured value \(y\):" />
                                                                • - +
                                                                  • @@ -188,6 +188,10 @@ Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry 24(11), 1895-1898.

                                                                  Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for measurement error in analytical chemistry. Technometrics 37(2), 176-184.

                                                                  +

                                                                  Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical +Degradation Data. Environments 6(12) 124 +doi: 10.3390/environments6120124 +.

                                                                  Examples

                                                                  times <- c(0, 1, 3, 7, 14, 28, 60, 90, 120)
diff --git a/docs/dev/reference/summary.mkinfit.html b/docs/dev/reference/summary.mkinfit.html
index f314dfa8..494731e9 100644
--- a/docs/dev/reference/summary.mkinfit.html
+++ b/docs/dev/reference/summary.mkinfit.html
@@ -76,7 +76,7 @@ values." />
       
       
         mkin
-        0.9.50.4
+        1.0.3.9000
       
     
                                                                  
 
@@ -125,7 +125,7 @@ values." />
       
                                                                    
         
                                                                  • 
   
-    
+    
      
   
 
                                                                    • 
@@ -236,17 +236,17 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
     
                                                                    Examples
                                                                    
     
                                                                    
   summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
-
                                                                    #> mkin version used for fitting:    0.9.50.4 
+
                                                                    #> mkin version used for fitting:    1.0.3.9000 
 #> R version used for fitting:       4.0.3 
-#> Date of fit:     Mon Nov 30 16:01:20 2020 
-#> Date of summary: Mon Nov 30 16:01:20 2020 
+#> Date of fit:     Mon Feb 15 17:13:09 2021 
+#> Date of summary: Mon Feb 15 17:13:09 2021 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 131 model solutions performed in 0.028 s
+#> Fitted using 131 model solutions performed in 0.027 s
 #> 
 #> Error model: Constant variance 
 #> 
diff --git a/docs/dev/reference/summary.nlme.mmkin.html b/docs/dev/reference/summary.nlme.mmkin.html
index 2aeadc46..b2f6624a 100644
--- a/docs/dev/reference/summary.nlme.mmkin.html
+++ b/docs/dev/reference/summary.nlme.mmkin.html
@@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally
       
       
         mkin
-        0.9.50.4
+        1.0.3.9000
       
     
                                                                    
 
@@ -125,7 +125,7 @@ endpoints such as formation fractions and DT50 values. Optionally
       
                                                                      
         
                                                                    • 
   
-    
+    
      
   
 
                                                                      • 
@@ -263,12 +263,12 @@ José Pinheiro and Douglas Bates for the components inherited from nlme
                                                                      
 f_mmkin <- mmkin("SFO", ds_sfo_syn, quiet = TRUE, error_model = "tc", cores = 1)
 
                                                                      #> Warning: Optimisation did not converge:
 #> iteration limit reached without convergence (10)
                                                                      f_nlme <- nlme(f_mmkin)
-
                                                                      #> Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!
                                                                      summary(f_nlme, data = TRUE)
-
                                                                      #> nlme version used for fitting:      3.1.150.1 
-#> mkin version used for pre-fitting:  0.9.50.4 
+
                                                                      #> Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
                                                                      summary(f_nlme, data = TRUE)
+
                                                                      #> nlme version used for fitting:      3.1.152 
+#> mkin version used for pre-fitting:  1.0.3.9000 
 #> R version used for fitting:         4.0.3 
-#> Date of fit:     Mon Nov 30 16:01:23 2020 
-#> Date of summary: Mon Nov 30 16:01:23 2020 
+#> Date of fit:     Mon Feb 15 17:13:12 2021 
+#> Date of summary: Mon Feb 15 17:13:12 2021 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -278,148 +278,146 @@ José Pinheiro and Douglas Bates for the components inherited from nlme
                                                                      
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted in 0.996 s using 6 iterations
+#> Fitted in 0.544 s using 4 iterations
 #> 
 #> Variance model: Two-component variance function 
 #> 
 #> Mean of starting values for individual parameters:
 #>     parent_0 log_k_parent 
-#>   101.568773    -4.454103 
+#>      101.569       -4.454 
 #> 
 #> Fixed degradation parameter values:
 #> None
 #> 
 #> Results:
 #> 
-#>        AIC      BIC    logLik
-#>   586.4159 603.9145 -286.2079
+#>     AIC   BIC logLik
+#>   584.5 599.5 -286.2
 #> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
 #>               lower    est.   upper
-#> parent_0     99.360 101.593 103.825
-#> log_k_parent -4.979  -4.451  -3.924
+#> parent_0     99.371 101.592 103.814
+#> log_k_parent -4.973  -4.449  -3.926
 #> 
 #> Correlation: 
 #>              prnt_0
-#> log_k_parent 0.155 
+#> log_k_parent 0.051 
 #> 
 #> Random effects:
 #>  Formula: list(parent_0 ~ 1, log_k_parent ~ 1)
 #>  Level: ds
-#>  Structure: General positive-definite, Log-Cholesky parametrization
-#>              StdDev    Corr  
-#> parent_0     0.2624248 prnt_0
-#> log_k_parent 0.5907146 0.999 
-#> Residual     1.0000000       
+#>  Structure: Diagonal
+#>          parent_0 log_k_parent Residual
+#> StdDev: 6.924e-05       0.5863        1
 #> 
 #> Variance function:
 #>  Structure: Constant plus proportion of variance covariate
 #>  Formula: ~fitted(.) 
 #>  Parameter estimates:
-#>       const        prop 
-#> 6.56706e-24 7.89583e-02 
+#>        const         prop 
+#> 0.0001208853 0.0789968036 
 #> 
 #> Backtransformed parameters with asymmetric confidence intervals:
 #>              lower      est.     upper
-#> parent_0 99.360213 101.59280 103.82539
-#> k_parent  0.006883   0.01166   0.01976
+#> parent_0 99.370882 101.59243 103.81398
+#> k_parent  0.006923   0.01168   0.01972
 #> 
 #> Estimated disappearance times:
 #>         DT50  DT90
-#> parent 59.44 197.4
+#> parent 59.32 197.1
 #> 
 #> Data:
 #>    ds   name time observed predicted  residual    std standardized
-#>  ds 1 parent    0    104.1   101.417   2.68283 8.0077     0.335030
-#>  ds 1 parent    0    105.0   101.417   3.58283 8.0077     0.447422
-#>  ds 1 parent    1     98.5   100.624  -2.12400 7.9451    -0.267334
-#>  ds 1 parent    1     96.1   100.624  -4.52400 7.9451    -0.569407
-#>  ds 1 parent    3    101.9    99.056   2.84379 7.8213     0.363595
-#>  ds 1 parent    3     85.2    99.056 -13.85621 7.8213    -1.771597
-#>  ds 1 parent    7     99.1    95.994   3.10646 7.5795     0.409850
-#>  ds 1 parent    7     93.0    95.994  -2.99354 7.5795    -0.394953
-#>  ds 1 parent   14     88.1    90.860  -2.75997 7.1741    -0.384710
-#>  ds 1 parent   14     84.1    90.860  -6.75997 7.1741    -0.942268
-#>  ds 1 parent   28     80.2    81.402  -1.20174 6.4273    -0.186973
-#>  ds 1 parent   28     91.3    81.402   9.89826 6.4273     1.540024
-#>  ds 1 parent   60     65.1    63.316   1.78361 4.9994     0.356767
-#>  ds 1 parent   60     65.8    63.316   2.48361 4.9994     0.496785
-#>  ds 1 parent   90     47.8    50.029  -2.22862 3.9502    -0.564182
-#>  ds 1 parent   90     53.5    50.029   3.47138 3.9502     0.878792
-#>  ds 1 parent  120     37.6    39.529  -1.92946 3.1212    -0.618182
-#>  ds 1 parent  120     39.3    39.529  -0.22946 3.1212    -0.073516
-#>  ds 2 parent    0    107.9   101.711   6.18875 8.0309     0.770613
-#>  ds 2 parent    0    102.1   101.711   0.38875 8.0309     0.048406
-#>  ds 2 parent    1    103.8   100.174   3.62597 7.9096     0.458428
-#>  ds 2 parent    1    108.6   100.174   8.42597 7.9096     1.065287
-#>  ds 2 parent    3     91.0    97.169  -6.16895 7.6723    -0.804055
-#>  ds 2 parent    3     84.9    97.169 -12.26895 7.6723    -1.599124
-#>  ds 2 parent    7     79.3    91.427 -12.12652 7.2189    -1.679833
-#>  ds 2 parent    7    100.9    91.427   9.47348 7.2189     1.312320
-#>  ds 2 parent   14     77.3    82.182  -4.88174 6.4889    -0.752318
-#>  ds 2 parent   14     83.5    82.182   1.31826 6.4889     0.203155
-#>  ds 2 parent   28     66.8    66.402   0.39792 5.2430     0.075895
-#>  ds 2 parent   28     63.3    66.402  -3.10208 5.2430    -0.591662
-#>  ds 2 parent   60     40.8    40.789   0.01138 3.2206     0.003532
-#>  ds 2 parent   60     44.8    40.789   4.01138 3.2206     1.245537
-#>  ds 2 parent   90     27.8    25.830   1.97002 2.0395     0.965937
-#>  ds 2 parent   90     27.0    25.830   1.17002 2.0395     0.573682
-#>  ds 2 parent  120     15.2    16.357  -1.15721 1.2915    -0.895991
-#>  ds 2 parent  120     15.5    16.357  -0.85721 1.2915    -0.663710
-#>  ds 3 parent    0     97.7   101.907  -4.20726 8.0464    -0.522873
-#>  ds 3 parent    0     88.2   101.907 -13.70726 8.0464    -1.703521
-#>  ds 3 parent    1    109.9    99.522  10.37805 7.8581     1.320685
-#>  ds 3 parent    1     97.8    99.522  -1.72195 7.8581    -0.219130
-#>  ds 3 parent    3    100.5    94.918   5.58248 7.4945     0.744875
-#>  ds 3 parent    3     77.4    94.918 -17.51752 7.4945    -2.337375
-#>  ds 3 parent    7     78.3    86.338  -8.03788 6.8171    -1.179077
-#>  ds 3 parent    7     90.3    86.338   3.96212 6.8171     0.581204
-#>  ds 3 parent   14     76.0    73.147   2.85281 5.7756     0.493944
-#>  ds 3 parent   14     79.1    73.147   5.95281 5.7756     1.030687
-#>  ds 3 parent   28     46.0    52.504  -6.50373 4.1456    -1.568826
-#>  ds 3 parent   28     53.4    52.504   0.89627 4.1456     0.216197
-#>  ds 3 parent   60     25.1    24.605   0.49461 1.9428     0.254586
-#>  ds 3 parent   60     21.4    24.605  -3.20539 1.9428    -1.649882
-#>  ds 3 parent   90     11.0    12.090  -1.09046 0.9546    -1.142271
-#>  ds 3 parent   90     14.2    12.090   2.10954 0.9546     2.209770
-#>  ds 3 parent  120      5.8     5.941  -0.14094 0.4691    -0.300464
-#>  ds 3 parent  120      6.1     5.941   0.15906 0.4691     0.339077
-#>  ds 4 parent    0     95.3   101.177  -5.87672 7.9887    -0.735625
-#>  ds 4 parent    0    102.0   101.177   0.82328 7.9887     0.103056
-#>  ds 4 parent    1    104.4   100.716   3.68438 7.9523     0.463307
-#>  ds 4 parent    1    105.4   100.716   4.68438 7.9523     0.589057
-#>  ds 4 parent    3    113.7    99.800  13.90026 7.8800     1.763989
-#>  ds 4 parent    3     82.3    99.800 -17.49974 7.8800    -2.220774
-#>  ds 4 parent    7     98.1    97.993   0.10713 7.7374     0.013846
-#>  ds 4 parent    7     87.8    97.993 -10.19287 7.7374    -1.317359
-#>  ds 4 parent   14     97.9    94.909   2.99079 7.4939     0.399098
-#>  ds 4 parent   14    104.8    94.909   9.89079 7.4939     1.319851
-#>  ds 4 parent   28     85.0    89.030  -4.02995 7.0297    -0.573279
-#>  ds 4 parent   28     77.2    89.030 -11.82995 7.0297    -1.682864
-#>  ds 4 parent   60     82.2    76.923   5.27690 6.0737     0.868808
-#>  ds 4 parent   60     86.1    76.923   9.17690 6.0737     1.510919
-#>  ds 4 parent   90     70.5    67.073   3.42743 5.2959     0.647182
-#>  ds 4 parent   90     61.7    67.073  -5.37257 5.2959    -1.014470
-#>  ds 4 parent  120     60.0    58.483   1.51654 4.6178     0.328416
-#>  ds 4 parent  120     56.4    58.483  -2.08346 4.6178    -0.451184
-#>  ds 5 parent    0     92.6   101.752  -9.15161 8.0341    -1.139091
-#>  ds 5 parent    0    116.5   101.752  14.74839 8.0341     1.835716
-#>  ds 5 parent    1    108.0   100.069   7.93112 7.9013     1.003778
-#>  ds 5 parent    1    104.9   100.069   4.83112 7.9013     0.611436
-#>  ds 5 parent    3    100.5    96.786   3.71355 7.6421     0.485934
-#>  ds 5 parent    3     89.5    96.786  -7.28645 7.6421    -0.953462
-#>  ds 5 parent    7     91.7    90.541   1.15895 7.1490     0.162114
-#>  ds 5 parent    7     95.1    90.541   4.55895 7.1490     0.637707
-#>  ds 5 parent   14     82.2    80.566   1.63437 6.3613     0.256923
-#>  ds 5 parent   14     84.5    80.566   3.93437 6.3613     0.618483
-#>  ds 5 parent   28     60.5    63.791  -3.29084 5.0368    -0.653357
-#>  ds 5 parent   28     72.8    63.791   9.00916 5.0368     1.788662
-#>  ds 5 parent   60     38.3    37.412   0.88840 2.9540     0.300748
-#>  ds 5 parent   60     40.7    37.412   3.28840 2.9540     1.113217
-#>  ds 5 parent   90     22.5    22.685  -0.18500 1.7912    -0.103287
-#>  ds 5 parent   90     20.8    22.685  -1.88500 1.7912    -1.052387
-#>  ds 5 parent  120     13.4    13.755  -0.35534 1.0861    -0.327173
-#>  ds 5 parent  120     13.8    13.755   0.04466 1.0861     0.041118
                                                                      
+#>  ds 1 parent    0    104.1   101.592   2.50757 8.0255     0.312451
+#>  ds 1 parent    0    105.0   101.592   3.40757 8.0255     0.424594
+#>  ds 1 parent    1     98.5   100.796  -2.29571 7.9625    -0.288313
+#>  ds 1 parent    1     96.1   100.796  -4.69571 7.9625    -0.589725
+#>  ds 1 parent    3    101.9    99.221   2.67904 7.8381     0.341796
+#>  ds 1 parent    3     85.2    99.221 -14.02096 7.8381    -1.788812
+#>  ds 1 parent    7     99.1    96.145   2.95512 7.5951     0.389081
+#>  ds 1 parent    7     93.0    96.145  -3.14488 7.5951    -0.414065
+#>  ds 1 parent   14     88.1    90.989  -2.88944 7.1879    -0.401987
+#>  ds 1 parent   14     84.1    90.989  -6.88944 7.1879    -0.958480
+#>  ds 1 parent   28     80.2    81.493  -1.29305 6.4377    -0.200857
+#>  ds 1 parent   28     91.3    81.493   9.80695 6.4377     1.523364
+#>  ds 1 parent   60     65.1    63.344   1.75642 5.0039     0.351008
+#>  ds 1 parent   60     65.8    63.344   2.45642 5.0039     0.490898
+#>  ds 1 parent   90     47.8    50.018  -2.21764 3.9512    -0.561252
+#>  ds 1 parent   90     53.5    50.018   3.48236 3.9512     0.881335
+#>  ds 1 parent  120     37.6    39.495  -1.89515 3.1200    -0.607423
+#>  ds 1 parent  120     39.3    39.495  -0.19515 3.1200    -0.062549
+#>  ds 2 parent    0    107.9   101.592   6.30757 8.0255     0.785943
+#>  ds 2 parent    0    102.1   101.592   0.50757 8.0255     0.063245
+#>  ds 2 parent    1    103.8   100.058   3.74159 7.9043     0.473361
+#>  ds 2 parent    1    108.6   100.058   8.54159 7.9043     1.080626
+#>  ds 2 parent    3     91.0    97.060  -6.05952 7.6674    -0.790297
+#>  ds 2 parent    3     84.9    97.060 -12.15952 7.6674    -1.585874
+#>  ds 2 parent    7     79.3    91.329 -12.02867 7.2147    -1.667251
+#>  ds 2 parent    7    100.9    91.329   9.57133 7.2147     1.326647
+#>  ds 2 parent   14     77.3    82.102  -4.80185 6.4858    -0.740366
+#>  ds 2 parent   14     83.5    82.102   1.39815 6.4858     0.215571
+#>  ds 2 parent   28     66.8    66.351   0.44945 5.2415     0.085748
+#>  ds 2 parent   28     63.3    66.351  -3.05055 5.2415    -0.582002
+#>  ds 2 parent   60     40.8    40.775   0.02474 3.2211     0.007679
+#>  ds 2 parent   60     44.8    40.775   4.02474 3.2211     1.249485
+#>  ds 2 parent   90     27.8    25.832   1.96762 2.0407     0.964198
+#>  ds 2 parent   90     27.0    25.832   1.16762 2.0407     0.572171
+#>  ds 2 parent  120     15.2    16.366  -1.16561 1.2928    -0.901595
+#>  ds 2 parent  120     15.5    16.366  -0.86561 1.2928    -0.669547
+#>  ds 3 parent    0     97.7   101.592  -3.89243 8.0255    -0.485009
+#>  ds 3 parent    0     88.2   101.592 -13.39243 8.0255    -1.668739
+#>  ds 3 parent    1    109.9    99.218  10.68196 7.8379     1.362858
+#>  ds 3 parent    1     97.8    99.218  -1.41804 7.8379    -0.180921
+#>  ds 3 parent    3    100.5    94.634   5.86555 7.4758     0.784603
+#>  ds 3 parent    3     77.4    94.634 -17.23445 7.4758    -2.305360
+#>  ds 3 parent    7     78.3    86.093  -7.79273 6.8011    -1.145813
+#>  ds 3 parent    7     90.3    86.093   4.20727 6.8011     0.618620
+#>  ds 3 parent   14     76.0    72.958   3.04222 5.7634     0.527848
+#>  ds 3 parent   14     79.1    72.958   6.14222 5.7634     1.065722
+#>  ds 3 parent   28     46.0    52.394  -6.39404 4.1390    -1.544842
+#>  ds 3 parent   28     53.4    52.394   1.00596 4.1390     0.243046
+#>  ds 3 parent   60     25.1    24.582   0.51786 1.9419     0.266676
+#>  ds 3 parent   60     21.4    24.582  -3.18214 1.9419    -1.638664
+#>  ds 3 parent   90     11.0    12.092  -1.09202 0.9552    -1.143199
+#>  ds 3 parent   90     14.2    12.092   2.10798 0.9552     2.206776
+#>  ds 3 parent  120      5.8     5.948  -0.14810 0.4699    -0.315178
+#>  ds 3 parent  120      6.1     5.948   0.15190 0.4699     0.323282
+#>  ds 4 parent    0     95.3   101.592  -6.29243 8.0255    -0.784057
+#>  ds 4 parent    0    102.0   101.592   0.40757 8.0255     0.050784
+#>  ds 4 parent    1    104.4   101.125   3.27549 7.9885     0.410025
+#>  ds 4 parent    1    105.4   101.125   4.27549 7.9885     0.535205
+#>  ds 4 parent    3    113.7   100.195  13.50487 7.9151     1.706218
+#>  ds 4 parent    3     82.3   100.195 -17.89513 7.9151    -2.260886
+#>  ds 4 parent    7     98.1    98.362  -0.26190 7.7703    -0.033706
+#>  ds 4 parent    7     87.8    98.362 -10.56190 7.7703    -1.359270
+#>  ds 4 parent   14     97.9    95.234   2.66590 7.5232     0.354357
+#>  ds 4 parent   14    104.8    95.234   9.56590 7.5232     1.271521
+#>  ds 4 parent   28     85.0    89.274  -4.27372 7.0523    -0.606001
+#>  ds 4 parent   28     77.2    89.274 -12.07372 7.0523    -1.712017
+#>  ds 4 parent   60     82.2    77.013   5.18661 6.0838     0.852526
+#>  ds 4 parent   60     86.1    77.013   9.08661 6.0838     1.493571
+#>  ds 4 parent   90     70.5    67.053   3.44692 5.2970     0.650733
+#>  ds 4 parent   90     61.7    67.053  -5.35308 5.2970    -1.010591
+#>  ds 4 parent  120     60.0    58.381   1.61905 4.6119     0.351058
+#>  ds 4 parent  120     56.4    58.381  -1.98095 4.6119    -0.429530
+#>  ds 5 parent    0     92.6   101.592  -8.99243 8.0255    -1.120485
+#>  ds 5 parent    0    116.5   101.592  14.90757 8.0255     1.857531
+#>  ds 5 parent    1    108.0    99.914   8.08560 7.8929     1.024413
+#>  ds 5 parent    1    104.9    99.914   4.98560 7.8929     0.631655
+#>  ds 5 parent    3    100.5    96.641   3.85898 7.6343     0.505477
+#>  ds 5 parent    3     89.5    96.641  -7.14102 7.6343    -0.935382
+#>  ds 5 parent    7     91.7    90.412   1.28752 7.1423     0.180267
+#>  ds 5 parent    7     95.1    90.412   4.68752 7.1423     0.656304
+#>  ds 5 parent   14     82.2    80.463   1.73715 6.3563     0.273295
+#>  ds 5 parent   14     84.5    80.463   4.03715 6.3563     0.635141
+#>  ds 5 parent   28     60.5    63.728  -3.22788 5.0343    -0.641178
+#>  ds 5 parent   28     72.8    63.728   9.07212 5.0343     1.802062
+#>  ds 5 parent   60     38.3    37.399   0.90061 2.9544     0.304835
+#>  ds 5 parent   60     40.7    37.399   3.30061 2.9544     1.117174
+#>  ds 5 parent   90     22.5    22.692  -0.19165 1.7926    -0.106913
+#>  ds 5 parent   90     20.8    22.692  -1.89165 1.7926    -1.055273
+#>  ds 5 parent  120     13.4    13.768  -0.36790 1.0876    -0.338259
+#>  ds 5 parent  120     13.8    13.768   0.03210 1.0876     0.029517
                                                                      
 
                                                                    
   
   
 
@@ -260,15 +260,15 @@ saemix authors for the parts inherited from saemix.
                                                                    
   quiet = TRUE, error_model = "tc", cores = 5)
 f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
 
                                                                    #> Running main SAEM algorithm
-#> [1] "Sat Feb 13 12:33:52 2021"
+#> [1] "Mon Feb 15 17:13:15 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Sat Feb 13 12:34:03 2021"
                                                                    summary(f_saem_dfop_sfo, data = TRUE)
+#> [1] "Mon Feb 15 17:13:26 2021"
                                                                    summary(f_saem_dfop_sfo, data = TRUE)
 
                                                                    #> saemix version used for fitting:      3.1.9000 
-#> mkin version used for pre-fitting:  1.0.2.9000 
+#> mkin version used for pre-fitting:  1.0.3.9000 
 #> R version used for fitting:         4.0.3 
-#> Date of fit:     Sat Feb 13 12:34:04 2021 
-#> Date of summary: Sat Feb 13 12:34:04 2021 
+#> Date of fit:     Mon Feb 15 17:13:27 2021 
+#> Date of summary: Mon Feb 15 17:13:27 2021 
 #> 
 #> Equations:
 #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -283,7 +283,7 @@ saemix authors for the parts inherited from saemix.
                                                                    
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted in 11.899 s using 300, 100 iterations
+#> Fitted in 12.204 s using 300, 100 iterations
 #> 
 #> Variance model: Two-component variance function 
 #> 
diff --git a/docs/dev/reference/synthetic_data_for_UBA_2014-1.png b/docs/dev/reference/synthetic_data_for_UBA_2014-1.png
index 351b21aa..89975db5 100644
Binary files a/docs/dev/reference/synthetic_data_for_UBA_2014-1.png and b/docs/dev/reference/synthetic_data_for_UBA_2014-1.png differ
diff --git a/docs/dev/reference/synthetic_data_for_UBA_2014.html b/docs/dev/reference/synthetic_data_for_UBA_2014.html
index 1edc7c1e..33a0ace2 100644
--- a/docs/dev/reference/synthetic_data_for_UBA_2014.html
+++ b/docs/dev/reference/synthetic_data_for_UBA_2014.html
@@ -87,7 +87,7 @@ Compare also the code in the example section to see the degradation models." />
       
       
         mkin
-        0.9.50.4
+        1.0.3.9000
       
     
                                                                    
 
@@ -136,7 +136,7 @@ Compare also the code in the example section to see the degradation models." />
       
                                                                      
         
                                                                    • 
   
-    
+    
      
   
 
                                                                      • 
@@ -297,10 +297,10 @@ Compare also the code in the example section to see the degradation models." />
                  quiet = TRUE)
   plot_sep(fit)
 
                                                                        summary(fit)
-
                                                                      #> mkin version used for fitting:    0.9.50.4 
+
                                                                      #> mkin version used for fitting:    1.0.3.9000 
 #> R version used for fitting:       4.0.3 
-#> Date of fit:     Mon Nov 30 16:01:42 2020 
-#> Date of summary: Mon Nov 30 16:01:42 2020 
+#> Date of fit:     Mon Feb 15 17:13:29 2021 
+#> Date of summary: Mon Feb 15 17:13:29 2021 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -309,7 +309,7 @@ Compare also the code in the example section to see the degradation models." />
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 822 model solutions performed in 0.652 s
+#> Fitted using 833 model solutions performed in 0.649 s
 #> 
 #> Error model: Constant variance 
 #> 
@@ -361,15 +361,15 @@ Compare also the code in the example section to see the degradation models." />
 #> log_k_M2         2.819e-02    7.166e-02 -3.929e-01  1.000e+00      -2.658e-01
 #> f_parent_qlogis -4.624e-01   -5.682e-01  7.478e-01 -2.658e-01       1.000e+00
 #> f_M1_qlogis      1.614e-01    4.102e-01 -8.109e-01  5.419e-01      -8.605e-01
-#> sigma           -7.941e-08   -9.143e-09 -1.268e-08  5.947e-08       5.657e-08
+#> sigma           -2.900e-08   -8.030e-09 -2.741e-08  3.938e-08      -2.681e-08
 #>                 f_M1_qlogis      sigma
-#> parent_0          1.614e-01 -7.941e-08
-#> log_k_parent      4.102e-01 -9.143e-09
-#> log_k_M1         -8.109e-01 -1.268e-08
-#> log_k_M2          5.419e-01  5.947e-08
-#> f_parent_qlogis  -8.605e-01  5.657e-08
-#> f_M1_qlogis       1.000e+00 -2.382e-10
-#> sigma            -2.382e-10  1.000e+00
+#> parent_0          1.614e-01 -2.900e-08
+#> log_k_parent      4.102e-01 -8.030e-09
+#> log_k_M1         -8.109e-01 -2.741e-08
+#> log_k_M2          5.419e-01  3.938e-08
+#> f_parent_qlogis  -8.605e-01 -2.681e-08
+#> f_M1_qlogis       1.000e+00  4.971e-08
+#> sigma             4.971e-08  1.000e+00
 #> 
 #> Backtransformed parameters:
 #> Confidence intervals for internally transformed parameters are asymmetric.
@@ -416,7 +416,7 @@ Compare also the code in the example section to see the degradation models." />
 #>     7   parent      0.3  5.772e-01 -0.27717
 #>    14   parent      3.5  3.264e-03  3.49674
 #>    28   parent      3.2  1.045e-07  3.20000
-#>    90   parent      0.6  9.532e-10  0.60000
+#>    90   parent      0.6  9.530e-10  0.60000
 #>   120   parent      3.5 -5.940e-10  3.50000
 #>     1       M1     36.4  3.479e+01  1.61088
 #>     1       M1     37.4  3.479e+01  2.61088
@@ -427,7 +427,7 @@ Compare also the code in the example section to see the degradation models." />
 #>    14       M1      5.8  1.995e+00  3.80469
 #>    14       M1      1.2  1.995e+00 -0.79531
 #>    60       M1      0.5  2.111e-06  0.50000
-#>    90       M1      3.2 -9.671e-10  3.20000
+#>    90       M1      3.2 -9.670e-10  3.20000
 #>   120       M1      1.5  7.670e-10  1.50000
 #>   120       M1      0.6  7.670e-10  0.60000
 #>     1       M2      4.8  4.455e+00  0.34517
diff --git a/docs/dev/reference/test_data_from_UBA_2014-1.png b/docs/dev/reference/test_data_from_UBA_2014-1.png
index 9e0afad2..7bf0bd0f 100644
Binary files a/docs/dev/reference/test_data_from_UBA_2014-1.png and b/docs/dev/reference/test_data_from_UBA_2014-1.png differ
diff --git a/docs/dev/reference/test_data_from_UBA_2014-2.png b/docs/dev/reference/test_data_from_UBA_2014-2.png
index e889efde..fc1f77e0 100644
Binary files a/docs/dev/reference/test_data_from_UBA_2014-2.png and b/docs/dev/reference/test_data_from_UBA_2014-2.png differ
diff --git a/docs/dev/reference/test_data_from_UBA_2014.html b/docs/dev/reference/test_data_from_UBA_2014.html
index 89ec3480..539b8287 100644
--- a/docs/dev/reference/test_data_from_UBA_2014.html
+++ b/docs/dev/reference/test_data_from_UBA_2014.html
@@ -73,7 +73,7 @@
       
       
         mkin
-        0.9.50.4
+        1.0.3.9000
       
     
                                                                      
 
@@ -122,7 +122,7 @@
       
                                                                        
         
                                                                      • 
   
-    
+    
      
   
 
                                                                        • 
@@ -203,25 +203,25 @@
 
                                                                        #> Warning: Observations with value of zero were removed from the data
                                                                          plot_sep(f_soil, lpos = c("topright", "topright", "topright", "bottomright"))
 
                                                                          summary(f_soil)$bpar
 
                                                                        #>                   Estimate  se_notrans    t value       Pr(>t)        Lower
-#> parent_0       76.55425649 0.859186399 89.1008710 1.113861e-26 74.755959406
+#> parent_0       76.55425650 0.859186399 89.1008710 1.113861e-26 74.755959418
 #> k_parent        0.12081956 0.004601918 26.2541722 1.077359e-16  0.111561575
-#> k_M1            0.84258614 0.806159820  1.0451850 1.545267e-01  0.113779670
-#> k_M2            0.04210880 0.017083035  2.4649483 1.170188e-02  0.018013857
-#> k_M3            0.01122918 0.007245855  1.5497385 6.885052e-02  0.002909431
-#> f_parent_to_M1  0.32240200 0.240783909  1.3389682 9.819073e-02           NA
-#> f_parent_to_M2  0.16099855 0.033691953  4.7785463 6.531137e-05           NA
-#> f_M1_to_M3      0.27921507 0.269423745  1.0363417 1.565266e-01  0.022978220
-#> f_M2_to_M3      0.55641253 0.595119954  0.9349586 1.807707e-01  0.008002509
+#> k_M1            0.84258615 0.806160102  1.0451846 1.545268e-01  0.113779609
+#> k_M2            0.04210880 0.017083034  2.4649483 1.170188e-02  0.018013857
+#> k_M3            0.01122918 0.007245856  1.5497385 6.885052e-02  0.002909431
+#> f_parent_to_M1  0.32240200 0.240783943  1.3389680 9.819076e-02           NA
+#> f_parent_to_M2  0.16099855 0.033691952  4.7785464 6.531136e-05           NA
+#> f_M1_to_M3      0.27921507 0.269423780  1.0363416 1.565267e-01  0.022978205
+#> f_M2_to_M3      0.55641252 0.595119966  0.9349586 1.807707e-01  0.008002509
 #> sigma           1.14005399 0.149696423  7.6157731 1.727024e-07  0.826735778
 #>                      Upper
-#> parent_0       78.35255357
+#> parent_0       78.35255358
 #> k_parent        0.13084582
-#> k_M1            6.23970352
+#> k_M1            6.23970702
 #> k_M2            0.09843260
 #> k_M3            0.04333992
 #> f_parent_to_M1          NA
 #> f_parent_to_M2          NA
-#> f_M1_to_M3      0.86450768
+#> f_M1_to_M3      0.86450775
 #> f_M2_to_M3      0.99489895
 #> sigma           1.45337221
                                                                          mkinerrmin(f_soil)
 
                                                                        #>             err.min n.optim df
diff --git a/docs/dev/reference/transform_odeparms.html b/docs/dev/reference/transform_odeparms.html
index 6e19505f..75d6a1f9 100644
--- a/docs/dev/reference/transform_odeparms.html
+++ b/docs/dev/reference/transform_odeparms.html
@@ -77,7 +77,7 @@ the ilr transformation is used." />
       
       
         mkin
-        1.0.1.9000
+        1.0.3.9000
       
     
                                                                        
 
diff --git a/docs/dev/reference/update.mkinfit-1.png b/docs/dev/reference/update.mkinfit-1.png
index 7d2f1bdb..df8473c1 100644
Binary files a/docs/dev/reference/update.mkinfit-1.png and b/docs/dev/reference/update.mkinfit-1.png differ
diff --git a/docs/dev/reference/update.mkinfit-2.png b/docs/dev/reference/update.mkinfit-2.png
index 8dcabcdc..13c99b44 100644
Binary files a/docs/dev/reference/update.mkinfit-2.png and b/docs/dev/reference/update.mkinfit-2.png differ
diff --git a/docs/dev/reference/update.mkinfit.html b/docs/dev/reference/update.mkinfit.html
index 10a93373..83f45028 100644
--- a/docs/dev/reference/update.mkinfit.html
+++ b/docs/dev/reference/update.mkinfit.html
@@ -75,7 +75,7 @@ override these starting values." />
       
       
         mkin
-        0.9.50.4
+        1.0.3.9000
       
     
 
@@ -124,7 +124,7 @@ override these starting values." />
       
                                                                          
         
                                                                        • 
   
-    
+    
      
   
 
                                                                          • 
diff --git a/test.log b/test.log
index 7a755af8..2c77a113 100644
--- a/test.log
+++ b/test.log
@@ -9,34 +9,34 @@ Testing mkin
 ✔ |   7       | Fitting the SFORB model [3.6 s]
 ✔ |   5       | Analytical solutions for coupled models [3.3 s]
 ✔ |   5       | Calculation of Akaike weights
-✔ |  12       | Confidence intervals and p-values [1.1 s]
+✔ |  12       | Confidence intervals and p-values [1.0 s]
 ✔ |  14       | Error model fitting [4.6 s]
 ✔ |   5       | Time step normalisation
 ✔ |   4       | Test fitting the decline of metabolites from their maximum [0.3 s]
 ✔ |   1       | Fitting the logistic model [0.2 s]
-✔ |  34     1 | Nonlinear mixed-effects models [26.1 s]
+✔ |  34     1 | Nonlinear mixed-effects models [40.8 s]
 ────────────────────────────────────────────────────────────────────────────────
 Skip (test_mixed.R:150:3): saem results are reproducible for biphasic fits
 Reason: Fitting with saemix takes around 10 minutes when using deSolve
 ────────────────────────────────────────────────────────────────────────────────
 ✔ |   2       | Test dataset classes mkinds and mkindsg
-✔ |   1       | mkinfit features [0.3 s]
-✔ |  10       | Special cases of mkinfit calls [0.3 s]
-✔ |   8       | mkinmod model generation and printing [0.2 s]
-✔ |   3       | Model predictions with mkinpredict [0.3 s]
-✔ |  16       | Evaluations according to 2015 NAFTA guidance [1.7 s]
-✔ |   9       | Nonlinear mixed-effects models [8.5 s]
-✔ |  16       | Plotting [2.0 s]
+✔ |   1       | mkinfit features [0.5 s]
+✔ |  10       | Special cases of mkinfit calls [0.6 s]
+✔ |   8       | mkinmod model generation and printing [0.4 s]
+✔ |   3       | Model predictions with mkinpredict [0.7 s]
+✔ |  16       | Evaluations according to 2015 NAFTA guidance [3.1 s]
+✔ |   9       | Nonlinear mixed-effects models [14.5 s]
+✔ |  16       | Plotting [2.1 s]
 ✔ |   4       | Residuals extracted from mkinfit models
-✔ |   2       | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s]
-✔ |   4       | Summary [0.1 s]
+✔ |   2       | Complex test case from Schaefer et al. (2007) Piacenza paper [1.7 s]
+✔ |   4       | Summary [0.2 s]
 ✔ |   1       | Summaries of old mkinfit objects
-✔ |   4       | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2 s]
-✔ |   9       | Hypothesis tests [8.3 s]
+✔ |   4       | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s]
+✔ |   9       | Hypothesis tests [8.4 s]
 ✔ |   4       | Calculation of maximum time weighted average concentrations (TWAs) [2.5 s]
 
 ══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 68.9 s
+Duration: 92.7 s
 
 ── Skipped tests  ──────────────────────────────────────────────────────────────
 ● Fitting with saemix takes around 10 minutes when using deSolve (1)
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index f1f078db..a158629a 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -463,8 +463,8 @@ print(FOCUS_2006_D)
                                                                  summary(fit)
                                                                  ## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Mon Feb 15 14:11:17 2021 
-## Date of summary: Mon Feb 15 14:11:17 2021 
+## Date of fit:     Mon Feb 15 17:29:02 2021 
+## Date of summary: Mon Feb 15 17:29:03 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 0ed46483..3d8e02c2 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -1561,8 +1561,8 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)
                                                                  summary(m.L1.SFO) 
                                                                  ## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Mon Feb 15 14:11:19 2021 
-## Date of summary: Mon Feb 15 14:11:19 2021 
+## Date of fit:     Mon Feb 15 17:29:04 2021 
+## Date of summary: Mon Feb 15 17:29:04 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -1662,15 +1662,15 @@ summary(m.L1.SFO)
                                                                  ## doubtful 
                                                                  ## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Mon Feb 15 14:11:19 2021 
-## Date of summary: Mon Feb 15 14:11:19 2021 
+## Date of fit:     Mon Feb 15 17:29:04 2021 
+## Date of summary: Mon Feb 15 17:29:04 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 369 model solutions performed in 0.082 s
+## Fitted using 369 model solutions performed in 0.083 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1767,8 +1767,8 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 
                                                                  summary(m.L2.FOMC, data = FALSE)
                                                                  
 
                                                                  ## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Mon Feb 15 14:11:19 2021 
-## Date of summary: Mon Feb 15 14:11:19 2021 
+## Date of fit:     Mon Feb 15 17:29:04 2021 
+## Date of summary: Mon Feb 15 17:29:04 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -1845,8 +1845,8 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 
                                                                  summary(m.L2.DFOP, data = FALSE)
                                                                  
 
                                                                  ## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Mon Feb 15 14:11:20 2021 
-## Date of summary: Mon Feb 15 14:11:20 2021 
+## Date of fit:     Mon Feb 15 17:29:05 2021 
+## Date of summary: Mon Feb 15 17:29:05 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1945,8 +1945,8 @@ plot(mm.L3)
                                                                  
 
                                                                  summary(mm.L3[["DFOP", 1]])
                                                                  
 
                                                                  ## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Mon Feb 15 14:11:20 2021 
-## Date of summary: Mon Feb 15 14:11:20 2021 
+## Date of fit:     Mon Feb 15 17:29:05 2021 
+## Date of summary: Mon Feb 15 17:29:05 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -2053,8 +2053,8 @@ plot(mm.L4)
                                                                  
 
                                                                  summary(mm.L4[["SFO", 1]], data = FALSE)
                                                                  
 
                                                                  ## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Mon Feb 15 14:11:20 2021 
-## Date of summary: Mon Feb 15 14:11:20 2021 
+## Date of fit:     Mon Feb 15 17:29:05 2021 
+## Date of summary: Mon Feb 15 17:29:05 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -2117,15 +2117,15 @@ plot(mm.L4)
                                                                  
 
                                                                  summary(mm.L4[["FOMC", 1]], data = FALSE)
                                                                  
 
                                                                  ## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Mon Feb 15 14:11:20 2021 
-## Date of summary: Mon Feb 15 14:11:20 2021 
+## Date of fit:     Mon Feb 15 17:29:05 2021 
+## Date of summary: Mon Feb 15 17:29:05 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 224 model solutions performed in 0.046 s
+## Fitted using 224 model solutions performed in 0.047 s
 ## 
 ## Error model: Constant variance 
 ## 
diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda
index d2b82805..8c3369a2 100644
Binary files a/vignettes/web_only/mkin_benchmarks.rda and b/vignettes/web_only/mkin_benchmarks.rda differ
-- 
cgit v1.2.1


From 28c0dff7d7191f854be610b5384e965d9b191f98 Mon Sep 17 00:00:00 2001
From: Johannes Ranke 
Date: Wed, 24 Feb 2021 14:46:10 +0100
Subject: Reset graphical parameters with on.exit()

plot.mixed.mmkin did not reset graphical parameters at all. The other
plotting functions did not use on.exit, so this change should make the
use of the plotting functions safer.
---
 R/mkinparplot.R      | 4 ++--
 R/plot.mixed.mmkin.R | 1 +
 R/plot.mkinfit.R     | 2 +-
 R/plot.mmkin.R       | 3 +--
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/R/mkinparplot.R b/R/mkinparplot.R
index f9abab5b..8cae30fb 100644
--- a/R/mkinparplot.R
+++ b/R/mkinparplot.R
@@ -32,7 +32,8 @@ mkinparplot <- function(object) {
               fractions.optim = length(fractions.optim))
   n.plot <- n.plot[n.plot > 0]
 
-  oldpars <- par(no.readonly = TRUE)
+  oldpar <- par(no.readonly = TRUE)
+  on.exit(par(oldpar, no.readonly = TRUE))
   layout(matrix(1:length(n.plot), ncol = 1), heights = n.plot + 1)
 
   s <- summary(object)
@@ -71,5 +72,4 @@ mkinparplot <- function(object) {
 	   as.numeric(values.upper.nonInf), parname_index,
 	   code = 3, angle = 90, length = 0.05))
   }
-  par(oldpars)
 }
diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R
index 1674d855..21399496 100644
--- a/R/plot.mixed.mmkin.R
+++ b/R/plot.mixed.mmkin.R
@@ -167,6 +167,7 @@ plot.mixed.mmkin <- function(x,
 
   # Start of graphical section
   oldpar <- par(no.readonly = TRUE)
+  on.exit(par(oldpar, no.readonly = TRUE))
 
   n_plot_rows = length(obs_vars)
   n_plots = n_plot_rows * 2
diff --git a/R/plot.mkinfit.R b/R/plot.mkinfit.R
index eced40a4..2e319aae 100644
--- a/R/plot.mkinfit.R
+++ b/R/plot.mkinfit.R
@@ -161,6 +161,7 @@ plot.mkinfit <- function(x, fit = x,
   if (do_layout) {
     # Layout should be restored afterwards
     oldpar <- par(no.readonly = TRUE)
+    on.exit(par(oldpar, no.readonly = TRUE))
 
     # If the observed variables are shown separately, or if requested, do row layout
     if (sep_obs | row_layout) {
@@ -287,7 +288,6 @@ plot.mkinfit <- function(x, fit = x,
         legend = FALSE, frame = frame)
     }
   }
-  if (do_layout) par(oldpar, no.readonly = TRUE)
 }
 
 #' @rdname plot.mkinfit
diff --git a/R/plot.mmkin.R b/R/plot.mmkin.R
index f8ed1f9a..2166b30e 100644
--- a/R/plot.mmkin.R
+++ b/R/plot.mmkin.R
@@ -65,6 +65,7 @@ plot.mmkin <- function(x, main = "auto", legends = 1,
 {
 
   oldpar <- par(no.readonly = TRUE)
+  on.exit(par(oldpar, no.readonly = TRUE))
 
   n.m <- nrow(x)
   n.d <- ncol(x)
@@ -153,6 +154,4 @@ plot.mmkin <- function(x, main = "auto", legends = 1,
     }
     mtext(paste(fit_name, "residuals"), cex = cex, line = 0.4)
   }
-
-  par(oldpar, no.readonly = TRUE)
 }
-- 
cgit v1.2.1


From c73b2f30ec836c949885784ab576e814eb8070a9 Mon Sep 17 00:00:00 2001
From: Johannes Ranke 
Date: Tue, 9 Mar 2021 17:35:47 +0100
Subject: Some improvements for borderline cases

- fit_with_errors for saem()
- test_log_parms for mean_degparms() and saem()
---
 NEWS.md                                            |    2 +
 R/nlme.R                                           |   37 +-
 R/nlme.mmkin.R                                     |    2 +-
 R/saem.R                                           |   31 +-
 build.log                                          |    2 +-
 check.log                                          |    6 +-
 docs/dev/404.html                                  |    2 +-
 docs/dev/articles/index.html                       |    2 +-
 docs/dev/authors.html                              |    2 +-
 docs/dev/index.html                                |    7 +-
 docs/dev/news/index.html                           |  161 +-
 docs/dev/pkgdown.yml                               |    2 +-
 docs/dev/reference/Rplot001.png                    |  Bin 13995 -> 1011 bytes
 docs/dev/reference/Rplot002.png                    |  Bin 13648 -> 16859 bytes
 docs/dev/reference/Rplot003.png                    |  Bin 28745 -> 28844 bytes
 docs/dev/reference/Rplot004.png                    |  Bin 49269 -> 49360 bytes
 docs/dev/reference/Rplot005.png                    |  Bin 59143 -> 59216 bytes
 docs/dev/reference/endpoints.html                  |    2 +-
 docs/dev/reference/index.html                      |    2 +-
 docs/dev/reference/nlme-1.png                      |  Bin 70133 -> 68233 bytes
 docs/dev/reference/nlme-2.png                      |  Bin 94031 -> 90552 bytes
 docs/dev/reference/nlme.html                       |   33 +-
 docs/dev/reference/nlme.mmkin-1.png                |  Bin 124677 -> 124827 bytes
 docs/dev/reference/nlme.mmkin-2.png                |  Bin 169523 -> 169698 bytes
 docs/dev/reference/nlme.mmkin-3.png                |  Bin 172692 -> 172809 bytes
 docs/dev/reference/nlme.mmkin.html                 |    2 +-
 docs/dev/reference/plot.mixed.mmkin-1.png          |  Bin 84734 -> 85433 bytes
 docs/dev/reference/plot.mixed.mmkin-2.png          |  Bin 173916 -> 174061 bytes
 docs/dev/reference/plot.mixed.mmkin-3.png          |  Bin 172396 -> 172540 bytes
 docs/dev/reference/plot.mixed.mmkin-4.png          |  Bin 175502 -> 175594 bytes
 docs/dev/reference/plot.mixed.mmkin.html           |    6 +-
 docs/dev/reference/saem-1.png                      |  Bin 47315 -> 47342 bytes
 docs/dev/reference/saem-2.png                      |  Bin 48720 -> 48819 bytes
 docs/dev/reference/saem-3.png                      |  Bin 82107 -> 82202 bytes
 docs/dev/reference/saem-4.png                      |  Bin 128231 -> 128213 bytes
 docs/dev/reference/saem-5.png                      |  Bin 173288 -> 173665 bytes
 docs/dev/reference/saem.html                       |   72 +-
 docs/dev/reference/summary.saem.mmkin.html         |  422 +-
 man/nlme.Rd                                        |   11 +-
 man/saem.Rd                                        |   19 +-
 test.log                                           |   36 +-
 .../plotting/mixed-model-fit-for-nlme-object.svg   | 2402 +++++------
 ...t-for-saem-object-with-mkin-transformations.svg | 4527 ++++++++++----------
 ...for-saem-object-with-saemix-transformations.svg |    5 +
 tests/testthat/print_mmkin_biphasic_mixed.txt      |    6 +-
 tests/testthat/print_nlme_biphasic.txt             |   10 +-
 tests/testthat/print_sfo_saem_1.txt                |   16 +-
 tests/testthat/setup_script.R                      |   19 +-
 tests/testthat/summary_nlme_biphasic_s.txt         |   46 +-
 tests/testthat/summary_saem_biphasic_s.txt         |   48 +-
 tests/testthat/test_mixed.R                        |   24 +-
 tests/testthat/test_nlme.R                         |    2 +-
 52 files changed, 4040 insertions(+), 3926 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 38cac245..5d0ea69a 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -12,6 +12,8 @@
 
 - 'saem': generic function to fit saemix models using 'saemix_model' and 'saemix_data', with a generator 'saem.mmkin', summary and plot methods
 
+- 'mean_degparms': New argument 'test_log_parms' that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to check more plausible parameters for 'saem'
+
 # mkin 1.0.4 (Unreleased)
 
 - 'plot.mixed.mmkin': Reset graphical parameters on exit
diff --git a/R/nlme.R b/R/nlme.R
index 9215aab0..d235a094 100644
--- a/R/nlme.R
+++ b/R/nlme.R
@@ -36,7 +36,7 @@
 #' nlme_f <- nlme_function(f)
 #' # These assignments are necessary for these objects to be
 #' # visible to nlme and augPred when evaluation is done by
-#' # pkgdown to generated the html docs.
+#' # pkgdown to generate the html docs.
 #' assign("nlme_f", nlme_f, globalenv())
 #' assign("grouped_data", grouped_data, globalenv())
 #'
@@ -130,13 +130,44 @@ nlme_function <- function(object) {
 #'   fixed and random effects, in the format required by the start argument of
 #'   nlme for the case of a single grouping variable ds.
 #' @param random Should a list with fixed and random effects be returned?
+#' @param test_log_parms If TRUE, log parameters are only considered in
+#'   the mean calculations if their untransformed counterparts (most likely
+#'   rate constants) pass the t-test for significant difference from zero.
+#' @param conf.level Possibility to adjust the required confidence level
+#'   for parameter that are tested if requested by 'test_log_parms'.
 #' @export
-mean_degparms <- function(object, random = FALSE) {
+mean_degparms <- function(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6)
+{
   if (nrow(object) > 1) stop("Only row objects allowed")
   parm_mat_trans <- sapply(object, parms, transformed = TRUE)
+
+  if (test_log_parms) {
+      parm_mat_dim <- dim(parm_mat_trans)
+      parm_mat_dimnames <- dimnames(parm_mat_trans)
+
+      log_parm_trans_names <- grep("^log_", rownames(parm_mat_trans), value = TRUE)
+      log_parm_names <- gsub("^log_", "", log_parm_trans_names)
+
+      t_test_back_OK <- matrix(
+        sapply(object, function(o) {
+          suppressWarnings(summary(o)$bpar[log_parm_names, "Pr(>t)"] < (1 - conf.level))
+        }), nrow = length(log_parm_names))
+      rownames(t_test_back_OK) <- log_parm_trans_names
+
+      parm_mat_trans_OK <- parm_mat_trans
+      for (trans_parm in log_parm_trans_names) {
+        parm_mat_trans_OK[trans_parm, ] <- ifelse(t_test_back_OK[trans_parm, ],
+          parm_mat_trans[trans_parm, ], NA)
+      }
+    } else {
+    parm_mat_trans_OK <- parm_mat_trans
+  }
+
   mean_degparm_names <- setdiff(rownames(parm_mat_trans), names(object[[1]]$errparms))
   degparm_mat_trans <- parm_mat_trans[mean_degparm_names, , drop = FALSE]
-  fixed <- apply(degparm_mat_trans, 1, mean)
+  degparm_mat_trans_OK <- parm_mat_trans_OK[mean_degparm_names, , drop = FALSE]
+
+  fixed <- apply(degparm_mat_trans_OK, 1, mean, na.rm = TRUE)
   if (random) {
     random <- t(apply(degparm_mat_trans[mean_degparm_names, , drop = FALSE], 2, function(column) column - fixed))
     # If we only have one parameter, apply returns a vector so we get a single row
diff --git a/R/nlme.mmkin.R b/R/nlme.mmkin.R
index ff1f2fff..306600c6 100644
--- a/R/nlme.mmkin.R
+++ b/R/nlme.mmkin.R
@@ -24,7 +24,7 @@ get_deg_func <- function() {
 #' This functions sets up a nonlinear mixed effects model for an mmkin row
 #' object. An mmkin row object is essentially a list of mkinfit objects that
 #' have been obtained by fitting the same model to a list of datasets.
-#' 
+#'
 #' Note that the convergence of the nlme algorithms depends on the quality
 #' of the data. In degradation kinetics, we often only have few datasets
 #' (e.g. data for few soils) and complicated degradation models, which may
diff --git a/R/saem.R b/R/saem.R
index fd2a77b4..460edede 100644
--- a/R/saem.R
+++ b/R/saem.R
@@ -24,8 +24,16 @@ utils::globalVariables(c("predicted", "std"))
 #'   SFO or DFOP is used for the parent and there is either no metabolite or one.
 #' @param degparms_start Parameter values given as a named numeric vector will
 #'   be used to override the starting values obtained from the 'mmkin' object.
+#' @param test_log_parms If TRUE, an attempt is made to use more robust starting
+#'   values for population parameters fitted as log parameters in mkin (like
+#'   rate constants) by only considering rate constants that pass the t-test
+#'   when calculating mean degradation parameters using [mean_degparms].
+#' @param conf.level Possibility to adjust the required confidence level
+#'   for parameter that are tested if requested by 'test_log_parms'.
 #' @param solution_type Possibility to specify the solution type in case the
 #'   automatic choice is not desired
+#' @param fail_with_errors Should a failure to compute standard errors
+#'   from the inverse of the Fisher Information Matrix be a failure?
 #' @param quiet Should we suppress the messages saemix prints at the beginning
 #'   and the end of the optimisation process?
 #' @param control Passed to [saemix::saemix]
@@ -51,7 +59,7 @@ utils::globalVariables(c("predicted", "std"))
 #' # The returned saem.mmkin object contains an SaemixObject, therefore we can use
 #' # functions from saemix
 #' library(saemix)
-#' compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so))
+#' compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)
 #' plot(f_saem_fomc$so, plot.type = "convergence")
 #' plot(f_saem_fomc$so, plot.type = "individual.fit")
 #' plot(f_saem_fomc$so, plot.type = "npde")
@@ -59,7 +67,7 @@ utils::globalVariables(c("predicted", "std"))
 #'
 #' f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
 #' f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
-#' compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
+#' compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
 #'
 #' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
 #'   A1 = mkinsub("SFO"))
@@ -104,19 +112,32 @@ saem <- function(object, ...) UseMethod("saem")
 saem.mmkin <- function(object,
   transformations = c("mkin", "saemix"),
   degparms_start = numeric(),
+  test_log_parms = FALSE,
+  conf.level = 0.6,
   solution_type = "auto",
   control = list(displayProgress = FALSE, print = FALSE,
     save = FALSE, save.graphs = FALSE),
+  fail_with_errors = TRUE,
   verbose = FALSE, quiet = FALSE, ...)
 {
   transformations <- match.arg(transformations)
   m_saemix <- saemix_model(object, verbose = verbose,
-    degparms_start = degparms_start, solution_type = solution_type,
+    degparms_start = degparms_start,
+    test_log_parms = test_log_parms, conf.level = conf.level,
+    solution_type = solution_type,
     transformations = transformations, ...)
   d_saemix <- saemix_data(object, verbose = verbose)
 
   fit_time <- system.time({
     utils::capture.output(f_saemix <- saemix::saemix(m_saemix, d_saemix, control), split = !quiet)
+    FIM_failed <- NULL
+    if (any(is.na(f_saemix@results@se.fixed))) FIM_failed <- c(FIM_failed, "fixed effects")
+    if (any(is.na(c(f_saemix@results@se.omega, f_saemix@results@se.respar)))) {
+      FIM_failed <- c(FIM_failed, "random effects and residual error parameters")
+    }
+    if (!is.null(FIM_failed) & fail_with_errors) {
+      stop("Could not invert FIM for ", paste(FIM_failed, collapse = " and "))
+    }
   })
 
   transparms_optim <- f_saemix@results@fixed.effects
@@ -203,13 +224,13 @@ print.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) {
 #' @return An [saemix::SaemixModel] object.
 #' @export
 saemix_model <- function(object, solution_type = "auto", transformations = c("mkin", "saemix"),
-  degparms_start = numeric(), verbose = FALSE, ...)
+  degparms_start = numeric(), test_log_parms = FALSE, verbose = FALSE, ...)
 {
   if (nrow(object) > 1) stop("Only row objects allowed")
 
   mkin_model <- object[[1]]$mkinmod
 
-  degparms_optim <-  mean_degparms(object)
+  degparms_optim <-  mean_degparms(object, test_log_parms = test_log_parms)
   if (transformations == "saemix") {
     degparms_optim <- backtransform_odeparms(degparms_optim,
       object[[1]]$mkinmod,
diff --git a/build.log b/build.log
index d50a4860..ca1c0481 100644
--- a/build.log
+++ b/build.log
@@ -6,5 +6,5 @@
 * creating vignettes ... OK
 * checking for LF line-endings in source and make files and shell scripts
 * checking for empty or unneeded directories
-* building ‘mkin_1.0.3.9000.tar.gz’
+* building ‘mkin_1.0.4.9000.tar.gz’
 
diff --git a/check.log b/check.log
index ac59f6af..6e19f958 100644
--- a/check.log
+++ b/check.log
@@ -1,16 +1,16 @@
 * using log directory ‘/home/jranke/git/mkin/mkin.Rcheck’
-* using R version 4.0.3 (2020-10-10)
+* using R version 4.0.4 (2021-02-15)
 * using platform: x86_64-pc-linux-gnu (64-bit)
 * using session charset: UTF-8
 * using options ‘--no-tests --as-cran’
 * checking for file ‘mkin/DESCRIPTION’ ... OK
 * checking extension type ... Package
-* this is package ‘mkin’ version ‘1.0.3.9000’
+* this is package ‘mkin’ version ‘1.0.4.9000’
 * package encoding: UTF-8
 * checking CRAN incoming feasibility ... NOTE
 Maintainer: ‘Johannes Ranke ’
 
-Version contains large components (1.0.3.9000)
+Version contains large components (1.0.4.9000)
 
 Unknown, possibly mis-spelled, fields in DESCRIPTION:
   ‘Remotes’
diff --git a/docs/dev/404.html b/docs/dev/404.html
index f9e51aa3..58591997 100644
--- a/docs/dev/404.html
+++ b/docs/dev/404.html
@@ -71,7 +71,7 @@
       
       
         mkin
-        1.0.3.9000
+        1.0.4.9000
       
     
 
diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html
index 17ee4a69..3c00526e 100644
--- a/docs/dev/articles/index.html
+++ b/docs/dev/articles/index.html
@@ -71,7 +71,7 @@
       
       
         mkin
-        1.0.3.9000
+        1.0.4.9000
       
     
 
diff --git a/docs/dev/authors.html b/docs/dev/authors.html
index 63050c0d..45db18f2 100644
--- a/docs/dev/authors.html
+++ b/docs/dev/authors.html
@@ -71,7 +71,7 @@
       
       
         mkin
-        1.0.3.9000
+        1.0.4.9000
       
     
 
diff --git a/docs/dev/index.html b/docs/dev/index.html
index 57328658..d1fa1a52 100644
--- a/docs/dev/index.html
+++ b/docs/dev/index.html
@@ -38,7 +38,7 @@
       
       
         mkin
-        1.0.3.9000
+        1.0.4.9000
       
     
 
@@ -192,11 +192,12 @@
 
                                                                  Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and at Eurofins Regulatory AG, and now as an independent consultant.
                                                                  
 
                                                                  Funding was received from the Umweltbundesamt in the course of the projects
                                                                  
 
                                                                    
-
                                                                  • Grant Number 112407 (Testing and validation of modelling software as an alternative to ModelMaker 4.0, 2014-2015)
                                                                    • 
+
                                                                  • Project Number 27452 (Testing and validation of modelling software as an alternative to ModelMaker 4.0, 2014-2015)
                                                                    • 
 
                                                                  • Project Number 56703 (Optimization of gmkin for routine use in the Umweltbundesamt, 2015)
                                                                    • 
+
                                                                  • Project Number 92570 (Update of Project Number 27452, 2017-2018)
                                                                    • 
 
                                                                  • Project Number 112407 (Testing the feasibility of using an error model according to Rocke and Lorenzato for more realistic parameter estimates in the kinetic evaluation of degradation data, 2018-2019)
                                                                    • 
 
                                                                  • Project Number 120667 (Development of objective criteria for the evaluation of the visual fit in the kinetic evaluation of degradation data, 2019-2020)
                                                                    • 
-
                                                                  • Project 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021)
                                                                    • 
+
                                                                  • Project Number 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021)
                                                                    • 
 
                                                                  
 
 
                                                                  
diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html
index 31c392f7..10585403 100644
--- a/docs/dev/news/index.html
+++ b/docs/dev/news/index.html
@@ -71,7 +71,7 @@
       
       
         mkin
-        1.0.3.9000
+        1.0.4.9000
       
     
                                                                  
 
@@ -141,10 +141,9 @@
       Source: NEWS.md
     
 
-    
                                                                  
-
                                                                  
-mkin 1.0.3.9000 Unreleased 
-
                                                                  
+    
                                                                  
+
                                                                  
+mkin 1.0.4.9000
                                                                  
 
                                                                  
 
                                                                  
 General
                                                                  
@@ -159,29 +158,35 @@
 
                                                                • Reintroduce the interface to the current development version of saemix, in particular:
                                                                  • 
 
                                                                • ‘saemix_model’ and ‘saemix_data’: Helper functions to set up nonlinear mixed-effects models for mmkin row objects
                                                                  • 
 
                                                                • ‘saem’: generic function to fit saemix models using ‘saemix_model’ and ‘saemix_data’, with a generator ‘saem.mmkin’, summary and plot methods
                                                                  • 
+
                                                                • ‘mean_degparms’: New argument ‘test_log_parms’ that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to check more plausible parameters for ‘saem’
                                                                  •
                                                                -
                                                                +
                                                                +

                                                                +mkin 1.0.4 (Unreleased)

                                                                +
                                                                  +

                                                                • ‘plot.mixed.mmkin’: Reset graphical parameters on exit

                                                                  • +

                                                                • All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters

                                                                  • +
                                                                +
                                                                +

                                                                -mkin 1.0.3 Unreleased -

                                                                +mkin 1.0.3 (2021-02-15)
                                                                • Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages
                                                                -
                                                                +

                                                                -mkin 1.0.2 Unreleased -

                                                                +mkin 1.0.2 (Unreleased)
                                                                • ‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’
                                                                -
                                                                +

                                                                -mkin 1.0.1 2021-02-10 -

                                                                +mkin 1.0.1 (2021-02-10)

                                                                • ‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults

                                                                • ‘logLik.mkinfit’: Improve example code to avoid warnings and show convenient syntax

                                                                  • @@ -190,10 +195,9 @@

                                                                • Increase test tolerance for some parameter comparisons that also proved to be platform dependent

                                                                -
                                                                +

                                                                -mkin 1.0.0 2021-02-03 -

                                                                +mkin 1.0.0 (2021-02-03)

                                                                General

                                                                @@ -221,8 +225,7 @@

                                                                -mkin 0.9.50.3 (2020-10-08) 2020-10-08 -

                                                                +mkin 0.9.50.3 (2020-10-08)

                                                                • ‘parms’: Add a method for mmkin objects

                                                                • ‘mmkin’ and ‘confint(method = ’profile’): Use all cores detected by parallel::detectCores() per default

                                                                  • @@ -237,8 +240,7 @@

                                                                -mkin 0.9.50.2 (2020-05-12) 2020-05-12 -

                                                                +mkin 0.9.50.2 (2020-05-12)

                                                                • Increase tolerance for a platform specific test results on the Solaris test machine on CRAN

                                                                • Updates and corrections (using the spelling package) to the documentation

                                                                  • @@ -246,8 +248,7 @@

                                                                -mkin 0.9.50.1 (2020-05-11) 2020-05-11 -

                                                                +mkin 0.9.50.1 (2020-05-11)

                                                                • Support SFORB with formation fractions

                                                                • ‘mkinmod’: Make ‘use_of_ff’ = “max” the default

                                                                  • @@ -256,16 +257,14 @@

                                                                -mkin 0.9.49.11 (2020-04-20) 2020-04-20 -

                                                                +mkin 0.9.49.11 (2020-04-20)
                                                                • Increase a test tolerance to make it pass on all CRAN check machines

                                                                -mkin 0.9.49.10 (2020-04-18) 2020-04-18 -

                                                                +mkin 0.9.49.10 (2020-04-18)

                                                                • ‘nlme.mmkin’: An nlme method for mmkin row objects and an associated S3 class with print, plot, anova and endpoint methods

                                                                • ‘mean_degparms, nlme_data, nlme_function’: Three new functions to facilitate building nlme models from mmkin row objects

                                                                  • @@ -277,8 +276,7 @@

                                                                -mkin 0.9.49.9 (2020-03-31) 2020-03-31 -

                                                                +mkin 0.9.49.9 (2020-03-31)

                                                                • ‘mkinmod’: Use pkgbuild::has_compiler instead of Sys.which(‘gcc’), as the latter will often fail even if Rtools are installed

                                                                • ‘mkinds’: Use roxygen for documenting fields and methods of this R6 class

                                                                  • @@ -286,8 +284,7 @@

                                                                -mkin 0.9.49.8 (2020-01-09) 2020-01-09 -

                                                                +mkin 0.9.49.8 (2020-01-09)

                                                                • ‘aw’: Generic function for calculating Akaike weights, methods for mkinfit objects and mmkin columns

                                                                • ‘loftest’: Add a lack-of-fit test

                                                                  • @@ -298,8 +295,7 @@

                                                                -mkin 0.9.49.7 (2019-11-01) 2019-11-02 -

                                                                +mkin 0.9.49.7 (2019-11-01)

                                                                • Fix a bug introduced in 0.9.49.6 that occurred if the direct optimisation yielded a higher likelihood than the three-step optimisation in the d_3 algorithm, which caused the fitted parameters of the three-step optimisation to be returned instead of the parameters of the direct optimisation

                                                                • Add a ‘nobs’ method for mkinfit objects, enabling the default ‘BIC’ method from the stats package. Also, add a ‘BIC’ method for mmkin column objects.

                                                                  • @@ -307,8 +303,7 @@

                                                                -mkin 0.9.49.6 (2019-10-31) 2019-10-31 -

                                                                +mkin 0.9.49.6 (2019-10-31)

                                                                • Implement a likelihood ratio test as a method for ‘lrtest’ from the lmtest package

                                                                • Add an ‘update’ method for mkinfit objects which remembers fitted parameters if appropriate

                                                                  • @@ -327,8 +322,7 @@

                                                                -mkin 0.9.49.5 (2019-07-04) 2019-07-04 -

                                                                +mkin 0.9.49.5 (2019-07-04)

                                                                • Several algorithms for minimization of the negative log-likelihood for non-constant error models (two-component and variance by variable). In the case the error model is constant variance, least squares is used as this is more stable. The default algorithm ‘d_3’ tries direct minimization and a three-step procedure, and returns the model with the highest likelihood.

                                                                • The argument ‘reweight.method’ to mkinfit and mmkin is now obsolete, use ‘error_model’ and ‘error_model_algorithm’ instead

                                                                  • @@ -346,8 +340,7 @@

                                                                -mkin 0.9.48.1 (2019-03-04) 2019-03-04 -

                                                                +mkin 0.9.48.1 (2019-03-04)

                                                                • Add the function ‘logLik.mkinfit’ which makes it possible to calculate an AIC for mkinfit objects

                                                                • Add the function ‘AIC.mmkin’ to make it easy to compare columns of mmkin objects

                                                                  • @@ -363,8 +356,7 @@

                                                                -mkin 0.9.47.5 (2018-09-14) 2018-09-14 -

                                                                +mkin 0.9.47.5 (2018-09-14)

                                                                • Make the two-component error model stop in cases where it is inadequate to avoid nls crashes on windows

                                                                • Move two vignettes to a location where they will not be built on CRAN (to avoid more NOTES from long execution times)

                                                                  • @@ -373,8 +365,7 @@

                                                                -mkin 0.9.47.3 Unreleased -

                                                                +mkin 0.9.47.3

                                                                • ‘mkinfit’: Improve fitting the error model for reweight.method = ‘tc’. Add ‘manual’ to possible arguments for ‘weight’

                                                                • Test that FOCUS_2006_C can be evaluated with DFOP and reweight.method = ‘tc’

                                                                  • @@ -382,8 +373,7 @@

                                                                -mkin 0.9.47.2 (2018-07-19) 2018-07-19 -

                                                                +mkin 0.9.47.2 (2018-07-19)

                                                                • ‘sigma_twocomp’: Rename ‘sigma_rl’ to ‘sigma_twocomp’ as the Rocke and Lorenzato model assumes lognormal distribution for large y. Correct references to the Rocke and Lorenzato model accordingly.

                                                                • ‘mkinfit’: Use 1.1 as starting value for N parameter of IORE models to obtain convergence in more difficult cases. Show parameter names when ‘trace_parms’ is ‘TRUE’.

                                                                  • @@ -391,8 +381,7 @@

                                                                -mkin 0.9.47.1 (2018-02-06) 2018-02-06 -

                                                                +mkin 0.9.47.1 (2018-02-06)

                                                                • Skip some tests on CRAN and winbuilder to avoid timeouts

                                                                • ‘test_data_from_UBA_2014’: Added this list of datasets containing experimental data used in the expertise from 2014

                                                                  • @@ -404,8 +393,7 @@

                                                                -mkin 0.9.46.3 (2017-11-16) 2017-11-16 -

                                                                +mkin 0.9.46.3 (2017-11-16)

                                                                • README.md, vignettes/mkin.Rmd: URLs were updated

                                                                • synthetic_data_for_UBA: Add the code used to generate the data in the interest of reproducibility

                                                                  • @@ -413,8 +401,7 @@

                                                                -mkin 0.9.46.2 (2017-10-10) 2017-10-10 -

                                                                +mkin 0.9.46.2 (2017-10-10)

                                                                • Converted the vignette FOCUS_Z from tex/pdf to markdown/html

                                                                • DESCRIPTION: Add ORCID

                                                                  • @@ -422,8 +409,7 @@

                                                                -mkin 0.9.46.1 (2017-09-14) 2017-09-14 -

                                                                +mkin 0.9.46.1 (2017-09-14)

                                                                • plot.mkinfit: Fix scaling of residual plots for the case of separate plots for each observed variable

                                                                • plot.mkinfit: Use all data points of the fitted curve for y axis scaling for the case of separate plots for each observed variable

                                                                  • @@ -432,16 +418,14 @@

                                                                -mkin 0.9.46 (2017-07-24) 2017-07-29 -

                                                                +mkin 0.9.46 (2017-07-24)
                                                                • Remove test_FOMC_ill-defined.R as it is too platform dependent

                                                                -mkin 0.9.45.2 (2017-07-24) 2017-07-22 -

                                                                +mkin 0.9.45.2 (2017-07-24)

                                                                • Rename twa to max_twa_parent to avoid conflict with twa from my pfm package

                                                                • Update URLs in documentation

                                                                  • @@ -451,8 +435,7 @@

                                                                -mkin 0.9.45.1 (2016-12-20) Unreleased -

                                                                +mkin 0.9.45.1 (2016-12-20)

                                                                New features

                                                                @@ -463,8 +446,7 @@

                                                                -mkin 0.9.45 (2016-12-08) 2016-12-08 -

                                                                +mkin 0.9.45 (2016-12-08)

                                                                Minor changes

                                                                @@ -477,8 +459,7 @@

                                                                -mkin 0.9.44 (2016-06-29) 2016-06-29 -

                                                                +mkin 0.9.44 (2016-06-29)

                                                                Bug fixes

                                                                @@ -489,8 +470,7 @@

                                                                -mkin 0.9.43 (2016-06-28) 2016-06-28 -

                                                                +mkin 0.9.43 (2016-06-28)

                                                                Major changes

                                                                @@ -528,8 +508,7 @@

                                                                -mkin 0.9.42 (2016-03-25) 2016-03-25 -

                                                                +mkin 0.9.42 (2016-03-25)

                                                                Major changes

                                                                @@ -549,8 +528,7 @@

                                                                -mkin 0.9-41 (2015-11-09) 2015-11-09 -

                                                                +mkin 0.9-41 (2015-11-09)

                                                                Minor changes

                                                                @@ -572,8 +550,7 @@

                                                                -mkin 0.9-40 (2015-07-21) 2015-07-21 -

                                                                +mkin 0.9-40 (2015-07-21)

                                                                Bug fixes

                                                                @@ -593,8 +570,7 @@

                                                                -mkin 0.9-39 (2015-06-26) 2015-06-26 -

                                                                +mkin 0.9-39 (2015-06-26)

                                                                Major changes

                                                                @@ -614,8 +590,7 @@

                                                                -mkin 0.9-38 (2015-06-24) 2015-06-23 -

                                                                +mkin 0.9-38 (2015-06-24)

                                                                Minor changes

                                                                @@ -635,8 +610,7 @@

                                                                -mkin 0.9-36 (2015-06-21) 2015-06-21 -

                                                                +mkin 0.9-36 (2015-06-21)

                                                                Major changes

                                                                @@ -657,8 +631,7 @@

                                                                -mkin 0.9-35 (2015-05-15) 2015-05-15 -

                                                                +mkin 0.9-35 (2015-05-15)

                                                                Major changes

                                                                @@ -689,8 +662,7 @@

                                                                -mkin 0.9-34 (2014-11-22) 2014-11-22 -

                                                                +mkin 0.9-34 (2014-11-22)

                                                                New features

                                                                @@ -711,8 +683,7 @@

                                                                -mkin 0.9-33 (2014-10-22) 2014-10-12 -

                                                                +mkin 0.9-33 (2014-10-22)

                                                                New features

                                                                @@ -744,8 +715,7 @@

                                                                -mkin 0.9-32 (2014-07-24) 2014-07-24 -

                                                                +mkin 0.9-32 (2014-07-24)

                                                                New features

                                                                @@ -781,8 +751,7 @@

                                                                -mkin 0.9-31 (2014-07-14) 2014-07-14 -

                                                                +mkin 0.9-31 (2014-07-14)

                                                                Bug fixes

                                                                @@ -793,8 +762,7 @@

                                                                -mkin 0.9-30 (2014-07-11) 2014-07-11 -

                                                                +mkin 0.9-30 (2014-07-11)

                                                                New features

                                                                @@ -825,8 +793,7 @@

                                                                -mkin 0.9-29 (2014-06-27) 2014-06-27 -

                                                                +mkin 0.9-29 (2014-06-27)

                                                                • R/mkinresplot.R: Make it possible to specify xlim

                                                                • R/geometric_mean.R, man/geometric_mean.Rd: Add geometric mean function

                                                                  • @@ -835,8 +802,7 @@

                                                                -mkin 0.9-28 (2014-05-20) 2014-05-20 -

                                                                +mkin 0.9-28 (2014-05-20)

                                                                • Do not backtransform confidence intervals for formation fractions if more than one compound is formed, as such parameters only define the pathways as a set

                                                                • Add historical remarks and some background to the main package vignette

                                                                  • @@ -845,8 +811,7 @@

                                                                -mkin 0.9-27 (2014-05-10) 2014-05-10 -

                                                                +mkin 0.9-27 (2014-05-10)

                                                                • Fork the GUI into a separate package gmkin

                                                                • DESCRIPTION, NAMESPACE, TODO: Adapt and add copyright information

                                                                  • @@ -869,8 +834,7 @@

                                                                -mkin 0.9-24 (2013-11-06) 2013-11-06 -

                                                                +mkin 0.9-24 (2013-11-06)

                                                                • Bugfix re-enabling the fixing of any combination of initial values for state variables

                                                                • Default values for kinetic rate constants are not all 0.1 any more but are “salted” with a small increment to avoid numeric artefacts with the eigenvalue based solutions

                                                                  • @@ -879,8 +843,7 @@

                                                                -mkin 0.9-22 (2013-10-26) 2013-10-26 -

                                                                +mkin 0.9-22 (2013-10-26)

                                                                • Get rid of the optimisation step in mkinerrmin - this was unnecessary. Thanks to KinGUII for the inspiration - actually this is equation 6-2 in FOCUS kinetics p. 91 that I had overlooked originally

                                                                • Fix plot.mkinfit as it passed graphical arguments like main to the solver

                                                                  • diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 4df60994..dbacd0ab 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-02-15T16:08Z +last_built: 2021-03-09T16:32Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png index 7f498242..17a35806 100644 Binary files a/docs/dev/reference/Rplot001.png and b/docs/dev/reference/Rplot001.png differ diff --git a/docs/dev/reference/Rplot002.png b/docs/dev/reference/Rplot002.png index 54c31a3f..a9a972e5 100644 Binary files a/docs/dev/reference/Rplot002.png and b/docs/dev/reference/Rplot002.png differ diff --git a/docs/dev/reference/Rplot003.png b/docs/dev/reference/Rplot003.png index 2b011ec1..d077f01c 100644 Binary files a/docs/dev/reference/Rplot003.png and b/docs/dev/reference/Rplot003.png differ diff --git a/docs/dev/reference/Rplot004.png b/docs/dev/reference/Rplot004.png index 98dd019e..ffcd2d96 100644 Binary files a/docs/dev/reference/Rplot004.png and b/docs/dev/reference/Rplot004.png differ diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png index 8c91d61e..dfb5965b 100644 Binary files a/docs/dev/reference/Rplot005.png and b/docs/dev/reference/Rplot005.png differ diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index c9912f9c..63bec6a8 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> mkin - 1.0.3.9000 + 1.0.4.9000
                                                                diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index 03a21517..5533a01f 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ mkin - 1.0.3.9000 + 1.0.4.9000
                                                                diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png index 728cc557..fd68ae43 100644 Binary files a/docs/dev/reference/nlme-1.png and b/docs/dev/reference/nlme-1.png differ diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png index e8167455..853cae40 100644 Binary files a/docs/dev/reference/nlme-2.png and b/docs/dev/reference/nlme-2.png differ diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index b850eb3d..78d132e9 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> mkin - 1.0.3.9000 + 1.0.4.9000
                                                                @@ -155,7 +155,7 @@ datasets. They are used internally by the nlme.m 
                                                                nlme_function(object)
 
-mean_degparms(object, random = FALSE)
+mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6)
 
 nlme_data(object)
                                                                @@ -170,6 +170,17 @@ datasets. They are used internally by the                                                                 nlme.m random

                                                                Should a list with fixed and random effects be returned?

                                                                + + test_log_parms +

                                                                If TRUE, log parameters are only considered in +the mean calculations if their untransformed counterparts (most likely +rate constants) pass the t-test for significant difference from zero.

                                                                + + + conf.level +

                                                                Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.

                                                                +

                                                                Value

                                                                @@ -211,7 +222,7 @@ nlme for the case of a single grouping variable ds.

                                                                nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be # visible to nlme and augPred when evaluation is done by -# pkgdown to generated the html docs. +# pkgdown to generate the html docs. assign("nlme_f", nlme_f, globalenv()) assign("grouped_data", grouped_data, globalenv()) @@ -226,28 +237,28 @@ nlme for the case of a single grouping variable ds.

                                                                #> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) #> Data: grouped_data #> AIC BIC logLik -#> 300.6824 310.2426 -145.3412 +#> 298.2781 307.7372 -144.1391 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual -#> StdDev: 1.697361 0.6801209 3.666073 +#> StdDev: 0.937473 0.7098105 3.83543 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 #> Value Std.Error DF t-value p-value -#> parent_0 100.99378 1.3890416 46 72.70753 0 -#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 +#> parent_0 101.76838 1.1445443 45 88.91607 0 +#> log_k_parent_sink -3.05444 0.4195622 45 -7.28008 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.027 +#> log_k_parent_sink 0.034 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 +#> Min Q1 Med Q3 Max +#> -2.61693595 -0.21853231 0.05740682 0.57209372 3.04598764 #> -#> Number of Observations: 50 +#> Number of Observations: 49 #> Number of Groups: 3
                                                                plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
                                                                # augPred does not work on fits with more than one state # variable diff --git a/docs/dev/reference/nlme.mmkin-1.png b/docs/dev/reference/nlme.mmkin-1.png index 9186c135..90ede880 100644 Binary files a/docs/dev/reference/nlme.mmkin-1.png and b/docs/dev/reference/nlme.mmkin-1.png differ diff --git a/docs/dev/reference/nlme.mmkin-2.png b/docs/dev/reference/nlme.mmkin-2.png index d395fe02..0d140fd1 100644 Binary files a/docs/dev/reference/nlme.mmkin-2.png and b/docs/dev/reference/nlme.mmkin-2.png differ diff --git a/docs/dev/reference/nlme.mmkin-3.png b/docs/dev/reference/nlme.mmkin-3.png index 40518a59..8a60b52b 100644 Binary files a/docs/dev/reference/nlme.mmkin-3.png and b/docs/dev/reference/nlme.mmkin-3.png differ diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index 925cf7cf..f308d8b7 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> mkin - 1.0.3.9000 + 1.0.4.9000
                                                                diff --git a/docs/dev/reference/plot.mixed.mmkin-1.png b/docs/dev/reference/plot.mixed.mmkin-1.png index 9c9a2211..2224d96e 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-1.png and b/docs/dev/reference/plot.mixed.mmkin-1.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-2.png b/docs/dev/reference/plot.mixed.mmkin-2.png index 0f66ff0f..28168495 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-2.png and b/docs/dev/reference/plot.mixed.mmkin-2.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-3.png b/docs/dev/reference/plot.mixed.mmkin-3.png index 34212f1c..d18275dd 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-3.png and b/docs/dev/reference/plot.mixed.mmkin-3.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-4.png b/docs/dev/reference/plot.mixed.mmkin-4.png index c1450d24..2fd52425 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-4.png and b/docs/dev/reference/plot.mixed.mmkin-4.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index 630e95a3..36796580 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ mkin - 1.0.3.9000 + 1.0.4.9000
                                                                @@ -283,10 +283,10 @@ corresponding model prediction lines for the different datasets.

                                                                f_saem <- saem(f, transformations = "saemix")
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:17 2021" +#> [1] "Tue Mar 9 17:34:35 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:24 2021"
                                                                plot(f_saem) +#> [1] "Tue Mar 9 17:34:42 2021"
                                                                plot(f_saem)
                                                                # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo, diff --git a/docs/dev/reference/saem-1.png b/docs/dev/reference/saem-1.png index 2df248bb..0da31388 100644 Binary files a/docs/dev/reference/saem-1.png and b/docs/dev/reference/saem-1.png differ diff --git a/docs/dev/reference/saem-2.png b/docs/dev/reference/saem-2.png index d4a2c1be..010950ba 100644 Binary files a/docs/dev/reference/saem-2.png and b/docs/dev/reference/saem-2.png differ diff --git a/docs/dev/reference/saem-3.png b/docs/dev/reference/saem-3.png index 4474b1f1..829f22bf 100644 Binary files a/docs/dev/reference/saem-3.png and b/docs/dev/reference/saem-3.png differ diff --git a/docs/dev/reference/saem-4.png b/docs/dev/reference/saem-4.png index bf24d6b0..4e976fa2 100644 Binary files a/docs/dev/reference/saem-4.png and b/docs/dev/reference/saem-4.png differ diff --git a/docs/dev/reference/saem-5.png b/docs/dev/reference/saem-5.png index 27ed3f8f..f50969b4 100644 Binary files a/docs/dev/reference/saem-5.png and b/docs/dev/reference/saem-5.png differ diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index bdb1226e..23102df3 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.3.9000 + 1.0.4.9000
                                                                @@ -158,9 +158,12 @@ Expectation Maximisation algorithm (SAEM).

                                                                object, transformations = c("mkin", "saemix"), degparms_start = numeric(), + test_log_parms = FALSE, + conf.level = 0.6, solution_type = "auto", control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = FALSE), + fail_with_errors = TRUE, verbose = FALSE, quiet = FALSE, ... @@ -174,6 +177,7 @@ Expectation Maximisation algorithm (SAEM).

                                                                solution_type = "auto", transformations = c("mkin", "saemix"), degparms_start = numeric(), + test_log_parms = FALSE, verbose = FALSE, ... ) @@ -204,6 +208,18 @@ SFO or DFOP is used for the parent and there is either no metabolite or one.

                                                                degparms_start

                                                                Parameter values given as a named numeric vector will be used to override the starting values obtained from the 'mmkin' object.

                                                                + + + test_log_parms +

                                                                If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using mean_degparms.

                                                                + + + conf.level +

                                                                Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.

                                                                solution_type @@ -214,6 +230,11 @@ automatic choice is not desired

                                                                control

                                                                Passed to saemix::saemix

                                                                + + fail_with_errors +

                                                                Should a failure to compute standard errors +from the inverse of the Fisher Information Matrix be a failure?

                                                                + verbose

                                                                Should we print information about created objects of @@ -261,33 +282,36 @@ using mmkin.

                                                                state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:32 2021" +#> [1] "Tue Mar 9 17:34:44 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:34 2021"
                                                                +#> [1] "Tue Mar 9 17:34:45 2021"
                                                                f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:35 2021" +#> [1] "Tue Mar 9 17:34:46 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:36 2021"
                                                                f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Tue Mar 9 17:34:48 2021"
                                                                f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:36 2021" +#> [1] "Tue Mar 9 17:34:48 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:38 2021"
                                                                f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Tue Mar 9 17:34:50 2021"
                                                                f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:39 2021" +#> [1] "Tue Mar 9 17:34:51 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:42 2021"
                                                                +#> [1] "Tue Mar 9 17:34:53 2021"
                                                                # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix)
                                                                #> Package saemix, version 3.1.9000 -#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
                                                                compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)) -
                                                                #> Error in compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)): 'compare.saemix' requires at least two models.
                                                                plot(f_saem_fomc$so, plot.type = "convergence") +#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
                                                                compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) +
                                                                #> Likelihoods calculated by importance sampling
                                                                #> AIC BIC +#> 1 624.2484 622.2956 +#> 2 467.7096 464.9757 +#> 3 495.4373 491.9222
                                                                plot(f_saem_fomc$so, plot.type = "convergence")
                                                                #> Plotting convergence plots
                                                                plot(f_saem_fomc$so, plot.type = "individual.fit")
                                                                #> Plotting individual fits
                                                                plot(f_saem_fomc$so, plot.type = "npde")
                                                                #> Simulating data using nsim = 1000 simulated datasets @@ -324,11 +348,13 @@ using mmkin.

                                                                f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:44 2021" +#> [1] "Tue Mar 9 17:34:55 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:49 2021"
                                                                compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) -
                                                                #> Error in compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)): 'compare.saemix' requires at least two models.
                                                                +#> [1] "Tue Mar 9 17:35:00 2021"
                                                                compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +
                                                                #> Likelihoods calculated by importance sampling
                                                                #> AIC BIC +#> 1 467.7096 464.9757 +#> 2 469.6831 466.5586
                                                                sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO"))
                                                                #> Temporary DLL for differentials generated and loaded
                                                                fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), @@ -346,15 +372,15 @@ using mmkin.

                                                                # four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:51 2021" +#> [1] "Tue Mar 9 17:35:02 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:12:56 2021"
                                                                f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Tue Mar 9 17:35:07 2021"
                                                                f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:12:56 2021" +#> [1] "Tue Mar 9 17:35:07 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:13:05 2021"
                                                                # We can use print, plot and summary methods to check the results +#> [1] "Tue Mar 9 17:35:15 2021"
                                                                # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo)
                                                                #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -395,10 +421,10 @@ using mmkin.

                                                                #> SD.g_qlogis 0.44771 -0.86417 1.7596
                                                                plot(f_saem_dfop_sfo)
                                                                summary(f_saem_dfop_sfo, data = TRUE)
                                                                #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.3.9000 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Feb 15 17:13:05 2021 -#> Date of summary: Mon Feb 15 17:13:06 2021 +#> mkin version used for pre-fitting: 1.0.4.9000 +#> R version used for fitting: 4.0.4 +#> Date of fit: Tue Mar 9 17:35:16 2021 +#> Date of summary: Tue Mar 9 17:35:16 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -413,7 +439,7 @@ using mmkin.

                                                                #> #> Model predictions using solution type analytical #> -#> Fitted in 8.985 s using 300, 100 iterations +#> Fitted in 8.668 s using 300, 100 iterations #> #> Variance model: Constant variance #> diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html index 0d661ee9..1166abb1 100644 --- a/docs/dev/reference/summary.saem.mmkin.html +++ b/docs/dev/reference/summary.saem.mmkin.html @@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally mkin - 1.0.3.9000 + 1.0.4.9000
                                                                @@ -260,15 +260,15 @@ saemix authors for the parts inherited from saemix.

                                                                quiet = TRUE, error_model = "tc", cores = 5) f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
                                                                #> Running main SAEM algorithm -#> [1] "Mon Feb 15 17:13:15 2021" +#> [1] "Tue Mar 9 17:35:19 2021" #> .... #> Minimisation finished -#> [1] "Mon Feb 15 17:13:26 2021"
                                                                summary(f_saem_dfop_sfo, data = TRUE) +#> [1] "Tue Mar 9 17:35:30 2021"
                                                                summary(f_saem_dfop_sfo, data = TRUE)
                                                                #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.3.9000 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Feb 15 17:13:27 2021 -#> Date of summary: Mon Feb 15 17:13:27 2021 +#> mkin version used for pre-fitting: 1.0.4.9000 +#> R version used for fitting: 4.0.4 +#> Date of fit: Tue Mar 9 17:35:31 2021 +#> Date of summary: Tue Mar 9 17:35:31 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -283,7 +283,7 @@ saemix authors for the parts inherited from saemix.

                                                                #> #> Model predictions using solution type analytical #> -#> Fitted in 12.204 s using 300, 100 iterations +#> Fitted in 12.058 s using 300, 100 iterations #> #> Variance model: Two-component variance function #> @@ -300,231 +300,231 @@ saemix authors for the parts inherited from saemix.

                                                                #> #> Likelihood computed by importance sampling #> AIC BIC logLik -#> 829.3 823.9 -400.7 +#> 825.9 820.4 -398.9 #> #> Optimised parameters: -#> est. lower upper -#> parent_0 101.29457 97.855 104.7344 -#> log_k_m1 -4.06337 -4.182 -3.9445 -#> f_parent_qlogis -0.94546 -1.307 -0.5841 -#> log_k1 -2.98794 -3.844 -2.1321 -#> log_k2 -3.47891 -4.253 -2.7050 -#> g_qlogis -0.03211 -1.157 1.0931 +#> est. lower upper +#> parent_0 101.118986 97.368 104.8695 +#> log_k_m1 -4.057591 -4.177 -3.9379 +#> f_parent_qlogis -0.933087 -1.290 -0.5763 +#> log_k1 -2.945520 -3.833 -2.0576 +#> log_k2 -3.531954 -4.310 -2.7542 +#> g_qlogis -0.009584 -1.688 1.6687 #> #> Correlation: #> prnt_0 lg_k_1 f_prn_ log_k1 log_k2 -#> log_k_m1 -0.202 -#> f_parent_qlogis -0.145 0.195 -#> log_k1 0.094 -0.099 -0.049 -#> log_k2 -0.042 0.056 0.024 -0.097 -#> g_qlogis -0.005 0.000 0.007 -0.160 -0.113 +#> log_k_m1 -0.198 +#> f_parent_qlogis -0.153 0.184 +#> log_k1 0.080 -0.077 -0.045 +#> log_k2 0.005 0.008 -0.003 -0.019 +#> g_qlogis -0.059 0.048 0.041 -0.334 -0.253 #> #> Random effects: #> est. lower upper -#> SD.parent_0 2.70085 -0.64980 6.0515 -#> SD.log_k_m1 0.08408 -0.04023 0.2084 -#> SD.f_parent_qlogis 0.39215 0.13695 0.6473 -#> SD.log_k1 0.89280 0.27466 1.5109 -#> SD.log_k2 0.82387 0.26388 1.3838 -#> SD.g_qlogis 0.36468 -0.86978 1.5991 +#> SD.parent_0 2.97797 -0.62927 6.5852 +#> SD.log_k_m1 0.09235 -0.02448 0.2092 +#> SD.f_parent_qlogis 0.38712 0.13469 0.6396 +#> SD.log_k1 0.88671 0.27052 1.5029 +#> SD.log_k2 0.80497 0.25587 1.3541 +#> SD.g_qlogis 0.36812 -3.56188 4.2981 #> #> Variance model: #> est. lower upper -#> a.1 0.65724 0.49361 0.82086 -#> b.1 0.06434 0.05034 0.07835 +#> a.1 0.85879 0.68143 1.03615 +#> b.1 0.07787 0.06288 0.09286 #> #> Backtransformed parameters: #> est. lower upper -#> parent_0 101.29457 97.85477 104.73437 -#> k_m1 0.01719 0.01526 0.01936 -#> f_parent_to_m1 0.27980 0.21302 0.35798 -#> k1 0.05039 0.02141 0.11859 -#> k2 0.03084 0.01422 0.06687 -#> g 0.49197 0.23916 0.74896 +#> parent_0 101.11899 97.36850 104.86947 +#> k_m1 0.01729 0.01534 0.01949 +#> f_parent_to_m1 0.28230 0.21587 0.35979 +#> k1 0.05257 0.02163 0.12776 +#> k2 0.02925 0.01344 0.06366 +#> g 0.49760 0.15606 0.84140 #> #> Resulting formation fractions: #> ff -#> parent_m1 0.2798 -#> parent_sink 0.7202 +#> parent_m1 0.2823 +#> parent_sink 0.7177 #> #> Estimated disappearance times: #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 17.49 61.05 18.38 13.76 22.47 -#> m1 40.32 133.94 NA NA NA +#> parent 17.47 62.31 18.76 13.18 23.7 +#> m1 40.09 133.17 NA NA NA #> #> Data: -#> ds name time observed predicted residual std standardized -#> ds 1 parent 0 89.8 9.878e+01 8.98039 6.3899 1.40541 -#> ds 1 parent 0 104.1 9.878e+01 -5.31961 6.3899 -0.83251 -#> ds 1 parent 1 88.7 9.422e+01 5.52084 6.0981 0.90533 -#> ds 1 parent 1 95.5 9.422e+01 -1.27916 6.0981 -0.20976 -#> ds 1 parent 3 81.8 8.587e+01 4.06752 5.5641 0.73103 -#> ds 1 parent 3 94.5 8.587e+01 -8.63248 5.5641 -1.55147 -#> ds 1 parent 7 71.5 7.180e+01 0.29615 4.6662 0.06347 -#> ds 1 parent 7 70.3 7.180e+01 1.49615 4.6662 0.32063 -#> ds 1 parent 14 54.2 5.360e+01 -0.59602 3.5112 -0.16975 -#> ds 1 parent 14 49.6 5.360e+01 4.00398 3.5112 1.14035 -#> ds 1 parent 28 31.5 3.213e+01 0.62529 2.1691 0.28828 -#> ds 1 parent 28 28.8 3.213e+01 3.32529 2.1691 1.53306 -#> ds 1 parent 60 12.1 1.271e+01 0.60718 1.0490 0.57879 -#> ds 1 parent 60 13.6 1.271e+01 -0.89282 1.0490 -0.85108 -#> ds 1 parent 90 6.2 6.080e+00 -0.12020 0.7649 -0.15716 -#> ds 1 parent 90 8.3 6.080e+00 -2.22020 0.7649 -2.90279 -#> ds 1 parent 120 2.2 3.011e+00 0.81059 0.6852 1.18302 -#> ds 1 parent 120 2.4 3.011e+00 0.61059 0.6852 0.89113 -#> ds 1 m1 1 0.3 1.131e+00 0.83071 0.6613 1.25628 -#> ds 1 m1 1 0.2 1.131e+00 0.93071 0.6613 1.40750 -#> ds 1 m1 3 2.2 3.147e+00 0.94691 0.6877 1.37688 -#> ds 1 m1 3 3.0 3.147e+00 0.14691 0.6877 0.21361 -#> ds 1 m1 7 6.5 6.341e+00 -0.15949 0.7736 -0.20618 -#> ds 1 m1 7 5.0 6.341e+00 1.34051 0.7736 1.73290 -#> ds 1 m1 14 10.2 9.910e+00 -0.28991 0.9157 -0.31659 -#> ds 1 m1 14 9.5 9.910e+00 0.41009 0.9157 0.44783 -#> ds 1 m1 28 12.2 1.255e+01 0.34690 1.0410 0.33323 -#> ds 1 m1 28 13.4 1.255e+01 -0.85310 1.0410 -0.81949 -#> ds 1 m1 60 11.8 1.087e+01 -0.92713 0.9599 -0.96586 -#> ds 1 m1 60 13.2 1.087e+01 -2.32713 0.9599 -2.42434 -#> ds 1 m1 90 6.6 7.813e+00 1.21254 0.8274 1.46541 -#> ds 1 m1 90 9.3 7.813e+00 -1.48746 0.8274 -1.79766 -#> ds 1 m1 120 3.5 5.295e+00 1.79489 0.7403 2.42457 -#> ds 1 m1 120 5.4 5.295e+00 -0.10511 0.7403 -0.14198 -#> ds 2 parent 0 118.0 1.074e+02 -10.63436 6.9396 -1.53242 -#> ds 2 parent 0 99.8 1.074e+02 7.56564 6.9396 1.09021 -#> ds 2 parent 1 90.2 1.012e+02 10.96486 6.5425 1.67594 -#> ds 2 parent 1 94.6 1.012e+02 6.56486 6.5425 1.00342 -#> ds 2 parent 3 96.1 9.054e+01 -5.56014 5.8627 -0.94839 -#> ds 2 parent 3 78.4 9.054e+01 12.13986 5.8627 2.07069 -#> ds 2 parent 7 77.9 7.468e+01 -3.21805 4.8501 -0.66350 -#> ds 2 parent 7 77.7 7.468e+01 -3.01805 4.8501 -0.62226 -#> ds 2 parent 14 56.0 5.748e+01 1.47774 3.7563 0.39340 -#> ds 2 parent 14 54.7 5.748e+01 2.77774 3.7563 0.73948 -#> ds 2 parent 28 36.6 3.996e+01 3.36317 2.6541 1.26717 -#> ds 2 parent 28 36.8 3.996e+01 3.16317 2.6541 1.19182 -#> ds 2 parent 60 22.1 2.132e+01 -0.78225 1.5210 -0.51430 -#> ds 2 parent 60 24.7 2.132e+01 -3.38225 1.5210 -2.22369 -#> ds 2 parent 90 12.4 1.215e+01 -0.25010 1.0213 -0.24487 -#> ds 2 parent 90 10.8 1.215e+01 1.34990 1.0213 1.32169 -#> ds 2 parent 120 6.8 6.931e+00 0.13105 0.7943 0.16500 -#> ds 2 parent 120 7.9 6.931e+00 -0.96895 0.7943 -1.21994 -#> ds 2 m1 1 1.3 1.519e+00 0.21924 0.6645 0.32995 -#> ds 2 m1 3 3.7 4.049e+00 0.34891 0.7070 0.49351 -#> ds 2 m1 3 4.7 4.049e+00 -0.65109 0.7070 -0.92094 -#> ds 2 m1 7 8.1 7.565e+00 -0.53526 0.8179 -0.65448 -#> ds 2 m1 7 7.9 7.565e+00 -0.33526 0.8179 -0.40993 -#> ds 2 m1 14 10.1 1.071e+01 0.60614 0.9521 0.63663 -#> ds 2 m1 14 10.3 1.071e+01 0.40614 0.9521 0.42657 -#> ds 2 m1 28 10.7 1.224e+01 1.54440 1.0260 1.50526 -#> ds 2 m1 28 12.2 1.224e+01 0.04440 1.0260 0.04327 -#> ds 2 m1 60 10.7 1.056e+01 -0.14005 0.9453 -0.14815 -#> ds 2 m1 60 12.5 1.056e+01 -1.94005 0.9453 -2.05226 -#> ds 2 m1 90 9.1 8.089e+00 -1.01088 0.8384 -1.20577 -#> ds 2 m1 90 7.4 8.089e+00 0.68912 0.8384 0.82197 -#> ds 2 m1 120 6.1 5.855e+00 -0.24463 0.7576 -0.32292 -#> ds 2 m1 120 4.5 5.855e+00 1.35537 0.7576 1.78911 -#> ds 3 parent 0 106.2 1.095e+02 3.30335 7.0765 0.46680 -#> ds 3 parent 0 106.9 1.095e+02 2.60335 7.0765 0.36788 -#> ds 3 parent 1 107.4 9.939e+01 -8.01282 6.4287 -1.24641 -#> ds 3 parent 1 96.1 9.939e+01 3.28718 6.4287 0.51133 -#> ds 3 parent 3 79.4 8.365e+01 4.24698 5.4222 0.78326 -#> ds 3 parent 3 82.6 8.365e+01 1.04698 5.4222 0.19309 -#> ds 3 parent 7 63.9 6.405e+01 0.14704 4.1732 0.03523 -#> ds 3 parent 7 62.4 6.405e+01 1.64704 4.1732 0.39467 -#> ds 3 parent 14 51.0 4.795e+01 -3.04985 3.1546 -0.96681 -#> ds 3 parent 14 47.1 4.795e+01 0.85015 3.1546 0.26950 -#> ds 3 parent 28 36.1 3.501e+01 -1.09227 2.3465 -0.46549 -#> ds 3 parent 28 36.6 3.501e+01 -1.59227 2.3465 -0.67858 -#> ds 3 parent 60 20.1 2.012e+01 0.02116 1.4520 0.01457 -#> ds 3 parent 60 19.8 2.012e+01 0.32116 1.4520 0.22119 -#> ds 3 parent 90 11.3 1.206e+01 0.76096 1.0170 0.74826 -#> ds 3 parent 90 10.7 1.206e+01 1.36096 1.0170 1.33825 -#> ds 3 parent 120 8.2 7.230e+00 -0.97022 0.8052 -1.20493 -#> ds 3 parent 120 7.3 7.230e+00 -0.07022 0.8052 -0.08721 -#> ds 3 m1 0 0.8 -5.684e-13 -0.80000 0.6572 -1.21722 -#> ds 3 m1 1 1.8 2.045e+00 0.24538 0.6703 0.36608 -#> ds 3 m1 1 2.3 2.045e+00 -0.25462 0.6703 -0.37987 -#> ds 3 m1 3 4.2 5.136e+00 0.93594 0.7356 1.27228 -#> ds 3 m1 3 4.1 5.136e+00 1.03594 0.7356 1.40822 -#> ds 3 m1 7 6.8 8.674e+00 1.87438 0.8623 2.17381 -#> ds 3 m1 7 10.1 8.674e+00 -1.42562 0.8623 -1.65335 -#> ds 3 m1 14 11.4 1.083e+01 -0.56746 0.9580 -0.59233 -#> ds 3 m1 14 12.8 1.083e+01 -1.96746 0.9580 -2.05369 -#> ds 3 m1 28 11.5 1.098e+01 -0.51762 0.9651 -0.53637 -#> ds 3 m1 28 10.6 1.098e+01 0.38238 0.9651 0.39623 -#> ds 3 m1 60 7.5 8.889e+00 1.38911 0.8713 1.59436 -#> ds 3 m1 60 8.6 8.889e+00 0.28911 0.8713 0.33183 -#> ds 3 m1 90 7.3 6.774e+00 -0.52608 0.7886 -0.66708 -#> ds 3 m1 90 8.1 6.774e+00 -1.32608 0.7886 -1.68150 -#> ds 3 m1 120 5.3 4.954e+00 -0.34584 0.7305 -0.47345 -#> ds 3 m1 120 3.8 4.954e+00 1.15416 0.7305 1.58004 -#> ds 4 parent 0 104.7 9.957e+01 -5.13169 6.4403 -0.79681 -#> ds 4 parent 0 88.3 9.957e+01 11.26831 6.4403 1.74966 -#> ds 4 parent 1 94.2 9.644e+01 2.23888 6.2400 0.35879 -#> ds 4 parent 1 94.6 9.644e+01 1.83888 6.2400 0.29469 -#> ds 4 parent 3 78.1 9.054e+01 12.43946 5.8627 2.12180 -#> ds 4 parent 3 96.5 9.054e+01 -5.96054 5.8627 -1.01669 -#> ds 4 parent 7 76.2 8.004e+01 3.83771 5.1918 0.73919 -#> ds 4 parent 7 77.8 8.004e+01 2.23771 5.1918 0.43101 -#> ds 4 parent 14 70.8 6.511e+01 -5.69246 4.2406 -1.34238 -#> ds 4 parent 14 67.3 6.511e+01 -2.19246 4.2406 -0.51702 -#> ds 4 parent 28 43.1 4.454e+01 1.43744 2.9401 0.48890 -#> ds 4 parent 28 45.1 4.454e+01 -0.56256 2.9401 -0.19134 -#> ds 4 parent 60 21.3 2.132e+01 0.02005 1.5211 0.01318 -#> ds 4 parent 60 23.5 2.132e+01 -2.17995 1.5211 -1.43310 -#> ds 4 parent 90 11.8 1.182e+01 0.02167 1.0053 0.02156 -#> ds 4 parent 90 12.1 1.182e+01 -0.27833 1.0053 -0.27687 -#> ds 4 parent 120 7.0 6.852e+00 -0.14780 0.7914 -0.18675 -#> ds 4 parent 120 6.2 6.852e+00 0.65220 0.7914 0.82408 -#> ds 4 m1 0 1.6 -5.684e-14 -1.60000 0.6572 -2.43444 -#> ds 4 m1 1 0.9 6.918e-01 -0.20821 0.6587 -0.31607 -#> ds 4 m1 3 3.7 1.959e+00 -1.74131 0.6692 -2.60204 -#> ds 4 m1 3 2.0 1.959e+00 -0.04131 0.6692 -0.06173 -#> ds 4 m1 7 3.6 4.076e+00 0.47590 0.7076 0.67252 -#> ds 4 m1 7 3.8 4.076e+00 0.27590 0.7076 0.38989 -#> ds 4 m1 14 7.1 6.698e+00 -0.40189 0.7859 -0.51135 -#> ds 4 m1 14 6.6 6.698e+00 0.09811 0.7859 0.12483 -#> ds 4 m1 28 9.5 9.175e+00 -0.32492 0.8835 -0.36779 -#> ds 4 m1 28 9.3 9.175e+00 -0.12492 0.8835 -0.14141 -#> ds 4 m1 60 8.3 8.818e+00 0.51810 0.8683 0.59671 -#> ds 4 m1 60 9.0 8.818e+00 -0.18190 0.8683 -0.20949 -#> ds 4 m1 90 6.6 6.645e+00 0.04480 0.7841 0.05713 -#> ds 4 m1 90 7.7 6.645e+00 -1.05520 0.7841 -1.34581 -#> ds 4 m1 120 3.7 4.648e+00 0.94805 0.7221 1.31293 -#> ds 4 m1 120 3.5 4.648e+00 1.14805 0.7221 1.58991 -#> ds 5 parent 0 110.4 1.026e+02 -7.81752 6.6333 -1.17853 -#> ds 5 parent 0 112.1 1.026e+02 -9.51752 6.6333 -1.43482 -#> ds 5 parent 1 93.5 9.560e+01 2.10274 6.1865 0.33989 -#> ds 5 parent 1 91.0 9.560e+01 4.60274 6.1865 0.74399 -#> ds 5 parent 3 71.0 8.356e+01 12.55799 5.4165 2.31846 -#> ds 5 parent 3 89.7 8.356e+01 -6.14201 5.4165 -1.13394 -#> ds 5 parent 7 60.4 6.550e+01 5.09732 4.2653 1.19506 -#> ds 5 parent 7 59.1 6.550e+01 6.39732 4.2653 1.49984 -#> ds 5 parent 14 56.5 4.641e+01 -10.09145 3.0576 -3.30044 -#> ds 5 parent 14 47.0 4.641e+01 -0.59145 3.0576 -0.19344 -#> ds 5 parent 28 30.2 2.982e+01 -0.37647 2.0284 -0.18560 -#> ds 5 parent 28 23.9 2.982e+01 5.92353 2.0284 2.92028 -#> ds 5 parent 60 17.0 1.754e+01 0.53981 1.3060 0.41332 -#> ds 5 parent 60 18.7 1.754e+01 -1.16019 1.3060 -0.88834 -#> ds 5 parent 90 11.3 1.175e+01 0.45050 1.0018 0.44969 -#> ds 5 parent 90 11.9 1.175e+01 -0.14950 1.0018 -0.14923 -#> ds 5 parent 120 9.0 7.915e+00 -1.08476 0.8315 -1.30462 -#> ds 5 parent 120 8.1 7.915e+00 -0.18476 0.8315 -0.22220 -#> ds 5 m1 0 0.7 0.000e+00 -0.70000 0.6572 -1.06507 -#> ds 5 m1 1 3.0 3.062e+00 0.06170 0.6861 0.08992 -#> ds 5 m1 1 2.6 3.062e+00 0.46170 0.6861 0.67290 -#> ds 5 m1 3 5.1 8.209e+00 3.10938 0.8432 3.68760 -#> ds 5 m1 3 7.5 8.209e+00 0.70938 0.8432 0.84130 -#> ds 5 m1 7 16.5 1.544e+01 -1.05567 1.1914 -0.88605 -#> ds 5 m1 7 19.0 1.544e+01 -3.55567 1.1914 -2.98436 -#> ds 5 m1 14 22.9 2.181e+01 -1.08765 1.5498 -0.70181 -#> ds 5 m1 14 23.2 2.181e+01 -1.38765 1.5498 -0.89539 -#> ds 5 m1 28 22.2 2.404e+01 1.83624 1.6805 1.09270 -#> ds 5 m1 28 24.4 2.404e+01 -0.36376 1.6805 -0.21647 -#> ds 5 m1 60 15.5 1.875e+01 3.25390 1.3741 2.36805 -#> ds 5 m1 60 19.8 1.875e+01 -1.04610 1.3741 -0.76131 -#> ds 5 m1 90 14.9 1.380e+01 -1.09507 1.1050 -0.99102 -#> ds 5 m1 90 14.2 1.380e+01 -0.39507 1.1050 -0.35753 -#> ds 5 m1 120 10.9 1.002e+01 -0.88429 0.9205 -0.96069 -#> ds 5 m1 120 10.4 1.002e+01 -0.38429 0.9205 -0.41749
                                                                # } +#> ds name time observed predicted residual std standardized +#> ds 1 parent 0 89.8 9.838e+01 8.584661 7.7094 1.113536 +#> ds 1 parent 0 104.1 9.838e+01 -5.715339 7.7094 -0.741350 +#> ds 1 parent 1 88.7 9.388e+01 5.182489 7.3611 0.704041 +#> ds 1 parent 1 95.5 9.388e+01 -1.617511 7.3611 -0.219739 +#> ds 1 parent 3 81.8 8.563e+01 3.825382 6.7229 0.569010 +#> ds 1 parent 3 94.5 8.563e+01 -8.874618 6.7229 -1.320062 +#> ds 1 parent 7 71.5 7.169e+01 0.188290 5.6482 0.033336 +#> ds 1 parent 7 70.3 7.169e+01 1.388290 5.6482 0.245795 +#> ds 1 parent 14 54.2 5.361e+01 -0.586595 4.2624 -0.137621 +#> ds 1 parent 14 49.6 5.361e+01 4.013405 4.2624 0.941587 +#> ds 1 parent 28 31.5 3.219e+01 0.688936 2.6496 0.260011 +#> ds 1 parent 28 28.8 3.219e+01 3.388936 2.6496 1.279016 +#> ds 1 parent 60 12.1 1.278e+01 0.678998 1.3145 0.516562 +#> ds 1 parent 60 13.6 1.278e+01 -0.821002 1.3145 -0.624595 +#> ds 1 parent 90 6.2 6.157e+00 -0.043461 0.9835 -0.044188 +#> ds 1 parent 90 8.3 6.157e+00 -2.143461 0.9835 -2.179316 +#> ds 1 parent 120 2.2 3.076e+00 0.876218 0.8916 0.982775 +#> ds 1 parent 120 2.4 3.076e+00 0.676218 0.8916 0.758453 +#> ds 1 m1 1 0.3 1.134e+00 0.833749 0.8633 0.965750 +#> ds 1 m1 1 0.2 1.134e+00 0.933749 0.8633 1.081583 +#> ds 1 m1 3 2.2 3.157e+00 0.957400 0.8933 1.071763 +#> ds 1 m1 3 3.0 3.157e+00 0.157400 0.8933 0.176202 +#> ds 1 m1 7 6.5 6.369e+00 -0.130995 0.9917 -0.132090 +#> ds 1 m1 7 5.0 6.369e+00 1.369005 0.9917 1.380438 +#> ds 1 m1 14 10.2 9.971e+00 -0.229362 1.1577 -0.198112 +#> ds 1 m1 14 9.5 9.971e+00 0.470638 1.1577 0.406513 +#> ds 1 m1 28 12.2 1.265e+01 0.447735 1.3067 0.342637 +#> ds 1 m1 28 13.4 1.265e+01 -0.752265 1.3067 -0.575683 +#> ds 1 m1 60 11.8 1.097e+01 -0.832027 1.2112 -0.686945 +#> ds 1 m1 60 13.2 1.097e+01 -2.232027 1.2112 -1.842825 +#> ds 1 m1 90 6.6 7.876e+00 1.275985 1.0553 1.209109 +#> ds 1 m1 90 9.3 7.876e+00 -1.424015 1.0553 -1.349381 +#> ds 1 m1 120 3.5 5.336e+00 1.835829 0.9540 1.924292 +#> ds 1 m1 120 5.4 5.336e+00 -0.064171 0.9540 -0.067263 +#> ds 2 parent 0 118.0 1.092e+02 -8.812058 8.5459 -1.031142 +#> ds 2 parent 0 99.8 1.092e+02 9.387942 8.5459 1.098529 +#> ds 2 parent 1 90.2 1.023e+02 12.114268 8.0135 1.511724 +#> ds 2 parent 1 94.6 1.023e+02 7.714268 8.0135 0.962654 +#> ds 2 parent 3 96.1 9.066e+01 -5.436165 7.1122 -0.764344 +#> ds 2 parent 3 78.4 9.066e+01 12.263835 7.1122 1.724339 +#> ds 2 parent 7 77.9 7.365e+01 -4.245773 5.7995 -0.732090 +#> ds 2 parent 7 77.7 7.365e+01 -4.045773 5.7995 -0.697604 +#> ds 2 parent 14 56.0 5.593e+01 -0.073803 4.4389 -0.016626 +#> ds 2 parent 14 54.7 5.593e+01 1.226197 4.4389 0.276236 +#> ds 2 parent 28 36.6 3.892e+01 2.320837 3.1502 0.736737 +#> ds 2 parent 28 36.8 3.892e+01 2.120837 3.1502 0.673248 +#> ds 2 parent 60 22.1 2.136e+01 -0.741020 1.8719 -0.395868 +#> ds 2 parent 60 24.7 2.136e+01 -3.341020 1.8719 -1.784841 +#> ds 2 parent 90 12.4 1.251e+01 0.113999 1.2989 0.087765 +#> ds 2 parent 90 10.8 1.251e+01 1.713999 1.2989 1.319575 +#> ds 2 parent 120 6.8 7.338e+00 0.537708 1.0315 0.521281 +#> ds 2 parent 120 7.9 7.338e+00 -0.562292 1.0315 -0.545113 +#> ds 2 m1 1 1.3 1.576e+00 0.276176 0.8675 0.318352 +#> ds 2 m1 3 3.7 4.177e+00 0.476741 0.9183 0.519146 +#> ds 2 m1 3 4.7 4.177e+00 -0.523259 0.9183 -0.569801 +#> ds 2 m1 7 8.1 7.724e+00 -0.376365 1.0485 -0.358970 +#> ds 2 m1 7 7.9 7.724e+00 -0.176365 1.0485 -0.168214 +#> ds 2 m1 14 10.1 1.077e+01 0.674433 1.2006 0.561738 +#> ds 2 m1 14 10.3 1.077e+01 0.474433 1.2006 0.395158 +#> ds 2 m1 28 10.7 1.212e+01 1.416179 1.2758 1.110010 +#> ds 2 m1 28 12.2 1.212e+01 -0.083821 1.2758 -0.065699 +#> ds 2 m1 60 10.7 1.041e+01 -0.294930 1.1807 -0.249793 +#> ds 2 m1 60 12.5 1.041e+01 -2.094930 1.1807 -1.774316 +#> ds 2 m1 90 9.1 8.079e+00 -1.020859 1.0646 -0.958929 +#> ds 2 m1 90 7.4 8.079e+00 0.679141 1.0646 0.637941 +#> ds 2 m1 120 6.1 5.968e+00 -0.131673 0.9765 -0.134843 +#> ds 2 m1 120 4.5 5.968e+00 1.468327 0.9765 1.503683 +#> ds 3 parent 0 106.2 1.036e+02 -2.638248 8.1101 -0.325303 +#> ds 3 parent 0 106.9 1.036e+02 -3.338248 8.1101 -0.411614 +#> ds 3 parent 1 107.4 9.580e+01 -11.600063 7.5094 -1.544743 +#> ds 3 parent 1 96.1 9.580e+01 -0.300063 7.5094 -0.039958 +#> ds 3 parent 3 79.4 8.297e+01 3.574516 6.5182 0.548391 +#> ds 3 parent 3 82.6 8.297e+01 0.374516 6.5182 0.057457 +#> ds 3 parent 7 63.9 6.517e+01 1.272397 5.1472 0.247200 +#> ds 3 parent 7 62.4 6.517e+01 2.772397 5.1472 0.538618 +#> ds 3 parent 14 51.0 4.821e+01 -2.790075 3.8512 -0.724475 +#> ds 3 parent 14 47.1 4.821e+01 1.109925 3.8512 0.288205 +#> ds 3 parent 28 36.1 3.385e+01 -2.250573 2.7723 -0.811811 +#> ds 3 parent 28 36.6 3.385e+01 -2.750573 2.7723 -0.992168 +#> ds 3 parent 60 20.1 1.964e+01 -0.455700 1.7543 -0.259760 +#> ds 3 parent 60 19.8 1.964e+01 -0.155700 1.7543 -0.088753 +#> ds 3 parent 90 11.3 1.210e+01 0.795458 1.2746 0.624068 +#> ds 3 parent 90 10.7 1.210e+01 1.395458 1.2746 1.094792 +#> ds 3 parent 120 8.2 7.451e+00 -0.749141 1.0364 -0.722816 +#> ds 3 parent 120 7.3 7.451e+00 0.150859 1.0364 0.145558 +#> ds 3 m1 0 0.8 3.695e-13 -0.800000 0.8588 -0.931542 +#> ds 3 m1 1 1.8 1.740e+00 -0.059741 0.8694 -0.068714 +#> ds 3 m1 1 2.3 1.740e+00 -0.559741 0.8694 -0.643812 +#> ds 3 m1 3 4.2 4.531e+00 0.331379 0.9285 0.356913 +#> ds 3 m1 3 4.1 4.531e+00 0.431379 0.9285 0.464618 +#> ds 3 m1 7 6.8 8.113e+00 1.312762 1.0661 1.231333 +#> ds 3 m1 7 10.1 8.113e+00 -1.987238 1.0661 -1.863971 +#> ds 3 m1 14 11.4 1.079e+01 -0.613266 1.2013 -0.510507 +#> ds 3 m1 14 12.8 1.079e+01 -2.013266 1.2013 -1.675923 +#> ds 3 m1 28 11.5 1.133e+01 -0.174252 1.2310 -0.141553 +#> ds 3 m1 28 10.6 1.133e+01 0.725748 1.2310 0.589558 +#> ds 3 m1 60 7.5 8.948e+00 1.448281 1.1059 1.309561 +#> ds 3 m1 60 8.6 8.948e+00 0.348281 1.1059 0.314922 +#> ds 3 m1 90 7.3 6.665e+00 -0.634932 1.0034 -0.632752 +#> ds 3 m1 90 8.1 6.665e+00 -1.434932 1.0034 -1.430004 +#> ds 3 m1 120 5.3 4.795e+00 -0.504936 0.9365 -0.539199 +#> ds 3 m1 120 3.8 4.795e+00 0.995064 0.9365 1.062586 +#> ds 4 parent 0 104.7 9.985e+01 -4.850494 7.8227 -0.620050 +#> ds 4 parent 0 88.3 9.985e+01 11.549506 7.8227 1.476402 +#> ds 4 parent 1 94.2 9.676e+01 2.556304 7.5834 0.337093 +#> ds 4 parent 1 94.6 9.676e+01 2.156304 7.5834 0.284346 +#> ds 4 parent 3 78.1 9.092e+01 12.817485 7.1318 1.797230 +#> ds 4 parent 3 96.5 9.092e+01 -5.582515 7.1318 -0.782764 +#> ds 4 parent 7 76.2 8.050e+01 4.297338 6.3270 0.679204 +#> ds 4 parent 7 77.8 8.050e+01 2.697338 6.3270 0.426320 +#> ds 4 parent 14 70.8 6.562e+01 -5.179989 5.1816 -0.999687 +#> ds 4 parent 14 67.3 6.562e+01 -1.679989 5.1816 -0.324222 +#> ds 4 parent 28 43.1 4.499e+01 1.886936 3.6069 0.523140 +#> ds 4 parent 28 45.1 4.499e+01 -0.113064 3.6069 -0.031346 +#> ds 4 parent 60 21.3 2.151e+01 0.214840 1.8827 0.114114 +#> ds 4 parent 60 23.5 2.151e+01 -1.985160 1.8827 -1.054433 +#> ds 4 parent 90 11.8 1.190e+01 0.098528 1.2633 0.077990 +#> ds 4 parent 90 12.1 1.190e+01 -0.201472 1.2633 -0.159475 +#> ds 4 parent 120 7.0 6.886e+00 -0.113832 1.0125 -0.112431 +#> ds 4 parent 120 6.2 6.886e+00 0.686168 1.0125 0.677724 +#> ds 4 m1 0 1.6 4.263e-14 -1.600000 0.8588 -1.863085 +#> ds 4 m1 1 0.9 7.140e-01 -0.185984 0.8606 -0.216112 +#> ds 4 m1 3 3.7 2.022e+00 -1.678243 0.8731 -1.922160 +#> ds 4 m1 3 2.0 2.022e+00 0.021757 0.8731 0.024919 +#> ds 4 m1 7 3.6 4.207e+00 0.607229 0.9192 0.660633 +#> ds 4 m1 7 3.8 4.207e+00 0.407229 0.9192 0.443044 +#> ds 4 m1 14 7.1 6.912e+00 -0.188339 1.0135 -0.185828 +#> ds 4 m1 14 6.6 6.912e+00 0.311661 1.0135 0.307506 +#> ds 4 m1 28 9.5 9.449e+00 -0.050714 1.1309 -0.044843 +#> ds 4 m1 28 9.3 9.449e+00 0.149286 1.1309 0.132004 +#> ds 4 m1 60 8.3 8.997e+00 0.697403 1.1083 0.629230 +#> ds 4 m1 60 9.0 8.997e+00 -0.002597 1.1083 -0.002343 +#> ds 4 m1 90 6.6 6.697e+00 0.096928 1.0047 0.096472 +#> ds 4 m1 90 7.7 6.697e+00 -1.003072 1.0047 -0.998348 +#> ds 4 m1 120 3.7 4.622e+00 0.921607 0.9312 0.989749 +#> ds 4 m1 120 3.5 4.622e+00 1.121607 0.9312 1.204537 +#> ds 5 parent 0 110.4 1.045e+02 -5.942426 8.1795 -0.726502 +#> ds 5 parent 0 112.1 1.045e+02 -7.642426 8.1795 -0.934338 +#> ds 5 parent 1 93.5 9.739e+01 3.893915 7.6327 0.510162 +#> ds 5 parent 1 91.0 9.739e+01 6.393915 7.6327 0.837700 +#> ds 5 parent 3 71.0 8.519e+01 14.188275 6.6891 2.121098 +#> ds 5 parent 3 89.7 8.519e+01 -4.511725 6.6891 -0.674487 +#> ds 5 parent 7 60.4 6.684e+01 6.439546 5.2753 1.220701 +#> ds 5 parent 7 59.1 6.684e+01 7.739546 5.2753 1.467133 +#> ds 5 parent 14 56.5 4.736e+01 -9.138979 3.7868 -2.413407 +#> ds 5 parent 14 47.0 4.736e+01 0.361021 3.7868 0.095338 +#> ds 5 parent 28 30.2 3.033e+01 0.131178 2.5132 0.052195 +#> ds 5 parent 28 23.9 3.033e+01 6.431178 2.5132 2.558936 +#> ds 5 parent 60 17.0 1.771e+01 0.705246 1.6243 0.434177 +#> ds 5 parent 60 18.7 1.771e+01 -0.994754 1.6243 -0.612409 +#> ds 5 parent 90 11.3 1.180e+01 0.504856 1.2580 0.401315 +#> ds 5 parent 90 11.9 1.180e+01 -0.095144 1.2580 -0.075631 +#> ds 5 parent 120 9.0 7.917e+00 -1.083499 1.0571 -1.024928 +#> ds 5 parent 120 8.1 7.917e+00 -0.183499 1.0571 -0.173579 +#> ds 5 m1 0 0.7 3.553e-15 -0.700000 0.8588 -0.815100 +#> ds 5 m1 1 3.0 3.204e+00 0.204414 0.8943 0.228572 +#> ds 5 m1 1 2.6 3.204e+00 0.604414 0.8943 0.675845 +#> ds 5 m1 3 5.1 8.586e+00 3.485889 1.0884 3.202858 +#> ds 5 m1 3 7.5 8.586e+00 1.085889 1.0884 0.997722 +#> ds 5 m1 7 16.5 1.612e+01 -0.376855 1.5211 -0.247743 +#> ds 5 m1 7 19.0 1.612e+01 -2.876855 1.5211 -1.891237 +#> ds 5 m1 14 22.9 2.267e+01 -0.228264 1.9633 -0.116267 +#> ds 5 m1 14 23.2 2.267e+01 -0.528264 1.9633 -0.269072 +#> ds 5 m1 28 22.2 2.468e+01 2.480178 2.1050 1.178211 +#> ds 5 m1 28 24.4 2.468e+01 0.280178 2.1050 0.133099 +#> ds 5 m1 60 15.5 1.860e+01 3.099615 1.6838 1.840794 +#> ds 5 m1 60 19.8 1.860e+01 -1.200385 1.6838 -0.712883 +#> ds 5 m1 90 14.9 1.326e+01 -1.636345 1.3433 -1.218195 +#> ds 5 m1 90 14.2 1.326e+01 -0.936345 1.3433 -0.697072 +#> ds 5 m1 120 10.9 9.348e+00 -1.551535 1.1258 -1.378133 +#> ds 5 m1 120 10.4 9.348e+00 -1.051535 1.1258 -0.934014
                                                                # }
                                                                diff --git a/man/nlme.Rd b/man/nlme.Rd index 307cca82..c367868b 100644 --- a/man/nlme.Rd +++ b/man/nlme.Rd @@ -8,7 +8,7 @@ \usage{ nlme_function(object) -mean_degparms(object, random = FALSE) +mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) nlme_data(object) } @@ -16,6 +16,13 @@ nlme_data(object) \item{object}{An mmkin row object containing several fits of the same model to different datasets} \item{random}{Should a list with fixed and random effects be returned?} + +\item{test_log_parms}{If TRUE, log parameters are only considered in +the mean calculations if their untransformed counterparts (most likely +rate constants) pass the t-test for significant difference from zero.} + +\item{conf.level}{Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.} } \value{ A function that can be used with nlme @@ -60,7 +67,7 @@ grouped_data <- nlme_data(f) nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be # visible to nlme and augPred when evaluation is done by -# pkgdown to generated the html docs. +# pkgdown to generate the html docs. assign("nlme_f", nlme_f, globalenv()) assign("grouped_data", grouped_data, globalenv()) diff --git a/man/saem.Rd b/man/saem.Rd index d5a8f17e..45f74e44 100644 --- a/man/saem.Rd +++ b/man/saem.Rd @@ -14,9 +14,12 @@ saem(object, ...) object, transformations = c("mkin", "saemix"), degparms_start = numeric(), + test_log_parms = FALSE, + conf.level = 0.6, solution_type = "auto", control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = FALSE), + fail_with_errors = TRUE, verbose = FALSE, quiet = FALSE, ... @@ -29,6 +32,7 @@ saemix_model( solution_type = "auto", transformations = c("mkin", "saemix"), degparms_start = numeric(), + test_log_parms = FALSE, verbose = FALSE, ... ) @@ -50,11 +54,22 @@ SFO or DFOP is used for the parent and there is either no metabolite or one.} \item{degparms_start}{Parameter values given as a named numeric vector will be used to override the starting values obtained from the 'mmkin' object.} +\item{test_log_parms}{If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using \link{mean_degparms}.} + +\item{conf.level}{Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.} + \item{solution_type}{Possibility to specify the solution type in case the automatic choice is not desired} \item{control}{Passed to \link[saemix:saemix]{saemix::saemix}} +\item{fail_with_errors}{Should a failure to compute standard errors +from the inverse of the Fisher Information Matrix be a failure?} + \item{verbose}{Should we print information about created objects of type \link[saemix:SaemixModel-class]{saemix::SaemixModel} and \link[saemix:SaemixData-class]{saemix::SaemixData}?} @@ -104,7 +119,7 @@ f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) -compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)) +compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) plot(f_saem_fomc$so, plot.type = "convergence") plot(f_saem_fomc$so, plot.type = "individual.fit") plot(f_saem_fomc$so, plot.type = "npde") @@ -112,7 +127,7 @@ plot(f_saem_fomc$so, plot.type = "vpc") f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ]) -compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)) +compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) diff --git a/test.log b/test.log index 2c77a113..5f50c623 100644 --- a/test.log +++ b/test.log @@ -6,39 +6,39 @@ Testing mkin ✔ | 2 | Export dataset for reading into CAKE ✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [1.0 s] ✔ | 4 | Calculation of FOCUS chi2 error levels [0.5 s] -✔ | 7 | Fitting the SFORB model [3.6 s] -✔ | 5 | Analytical solutions for coupled models [3.3 s] +✔ | 7 | Fitting the SFORB model [3.5 s] +✔ | 5 | Analytical solutions for coupled models [3.2 s] ✔ | 5 | Calculation of Akaike weights -✔ | 12 | Confidence intervals and p-values [1.0 s] -✔ | 14 | Error model fitting [4.6 s] +✔ | 12 | Confidence intervals and p-values [1.1 s] +✔ | 14 | Error model fitting [4.5 s] ✔ | 5 | Time step normalisation ✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s] ✔ | 1 | Fitting the logistic model [0.2 s] -✔ | 34 1 | Nonlinear mixed-effects models [40.8 s] +✔ | 35 1 | Nonlinear mixed-effects models [27.1 s] ──────────────────────────────────────────────────────────────────────────────── -Skip (test_mixed.R:150:3): saem results are reproducible for biphasic fits +Skip (test_mixed.R:161:3): saem results are reproducible for biphasic fits Reason: Fitting with saemix takes around 10 minutes when using deSolve ──────────────────────────────────────────────────────────────────────────────── ✔ | 2 | Test dataset classes mkinds and mkindsg -✔ | 1 | mkinfit features [0.5 s] -✔ | 10 | Special cases of mkinfit calls [0.6 s] -✔ | 8 | mkinmod model generation and printing [0.4 s] -✔ | 3 | Model predictions with mkinpredict [0.7 s] -✔ | 16 | Evaluations according to 2015 NAFTA guidance [3.1 s] -✔ | 9 | Nonlinear mixed-effects models [14.5 s] -✔ | 16 | Plotting [2.1 s] +✔ | 1 | mkinfit features [0.3 s] +✔ | 10 | Special cases of mkinfit calls [0.3 s] +✔ | 8 | mkinmod model generation and printing [0.2 s] +✔ | 3 | Model predictions with mkinpredict [0.2 s] +✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.8 s] +✔ | 9 | Nonlinear mixed-effects models with nlme [8.1 s] +✔ | 16 | Plotting [2.0 s] ✔ | 4 | Residuals extracted from mkinfit models -✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.7 s] -✔ | 4 | Summary [0.2 s] +✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s] +✔ | 4 | Summary [0.1 s] ✔ | 1 | Summaries of old mkinfit objects ✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s] -✔ | 9 | Hypothesis tests [8.4 s] +✔ | 9 | Hypothesis tests [8.3 s] ✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.5 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 92.7 s +Duration: 69.4 s ── Skipped tests ────────────────────────────────────────────────────────────── ● Fitting with saemix takes around 10 minutes when using deSolve (1) -[ FAIL 0 | WARN 0 | SKIP 1 | PASS 205 ] +[ FAIL 0 | WARN 0 | SKIP 1 | PASS 206 ] diff --git a/tests/figs/plotting/mixed-model-fit-for-nlme-object.svg b/tests/figs/plotting/mixed-model-fit-for-nlme-object.svg index 3eb2b0f8..db13b159 100644 --- a/tests/figs/plotting/mixed-model-fit-for-nlme-object.svg +++ b/tests/figs/plotting/mixed-model-fit-for-nlme-object.svg @@ -86,7 +86,7 @@ - + @@ -107,19 +107,19 @@ 80 100 120 - + - - - - - + + + + + 0 -20 -40 -60 -80 -100 +20 +40 +60 +80 +100 @@ -133,597 +133,597 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -734,30 +734,34 @@ - + - - - - - + + + + + 0 -20 -40 -60 -80 -100 - - - +20 +40 +60 +80 +100 + + + + - - --4 --2 + + + +-3 +-2 +-1 0 -2 -4 +1 +2 +3 @@ -772,582 +776,582 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/figs/plotting/mixed-model-fit-for-saem-object-with-mkin-transformations.svg b/tests/figs/plotting/mixed-model-fit-for-saem-object-with-mkin-transformations.svg index 0c2992d5..209b3dee 100644 --- a/tests/figs/plotting/mixed-model-fit-for-saem-object-with-mkin-transformations.svg +++ b/tests/figs/plotting/mixed-model-fit-for-saem-object-with-mkin-transformations.svg @@ -86,7 +86,7 @@ - + @@ -107,19 +107,19 @@ 80 100 120 - + - - - - - + + + + + 0 -20 -40 -60 -80 -100 +20 +40 +60 +80 +100 @@ -133,597 +133,597 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -734,30 +734,30 @@ - + - - - - - + + + + + 0 -20 -40 -60 -80 -100 - - - +20 +40 +60 +80 +100 + + + - - --4 --2 + + +-4 +-2 0 -2 -4 +2 +4 @@ -772,588 +772,588 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + @@ -1374,17 +1374,17 @@ 80 100 120 - + - - - - + + + + 0 -10 -20 -30 -40 +10 +20 +30 +40 @@ -1398,531 +1398,530 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -1933,28 +1932,28 @@ - + - - - - + + + + 0 -10 -20 -30 -40 - - - +10 +20 +30 +40 + + + - - --4 --2 + + +-4 +-2 0 -2 -4 +2 +4 @@ -1969,514 +1968,518 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/figs/plotting/mixed-model-fit-for-saem-object-with-saemix-transformations.svg b/tests/figs/plotting/mixed-model-fit-for-saem-object-with-saemix-transformations.svg index e65bc3bb..66d1d172 100644 --- a/tests/figs/plotting/mixed-model-fit-for-saem-object-with-saemix-transformations.svg +++ b/tests/figs/plotting/mixed-model-fit-for-saem-object-with-saemix-transformations.svg @@ -710,4 +710,9 @@ + + + + + diff --git a/tests/testthat/print_mmkin_biphasic_mixed.txt b/tests/testthat/print_mmkin_biphasic_mixed.txt index 11e11bfc..0b23fe58 100644 --- a/tests/testthat/print_mmkin_biphasic_mixed.txt +++ b/tests/testthat/print_mmkin_biphasic_mixed.txt @@ -8,7 +8,7 @@ d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) exp(-k2 * time))) * parent - k_m1 * m1 Data: -509 observations of 2 variable(s) grouped in 15 datasets +507 observations of 2 variable(s) grouped in 15 datasets object Status of individual fits: @@ -21,6 +21,6 @@ OK: No warnings Mean fitted parameters: parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 - 100.702 -5.347 -0.078 -2.681 -4.366 + 100.667 -5.378 -0.095 -2.743 -4.510 g_qlogis - -0.335 + -0.180 diff --git a/tests/testthat/print_nlme_biphasic.txt b/tests/testthat/print_nlme_biphasic.txt index f86bda76..f40d438d 100644 --- a/tests/testthat/print_nlme_biphasic.txt +++ b/tests/testthat/print_nlme_biphasic.txt @@ -9,21 +9,21 @@ d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) exp(-k2 * time))) * parent - k_m1 * m1 Data: -509 observations of 2 variable(s) grouped in 15 datasets +507 observations of 2 variable(s) grouped in 15 datasets -Log-likelihood: -1329 +Log-likelihood: -1326 Fixed effects: list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 - 100.43 -5.34 -0.08 -2.90 -4.34 + 100.7 -5.4 -0.1 -2.8 -4.5 g_qlogis - -0.19 + -0.1 Random effects: Formula: list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) Level: ds Structure: Diagonal parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis Residual -StdDev: 1 0.1 0.3 0.6 0.5 0.3 3 +StdDev: 1 0.03 0.3 0.3 0.2 0.3 3 diff --git a/tests/testthat/print_sfo_saem_1.txt b/tests/testthat/print_sfo_saem_1.txt index d341e9e7..0c0e32ce 100644 --- a/tests/testthat/print_sfo_saem_1.txt +++ b/tests/testthat/print_sfo_saem_1.txt @@ -3,19 +3,19 @@ Structural model: d_parent/dt = - k_parent * parent Data: -264 observations of 1 variable(s) grouped in 15 datasets +262 observations of 1 variable(s) grouped in 15 datasets Likelihood computed by importance sampling AIC BIC logLik - 1320 1324 -654 + 1310 1315 -649 Fitted parameters: estimate lower upper -parent_0 1e+02 98.78 1e+02 +parent_0 1e+02 98.87 1e+02 k_parent 4e-02 0.03 4e-02 -Var.parent_0 8e-01 -1.76 3e+00 -Var.k_parent 9e-02 0.03 2e-01 -a.1 9e-01 0.70 1e+00 -b.1 4e-02 0.03 4e-02 -SD.parent_0 9e-01 -0.57 2e+00 +Var.parent_0 1e+00 -1.72 5e+00 +Var.k_parent 1e-01 0.03 2e-01 +a.1 9e-01 0.75 1e+00 +b.1 5e-02 0.04 5e-02 +SD.parent_0 1e+00 -0.12 3e+00 SD.k_parent 3e-01 0.20 4e-01 diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R index 9229c198..96e865d4 100644 --- a/tests/testthat/setup_script.R +++ b/tests/testthat/setup_script.R @@ -106,6 +106,7 @@ const <- function(value) 2 set.seed(123456) SFO <- mkinmod(parent = mkinsub("SFO")) k_parent = rlnorm(n, log(0.03), log_sd) +set.seed(123456) ds_sfo <- lapply(1:n, function(i) { ds_mean <- mkinpredict(SFO, c(k_parent = k_parent[i]), c(parent = 100), sampling_times) @@ -118,6 +119,7 @@ fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8) fomc_parms <- as.matrix(data.frame( alpha = rlnorm(n, log(fomc_pop$alpha), 0.4), beta = rlnorm(n, log(fomc_pop$beta), 0.2))) +set.seed(123456) ds_fomc <- lapply(1:3, function(i) { ds_mean <- mkinpredict(FOMC, fomc_parms[i, ], c(parent = 100), sampling_times) @@ -131,6 +133,7 @@ dfop_parms <- as.matrix(data.frame( k1 = rlnorm(n, log(dfop_pop$k1), log_sd), k2 = rlnorm(n, log(dfop_pop$k2), log_sd), g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd)))) +set.seed(123456) ds_dfop <- lapply(1:n, function(i) { ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], c(parent = dfop_pop$parent_0), sampling_times) @@ -144,6 +147,7 @@ hs_parms <- as.matrix(data.frame( k1 = rlnorm(n, log(hs_pop$k1), log_sd), k2 = rlnorm(n, log(hs_pop$k2), log_sd), tb = rlnorm(n, log(hs_pop$tb), 0.1))) +set.seed(123456) ds_hs <- lapply(1:10, function(i) { ds_mean <- mkinpredict(HS, hs_parms[i, ], c(parent = hs_pop$parent_0), sampling_times) @@ -171,6 +175,7 @@ ds_biphasic_mean <- lapply(1:n_biphasic, c(parent = 100, m1 = 0), sampling_times) } ) +set.seed(123456) ds_biphasic <- lapply(ds_biphasic_mean, function(ds) { add_err(ds, sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), @@ -193,8 +198,18 @@ nlme_biphasic <- nlme(mmkin_biphasic) if (saemix_available) { sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix") - dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin") - dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix") + # With default control parameters, we do not get good results with mkin + # transformations here + dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", + control = list( + displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = FALSE, + rw.init = 1, nbiter.saemix = c(600, 100)) + ) + dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", + control = list( + displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = FALSE, + rw.init = 0.5, nbiter.saemix = c(600, 100)) + ) saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE) saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE) diff --git a/tests/testthat/summary_nlme_biphasic_s.txt b/tests/testthat/summary_nlme_biphasic_s.txt index 65aead62..86049775 100644 --- a/tests/testthat/summary_nlme_biphasic_s.txt +++ b/tests/testthat/summary_nlme_biphasic_s.txt @@ -13,19 +13,19 @@ d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) exp(-k2 * time))) * parent - k_m1 * m1 Data: -509 observations of 2 variable(s) grouped in 15 datasets +507 observations of 2 variable(s) grouped in 15 datasets Model predictions using solution type analytical -Fitted in test time 0 s using 3 iterations +Fitted in test time 0 s using 4 iterations Variance model: Constant variance Mean of starting values for individual parameters: parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 - 100.70 -5.35 -0.08 -2.68 -4.37 + 100.67 -5.38 -0.09 -2.74 -4.51 g_qlogis - -0.33 + -0.18 Fixed degradation parameter values: value type @@ -34,40 +34,40 @@ m1_0 0 state Results: AIC BIC logLik - 2683 2738 -1329 + 2678 2733 -1326 Optimised, transformed parameters with symmetric confidence intervals: - lower est. upper -parent_0 99.6 100.43 101.26 -log_k_m1 -5.5 -5.34 -5.18 -f_parent_qlogis -0.3 -0.08 0.09 -log_k1 -3.2 -2.90 -2.60 -log_k2 -4.6 -4.34 -4.07 -g_qlogis -0.5 -0.19 0.08 + lower est. upper +parent_0 99.8 100.7 101.62 +log_k_m1 -5.6 -5.4 -5.25 +f_parent_qlogis -0.3 -0.1 0.06 +log_k1 -3.0 -2.8 -2.57 +log_k2 -4.7 -4.5 -4.35 +g_qlogis -0.4 -0.1 0.17 Correlation: prnt_0 lg_k_1 f_prn_ log_k1 log_k2 -log_k_m1 -0.177 -f_parent_qlogis -0.164 0.385 -log_k1 0.108 -0.017 -0.025 -log_k2 0.036 0.054 0.008 0.096 -g_qlogis -0.068 -0.110 -0.030 -0.269 -0.267 +log_k_m1 -0.167 +f_parent_qlogis -0.145 0.380 +log_k1 0.170 0.005 -0.026 +log_k2 0.083 0.168 0.032 0.365 +g_qlogis -0.088 -0.170 -0.044 -0.472 -0.631 Random effects: Formula: list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) Level: ds Structure: Diagonal parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis Residual -StdDev: 1 0.1 0.3 0.6 0.5 0.3 3 +StdDev: 1 0.03 0.3 0.3 0.2 0.3 3 Backtransformed parameters with asymmetric confidence intervals: lower est. upper parent_0 1e+02 1e+02 1e+02 -k_m1 4e-03 5e-03 6e-03 +k_m1 4e-03 4e-03 5e-03 f_parent_to_m1 4e-01 5e-01 5e-01 -k1 4e-02 6e-02 7e-02 -k2 1e-02 1e-02 2e-02 +k1 5e-02 6e-02 8e-02 +k2 9e-03 1e-02 1e-02 g 4e-01 5e-01 5e-01 Resulting formation fractions: @@ -77,5 +77,5 @@ parent_sink 0.5 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -parent 26 131 39 13 53 -m1 144 479 NA NA NA +parent 25 150 45 11 63 +m1 154 512 NA NA NA diff --git a/tests/testthat/summary_saem_biphasic_s.txt b/tests/testthat/summary_saem_biphasic_s.txt index 1e0f1ccc..8dfae367 100644 --- a/tests/testthat/summary_saem_biphasic_s.txt +++ b/tests/testthat/summary_saem_biphasic_s.txt @@ -13,7 +13,7 @@ d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) exp(-k2 * time))) * parent - k_m1 * m1 Data: -509 observations of 2 variable(s) grouped in 15 datasets +507 observations of 2 variable(s) grouped in 15 datasets Model predictions using solution type analytical @@ -23,9 +23,9 @@ Variance model: Constant variance Mean of starting values for individual parameters: parent_0 k_m1 f_parent_to_m1 k1 k2 - 1.0e+02 4.8e-03 4.8e-01 6.8e-02 1.3e-02 + 1.0e+02 4.6e-03 4.8e-01 6.4e-02 1.1e-02 g - 4.2e-01 + 4.6e-01 Fixed degradation parameter values: None @@ -34,37 +34,37 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2645 2654 -1310 + 2702 2711 -1338 Optimised parameters: est. lower upper -parent_0 1.0e+02 99.627 1.0e+02 -k_m1 4.8e-03 0.004 5.6e-03 -f_parent_to_m1 4.8e-01 0.437 5.2e-01 -k1 6.5e-02 0.051 8.0e-02 -k2 1.2e-02 0.010 1.4e-02 -g 4.3e-01 0.362 5.0e-01 +parent_0 1.0e+02 1.0e+02 1.0e+02 +k_m1 4.7e-03 3.9e-03 5.6e-03 +f_parent_to_m1 4.8e-01 4.3e-01 5.2e-01 +k1 4.8e-02 3.1e-02 6.5e-02 +k2 1.3e-02 8.7e-03 1.7e-02 +g 5.0e-01 4.1e-01 5.8e-01 Correlation: prnt_0 k_m1 f_p__1 k1 k2 -k_m1 -0.156 -f_parent_to_m1 -0.157 0.372 -k1 0.159 0.000 -0.029 -k2 0.074 0.145 0.032 0.332 -g -0.072 -0.142 -0.044 -0.422 -0.570 +k_m1 -0.152 +f_parent_to_m1 -0.143 0.366 +k1 0.097 -0.014 -0.021 +k2 0.022 0.083 0.023 0.101 +g -0.084 -0.144 -0.044 -0.303 -0.364 Random effects: est. lower upper -SD.parent_0 1.14 0.251 2.03 -SD.k_m1 0.14 -0.073 0.35 -SD.f_parent_to_m1 0.29 0.176 0.41 -SD.k1 0.36 0.211 0.52 -SD.k2 0.18 0.089 0.27 -SD.g 0.32 0.098 0.53 +SD.parent_0 1.22 0.316 2.12 +SD.k_m1 0.15 -0.079 0.38 +SD.f_parent_to_m1 0.32 0.191 0.44 +SD.k1 0.66 0.416 0.90 +SD.k2 0.59 0.368 0.80 +SD.g 0.16 -0.373 0.70 Variance model: est. lower upper -a.1 2.7 2.5 2.9 +a.1 2.9 2.7 3 Resulting formation fractions: ff @@ -73,5 +73,5 @@ parent_sink 0.52 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -parent 25 145 44 11 58 -m1 145 481 NA NA NA +parent 26 127 38 14 54 +m1 146 485 NA NA NA diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R index 0eb1f0d5..5d15530b 100644 --- a/tests/testthat/test_mixed.R +++ b/tests/testthat/test_mixed.R @@ -53,20 +53,26 @@ test_that("Parent fits using saemix are correctly implemented", { expect_true(all(s_dfop_s2$confint_back[, "lower"] < dfop_pop)) expect_true(all(s_dfop_s2$confint_back[, "upper"] > dfop_pop)) + dfop_mmkin_means_trans_tested <- mean_degparms(mmkin_dfop_1, test_log_parms = TRUE) dfop_mmkin_means_trans <- apply(parms(mmkin_dfop_1, transformed = TRUE), 1, mean) + + dfop_mmkin_means_tested <- backtransform_odeparms(dfop_mmkin_means_trans_tested, mmkin_dfop_1$mkinmod) dfop_mmkin_means <- backtransform_odeparms(dfop_mmkin_means_trans, mmkin_dfop_1$mkinmod) - # We get < 22% deviations by averaging the transformed parameters + # We get < 20% deviations for parent_0 and k1 by averaging the transformed parameters + # If we average only parameters passing the t-test, the deviation for k2 is also < 20% rel_diff_mmkin <- (dfop_mmkin_means - dfop_pop) / dfop_pop - expect_true(all(rel_diff_mmkin < 0.22)) + rel_diff_mmkin_tested <- (dfop_mmkin_means_tested - dfop_pop) / dfop_pop + expect_true(all(rel_diff_mmkin[c("parent_0", "k1")] < 0.20)) + expect_true(all(rel_diff_mmkin_tested[c("parent_0", "k1", "k2")] < 0.20)) - # We get < 50% deviations with transformations made in mkin + # We get < 30% deviations with transformations made in mkin rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop expect_true(all(rel_diff_1 < 0.5)) - # We get < 12% deviations with transformations made in saemix + # We get < 20% deviations with transformations made in saemix rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop - expect_true(all(rel_diff_2 < 0.12)) + expect_true(all(rel_diff_2 < 0.2)) mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores) hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE) @@ -107,9 +113,14 @@ test_that("nlme results are reproducible to some degree", { expect_known_output(print(test_summary, digits = 1), "summary_nlme_biphasic_s.txt") + # k1 just fails the first test (lower bound of the ci), so we need to excluded it + dfop_no_k1 <- c("parent_0", "k_m1", "f_parent_to_m1", "k2", "g") + dfop_sfo_pop_no_k1 <- as.numeric(dfop_sfo_pop[dfop_no_k1]) dfop_sfo_pop <- as.numeric(dfop_sfo_pop) + ci_dfop_sfo_n <- summary(nlme_biphasic)$confint_back - expect_true(all(ci_dfop_sfo_n[, "lower"] < dfop_sfo_pop)) + + expect_true(all(ci_dfop_sfo_n[dfop_no_k1, "lower"] < dfop_sfo_pop_no_k1)) expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop)) }) @@ -155,4 +166,3 @@ test_that("saem results are reproducible for biphasic fits", { expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2])) expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1])) }) - diff --git a/tests/testthat/test_nlme.R b/tests/testthat/test_nlme.R index 989914da..a3bc9413 100644 --- a/tests/testthat/test_nlme.R +++ b/tests/testthat/test_nlme.R @@ -1,4 +1,4 @@ -context("Nonlinear mixed-effects models") +context("Nonlinear mixed-effects models with nlme") library(nlme) -- cgit v1.2.1 From cb112e53163f9dc63d439dba50ca051877d67a79 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 16 Mar 2021 16:47:17 +0100 Subject: Convenience option to set nbiter.saemix --- R/saem.R | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/R/saem.R b/R/saem.R index 460edede..184890f4 100644 --- a/R/saem.R +++ b/R/saem.R @@ -36,7 +36,9 @@ utils::globalVariables(c("predicted", "std")) #' from the inverse of the Fisher Information Matrix be a failure? #' @param quiet Should we suppress the messages saemix prints at the beginning #' and the end of the optimisation process? -#' @param control Passed to [saemix::saemix] +#' @param nbiter.saemix Convenience option to increase the number of +#' iterations +#' @param control Passed to [saemix::saemix]. #' @param \dots Further parameters passed to [saemix::saemixModel]. #' @return An S3 object of class 'saem.mmkin', containing the fitted #' [saemix::SaemixObject] as a list component named 'so'. The @@ -115,7 +117,9 @@ saem.mmkin <- function(object, test_log_parms = FALSE, conf.level = 0.6, solution_type = "auto", + nbiter.saemix = c(300, 100), control = list(displayProgress = FALSE, print = FALSE, + nbiter.saemix = nbiter.saemix, save = FALSE, save.graphs = FALSE), fail_with_errors = TRUE, verbose = FALSE, quiet = FALSE, ...) -- cgit v1.2.1 From 6d6dc7d53bf99b088af3488588574afc832fb7fe Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 19 Mar 2021 11:22:07 +0100 Subject: test_log_parms for plot.mixed.mmkin, roxygen run --- NEWS.md | 4 +++- R/mixed.mmkin.R | 3 ++- R/plot.mixed.mmkin.R | 8 +++++++- man/mixed.Rd | 4 ++++ man/plot.mixed.mmkin.Rd | 8 ++++++++ man/saem.Rd | 10 +++++++--- 6 files changed, 31 insertions(+), 6 deletions(-) diff --git a/NEWS.md b/NEWS.md index 5d0ea69a..a91523bd 100644 --- a/NEWS.md +++ b/NEWS.md @@ -12,7 +12,9 @@ - 'saem': generic function to fit saemix models using 'saemix_model' and 'saemix_data', with a generator 'saem.mmkin', summary and plot methods -- 'mean_degparms': New argument 'test_log_parms' that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to check more plausible parameters for 'saem' +- 'mean_degparms': New argument 'test_log_parms' that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for 'saem' + +- 'plot.mixed.mmkin': Gains arguments 'test_log_parms' and 'conf.level' # mkin 1.0.4 (Unreleased) diff --git a/R/mixed.mmkin.R b/R/mixed.mmkin.R index 7aa5edd5..682a9a34 100644 --- a/R/mixed.mmkin.R +++ b/R/mixed.mmkin.R @@ -3,6 +3,8 @@ #' @param object An [mmkin] row object #' @param method The method to be used #' @param \dots Currently not used +#' @return An object of class 'mixed.mmkin' which has the observed data in a +#' single dataframe which is convenient for plotting #' @examples #' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) #' n_biphasic <- 8 @@ -54,7 +56,6 @@ mixed.mmkin <- function(object, method = c("none"), ...) { if (nrow(object) > 1) stop("Only row objects allowed") method <- match.arg(method) - if (method == "default") method = c("naive", "nlme") ds_names <- colnames(object) res <- list(mmkin = object, mkinmod = object[[1]]$mkinmod) diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R index 21399496..f0682c10 100644 --- a/R/plot.mixed.mmkin.R +++ b/R/plot.mixed.mmkin.R @@ -10,6 +10,10 @@ utils::globalVariables("ds") #' `resplot = "time"`. #' @param pred_over Named list of alternative predictions as obtained #' from [mkinpredict] with a compatible [mkinmod]. +#' @param test_log_parms Passed to [mean_degparms] in the case of an +#' [mixed.mmkin] object +#' @param conf.level Passed to [mean_degparms] in the case of an +#' [mixed.mmkin] object #' @param rel.height.legend The relative height of the legend shown on top #' @param rel.height.bottom The relative height of the bottom plot row #' @param ymax Vector of maximum y axis values @@ -58,6 +62,8 @@ plot.mixed.mmkin <- function(x, xlim = range(x$data$time), resplot = c("predicted", "time"), pred_over = NULL, + test_log_parms = FALSE, + conf.level = 0.6, ymax = "auto", maxabs = "auto", ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), nrow.legend = ceiling((length(i) + 1) / ncol.legend), @@ -76,7 +82,7 @@ plot.mixed.mmkin <- function(x, backtransform = TRUE if (identical(class(x), "mixed.mmkin")) { - degparms_pop <- mean_degparms(x$mmkin) + degparms_pop <- mean_degparms(x$mmkin, test_log_parms = test_log_parms, conf.level = conf.level) degparms_tmp <- parms(x$mmkin, transformed = TRUE) degparms_i <- as.data.frame(t(degparms_tmp[setdiff(rownames(degparms_tmp), names(fit_1$errparms)), ])) diff --git a/man/mixed.Rd b/man/mixed.Rd index 8b00382d..95cae364 100644 --- a/man/mixed.Rd +++ b/man/mixed.Rd @@ -23,6 +23,10 @@ mixed(object, ...) \item{digits}{Number of digits to use for printing.} } +\value{ +An object of class 'mixed.mmkin' which has the observed data in a +single dataframe which is convenient for plotting +} \description{ Create a mixed effects model from an mmkin row object } diff --git a/man/plot.mixed.mmkin.Rd b/man/plot.mixed.mmkin.Rd index b1200729..bcab3e74 100644 --- a/man/plot.mixed.mmkin.Rd +++ b/man/plot.mixed.mmkin.Rd @@ -13,6 +13,8 @@ xlim = range(x$data$time), resplot = c("predicted", "time"), pred_over = NULL, + test_log_parms = FALSE, + conf.level = 0.6, ymax = "auto", maxabs = "auto", ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), @@ -49,6 +51,12 @@ predicted values?} \item{pred_over}{Named list of alternative predictions as obtained from \link{mkinpredict} with a compatible \link{mkinmod}.} +\item{test_log_parms}{Passed to \link{mean_degparms} in the case of an +\link{mixed.mmkin} object} + +\item{conf.level}{Passed to \link{mean_degparms} in the case of an +\link{mixed.mmkin} object} + \item{ymax}{Vector of maximum y axis values} \item{maxabs}{Maximum absolute value of the residuals. This is used for the diff --git a/man/saem.Rd b/man/saem.Rd index 45f74e44..f462f405 100644 --- a/man/saem.Rd +++ b/man/saem.Rd @@ -17,8 +17,9 @@ saem(object, ...) test_log_parms = FALSE, conf.level = 0.6, solution_type = "auto", - control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = - FALSE), + nbiter.saemix = c(300, 100), + control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix, + save = FALSE, save.graphs = FALSE), fail_with_errors = TRUE, verbose = FALSE, quiet = FALSE, @@ -65,7 +66,10 @@ for parameter that are tested if requested by 'test_log_parms'.} \item{solution_type}{Possibility to specify the solution type in case the automatic choice is not desired} -\item{control}{Passed to \link[saemix:saemix]{saemix::saemix}} +\item{nbiter.saemix}{Convenience option to increase the number of +iterations} + +\item{control}{Passed to \link[saemix:saemix]{saemix::saemix}.} \item{fail_with_errors}{Should a failure to compute standard errors from the inverse of the Fisher Information Matrix be a failure?} -- cgit v1.2.1 From 34d1c5f23edfb60548bc5a9dd99c2f3af92acef1 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 20 Mar 2021 21:26:40 +0100 Subject: Fix mkin calculation of saemix residuals --- R/saem.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/saem.R b/R/saem.R index 184890f4..6f28a47a 100644 --- a/R/saem.R +++ b/R/saem.R @@ -164,7 +164,7 @@ saem.mmkin <- function(object, xidep = return_data[c("time", "name")]) return_data <- transform(return_data, - residual = predicted - value, + residual = value - predicted, std = sigma_twocomp(predicted, f_saemix@results@respar[1], f_saemix@results@respar[2])) return_data <- transform(return_data, -- cgit v1.2.1 From c6eb6b2bb598002523c3d34d71b0e4a99671ccd6 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 9 Jun 2021 16:53:31 +0200 Subject: Rudimentary support for setting up nlmixr models - All degradation models are specified as ODE models. This appears to be fast enough - Error models are being translated to nlmixr as close to the mkin error model as possible. When using the 'saem' backend, it appears not to be possible to use the same error model for more than one observed variable - No support yet for models with parallel formation of metabolites, where the ilr transformation is used in mkin per default - There is a bug in nlmixr which appears to be triggered if the data are not balanced, see nlmixrdevelopment/nlmixr#530 - There is a print and a plot method, the summary method is not finished --- .travis.yml | 2 + DESCRIPTION | 9 +- NAMESPACE | 8 + NEWS.md | 14 +- R/mean_degparms.R | 61 ++++++ R/nlme.R | 55 ------ R/nlme.mmkin.R | 2 +- R/nlmixr.R | 467 ++++++++++++++++++++++++++++++++++++++++++++ R/plot.mixed.mmkin.R | 17 ++ R/saem.R | 6 + R/summary.nlmixr.mmkin.R | 250 ++++++++++++++++++++++++ build.log | 2 +- check.log | 68 +++++-- man/mean_degparms.Rd | 27 +++ man/nlme.Rd | 17 -- man/nlme.mmkin.Rd | 2 +- man/nlmixr.mmkin.Rd | 188 ++++++++++++++++++ man/plot.mixed.mmkin.Rd | 5 + man/summary.nlmixr.mmkin.Rd | 100 ++++++++++ man/summary.saem.mmkin.Rd | 24 +-- 20 files changed, 1209 insertions(+), 115 deletions(-) create mode 100644 R/mean_degparms.R create mode 100644 R/nlmixr.R create mode 100644 R/summary.nlmixr.mmkin.R create mode 100644 man/mean_degparms.Rd create mode 100644 man/nlmixr.mmkin.Rd create mode 100644 man/summary.nlmixr.mmkin.Rd diff --git a/.travis.yml b/.travis.yml index 6c03b451..60e37230 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,6 +11,8 @@ addons: cache: packages repos: CRAN: https://cloud.r-project.org +r_github_packages: + - jranke/saemixextension@warp_combined script: - R CMD build . - R CMD check --no-tests mkin_*.tar.gz diff --git a/DESCRIPTION b/DESCRIPTION index 48aaf81f..5b90ef37 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data -Version: 1.0.4.9000 -Date: 2021-04-21 +Version: 1.0.5 +Date: 2021-06-03 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "jranke@uni-bremen.de", @@ -18,11 +18,10 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006, note that no warranty is implied for correctness of results or fitness for a particular purpose. Depends: R (>= 2.15.1), parallel -Imports: stats, graphics, methods, deSolve, R6, inline (>= 0.3.17), numDeriv, - lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix (>= 3.1.9000) +Imports: stats, graphics, methods, deSolve, R6, inline (>= 0.3.19), numDeriv, + lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix, nlmixr Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr, benchmarkme, tibble, stats4 -Remotes: github::saemixdevelopment/saemixextension License: GPL LazyLoad: yes LazyData: yes diff --git a/NAMESPACE b/NAMESPACE index f2497283..bb4f5f92 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -16,6 +16,7 @@ S3method(mixed,mmkin) S3method(mkinpredict,mkinfit) S3method(mkinpredict,mkinmod) S3method(nlme,mmkin) +S3method(nlmixr,mmkin) S3method(nobs,mkinfit) S3method(parms,mkinfit) S3method(parms,mmkin) @@ -30,6 +31,7 @@ S3method(print,mkinmod) S3method(print,mmkin) S3method(print,nafta) S3method(print,nlme.mmkin) +S3method(print,nlmixr.mmkin) S3method(print,saem.mmkin) S3method(print,summary.mkinfit) S3method(print,summary.nlme.mmkin) @@ -38,6 +40,7 @@ S3method(residuals,mkinfit) S3method(saem,mmkin) S3method(summary,mkinfit) S3method(summary,nlme.mmkin) +S3method(summary,nlmixr.mmkin) S3method(summary,saem.mmkin) S3method(update,mkinfit) S3method(update,mmkin) @@ -86,6 +89,8 @@ export(nafta) export(nlme) export(nlme_data) export(nlme_function) +export(nlmixr_data) +export(nlmixr_model) export(parms) export(plot_err) export(plot_res) @@ -102,6 +107,8 @@ importFrom(R6,R6Class) importFrom(grDevices,dev.cur) importFrom(lmtest,lrtest) importFrom(methods,signature) +importFrom(nlmixr,nlmixr) +importFrom(nlmixr,tableControl) importFrom(parallel,detectCores) importFrom(parallel,mclapply) importFrom(parallel,parLapply) @@ -135,4 +142,5 @@ importFrom(stats,shapiro.test) importFrom(stats,update) importFrom(stats,vcov) importFrom(utils,getFromNamespace) +importFrom(utils,packageVersion) importFrom(utils,write.table) diff --git a/NEWS.md b/NEWS.md index d80e152c..03098106 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,18 +1,12 @@ -# mkin 1.0.4.9000 - -## General - -- Switch to a versioning scheme where the fourth version component indicates development versions +# mkin 1.0.5 ## Mixed-effects models -- Reintroduce the interface to the current development version of saemix, in particular: - -- 'saemix_model' and 'saemix_data': Helper functions to set up nonlinear mixed-effects models for mmkin row objects +- Introduce an interface to nlmixr, supporting estimation methods 'saem' and 'focei': S3 method 'nlmixr.mmkin' using the helper functions 'nlmixr_model' and 'nlmixr_data' to set up nlmixr models for mmkin row objects, with summary and plot methods. -- 'saem': generic function to fit saemix models using 'saemix_model' and 'saemix_data', with a generator 'saem.mmkin', summary and plot methods +- Reintroduce the interface to current development versions (not on CRAN) of saemix, in particular the generic function 'saem' with a generator 'saem.mmkin', currently using 'saemix_model' and 'saemix_data', summary and plot methods -- 'mean_degparms': New argument 'test_log_parms' that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for 'saem' +- 'mean_degparms': New argument 'test_log_parms' that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends - 'plot.mixed.mmkin': Gains arguments 'test_log_parms' and 'conf.level' diff --git a/R/mean_degparms.R b/R/mean_degparms.R new file mode 100644 index 00000000..ec7f4342 --- /dev/null +++ b/R/mean_degparms.R @@ -0,0 +1,61 @@ +#' Calculate mean degradation parameters for an mmkin row object +#' +#' @return If random is FALSE (default), a named vector containing mean values +#' of the fitted degradation model parameters. If random is TRUE, a list with +#' fixed and random effects, in the format required by the start argument of +#' nlme for the case of a single grouping variable ds. +#' @param random Should a list with fixed and random effects be returned? +#' @param test_log_parms If TRUE, log parameters are only considered in +#' the mean calculations if their untransformed counterparts (most likely +#' rate constants) pass the t-test for significant difference from zero. +#' @param conf.level Possibility to adjust the required confidence level +#' for parameter that are tested if requested by 'test_log_parms'. +#' @export +mean_degparms <- function(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) +{ + if (nrow(object) > 1) stop("Only row objects allowed") + parm_mat_trans <- sapply(object, parms, transformed = TRUE) + + if (test_log_parms) { + parm_mat_dim <- dim(parm_mat_trans) + parm_mat_dimnames <- dimnames(parm_mat_trans) + + log_parm_trans_names <- grep("^log_", rownames(parm_mat_trans), value = TRUE) + log_parm_names <- gsub("^log_", "", log_parm_trans_names) + + t_test_back_OK <- matrix( + sapply(object, function(o) { + suppressWarnings(summary(o)$bpar[log_parm_names, "Pr(>t)"] < (1 - conf.level)) + }), nrow = length(log_parm_names)) + rownames(t_test_back_OK) <- log_parm_trans_names + + parm_mat_trans_OK <- parm_mat_trans + for (trans_parm in log_parm_trans_names) { + parm_mat_trans_OK[trans_parm, ] <- ifelse(t_test_back_OK[trans_parm, ], + parm_mat_trans[trans_parm, ], NA) + } + } else { + parm_mat_trans_OK <- parm_mat_trans + } + + mean_degparm_names <- setdiff(rownames(parm_mat_trans), names(object[[1]]$errparms)) + degparm_mat_trans <- parm_mat_trans[mean_degparm_names, , drop = FALSE] + degparm_mat_trans_OK <- parm_mat_trans_OK[mean_degparm_names, , drop = FALSE] + + fixed <- apply(degparm_mat_trans_OK, 1, mean, na.rm = TRUE) + if (random) { + random <- t(apply(degparm_mat_trans[mean_degparm_names, , drop = FALSE], 2, function(column) column - fixed)) + # If we only have one parameter, apply returns a vector so we get a single row + if (nrow(degparm_mat_trans) == 1) random <- t(random) + rownames(random) <- levels(nlme_data(object)$ds) + + # For nlmixr we can specify starting values for standard deviations eta, and + # we ignore uncertain parameters if test_log_parms is FALSE + eta <- apply(degparm_mat_trans_OK, 1, sd, na.rm = TRUE) + + return(list(fixed = fixed, random = list(ds = random), eta = eta)) + } else { + return(fixed) + } +} + diff --git a/R/nlme.R b/R/nlme.R index d235a094..8762f137 100644 --- a/R/nlme.R +++ b/R/nlme.R @@ -124,61 +124,6 @@ nlme_function <- function(object) { return(model_function) } -#' @rdname nlme -#' @return If random is FALSE (default), a named vector containing mean values -#' of the fitted degradation model parameters. If random is TRUE, a list with -#' fixed and random effects, in the format required by the start argument of -#' nlme for the case of a single grouping variable ds. -#' @param random Should a list with fixed and random effects be returned? -#' @param test_log_parms If TRUE, log parameters are only considered in -#' the mean calculations if their untransformed counterparts (most likely -#' rate constants) pass the t-test for significant difference from zero. -#' @param conf.level Possibility to adjust the required confidence level -#' for parameter that are tested if requested by 'test_log_parms'. -#' @export -mean_degparms <- function(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) -{ - if (nrow(object) > 1) stop("Only row objects allowed") - parm_mat_trans <- sapply(object, parms, transformed = TRUE) - - if (test_log_parms) { - parm_mat_dim <- dim(parm_mat_trans) - parm_mat_dimnames <- dimnames(parm_mat_trans) - - log_parm_trans_names <- grep("^log_", rownames(parm_mat_trans), value = TRUE) - log_parm_names <- gsub("^log_", "", log_parm_trans_names) - - t_test_back_OK <- matrix( - sapply(object, function(o) { - suppressWarnings(summary(o)$bpar[log_parm_names, "Pr(>t)"] < (1 - conf.level)) - }), nrow = length(log_parm_names)) - rownames(t_test_back_OK) <- log_parm_trans_names - - parm_mat_trans_OK <- parm_mat_trans - for (trans_parm in log_parm_trans_names) { - parm_mat_trans_OK[trans_parm, ] <- ifelse(t_test_back_OK[trans_parm, ], - parm_mat_trans[trans_parm, ], NA) - } - } else { - parm_mat_trans_OK <- parm_mat_trans - } - - mean_degparm_names <- setdiff(rownames(parm_mat_trans), names(object[[1]]$errparms)) - degparm_mat_trans <- parm_mat_trans[mean_degparm_names, , drop = FALSE] - degparm_mat_trans_OK <- parm_mat_trans_OK[mean_degparm_names, , drop = FALSE] - - fixed <- apply(degparm_mat_trans_OK, 1, mean, na.rm = TRUE) - if (random) { - random <- t(apply(degparm_mat_trans[mean_degparm_names, , drop = FALSE], 2, function(column) column - fixed)) - # If we only have one parameter, apply returns a vector so we get a single row - if (nrow(degparm_mat_trans) == 1) random <- t(random) - rownames(random) <- levels(nlme_data(object)$ds) - return(list(fixed = fixed, random = list(ds = random))) - } else { - return(fixed) - } -} - #' @rdname nlme #' @importFrom purrr map_dfr #' @return A \code{\link{groupedData}} object diff --git a/R/nlme.mmkin.R b/R/nlme.mmkin.R index 306600c6..a1aa32e5 100644 --- a/R/nlme.mmkin.R +++ b/R/nlme.mmkin.R @@ -135,7 +135,7 @@ nlme.mmkin <- function(model, data = "auto", function(el) eval(parse(text = paste(el, 1, sep = "~")))), random = pdDiag(fixed), groups, - start = mean_degparms(model, random = TRUE), + start = mean_degparms(model, random = TRUE, test_log_parms = TRUE), correlation = NULL, weights = NULL, subset, method = c("ML", "REML"), na.action = na.fail, naPattern, diff --git a/R/nlmixr.R b/R/nlmixr.R new file mode 100644 index 00000000..223b23a1 --- /dev/null +++ b/R/nlmixr.R @@ -0,0 +1,467 @@ +utils::globalVariables(c("predicted", "std")) + +#' Fit nonlinear mixed models using nlmixr +#' +#' This function uses [nlmixr::nlmixr()] as a backend for fitting nonlinear mixed +#' effects models created from [mmkin] row objects using the Stochastic Approximation +#' Expectation Maximisation algorithm (SAEM). +#' +#' An mmkin row object is essentially a list of mkinfit objects that have been +#' obtained by fitting the same model to a list of datasets using [mkinfit]. +#' +#' @importFrom nlmixr nlmixr tableControl +#' @param object An [mmkin] row object containing several fits of the same +#' [mkinmod] model to different datasets +#' @param est Estimation method passed to [nlmixr::nlmixr] +#' @param degparms_start Parameter values given as a named numeric vector will +#' be used to override the starting values obtained from the 'mmkin' object. +#' @param test_log_parms If TRUE, an attempt is made to use more robust starting +#' values for population parameters fitted as log parameters in mkin (like +#' rate constants) by only considering rate constants that pass the t-test +#' when calculating mean degradation parameters using [mean_degparms]. +#' @param conf.level Possibility to adjust the required confidence level +#' for parameter that are tested if requested by 'test_log_parms'. +#' @param solution_type Possibility to specify the solution type in case the +#' automatic choice is not desired +#' @param control Passed to [nlmixr::nlmixr]. +#' @param \dots Passed to [nlmixr_model] +#' @return An S3 object of class 'nlmixr.mmkin', containing the fitted +#' [nlmixr::nlmixr] object as a list component named 'nm'. The +#' object also inherits from 'mixed.mmkin'. +#' @seealso [summary.nlmixr.mmkin] [plot.mixed.mmkin] +#' @examples +#' ds <- lapply(experimental_data_for_UBA_2019[6:10], +#' function(x) subset(x$data[c("name", "time", "value")])) +#' names(ds) <- paste("Dataset", 6:10) +#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) +#' f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", +#' cores = 1, quiet = TRUE) +#' +#' f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem") +#' f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei") +#' +#' f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem") +#' f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei") +#' +#' f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem") +#' f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei") +#' +#' f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem") +#' f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei") +#' +#' f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem") +#' f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei") +#' +#' AIC( +#' f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm, +#' f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm, +#' f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm, +#' f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm, +#' f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm) +#' +#' AIC(nlme(f_mmkin_parent["FOMC", ])) +#' AIC(nlme(f_mmkin_parent["HS", ])) +#' +#' # nlme is comparable to nlmixr with focei, saem finds a better +#' # solution, the two-component error model does not improve it +#' plot(f_nlmixr_fomc_saem) +#' +#' \dontrun{ +#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), +#' A1 = mkinsub("SFO")) +#' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), +#' A1 = mkinsub("SFO")) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), +#' A1 = mkinsub("SFO")) +#' +#' f_mmkin_const <- mmkin(list( +#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), +#' ds, quiet = TRUE, error_model = "const") +#' f_mmkin_obs <- mmkin(list( +#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), +#' ds, quiet = TRUE, error_model = "obs") +#' f_mmkin_tc <- mmkin(list( +#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), +#' ds, quiet = TRUE, error_model = "tc") +#' +#' # A single constant variance is currently only possible with est = 'focei' in nlmixr +#' f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei") +#' f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") +#' f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") +#' +#' # Variance by variable is supported by 'saem' and 'focei' +#' f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem") +#' f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") +#' f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") +#' f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") +#' +#' # Identical two-component error for all variables is only possible with +#' # est = 'focei' in nlmixr +#' f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei") +#' f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") +#' +#' # Two-component error by variable is possible with both estimation methods +#' # Variance by variable is supported by 'saem' and 'focei' +#' f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem", +#' error_model = "obs_tc") +#' f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", +#' error_model = "obs_tc") +#' f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", +#' error_model = "obs_tc") +#' f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", +#' error_model = "obs_tc") +#' +#' AIC( +#' f_nlmixr_sfo_sfo_focei_const$nm, +#' f_nlmixr_fomc_sfo_focei_const$nm, +#' f_nlmixr_dfop_sfo_focei_const$nm, +#' f_nlmixr_fomc_sfo_saem_obs$nm, +#' f_nlmixr_fomc_sfo_focei_obs$nm, +#' f_nlmixr_dfop_sfo_saem_obs$nm, +#' f_nlmixr_dfop_sfo_focei_obs$nm, +#' f_nlmixr_fomc_sfo_focei_tc$nm, +#' f_nlmixr_dfop_sfo_focei_tc$nm, +#' f_nlmixr_fomc_sfo_saem_obs_tc$nm, +#' f_nlmixr_fomc_sfo_focei_obs_tc$nm, +#' f_nlmixr_dfop_sfo_saem_obs_tc$nm, +#' f_nlmixr_dfop_sfo_focei_obs_tc$nm +#' ) +#' # Currently, FOMC-SFO with two-component error by variable fitted by focei gives the +#' # lowest AIC +#' plot(f_nlmixr_fomc_sfo_focei_obs_tc) +#' summary(f_nlmixr_fomc_sfo_focei_obs_tc) +#' } +#' @export +nlmixr.mmkin <- function(object, data = NULL, + est = NULL, control = list(), + table = tableControl(), + error_model = object[[1]]$err_mod, + test_log_parms = TRUE, + conf.level = 0.6, + ..., + save = NULL, + envir = parent.frame() +) +{ + m_nlmixr <- nlmixr_model(object, est = est, + error_model = error_model, add_attributes = TRUE, + test_log_parms = test_log_parms, conf.level = conf.level) + d_nlmixr <- nlmixr_data(object) + + mean_dp_start <- attr(m_nlmixr, "mean_dp_start") + mean_ep_start <- attr(m_nlmixr, "mean_ep_start") + + attributes(m_nlmixr) <- NULL + + fit_time <- system.time({ + f_nlmixr <- nlmixr(m_nlmixr, d_nlmixr, est = est) + }) + + if (is.null(f_nlmixr$CMT)) { + nlmixr_df <- as.data.frame(f_nlmixr[c("ID", "TIME", "DV", "IPRED", "IRES", "IWRES")]) + nlmixr_df$CMT <- as.character(object[[1]]$data$variable[1]) + } else { + nlmixr_df <- as.data.frame(f_nlmixr[c("ID", "TIME", "DV", "CMT", "IPRED", "IRES", "IWRES")]) + } + + return_data <- nlmixr_df %>% + dplyr::transmute(ds = ID, name = CMT, time = TIME, value = DV, + predicted = IPRED, residual = IRES, + std = IRES/IWRES, standardized = IWRES) + + bparms_optim <- backtransform_odeparms(f_nlmixr$theta, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions) + + result <- list( + mkinmod = object[[1]]$mkinmod, + mmkin = object, + transform_rates = object[[1]]$transform_rates, + transform_fractions = object[[1]]$transform_fractions, + nm = f_nlmixr, + est = est, + time = fit_time, + mean_dp_start = mean_dp_start, + mean_ep_start = mean_ep_start, + bparms.optim = bparms_optim, + bparms.fixed = object[[1]]$bparms.fixed, + data = return_data, + err_mod = error_model, + date.fit = date(), + nlmixrversion = as.character(utils::packageVersion("nlmixr")), + mkinversion = as.character(utils::packageVersion("mkin")), + Rversion = paste(R.version$major, R.version$minor, sep=".") + ) + + class(result) <- c("nlmixr.mmkin", "mixed.mmkin") + return(result) +} + +#' @export +#' @rdname nlmixr.mmkin +#' @param x An nlmixr.mmkin object to print +#' @param digits Number of digits to use for printing +print.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) { + cat("Kinetic nonlinear mixed-effects model fit by", x$est, "using nlmixr") + cat("\nStructural model:\n") + diffs <- x$mmkin[[1]]$mkinmod$diffs + nice_diffs <- gsub("^(d.*) =", "\\1/dt =", diffs) + writeLines(strwrap(nice_diffs, exdent = 11)) + cat("\nData:\n") + cat(nrow(x$data), "observations of", + length(unique(x$data$name)), "variable(s) grouped in", + length(unique(x$data$ds)), "datasets\n") + + cat("\nLikelihood:\n") + print(data.frame( + AIC = AIC(x$nm), + BIC = BIC(x$nm), + logLik = logLik(x$nm), + row.names = " "), digits = digits) + + cat("\nFitted parameters:\n") + print(x$nm$parFixed, digits = digits) + + invisible(x) +} + +#' @rdname nlmixr.mmkin +#' @return An function defining a model suitable for fitting with [nlmixr::nlmixr]. +#' @export +nlmixr_model <- function(object, + est = c("saem", "focei"), + degparms_start = "auto", + test_log_parms = FALSE, conf.level = 0.6, + error_model = object[[1]]$err_mod, add_attributes = FALSE) +{ + if (nrow(object) > 1) stop("Only row objects allowed") + est = match.arg(est) + + mkin_model <- object[[1]]$mkinmod + obs_vars <- names(mkin_model$spec) + + if (error_model == object[[1]]$err_mod) { + + if (length(object[[1]]$mkinmod$spec) > 1 & est == "saem") { + if (error_model == "const") { + message( + "Constant variance for more than one variable is not supported for est = 'saem'\n", + "Changing the error model to 'obs' (variance by observed variable)") + error_model <- "obs" + } + if (error_model =="tc") { + message( + "With est = 'saem', a different error model is required for each observed variable", + "Changing the error model to 'obs_tc' (Two-component error for each observed variable)") + error_model <- "obs_tc" + } + } + } + + degparms_mmkin <- mean_degparms(object, + test_log_parms = test_log_parms, + conf.level = conf.level, random = TRUE) + + degparms_optim <- degparms_mmkin$fixed + + degparms_optim <- degparms_mmkin$fixed + + if (degparms_start[1] == "auto") { + degparms_start <- degparms_optim + } + degparms_fixed <- object[[1]]$bparms.fixed + + degparms_optim_back_names <- names(backtransform_odeparms(degparms_optim, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions)) + names(degparms_optim_back_names) <- names(degparms_optim) + + odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE) + odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE) + + odeparms_fixed_names <- setdiff(names(degparms_fixed), odeini_fixed_parm_names) + odeparms_fixed <- degparms_fixed[odeparms_fixed_names] + + odeini_fixed <- degparms_fixed[odeini_fixed_parm_names] + names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names) + + # Definition of the model function + f <- function(){} + + ini_block <- "ini({" + + # Initial values for all degradation parameters + for (parm_name in names(degparms_optim)) { + # As initials for state variables are not transformed, + # we need to modify the name here as we want to + # use the original name in the model block + ini_block <- paste0( + ini_block, + parm_name, " = ", + as.character(degparms_start[parm_name]), + "\n", + "eta.", parm_name, " ~ ", + as.character(degparms_mmkin$eta[parm_name]), + "\n" + ) + } + + # Error model parameters + error_model_mkin <- object[[1]]$err_mod + + errparm_names_mkin <- names(object[[1]]$errparms) + errparms_mkin <- sapply(errparm_names_mkin, function(parm_name) { + mean(sapply(object, function(x) x$errparms[parm_name])) + }) + + sigma_tc_mkin <- errparms_ini <- errparms_mkin[1] + + mean(unlist(sapply(object, function(x) x$data$observed)), na.rm = TRUE) * + errparms_mkin[2] + + if (error_model == "const") { + if (error_model_mkin == "tc") { + errparms_ini <- sigma_tc_mkin + } else { + errparms_ini <- mean(errparms_mkin) + } + names(errparms_ini) <- "sigma" + } + + if (error_model == "obs") { + errparms_ini <- switch(error_model_mkin, + const = rep(errparms_mkin["sigma"], length(obs_vars)), + obs = errparms_mkin, + tc = sigma_tc_mkin) + names(errparms_ini) <- paste0("sigma_", obs_vars) + } + + if (error_model == "tc") { + if (error_model_mkin != "tc") { + stop("Not supported") + } else { + errparms_ini <- errparms_mkin + } + } + + if (error_model == "obs_tc") { + if (error_model_mkin != "tc") { + stop("Not supported") + } else { + errparms_ini <- rep(errparms_mkin, length(obs_vars)) + names(errparms_ini) <- paste0( + rep(names(errparms_mkin), length(obs_vars)), + "_", + rep(obs_vars, each = 2)) + } + } + + for (parm_name in names(errparms_ini)) { + ini_block <- paste0( + ini_block, + parm_name, " = ", + as.character(errparms_ini[parm_name]), + "\n" + ) + } + + ini_block <- paste0(ini_block, "})") + + body(f)[2] <- parse(text = ini_block) + + model_block <- "model({" + + # Population initial values for the ODE state variables + for (parm_name in odeini_optim_parm_names) { + model_block <- paste0( + model_block, + parm_name, "_model = ", + parm_name, " + eta.", parm_name, "\n", + gsub("(.*)_0", "\\1(0)", parm_name), " = ", parm_name, "_model\n") + } + + # Population initial values for log rate constants + for (parm_name in grep("^log_", names(degparms_optim), value = TRUE)) { + model_block <- paste0( + model_block, + gsub("^log_", "", parm_name), " = ", + "exp(", parm_name, " + eta.", parm_name, ")\n") + } + + # Population initial values for logit transformed parameters + for (parm_name in grep("_qlogis$", names(degparms_optim), value = TRUE)) { + model_block <- paste0( + model_block, + degparms_optim_back_names[parm_name], " = ", + "expit(", parm_name, " + eta.", parm_name, ")\n") + } + + # Differential equations + model_block <- paste0( + model_block, + paste( + gsub("d_(.*) =", "d/dt(\\1) =", mkin_model$diffs), + collapse = "\n"), + "\n" + ) + + # Error model + if (error_model == "const") { + model_block <- paste0(model_block, + paste(paste0(obs_vars, " ~ add(sigma)"), collapse = "\n")) + } + if (error_model == "obs") { + model_block <- paste0(model_block, + paste(paste0(obs_vars, " ~ add(sigma_", obs_vars, ")"), collapse = "\n"), + "\n") + } + if (error_model == "tc") { + model_block <- paste0(model_block, + paste(paste0(obs_vars, " ~ add(sigma_low) + prop(rsd_high)"), collapse = "\n"), + "\n") + } + if (error_model == "obs_tc") { + model_block <- paste0(model_block, + paste( + paste0(obs_vars, " ~ add(sigma_low_", obs_vars, ") + ", + "prop(rsd_high_", obs_vars, ")"), collapse = "\n"), + "\n") + } + + model_block <- paste0(model_block, "})") + + body(f)[3] <- parse(text = model_block) + + if (add_attributes) { + attr(f, "mean_dp_start") <- degparms_optim + attr(f, "mean_ep_start") <- errparms_ini + } + + return(f) +} + +#' @rdname nlmixr.mmkin +#' @return An dataframe suitable for use with [nlmixr::nlmixr] +#' @export +nlmixr_data <- function(object, ...) { + if (nrow(object) > 1) stop("Only row objects allowed") + d <- lapply(object, function(x) x$data) + compartment_map <- 1:length(object[[1]]$mkinmod$spec) + names(compartment_map) <- names(object[[1]]$mkinmod$spec) + ds_names <- colnames(object) + + ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")]) + names(ds_list) <- ds_names + ds_nlmixr <- purrr::map_dfr(ds_list, function(x) x, .id = "ds") + ds_nlmixr$variable <- as.character(ds_nlmixr$variable) + ds_nlmixr_renamed <- data.frame( + ID = ds_nlmixr$ds, + TIME = ds_nlmixr$time, + AMT = 0, EVID = 0, + CMT = ds_nlmixr$variable, + DV = ds_nlmixr$observed, + stringsAsFactors = FALSE) + + return(ds_nlmixr_renamed) +} diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R index f0682c10..1ac62b07 100644 --- a/R/plot.mixed.mmkin.R +++ b/R/plot.mixed.mmkin.R @@ -40,12 +40,17 @@ utils::globalVariables("ds") #' #' # For this fit we need to increase pnlsMaxiter, and we increase the #' # tolerance in order to speed up the fit for this example evaluation +#' # It still takes 20 seconds to run #' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) #' plot(f_nlme) #' #' f_saem <- saem(f, transformations = "saemix") #' plot(f_saem) #' +#' f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs") +#' f_nlmix <- nlmix(f_obs) +#' plot(f_nlmix) +#' #' # We can overlay the two variants if we generate predictions #' pred_nlme <- mkinpredict(dfop_sfo, #' f_nlme$bparms.optim[-1], @@ -109,6 +114,18 @@ plot.mixed.mmkin <- function(x, names(degparms_pop) <- degparms_i_names } + if (inherits(x, "nlmixr.mmkin")) { + eta_i <- random.effects(x$nm)[-1] + names(eta_i) <- gsub("^eta.", "", names(eta_i)) + degparms_i <- eta_i + degparms_pop <- x$nm$theta + for (parm_name in names(degparms_i)) { + degparms_i[parm_name] <- eta_i[parm_name] + degparms_pop[parm_name] + } + residual_type = ifelse(standardized, "standardized", "residual") + residuals <- x$data[[residual_type]] + } + degparms_fixed <- fit_1$fixed$value names(degparms_fixed) <- rownames(fit_1$fixed) degparms_all <- cbind(as.matrix(degparms_i), diff --git a/R/saem.R b/R/saem.R index 6f28a47a..5daf4be8 100644 --- a/R/saem.R +++ b/R/saem.R @@ -13,6 +13,7 @@ utils::globalVariables(c("predicted", "std")) #' psi0 of [saemix::saemixModel()] are the mean values of the parameters found #' using [mmkin]. #' +#' @importFrom utils packageVersion #' @param object An [mmkin] row object containing several fits of the same #' [mkinmod] model to different datasets #' @param verbose Should we print information about created objects of @@ -230,6 +231,11 @@ print.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) { saemix_model <- function(object, solution_type = "auto", transformations = c("mkin", "saemix"), degparms_start = numeric(), test_log_parms = FALSE, verbose = FALSE, ...) { + if (packageVersion("saemix") < "3.1.9000") { + stop("To use the interface to saemix, you need to install a development version\n", + "preferably https://github.com/jranke/saemixextension@warp_combined") + } + if (nrow(object) > 1) stop("Only row objects allowed") mkin_model <- object[[1]]$mkinmod diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R new file mode 100644 index 00000000..ae8e32cf --- /dev/null +++ b/R/summary.nlmixr.mmkin.R @@ -0,0 +1,250 @@ +#' Summary method for class "nlmixr.mmkin" +#' +#' Lists model equations, initial parameter values, optimised parameters +#' for fixed effects (population), random effects (deviations from the +#' population mean) and residual error model, as well as the resulting +#' endpoints such as formation fractions and DT50 values. Optionally +#' (default is FALSE), the data are listed in full. +#' +#' @param object an object of class [nlmix.mmkin] +#' @param x an object of class [summary.nlmix.mmkin] +#' @param data logical, indicating whether the full data should be included in +#' the summary. +#' @param verbose Should the summary be verbose? +#' @param distimes logical, indicating whether DT50 and DT90 values should be +#' included. +#' @param digits Number of digits to use for printing +#' @param \dots optional arguments passed to methods like \code{print}. +#' @return The summary function returns a list obtained in the fit, with at +#' least the following additional components +#' \item{nlmixrversion, mkinversion, Rversion}{The nlmixr, mkin and R versions used} +#' \item{date.fit, date.summary}{The dates where the fit and the summary were +#' produced} +#' \item{diffs}{The differential equations used in the degradation model} +#' \item{use_of_ff}{Was maximum or minimum use made of formation fractions} +#' \item{data}{The data} +#' \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals} +#' \item{confint_back}{Backtransformed parameters, with confidence intervals if available} +#' \item{confint_errmod}{Error model parameters with confidence intervals} +#' \item{ff}{The estimated formation fractions derived from the fitted +#' model.} +#' \item{distimes}{The DT50 and DT90 values for each observed variable.} +#' \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} +#' The print method is called for its side effect, i.e. printing the summary. +#' @importFrom stats predict vcov +#' @author Johannes Ranke for the mkin specific parts +#' nlmixr authors for the parts inherited from nlmixr. +#' @examples +#' # Generate five datasets following DFOP-SFO kinetics +#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), +#' m1 = mkinsub("SFO"), quiet = TRUE) +#' set.seed(1234) +#' k1_in <- rlnorm(5, log(0.1), 0.3) +#' k2_in <- rlnorm(5, log(0.02), 0.3) +#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +#' k_m1_in <- rlnorm(5, log(0.02), 0.3) +#' +#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' } +#' +#' ds_mean_dfop_sfo <- lapply(1:5, function(i) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], +#' f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' }) +#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5) +#' +#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { +#' add_err(ds, +#' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), +#' n = 1)[[1]] +#' }) +#' +#' \dontrun{ +#' # Evaluate using mmkin and nlmixr +#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, +#' quiet = TRUE, error_model = "tc", cores = 5) +#' f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo) +#' f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo) +#' f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem") +#' # The following takes a very long time but gives +#' f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") +#' AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm) +#' summary(f_nlmixr_dfop_sfo, data = TRUE) +#' } +#' +#' @export +summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) { + + mod_vars <- names(object$mkinmod$diffs) + + pnames <- names(object$mean_dp_start) + np <- length(pnames) + + conf.int <- confint(object$nm) + confint_trans <- as.matrix(conf.int[pnames, c(1, 3, 4)]) + colnames(confint_trans) <- c("est.", "lower", "upper") + + bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, + object$transform_rates, object$transform_fractions) + bpnames <- names(bp) + + # Transform boundaries of CI for one parameter at a time, + # with the exception of sets of formation fractions (single fractions are OK). + f_names_skip <- character(0) + for (box in mod_vars) { # Figure out sets of fractions to skip + f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE) + n_paths <- length(f_names) + if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) + } + + confint_back <- matrix(NA, nrow = length(bp), ncol = 3, + dimnames = list(bpnames, colnames(confint_trans))) + confint_back[, "est."] <- bp + + for (pname in pnames) { + if (!pname %in% f_names_skip) { + par.lower <- confint_trans[pname, "lower"] + par.upper <- confint_trans[pname, "upper"] + names(par.lower) <- names(par.upper) <- pname + bpl <- backtransform_odeparms(par.lower, object$mkinmod, + object$transform_rates, + object$transform_fractions) + bpu <- backtransform_odeparms(par.upper, object$mkinmod, + object$transform_rates, + object$transform_fractions) + confint_back[names(bpl), "lower"] <- bpl + confint_back[names(bpu), "upper"] <- bpu + } + } + + # Correlation of fixed effects (inspired by summary.nlme) + varFix <- vcov(object$nm) + stdFix <- sqrt(diag(varFix)) + object$corFixed <- array( + t(varFix/stdFix)/stdFix, + dim(varFix), + list(pnames, pnames)) + + object$confint_back <- confint_back + + object$date.summary = date() + object$use_of_ff = object$mkinmod$use_of_ff + + object$diffs <- object$mkinmod$diffs + object$print_data <- data # boolean: Should we print the data? + predict(object$nm) + so_pred <- object$so@results@predictions + + names(object$data)[4] <- "observed" # rename value to observed + + object$verbose <- verbose + + object$fixed <- object$mmkin_orig[[1]]$fixed + object$AIC = AIC(object$so) + object$BIC = BIC(object$so) + object$logLik = logLik(object$so, method = "is") + + ep <- endpoints(object) + if (length(ep$ff) != 0) + object$ff <- ep$ff + if (distimes) object$distimes <- ep$distimes + if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB + class(object) <- c("summary.saem.mmkin") + return(object) +} + +#' @rdname summary.saem.mmkin +#' @export +print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) { + cat("saemix version used for fitting: ", x$saemixversion, "\n") + cat("mkin version used for pre-fitting: ", x$mkinversion, "\n") + cat("R version used for fitting: ", x$Rversion, "\n") + + cat("Date of fit: ", x$date.fit, "\n") + cat("Date of summary:", x$date.summary, "\n") + + cat("\nEquations:\n") + nice_diffs <- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]]) + writeLines(strwrap(nice_diffs, exdent = 11)) + + cat("\nData:\n") + cat(nrow(x$data), "observations of", + length(unique(x$data$name)), "variable(s) grouped in", + length(unique(x$data$ds)), "datasets\n") + + cat("\nModel predictions using solution type", x$solution_type, "\n") + + cat("\nFitted in", x$time[["elapsed"]], "s using", paste(x$so@options$nbiter.saemix, collapse = ", "), "iterations\n") + + cat("\nVariance model: ") + cat(switch(x$err_mod, + const = "Constant variance", + obs = "Variance unique to each observed variable", + tc = "Two-component variance function"), "\n") + + cat("\nMean of starting values for individual parameters:\n") + print(x$mean_dp_start, digits = digits) + + cat("\nFixed degradation parameter values:\n") + if(length(x$fixed$value) == 0) cat("None\n") + else print(x$fixed, digits = digits) + + cat("\nResults:\n\n") + cat("Likelihood computed by importance sampling\n") + print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik, + row.names = " "), digits = digits) + + cat("\nOptimised parameters:\n") + print(x$confint_trans, digits = digits) + + if (nrow(x$confint_trans) > 1) { + corr <- x$corFixed + class(corr) <- "correlation" + print(corr, title = "\nCorrelation:", ...) + } + + cat("\nRandom effects:\n") + print(x$confint_ranef, digits = digits) + + cat("\nVariance model:\n") + print(x$confint_errmod, digits = digits) + + if (x$transformations == "mkin") { + cat("\nBacktransformed parameters:\n") + print(x$confint_back, digits = digits) + } + + printSFORB <- !is.null(x$SFORB) + if(printSFORB){ + cat("\nEstimated Eigenvalues of SFORB model(s):\n") + print(x$SFORB, digits = digits,...) + } + + printff <- !is.null(x$ff) + if(printff){ + cat("\nResulting formation fractions:\n") + print(data.frame(ff = x$ff), digits = digits,...) + } + + printdistimes <- !is.null(x$distimes) + if(printdistimes){ + cat("\nEstimated disappearance times:\n") + print(x$distimes, digits = digits,...) + } + + if (x$print_data){ + cat("\nData:\n") + print(format(x$data, digits = digits, ...), row.names = FALSE) + } + + invisible(x) +} diff --git a/build.log b/build.log index ca1c0481..13f76240 100644 --- a/build.log +++ b/build.log @@ -6,5 +6,5 @@ * creating vignettes ... OK * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories -* building ‘mkin_1.0.4.9000.tar.gz’ +* building ‘mkin_1.0.5.tar.gz’ diff --git a/check.log b/check.log index 7de944a5..f6ee39db 100644 --- a/check.log +++ b/check.log @@ -1,19 +1,14 @@ * using log directory ‘/home/jranke/git/mkin/mkin.Rcheck’ -* using R version 4.0.5 (2021-03-31) +* using R version 4.1.0 (2021-05-18) * using platform: x86_64-pc-linux-gnu (64-bit) * using session charset: UTF-8 * using options ‘--no-tests --as-cran’ * checking for file ‘mkin/DESCRIPTION’ ... OK * checking extension type ... Package -* this is package ‘mkin’ version ‘1.0.4.9000’ +* this is package ‘mkin’ version ‘1.0.5’ * package encoding: UTF-8 -* checking CRAN incoming feasibility ... NOTE +* checking CRAN incoming feasibility ... Note_to_CRAN_maintainers Maintainer: ‘Johannes Ranke ’ - -Version contains large components (1.0.4.9000) - -Unknown, possibly mis-spelled, fields in DESCRIPTION: - ‘Remotes’ * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK @@ -46,22 +41,68 @@ Unknown, possibly mis-spelled, fields in DESCRIPTION: * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK -* checking R code for possible problems ... OK +* checking R code for possible problems ... NOTE +saemix_model: no visible global function definition for + ‘packageVersion’ +Undefined global functions or variables: + packageVersion +Consider adding + importFrom("utils", "packageVersion") +to your NAMESPACE file. * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd line widths ... OK -* checking Rd cross-references ... OK +* checking Rd cross-references ... WARNING +Missing link or links in documentation object 'nlmixr.mmkin.Rd': + ‘nlmix_model’ ‘summary.nlmixr.mmkin’ + +See section 'Cross-references' in the 'Writing R Extensions' manual. * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK -* checking Rd \usage sections ... OK +* checking Rd \usage sections ... WARNING +Undocumented arguments in documentation object 'mean_degparms' + ‘object’ + +Undocumented arguments in documentation object 'nlmixr.mmkin' + ‘data’ ‘table’ ‘error_model’ ‘save’ ‘envir’ +Documented arguments not in \usage in documentation object 'nlmixr.mmkin': + ‘solution_type’ + +Functions with \usage entries need to have the appropriate \alias +entries, and all their arguments documented. +The \usage entries must correspond to syntactically valid R code. +See chapter ‘Writing R documentation files’ in the ‘Writing R +Extensions’ manual. * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... OK +* checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK -* checking examples ... OK +* checking examples ... ERROR +Running examples in ‘mkin-Ex.R’ failed +The error most likely occurred in: + +> base::assign(".ptime", proc.time(), pos = "CheckExEnv") +> ### Name: nlmixr.mmkin +> ### Title: Fit nonlinear mixed models using nlmixr +> ### Aliases: nlmixr.mmkin print.nlmixr.mmkin nlmixr_model nlmixr_data +> +> ### ** Examples +> +> ds <- lapply(experimental_data_for_UBA_2019[6:10], ++ function(x) subset(x$data[c("name", "time", "value")])) +> names(ds) <- paste("Dataset", 6:10) +> f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) +> f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", ++ cores = 1, quiet = TRUE) +> +> f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem") +Error in nlmixr(f_mmkin_parent["SFO", ], est = "saem") : + could not find function "nlmixr" +Execution halted * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... SKIPPED * checking for unstated dependencies in vignettes ... OK @@ -72,9 +113,8 @@ Unknown, possibly mis-spelled, fields in DESCRIPTION: * checking for detritus in the temp directory ... OK * DONE -Status: 1 NOTE +Status: 1 ERROR, 2 WARNINGs, 1 NOTE See ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ for details. - diff --git a/man/mean_degparms.Rd b/man/mean_degparms.Rd new file mode 100644 index 00000000..92ed4c9d --- /dev/null +++ b/man/mean_degparms.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mean_degparms.R +\name{mean_degparms} +\alias{mean_degparms} +\title{Calculate mean degradation parameters for an mmkin row object} +\usage{ +mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) +} +\arguments{ +\item{random}{Should a list with fixed and random effects be returned?} + +\item{test_log_parms}{If TRUE, log parameters are only considered in +the mean calculations if their untransformed counterparts (most likely +rate constants) pass the t-test for significant difference from zero.} + +\item{conf.level}{Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.} +} +\value{ +If random is FALSE (default), a named vector containing mean values +of the fitted degradation model parameters. If random is TRUE, a list with +fixed and random effects, in the format required by the start argument of +nlme for the case of a single grouping variable ds. +} +\description{ +Calculate mean degradation parameters for an mmkin row object +} diff --git a/man/nlme.Rd b/man/nlme.Rd index c367868b..e87b7a00 100644 --- a/man/nlme.Rd +++ b/man/nlme.Rd @@ -2,36 +2,19 @@ % Please edit documentation in R/nlme.R \name{nlme_function} \alias{nlme_function} -\alias{mean_degparms} \alias{nlme_data} \title{Helper functions to create nlme models from mmkin row objects} \usage{ nlme_function(object) -mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) - nlme_data(object) } \arguments{ \item{object}{An mmkin row object containing several fits of the same model to different datasets} - -\item{random}{Should a list with fixed and random effects be returned?} - -\item{test_log_parms}{If TRUE, log parameters are only considered in -the mean calculations if their untransformed counterparts (most likely -rate constants) pass the t-test for significant difference from zero.} - -\item{conf.level}{Possibility to adjust the required confidence level -for parameter that are tested if requested by 'test_log_parms'.} } \value{ A function that can be used with nlme -If random is FALSE (default), a named vector containing mean values -of the fitted degradation model parameters. If random is TRUE, a list with -fixed and random effects, in the format required by the start argument of -nlme for the case of a single grouping variable ds. - A \code{\link{groupedData}} object } \description{ diff --git a/man/nlme.mmkin.Rd b/man/nlme.mmkin.Rd index 2fb0488a..a2b45efa 100644 --- a/man/nlme.mmkin.Rd +++ b/man/nlme.mmkin.Rd @@ -13,7 +13,7 @@ paste(el, 1, sep = "~")))), random = pdDiag(fixed), groups, - start = mean_degparms(model, random = TRUE), + start = mean_degparms(model, random = TRUE, test_log_parms = TRUE), correlation = NULL, weights = NULL, subset, diff --git a/man/nlmixr.mmkin.Rd b/man/nlmixr.mmkin.Rd new file mode 100644 index 00000000..86bbdc9f --- /dev/null +++ b/man/nlmixr.mmkin.Rd @@ -0,0 +1,188 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/nlmixr.R +\name{nlmixr.mmkin} +\alias{nlmixr.mmkin} +\alias{print.nlmixr.mmkin} +\alias{nlmixr_model} +\alias{nlmixr_data} +\title{Fit nonlinear mixed models using nlmixr} +\usage{ +\method{nlmixr}{mmkin}( + object, + data = NULL, + est = NULL, + control = list(), + table = tableControl(), + error_model = object[[1]]$err_mod, + test_log_parms = TRUE, + conf.level = 0.6, + ..., + save = NULL, + envir = parent.frame() +) + +\method{print}{nlmixr.mmkin}(x, digits = max(3, getOption("digits") - 3), ...) + +nlmixr_model( + object, + est = c("saem", "focei"), + degparms_start = "auto", + test_log_parms = FALSE, + conf.level = 0.6, + error_model = object[[1]]$err_mod +) + +nlmixr_data(object, ...) +} +\arguments{ +\item{object}{An \link{mmkin} row object containing several fits of the same +\link{mkinmod} model to different datasets} + +\item{est}{Estimation method passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{control}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}.} + +\item{test_log_parms}{If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using \link{mean_degparms}.} + +\item{conf.level}{Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.} + +\item{\dots}{Passed to \link{nlmixr_model}} + +\item{x}{An nlmixr.mmkin object to print} + +\item{digits}{Number of digits to use for printing} + +\item{degparms_start}{Parameter values given as a named numeric vector will +be used to override the starting values obtained from the 'mmkin' object.} + +\item{solution_type}{Possibility to specify the solution type in case the +automatic choice is not desired} +} +\value{ +An S3 object of class 'nlmixr.mmkin', containing the fitted +\link[nlmixr:nlmixr]{nlmixr::nlmixr} object as a list component named 'nm'. The +object also inherits from 'mixed.mmkin'. + +An function defining a model suitable for fitting with \link[nlmixr:nlmixr]{nlmixr::nlmixr}. + +An dataframe suitable for use with \link[nlmixr:nlmixr]{nlmixr::nlmixr} +} +\description{ +This function uses \code{\link[nlmixr:nlmixr]{nlmixr::nlmixr()}} as a backend for fitting nonlinear mixed +effects models created from \link{mmkin} row objects using the Stochastic Approximation +Expectation Maximisation algorithm (SAEM). +} +\details{ +An mmkin row object is essentially a list of mkinfit objects that have been +obtained by fitting the same model to a list of datasets using \link{mkinfit}. +} +\examples{ +ds <- lapply(experimental_data_for_UBA_2019[6:10], + function(x) subset(x$data[c("name", "time", "value")])) +names(ds) <- paste("Dataset", 6:10) +f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) +f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", + cores = 1, quiet = TRUE) + +f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem") +f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei") + +f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem") +f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei") + +f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem") +f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei") + +f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem") +f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei") + +f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem") +f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei") + +AIC( + f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm, + f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm, + f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm, + f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm, + f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm) + +AIC(nlme(f_mmkin_parent["FOMC", ])) +AIC(nlme(f_mmkin_parent["HS", ])) + +# nlme is comparable to nlmixr with focei, saem finds a better +# solution, the two-component error model does not improve it +plot(f_nlmixr_fomc_saem) + +\dontrun{ +sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), + A1 = mkinsub("SFO")) +fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), + A1 = mkinsub("SFO")) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), + A1 = mkinsub("SFO")) + +f_mmkin_const <- mmkin(list( + "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), + ds, quiet = TRUE, error_model = "const") +f_mmkin_obs <- mmkin(list( + "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), + ds, quiet = TRUE, error_model = "obs") +f_mmkin_tc <- mmkin(list( + "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), + ds, quiet = TRUE, error_model = "tc") + +# A single constant variance is currently only possible with est = 'focei' in nlmixr +f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei") +f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") +f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") + +# Variance by variable is supported by 'saem' and 'focei' +f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem") +f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") +f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") +f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") + +# Identical two-component error for all variables is only possible with +# est = 'focei' in nlmixr +f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei") +f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") + +# Two-component error by variable is possible with both estimation methods +# Variance by variable is supported by 'saem' and 'focei' +f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem", + error_model = "obs_tc") +f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", + error_model = "obs_tc") +f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", + error_model = "obs_tc") +f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", + error_model = "obs_tc") + +AIC( + f_nlmixr_sfo_sfo_focei_const$nm, + f_nlmixr_fomc_sfo_focei_const$nm, + f_nlmixr_dfop_sfo_focei_const$nm, + f_nlmixr_fomc_sfo_saem_obs$nm, + f_nlmixr_fomc_sfo_focei_obs$nm, + f_nlmixr_dfop_sfo_saem_obs$nm, + f_nlmixr_dfop_sfo_focei_obs$nm, + f_nlmixr_fomc_sfo_focei_tc$nm, + f_nlmixr_dfop_sfo_focei_tc$nm, + f_nlmixr_fomc_sfo_saem_obs_tc$nm, + f_nlmixr_fomc_sfo_focei_obs_tc$nm, + f_nlmixr_dfop_sfo_saem_obs_tc$nm, + f_nlmixr_dfop_sfo_focei_obs_tc$nm +) +# Currently, FOMC-SFO with two-component error by variable fitted by focei gives the +# lowest AIC +plot(f_nlmixr_fomc_sfo_focei_obs_tc) +summary(f_nlmixr_fomc_sfo_focei_obs_tc) +} +} +\seealso{ +\link{summary.nlmixr.mmkin} \link{plot.mixed.mmkin} +} diff --git a/man/plot.mixed.mmkin.Rd b/man/plot.mixed.mmkin.Rd index bcab3e74..d87ca22c 100644 --- a/man/plot.mixed.mmkin.Rd +++ b/man/plot.mixed.mmkin.Rd @@ -99,12 +99,17 @@ plot(f[, 3:4], standardized = TRUE) # For this fit we need to increase pnlsMaxiter, and we increase the # tolerance in order to speed up the fit for this example evaluation +# It still takes 20 seconds to run f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) plot(f_nlme) f_saem <- saem(f, transformations = "saemix") plot(f_saem) +f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs") +f_nlmix <- nlmix(f_obs) +plot(f_nlmix) + # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo, f_nlme$bparms.optim[-1], diff --git a/man/summary.nlmixr.mmkin.Rd b/man/summary.nlmixr.mmkin.Rd new file mode 100644 index 00000000..03f0ffb2 --- /dev/null +++ b/man/summary.nlmixr.mmkin.Rd @@ -0,0 +1,100 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/summary.nlmixr.mmkin.R +\name{summary.nlmixr.mmkin} +\alias{summary.nlmixr.mmkin} +\title{Summary method for class "nlmixr.mmkin"} +\usage{ +\method{summary}{nlmixr.mmkin}(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) +} +\arguments{ +\item{object}{an object of class \link{nlmix.mmkin}} + +\item{data}{logical, indicating whether the full data should be included in +the summary.} + +\item{verbose}{Should the summary be verbose?} + +\item{distimes}{logical, indicating whether DT50 and DT90 values should be +included.} + +\item{\dots}{optional arguments passed to methods like \code{print}.} + +\item{x}{an object of class \link{summary.nlmix.mmkin}} + +\item{digits}{Number of digits to use for printing} +} +\value{ +The summary function returns a list obtained in the fit, with at +least the following additional components +\item{nlmixrversion, mkinversion, Rversion}{The nlmixr, mkin and R versions used} +\item{date.fit, date.summary}{The dates where the fit and the summary were +produced} +\item{diffs}{The differential equations used in the degradation model} +\item{use_of_ff}{Was maximum or minimum use made of formation fractions} +\item{data}{The data} +\item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals} +\item{confint_back}{Backtransformed parameters, with confidence intervals if available} +\item{confint_errmod}{Error model parameters with confidence intervals} +\item{ff}{The estimated formation fractions derived from the fitted +model.} +\item{distimes}{The DT50 and DT90 values for each observed variable.} +\item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} +The print method is called for its side effect, i.e. printing the summary. +} +\description{ +Lists model equations, initial parameter values, optimised parameters +for fixed effects (population), random effects (deviations from the +population mean) and residual error model, as well as the resulting +endpoints such as formation fractions and DT50 values. Optionally +(default is FALSE), the data are listed in full. +} +\examples{ +# Generate five datasets following DFOP-SFO kinetics +sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), + m1 = mkinsub("SFO"), quiet = TRUE) +set.seed(1234) +k1_in <- rlnorm(5, log(0.1), 0.3) +k2_in <- rlnorm(5, log(0.02), 0.3) +g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +k_m1_in <- rlnorm(5, log(0.02), 0.3) + +pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { + mkinpredict(dfop_sfo, + c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), + c(parent = 100, m1 = 0), + sampling_times) +} + +ds_mean_dfop_sfo <- lapply(1:5, function(i) { + mkinpredict(dfop_sfo, + c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], + f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), + c(parent = 100, m1 = 0), + sampling_times) +}) +names(ds_mean_dfop_sfo) <- paste("ds", 1:5) + +ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { + add_err(ds, + sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), + n = 1)[[1]] +}) + +\dontrun{ +# Evaluate using mmkin and nlmixr +f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, + quiet = TRUE, error_model = "obs", cores = 5) +f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo) +f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo) +f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem") +#f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") +summary(f_nlmixr_dfop_sfo, data = TRUE) +} + +} +\author{ +Johannes Ranke for the mkin specific parts +nlmixr authors for the parts inherited from nlmixr. +} diff --git a/man/summary.saem.mmkin.Rd b/man/summary.saem.mmkin.Rd index 67cb3cbb..86938d31 100644 --- a/man/summary.saem.mmkin.Rd +++ b/man/summary.saem.mmkin.Rd @@ -1,30 +1,32 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/summary.saem.mmkin.R -\name{summary.saem.mmkin} -\alias{summary.saem.mmkin} +% Please edit documentation in R/summary.nlmixr.mmkin.R, R/summary.saem.mmkin.R +\name{print.summary.saem.mmkin} \alias{print.summary.saem.mmkin} +\alias{summary.saem.mmkin} \title{Summary method for class "saem.mmkin"} \usage{ +\method{print}{summary.saem.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) + \method{summary}{saem.mmkin}(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) \method{print}{summary.saem.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) } \arguments{ -\item{object}{an object of class \link{saem.mmkin}} +\item{x}{an object of class \link{summary.saem.mmkin}} -\item{data}{logical, indicating whether the full data should be included in -the summary.} +\item{digits}{Number of digits to use for printing} \item{verbose}{Should the summary be verbose?} -\item{distimes}{logical, indicating whether DT50 and DT90 values should be -included.} - \item{\dots}{optional arguments passed to methods like \code{print}.} -\item{x}{an object of class \link{summary.saem.mmkin}} +\item{object}{an object of class \link{saem.mmkin}} -\item{digits}{Number of digits to use for printing} +\item{data}{logical, indicating whether the full data should be included in +the summary.} + +\item{distimes}{logical, indicating whether DT50 and DT90 values should be +included.} } \value{ The summary function returns a list based on the \link[saemix:SaemixObject-class]{saemix::SaemixObject} -- cgit v1.2.1 From 0c9b2f0e3c8ce65cb790c9e048476784cbbea070 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Jun 2021 11:14:45 +0200 Subject: Finished 'summary.nlmixr.mmkin', checks, docs --- DESCRIPTION | 2 +- NAMESPACE | 5 + NEWS.md | 2 +- R/endpoints.R | 4 +- R/mean_degparms.R | 3 +- R/nlmixr.R | 29 +- R/summary.nlmixr.mmkin.R | 50 +- _pkgdown.yml | 3 + check.log | 59 +- docs/dev/404.html | 2 +- docs/dev/articles/index.html | 2 +- docs/dev/authors.html | 2 +- docs/dev/index.html | 2 +- docs/dev/news/index.html | 34 +- docs/dev/pkgdown.yml | 2 +- docs/dev/reference/Rplot002.png | Bin 16859 -> 16858 bytes docs/dev/reference/Rplot003.png | Bin 28844 -> 28838 bytes docs/dev/reference/Rplot004.png | Bin 49360 -> 49360 bytes docs/dev/reference/Rplot005.png | Bin 59216 -> 59049 bytes docs/dev/reference/Rplot006.png | Bin 24545 -> 22144 bytes docs/dev/reference/endpoints.html | 6 +- docs/dev/reference/index.html | 22 +- docs/dev/reference/mean_degparms.html | 210 + docs/dev/reference/mixed-1.png | Bin 219866 -> 220057 bytes docs/dev/reference/mixed.html | 6 +- docs/dev/reference/mmkin-1.png | Bin 110459 -> 111120 bytes docs/dev/reference/mmkin-2.png | Bin 107057 -> 108016 bytes docs/dev/reference/mmkin-3.png | Bin 96062 -> 96433 bytes docs/dev/reference/mmkin-4.png | Bin 67191 -> 66723 bytes docs/dev/reference/mmkin-5.png | Bin 64880 -> 65113 bytes docs/dev/reference/mmkin.html | 11 +- docs/dev/reference/nlme-1.png | Bin 68233 -> 68943 bytes docs/dev/reference/nlme-2.png | Bin 90552 -> 94409 bytes docs/dev/reference/nlme.html | 41 +- docs/dev/reference/nlme.mmkin-1.png | Bin 124827 -> 124937 bytes docs/dev/reference/nlme.mmkin-2.png | Bin 169698 -> 169884 bytes docs/dev/reference/nlme.mmkin-3.png | Bin 172809 -> 172863 bytes docs/dev/reference/nlme.mmkin.html | 20 +- docs/dev/reference/nlmixr.mmkin-1.png | Bin 0 -> 127508 bytes docs/dev/reference/nlmixr.mmkin-2.png | Bin 0 -> 177016 bytes docs/dev/reference/nlmixr.mmkin.html | 13791 +++++++++++++++++++++++++ docs/dev/reference/plot.mixed.mmkin-1.png | Bin 85433 -> 85300 bytes docs/dev/reference/plot.mixed.mmkin-2.png | Bin 174061 -> 174111 bytes docs/dev/reference/plot.mixed.mmkin-3.png | Bin 172540 -> 173260 bytes docs/dev/reference/plot.mixed.mmkin-4.png | Bin 175594 -> 176346 bytes docs/dev/reference/plot.mixed.mmkin.html | 23 +- docs/dev/reference/reexports.html | 8 +- docs/dev/reference/saem-1.png | Bin 47342 -> 47337 bytes docs/dev/reference/saem-2.png | Bin 48819 -> 48793 bytes docs/dev/reference/saem-3.png | Bin 82202 -> 82192 bytes docs/dev/reference/saem-4.png | Bin 128213 -> 128209 bytes docs/dev/reference/saem-5.png | Bin 173665 -> 174406 bytes docs/dev/reference/saem.html | 399 +- docs/dev/reference/summary.nlmixr.mmkin.html | 1022 ++ docs/dev/reference/summary.saem.mmkin.html | 358 +- docs/dev/sitemap.xml | 9 + man/endpoints.Rd | 4 +- man/mean_degparms.Rd | 2 + man/nlmixr.mmkin.Rd | 24 +- man/reexports.Rd | 5 +- man/summary.nlmixr.mmkin.Rd | 17 +- man/summary.saem.mmkin.Rd | 24 +- 62 files changed, 15632 insertions(+), 571 deletions(-) create mode 100644 docs/dev/reference/mean_degparms.html create mode 100644 docs/dev/reference/nlmixr.mmkin-1.png create mode 100644 docs/dev/reference/nlmixr.mmkin-2.png create mode 100644 docs/dev/reference/nlmixr.mmkin.html create mode 100644 docs/dev/reference/summary.nlmixr.mmkin.html diff --git a/DESCRIPTION b/DESCRIPTION index 5b90ef37..e81fcb32 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -19,7 +19,7 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006, particular purpose. Depends: R (>= 2.15.1), parallel Imports: stats, graphics, methods, deSolve, R6, inline (>= 0.3.19), numDeriv, - lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix, nlmixr + dplyr, lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix, nlmixr Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr, benchmarkme, tibble, stats4 License: GPL diff --git a/NAMESPACE b/NAMESPACE index bb4f5f92..0f61396d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -35,6 +35,7 @@ S3method(print,nlmixr.mmkin) S3method(print,saem.mmkin) S3method(print,summary.mkinfit) S3method(print,summary.nlme.mmkin) +S3method(print,summary.nlmixr.mmkin) S3method(print,summary.saem.mmkin) S3method(residuals,mkinfit) S3method(saem,mmkin) @@ -89,6 +90,7 @@ export(nafta) export(nlme) export(nlme_data) export(nlme_function) +export(nlmixr) export(nlmixr_data) export(nlmixr_model) export(parms) @@ -104,6 +106,7 @@ import(deSolve) import(graphics) import(nlme) importFrom(R6,R6Class) +importFrom(dplyr,"%>%") importFrom(grDevices,dev.cur) importFrom(lmtest,lrtest) importFrom(methods,signature) @@ -119,6 +122,7 @@ importFrom(stats,aggregate) importFrom(stats,as.formula) importFrom(stats,coef) importFrom(stats,coefficients) +importFrom(stats,confint) importFrom(stats,cov2cor) importFrom(stats,dist) importFrom(stats,dnorm) @@ -138,6 +142,7 @@ importFrom(stats,qnorm) importFrom(stats,qt) importFrom(stats,residuals) importFrom(stats,rnorm) +importFrom(stats,sd) importFrom(stats,shapiro.test) importFrom(stats,update) importFrom(stats,vcov) diff --git a/NEWS.md b/NEWS.md index 03098106..e668f1e5 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# mkin 1.0.5 +# mkin 1.0.5 (unreleased) ## Mixed-effects models diff --git a/R/endpoints.R b/R/endpoints.R index f1f47581..6bf52f07 100644 --- a/R/endpoints.R +++ b/R/endpoints.R @@ -10,8 +10,8 @@ #' Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from #' HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models #' -#' @param fit An object of class [mkinfit], [nlme.mmkin] or -#' [saem.mmkin]. Or another object that has list components +#' @param fit An object of class [mkinfit], [nlme.mmkin], [saem.mmkin] or +#' [nlmixr.mmkin]. Or another object that has list components #' mkinmod containing an [mkinmod] degradation model, and two numeric vectors, #' bparms.optim and bparms.fixed, that contain parameter values #' for that model. diff --git a/R/mean_degparms.R b/R/mean_degparms.R index ec7f4342..ec20c068 100644 --- a/R/mean_degparms.R +++ b/R/mean_degparms.R @@ -4,6 +4,7 @@ #' of the fitted degradation model parameters. If random is TRUE, a list with #' fixed and random effects, in the format required by the start argument of #' nlme for the case of a single grouping variable ds. +#' @param object An mmkin row object containing several fits of the same model to different datasets #' @param random Should a list with fixed and random effects be returned? #' @param test_log_parms If TRUE, log parameters are only considered in #' the mean calculations if their untransformed counterparts (most likely @@ -51,7 +52,7 @@ mean_degparms <- function(object, random = FALSE, test_log_parms = FALSE, conf.l # For nlmixr we can specify starting values for standard deviations eta, and # we ignore uncertain parameters if test_log_parms is FALSE - eta <- apply(degparm_mat_trans_OK, 1, sd, na.rm = TRUE) + eta <- apply(degparm_mat_trans_OK, 1, stats::sd, na.rm = TRUE) return(list(fixed = fixed, random = list(ds = random), eta = eta)) } else { diff --git a/R/nlmixr.R b/R/nlmixr.R index 223b23a1..98783ca7 100644 --- a/R/nlmixr.R +++ b/R/nlmixr.R @@ -1,4 +1,7 @@ -utils::globalVariables(c("predicted", "std")) +utils::globalVariables(c("predicted", "std", "ID", "TIME", "CMT", "DV", "IPRED", "IRES", "IWRES")) + +#' @export +nlmixr::nlmixr #' Fit nonlinear mixed models using nlmixr #' @@ -10,8 +13,10 @@ utils::globalVariables(c("predicted", "std")) #' obtained by fitting the same model to a list of datasets using [mkinfit]. #' #' @importFrom nlmixr nlmixr tableControl +#' @importFrom dplyr %>% #' @param object An [mmkin] row object containing several fits of the same #' [mkinmod] model to different datasets +#' @param data Not used, the data are extracted from the mmkin row object #' @param est Estimation method passed to [nlmixr::nlmixr] #' @param degparms_start Parameter values given as a named numeric vector will #' be used to override the starting values obtained from the 'mmkin' object. @@ -21,22 +26,28 @@ utils::globalVariables(c("predicted", "std")) #' when calculating mean degradation parameters using [mean_degparms]. #' @param conf.level Possibility to adjust the required confidence level #' for parameter that are tested if requested by 'test_log_parms'. -#' @param solution_type Possibility to specify the solution type in case the -#' automatic choice is not desired -#' @param control Passed to [nlmixr::nlmixr]. +#' @param data Not used, as the data are extracted from the mmkin row object +#' @param table Passed to [nlmixr::nlmixr] +#' @param error_model Possibility to override the error model which is being +#' set based on the error model used in the mmkin row object. +#' @param control Passed to [nlmixr::nlmixr] #' @param \dots Passed to [nlmixr_model] +#' @param save Passed to [nlmixr::nlmixr] +#' @param envir Passed to [nlmixr::nlmixr] #' @return An S3 object of class 'nlmixr.mmkin', containing the fitted #' [nlmixr::nlmixr] object as a list component named 'nm'. The #' object also inherits from 'mixed.mmkin'. #' @seealso [summary.nlmixr.mmkin] [plot.mixed.mmkin] #' @examples +#' \dontrun{ #' ds <- lapply(experimental_data_for_UBA_2019[6:10], #' function(x) subset(x$data[c("name", "time", "value")])) #' names(ds) <- paste("Dataset", 6:10) +#' #' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) #' f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", #' cores = 1, quiet = TRUE) -#' +#' #' f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem") #' f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei") #' @@ -66,7 +77,6 @@ utils::globalVariables(c("predicted", "std")) #' # solution, the two-component error model does not improve it #' plot(f_nlmixr_fomc_saem) #' -#' \dontrun{ #' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), #' A1 = mkinsub("SFO")) #' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), @@ -167,7 +177,8 @@ nlmixr.mmkin <- function(object, data = NULL, return_data <- nlmixr_df %>% dplyr::transmute(ds = ID, name = CMT, time = TIME, value = DV, predicted = IPRED, residual = IRES, - std = IRES/IWRES, standardized = IWRES) + std = IRES/IWRES, standardized = IWRES) %>% + dplyr::arrange(ds, name, time) bparms_optim <- backtransform_odeparms(f_nlmixr$theta, object[[1]]$mkinmod, @@ -227,6 +238,9 @@ print.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) } #' @rdname nlmixr.mmkin +#' @param add_attributes Should the starting values used for degradation model +#' parameters and their distribution and for the error model parameters +#' be returned as attributes? #' @return An function defining a model suitable for fitting with [nlmixr::nlmixr]. #' @export nlmixr_model <- function(object, @@ -435,6 +449,7 @@ nlmixr_model <- function(object, if (add_attributes) { attr(f, "mean_dp_start") <- degparms_optim + attr(f, "eta_start") <- degparms_mmkin$eta attr(f, "mean_ep_start") <- errparms_ini } diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R index ae8e32cf..f2d7c607 100644 --- a/R/summary.nlmixr.mmkin.R +++ b/R/summary.nlmixr.mmkin.R @@ -6,8 +6,9 @@ #' endpoints such as formation fractions and DT50 values. Optionally #' (default is FALSE), the data are listed in full. #' -#' @param object an object of class [nlmix.mmkin] -#' @param x an object of class [summary.nlmix.mmkin] +#' @importFrom stats confint sd +#' @param object an object of class [nlmixr.mmkin] +#' @param x an object of class [summary.nlmixr.mmkin] #' @param data logical, indicating whether the full data should be included in #' the summary. #' @param verbose Should the summary be verbose? @@ -23,9 +24,7 @@ #' \item{diffs}{The differential equations used in the degradation model} #' \item{use_of_ff}{Was maximum or minimum use made of formation fractions} #' \item{data}{The data} -#' \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals} #' \item{confint_back}{Backtransformed parameters, with confidence intervals if available} -#' \item{confint_errmod}{Error model parameters with confidence intervals} #' \item{ff}{The estimated formation fractions derived from the fitted #' model.} #' \item{distimes}{The DT50 and DT90 values for each observed variable.} @@ -78,7 +77,7 @@ #' # The following takes a very long time but gives #' f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") #' AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm) -#' summary(f_nlmixr_dfop_sfo, data = TRUE) +#' summary(f_nlmixr_dfop_sfo_sfo, data = TRUE) #' } #' #' @export @@ -134,6 +133,7 @@ summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes dim(varFix), list(pnames, pnames)) + object$confint_trans <- confint_trans object$confint_back <- confint_back object$date.summary = date() @@ -141,31 +141,29 @@ summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes object$diffs <- object$mkinmod$diffs object$print_data <- data # boolean: Should we print the data? - predict(object$nm) - so_pred <- object$so@results@predictions names(object$data)[4] <- "observed" # rename value to observed object$verbose <- verbose object$fixed <- object$mmkin_orig[[1]]$fixed - object$AIC = AIC(object$so) - object$BIC = BIC(object$so) - object$logLik = logLik(object$so, method = "is") + object$AIC = AIC(object$nm) + object$BIC = BIC(object$nm) + object$logLik = logLik(object$nm) ep <- endpoints(object) if (length(ep$ff) != 0) object$ff <- ep$ff if (distimes) object$distimes <- ep$distimes if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB - class(object) <- c("summary.saem.mmkin") + class(object) <- c("summary.nlmixr.mmkin") return(object) } -#' @rdname summary.saem.mmkin +#' @rdname summary.nlmixr.mmkin #' @export -print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) { - cat("saemix version used for fitting: ", x$saemixversion, "\n") +print.summary.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) { + cat("nlmixr version used for fitting: ", x$nlmixrversion, "\n") cat("mkin version used for pre-fitting: ", x$mkinversion, "\n") cat("R version used for fitting: ", x$Rversion, "\n") @@ -181,25 +179,29 @@ print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3) length(unique(x$data$name)), "variable(s) grouped in", length(unique(x$data$ds)), "datasets\n") - cat("\nModel predictions using solution type", x$solution_type, "\n") + cat("\nDegradation model predictions using RxODE\n") - cat("\nFitted in", x$time[["elapsed"]], "s using", paste(x$so@options$nbiter.saemix, collapse = ", "), "iterations\n") + cat("\nFitted in", x$time[["elapsed"]], "s\n") cat("\nVariance model: ") cat(switch(x$err_mod, const = "Constant variance", obs = "Variance unique to each observed variable", - tc = "Two-component variance function"), "\n") + tc = "Two-component variance function", + obs_tc = "Two-component variance unique to each observed variable"), "\n") cat("\nMean of starting values for individual parameters:\n") print(x$mean_dp_start, digits = digits) + cat("\nMean of starting values for error model parameters:\n") + print(x$mean_ep_start, digits = digits) + cat("\nFixed degradation parameter values:\n") if(length(x$fixed$value) == 0) cat("None\n") else print(x$fixed, digits = digits) cat("\nResults:\n\n") - cat("Likelihood computed by importance sampling\n") + cat("Likelihood calculated by", nlmixr::getOfvType(x$nm), " \n") print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik, row.names = " "), digits = digits) @@ -212,16 +214,14 @@ print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3) print(corr, title = "\nCorrelation:", ...) } - cat("\nRandom effects:\n") - print(x$confint_ranef, digits = digits) + cat("\nRandom effects (omega):\n") + print(x$nm$omega, digits = digits) cat("\nVariance model:\n") - print(x$confint_errmod, digits = digits) + print(x$nm$sigma, digits = digits) - if (x$transformations == "mkin") { - cat("\nBacktransformed parameters:\n") - print(x$confint_back, digits = digits) - } + cat("\nBacktransformed parameters:\n") + print(x$confint_back, digits = digits) printSFORB <- !is.null(x$SFORB) if(printSFORB){ diff --git a/_pkgdown.yml b/_pkgdown.yml index 340004de..50c0685f 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -43,8 +43,10 @@ reference: contents: - nlme.mmkin - saem.mmkin + - nlmixr.mmkin - plot.mixed.mmkin - summary.nlme.mmkin + - summary.nlmixr.mmkin - summary.saem.mmkin - nlme_function - get_deg_func @@ -91,6 +93,7 @@ reference: - mkinresplot - mkinparplot - mkinerrplot + - mean_degparms - create_deg_func - title: Analytical solutions desc: Parent only model solutions diff --git a/check.log b/check.log index f6ee39db..2627695d 100644 --- a/check.log +++ b/check.log @@ -41,38 +41,14 @@ Maintainer: ‘Johannes Ranke ’ * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK -* checking R code for possible problems ... NOTE -saemix_model: no visible global function definition for - ‘packageVersion’ -Undefined global functions or variables: - packageVersion -Consider adding - importFrom("utils", "packageVersion") -to your NAMESPACE file. +* checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd line widths ... OK -* checking Rd cross-references ... WARNING -Missing link or links in documentation object 'nlmixr.mmkin.Rd': - ‘nlmix_model’ ‘summary.nlmixr.mmkin’ - -See section 'Cross-references' in the 'Writing R Extensions' manual. +* checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK -* checking Rd \usage sections ... WARNING -Undocumented arguments in documentation object 'mean_degparms' - ‘object’ - -Undocumented arguments in documentation object 'nlmixr.mmkin' - ‘data’ ‘table’ ‘error_model’ ‘save’ ‘envir’ -Documented arguments not in \usage in documentation object 'nlmixr.mmkin': - ‘solution_type’ - -Functions with \usage entries need to have the appropriate \alias -entries, and all their arguments documented. -The \usage entries must correspond to syntactically valid R code. -See chapter ‘Writing R documentation files’ in the ‘Writing R -Extensions’ manual. +* checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK @@ -81,28 +57,10 @@ Extensions’ manual. * checking data for ASCII and uncompressed saves ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK -* checking examples ... ERROR -Running examples in ‘mkin-Ex.R’ failed -The error most likely occurred in: - -> base::assign(".ptime", proc.time(), pos = "CheckExEnv") -> ### Name: nlmixr.mmkin -> ### Title: Fit nonlinear mixed models using nlmixr -> ### Aliases: nlmixr.mmkin print.nlmixr.mmkin nlmixr_model nlmixr_data -> -> ### ** Examples -> -> ds <- lapply(experimental_data_for_UBA_2019[6:10], -+ function(x) subset(x$data[c("name", "time", "value")])) -> names(ds) <- paste("Dataset", 6:10) -> f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) -> f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", -+ cores = 1, quiet = TRUE) -> -> f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem") -Error in nlmixr(f_mmkin_parent["SFO", ], est = "saem") : - could not find function "nlmixr" -Execution halted +* checking examples ... NOTE +Examples with CPU (user + system) or elapsed time > 5s + user system elapsed +nlmixr.mmkin 8.129 0.375 5.384 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... SKIPPED * checking for unstated dependencies in vignettes ... OK @@ -113,8 +71,9 @@ Execution halted * checking for detritus in the temp directory ... OK * DONE -Status: 1 ERROR, 2 WARNINGs, 1 NOTE +Status: 1 NOTE See ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ for details. + diff --git a/docs/dev/404.html b/docs/dev/404.html index 58591997..98c0b1e0 100644 --- a/docs/dev/404.html +++ b/docs/dev/404.html @@ -71,7 +71,7 @@ mkin - 1.0.4.9000 + 1.0.5
                                                                diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 3c00526e..3896120a 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -71,7 +71,7 @@ mkin - 1.0.4.9000 + 1.0.5
                                                                diff --git a/docs/dev/authors.html b/docs/dev/authors.html index 45db18f2..4208dc24 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -71,7 +71,7 @@ mkin - 1.0.4.9000 + 1.0.5
                                                                diff --git a/docs/dev/index.html b/docs/dev/index.html index d1fa1a52..6e3fa6e1 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -38,7 +38,7 @@ mkin - 1.0.4.9000 + 1.0.5
                                                                diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index 10585403..234ba02f 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -71,7 +71,7 @@ mkin - 1.0.4.9000 + 1.0.5
                                                                @@ -141,33 +141,27 @@ Source: NEWS.md
                                                                -
                                                                -

                                                                -mkin 1.0.4.9000

                                                                -
                                                                -

                                                                -General

                                                                -
                                                                  -
                                                                • Switch to a versioning scheme where the fourth version component indicates development versions
                                                                  • -
                                                                -
                                                                +
                                                                +

                                                                +mkin 1.0.5 (unreleased)

                                                                Mixed-effects models

                                                                  -

                                                                • Reintroduce the interface to the current development version of saemix, in particular:

                                                                  • -

                                                                • ‘saemix_model’ and ‘saemix_data’: Helper functions to set up nonlinear mixed-effects models for mmkin row objects

                                                                  • -

                                                                • ‘saem’: generic function to fit saemix models using ‘saemix_model’ and ‘saemix_data’, with a generator ‘saem.mmkin’, summary and plot methods

                                                                  • -

                                                                • ‘mean_degparms’: New argument ‘test_log_parms’ that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to check more plausible parameters for ‘saem’

                                                                  • +

                                                                • Introduce an interface to nlmixr, supporting estimation methods ‘saem’ and ‘focei’: S3 method ‘nlmixr.mmkin’ using the helper functions ‘nlmixr_model’ and ‘nlmixr_data’ to set up nlmixr models for mmkin row objects, with summary and plot methods.

                                                                  • +

                                                                • Reintroduce the interface to current development versions (not on CRAN) of saemix, in particular the generic function ‘saem’ with a generator ‘saem.mmkin’, currently using ‘saemix_model’ and ‘saemix_data’, summary and plot methods

                                                                  • +

                                                                • ‘mean_degparms’: New argument ‘test_log_parms’ that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends

                                                                  • +

                                                                • ‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ and ‘conf.level’

                                                                -
                                                                +

                                                                -mkin 1.0.4 (Unreleased)

                                                                +mkin 1.0.4 (2021-04-20)
                                                                  -

                                                                • ‘plot.mixed.mmkin’: Reset graphical parameters on exit

                                                                • All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters

                                                                  • +

                                                                • ‘plot.mkinfit’: Use xlab and xlim for the residual plot if show_residuals is TRUE

                                                                  • +

                                                                • ‘mmkin’: Use cores = 1 per default on Windows to make it easier for first time users

                                                                @@ -198,9 +192,9 @@

                                                                mkin 1.0.0 (2021-02-03)

                                                                -
                                                                +

                                                                -General

                                                                +General

                                                                • ‘mkinmod’ models gain arguments ‘name’ and ‘dll_dir’ which, in conjunction with a current version of the ‘inline’ package, make it possible to still use the DLL used for fast ODE solutions with ‘deSolve’ after saving and restoring the ‘mkinmod’ object.

                                                                • ‘mkindsg’ R6 class for groups of ‘mkinds’ datasets with metadata

                                                                  • diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index dbacd0ab..0b01e008 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-03-09T16:32Z +last_built: 2021-06-11T09:09Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/Rplot002.png b/docs/dev/reference/Rplot002.png index a9a972e5..32c64fcd 100644 Binary files a/docs/dev/reference/Rplot002.png and b/docs/dev/reference/Rplot002.png differ diff --git a/docs/dev/reference/Rplot003.png b/docs/dev/reference/Rplot003.png index d077f01c..5726488c 100644 Binary files a/docs/dev/reference/Rplot003.png and b/docs/dev/reference/Rplot003.png differ diff --git a/docs/dev/reference/Rplot004.png b/docs/dev/reference/Rplot004.png index ffcd2d96..c279f831 100644 Binary files a/docs/dev/reference/Rplot004.png and b/docs/dev/reference/Rplot004.png differ diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png index dfb5965b..55aa7eec 100644 Binary files a/docs/dev/reference/Rplot005.png and b/docs/dev/reference/Rplot005.png differ diff --git a/docs/dev/reference/Rplot006.png b/docs/dev/reference/Rplot006.png index 81525882..4c728f4e 100644 Binary files a/docs/dev/reference/Rplot006.png and b/docs/dev/reference/Rplot006.png differ diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index 63bec6a8..dc1d1f17 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> mkin - 1.0.4.9000 + 1.0.5
                                                                @@ -165,8 +165,8 @@ advantage that the SFORB model can also be used for metabolites.

                                                                fit -

                                                                An object of class mkinfit, nlme.mmkin or -saem.mmkin. Or another object that has list components +

                                                                An object of class mkinfit, nlme.mmkin, saem.mmkin or +nlmixr.mmkin. Or another object that has list components mkinmod containing an mkinmod degradation model, and two numeric vectors, bparms.optim and bparms.fixed, that contain parameter values for that model.

                                                                diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index 5533a01f..f5825742 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ mkin - 1.0.4.9000 + 1.0.5
                                                                @@ -324,6 +324,12 @@ of an mmkin object

                                                                Fit nonlinear mixed models with SAEM

                                                                + +

                                                                nlmixr(<mmkin>) print(<nlmixr.mmkin>) nlmixr_model() nlmixr_data()

                                                                + +

                                                                Fit nonlinear mixed models using nlmixr

                                                                + +

                                                                plot(<mixed.mmkin>)

                                                                @@ -336,6 +342,12 @@ of an mmkin object

                                                                Summary method for class "nlme.mmkin"

                                                                + +

                                                                summary(<nlmixr.mmkin>) print(<summary.nlmixr.mmkin>)

                                                                + +

                                                                Summary method for class "nlmixr.mmkin"

                                                                + +

                                                                summary(<saem.mmkin>) print(<summary.saem.mmkin>)

                                                                @@ -343,7 +355,7 @@ of an mmkin object

                                                                -

                                                                nlme_function() mean_degparms() nlme_data()

                                                                +

                                                                nlme_function() nlme_data()

                                                                Helper functions to create nlme models from mmkin row objects

                                                                @@ -605,6 +617,12 @@ kinetic models fitted with mkinfit

                                                                Function to plot squared residuals and the error model for an mkin object

                                                                + +

                                                                mean_degparms()

                                                                + +

                                                                Calculate mean degradation parameters for an mmkin row object

                                                                + +

                                                                create_deg_func()

                                                                diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html new file mode 100644 index 00000000..f63dbc31 --- /dev/null +++ b/docs/dev/reference/mean_degparms.html @@ -0,0 +1,210 @@ + + + + + + + + +Calculate mean degradation parameters for an mmkin row object — mean_degparms • mkin + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                +
                                                                + + + + +
                                                                + +
                                                                +
                                                                +
                                                                +

                                                                Calculate mean degradation parameters for an mmkin row object

                                                                + Source: R/mean_degparms.R + +
                                                                + +
                                                                +

                                                                Calculate mean degradation parameters for an mmkin row object

                                                                +
                                                                + + 
                                                                mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6)
                                                                + +

                                                                Arguments

                                                                + + + + + + + + + + + + + + + + + + +
                                                                object

                                                                An mmkin row object containing several fits of the same model to different datasets

                                                                random

                                                                Should a list with fixed and random effects be returned?

                                                                test_log_parms

                                                                If TRUE, log parameters are only considered in +the mean calculations if their untransformed counterparts (most likely +rate constants) pass the t-test for significant difference from zero.

                                                                conf.level

                                                                Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.

                                                                + +

                                                                Value

                                                                + +

                                                                If random is FALSE (default), a named vector containing mean values +of the fitted degradation model parameters. If random is TRUE, a list with +fixed and random effects, in the format required by the start argument of +nlme for the case of a single grouping variable ds.

                                                                + +
                                                                + +
                                                                + + +
                                                                +
                                                                +

                                                                Developed by Johannes Ranke.

                                                                +
                                                                + +
                                                                +

                                                                Site built with pkgdown 1.6.1.

                                                                +
                                                                + +
                                                                +
                                                                + + + + + + + + diff --git a/docs/dev/reference/mixed-1.png b/docs/dev/reference/mixed-1.png index 28a376f4..422ab6a0 100644 Binary files a/docs/dev/reference/mixed-1.png and b/docs/dev/reference/mixed-1.png differ diff --git a/docs/dev/reference/mixed.html b/docs/dev/reference/mixed.html index 7bf8dd56..338480ee 100644 --- a/docs/dev/reference/mixed.html +++ b/docs/dev/reference/mixed.html @@ -72,7 +72,7 @@ mkin - 1.0.3.9000 + 1.0.5
                                                                @@ -180,6 +180,10 @@ +

                                                                Value

                                                                + +

                                                                An object of class 'mixed.mmkin' which has the observed data in a +single dataframe which is convenient for plotting

                                                                Examples

                                                                sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
diff --git a/docs/dev/reference/mmkin-1.png b/docs/dev/reference/mmkin-1.png
index 0db3379f..701a6d6a 100644
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diff --git a/docs/dev/reference/mmkin-2.png b/docs/dev/reference/mmkin-2.png
index 024a9892..5277b389 100644
Binary files a/docs/dev/reference/mmkin-2.png and b/docs/dev/reference/mmkin-2.png differ
diff --git a/docs/dev/reference/mmkin-3.png b/docs/dev/reference/mmkin-3.png
index a23d7cb9..2659cd61 100644
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diff --git a/docs/dev/reference/mmkin-4.png b/docs/dev/reference/mmkin-4.png
index 89975db5..ae16ee79 100644
Binary files a/docs/dev/reference/mmkin-4.png and b/docs/dev/reference/mmkin-4.png differ
diff --git a/docs/dev/reference/mmkin-5.png b/docs/dev/reference/mmkin-5.png
index a2f34983..2b9dc831 100644
Binary files a/docs/dev/reference/mmkin-5.png and b/docs/dev/reference/mmkin-5.png differ
diff --git a/docs/dev/reference/mmkin.html b/docs/dev/reference/mmkin.html
index 5da1b1de..c385bbf6 100644
--- a/docs/dev/reference/mmkin.html
+++ b/docs/dev/reference/mmkin.html
@@ -75,7 +75,7 @@ datasets specified in its first two arguments." />
       
       
         mkin
-        1.0.3.9000
+        1.0.5
       
     
                                                                
 
@@ -155,7 +155,7 @@ datasets specified in its first two arguments.
                                                                
     
                                                                mmkin(
   models = c("SFO", "FOMC", "DFOP"),
   datasets,
-  cores = parallel::detectCores(),
+  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
   cluster = NULL,
   ...
 )
@@ -183,7 +183,8 @@ data for mkinfit.
                                                                
 is only used when the cluster argument is NULL. On Windows
 machines, cores > 1 is not supported, you need to use the cluster
 argument to use multiple logical processors. Per default, all cores
-detected by parallel::detectCores() are used.
                                                                
+detected by parallel::detectCores() are used, except on Windows where
+the default is 1.
                                                                
     
     
       cluster
@@ -234,9 +235,9 @@ plotting.
                                                                time_default
                                                                #> user system elapsed -#> 4.921 0.475 1.707
                                                                time_1 +#> 4.771 0.576 1.803
                                                                time_1
                                                                #> user system elapsed -#> 5.680 0.003 5.684
                                                                +#> 5.779 0.000 5.781
                                                                endpoints(fits.0[["SFO_lin", 2]])
                                                                #> $ff #> parent_M1 parent_sink M1_M2 M1_sink diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png index fd68ae43..365aaef0 100644 Binary files a/docs/dev/reference/nlme-1.png and b/docs/dev/reference/nlme-1.png differ diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png index 853cae40..40841404 100644 Binary files a/docs/dev/reference/nlme-2.png and b/docs/dev/reference/nlme-2.png differ diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index 78d132e9..55a94443 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> mkin - 1.0.4.9000 + 1.0.5
                                                                @@ -155,8 +155,6 @@ datasets. They are used internally by the nlme.m 
                                                                nlme_function(object)
 
-mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6)
-
 nlme_data(object)

                                                                Arguments

                                                                @@ -166,30 +164,11 @@ datasets. They are used internally by the nlme.m object

                                                                An mmkin row object containing several fits of the same model to different datasets

                                                                - - random -

                                                                Should a list with fixed and random effects be returned?

                                                                - - - test_log_parms -

                                                                If TRUE, log parameters are only considered in -the mean calculations if their untransformed counterparts (most likely -rate constants) pass the t-test for significant difference from zero.

                                                                - - - conf.level -

                                                                Possibility to adjust the required confidence level -for parameter that are tested if requested by 'test_log_parms'.

                                                                -

                                                                Value

                                                                A function that can be used with nlme

                                                                -

                                                                If random is FALSE (default), a named vector containing mean values -of the fitted degradation model parameters. If random is TRUE, a list with -fixed and random effects, in the format required by the start argument of -nlme for the case of a single grouping variable ds.

                                                                A groupedData object

                                                                See also

                                                                @@ -217,7 +196,7 @@ nlme for the case of a single grouping variable ds.

                                                                ds <- c(d1 = d1, d2 = d2, d3 = d3) f <- mmkin("SFO", ds, cores = 1, quiet = TRUE) -mean_dp <- mean_degparms(f) +mean_dp <- mean_degparms(f) grouped_data <- nlme_data(f) nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be @@ -237,28 +216,28 @@ nlme for the case of a single grouping variable ds.

                                                                #> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) #> Data: grouped_data #> AIC BIC logLik -#> 298.2781 307.7372 -144.1391 +#> 300.6824 310.2426 -145.3412 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual -#> StdDev: 0.937473 0.7098105 3.83543 +#> StdDev: 1.697361 0.6801209 3.666073 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 #> Value Std.Error DF t-value p-value -#> parent_0 101.76838 1.1445443 45 88.91607 0 -#> log_k_parent_sink -3.05444 0.4195622 45 -7.28008 0 +#> parent_0 100.99378 1.3890416 46 72.70753 0 +#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.034 +#> log_k_parent_sink 0.027 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -2.61693595 -0.21853231 0.05740682 0.57209372 3.04598764 +#> Min Q1 Med Q3 Max +#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 #> -#> Number of Observations: 49 +#> Number of Observations: 50 #> Number of Groups: 3
                                                                plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
                                                                # augPred does not work on fits with more than one state # variable diff --git a/docs/dev/reference/nlme.mmkin-1.png b/docs/dev/reference/nlme.mmkin-1.png index 90ede880..95adfafb 100644 Binary files a/docs/dev/reference/nlme.mmkin-1.png and b/docs/dev/reference/nlme.mmkin-1.png differ diff --git a/docs/dev/reference/nlme.mmkin-2.png b/docs/dev/reference/nlme.mmkin-2.png index 0d140fd1..53b6fc76 100644 Binary files a/docs/dev/reference/nlme.mmkin-2.png and b/docs/dev/reference/nlme.mmkin-2.png differ diff --git a/docs/dev/reference/nlme.mmkin-3.png b/docs/dev/reference/nlme.mmkin-3.png index 8a60b52b..8df1e73b 100644 Binary files a/docs/dev/reference/nlme.mmkin-3.png and b/docs/dev/reference/nlme.mmkin-3.png differ diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index f308d8b7..2bbf4f80 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> mkin - 1.0.4.9000 + 1.0.5
                                                                @@ -155,11 +155,11 @@ have been obtained by fitting the same model to a list of datasets.

                                                                nlme( model, data = "auto", - fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text = + fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text = paste(el, 1, sep = "~")))), random = pdDiag(fixed), groups, - start = mean_degparms(model, random = TRUE), + start = mean_degparms(model, random = TRUE, test_log_parms = TRUE), correlation = NULL, weights = NULL, subset, @@ -350,8 +350,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
                                                                anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)
                                                                #> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274 -#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3273 <.0001
                                                                +#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 +#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.0001
                                                                endpoints(f_nlme_sfo_sfo)
                                                                #> $ff #> parent_sink parent_A1 A1_sink @@ -364,12 +364,12 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #>
                                                                endpoints(f_nlme_dfop_sfo)
                                                                #> $ff #> parent_A1 parent_sink -#> 0.2768575 0.7231425 +#> 0.2768574 0.7231426 #> #> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 11.07091 104.6320 31.49737 4.462384 46.20825 -#> A1 162.30492 539.1653 NA NA NA +#> parent 11.07091 104.6320 31.49737 4.462383 46.20825 +#> A1 162.30519 539.1662 NA NA NA #>
                                                                if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available # Attempts to fit metabolite kinetics with the tc error model are possible, @@ -452,8 +452,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs)
                                                                #> Model df AIC BIC logLik Test L.Ratio -#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274 -#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32093 +#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 +#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32091 #> p-value #> f_nlme_dfop_sfo #> f_nlme_dfop_sfo_obs <.0001
                                                                diff --git a/docs/dev/reference/nlmixr.mmkin-1.png b/docs/dev/reference/nlmixr.mmkin-1.png new file mode 100644 index 00000000..851d363d Binary files /dev/null and b/docs/dev/reference/nlmixr.mmkin-1.png differ diff --git a/docs/dev/reference/nlmixr.mmkin-2.png b/docs/dev/reference/nlmixr.mmkin-2.png new file mode 100644 index 00000000..d0c74c31 Binary files /dev/null and b/docs/dev/reference/nlmixr.mmkin-2.png differ diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html new file mode 100644 index 00000000..d017e463 --- /dev/null +++ b/docs/dev/reference/nlmixr.mmkin.html @@ -0,0 +1,13791 @@ + + + + + + + + +Fit nonlinear mixed models using nlmixr — nlmixr.mmkin • mkin + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                +
                                                                + + + + +
                                                                + +
                                                                +
                                                                +
                                                                +

                                                                Fit nonlinear mixed models using nlmixr

                                                                + Source: R/nlmixr.R + +
                                                                + +
                                                                +

                                                                This function uses nlmixr::nlmixr() as a backend for fitting nonlinear mixed +effects models created from mmkin row objects using the Stochastic Approximation +Expectation Maximisation algorithm (SAEM).

                                                                +
                                                                + + 
                                                                # S3 method for mmkin
+nlmixr(
+  object,
+  data = NULL,
+  est = NULL,
+  control = list(),
+  table = tableControl(),
+  error_model = object[[1]]$err_mod,
+  test_log_parms = TRUE,
+  conf.level = 0.6,
+  ...,
+  save = NULL,
+  envir = parent.frame()
+)
+
+# S3 method for nlmixr.mmkin
+print(x, digits = max(3, getOption("digits") - 3), ...)
+
+nlmixr_model(
+  object,
+  est = c("saem", "focei"),
+  degparms_start = "auto",
+  test_log_parms = FALSE,
+  conf.level = 0.6,
+  error_model = object[[1]]$err_mod,
+  add_attributes = FALSE
+)
+
+nlmixr_data(object, ...)
                                                                + +

                                                                Arguments

                                                                + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                object

                                                                An mmkin row object containing several fits of the same +mkinmod model to different datasets

                                                                data

                                                                Not used, as the data are extracted from the mmkin row object

                                                                est

                                                                Estimation method passed to nlmixr::nlmixr

                                                                control

                                                                Passed to nlmixr::nlmixr

                                                                table

                                                                Passed to nlmixr::nlmixr

                                                                error_model

                                                                Possibility to override the error model which is being +set based on the error model used in the mmkin row object.

                                                                test_log_parms

                                                                If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using mean_degparms.

                                                                conf.level

                                                                Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.

                                                                ...

                                                                Passed to nlmixr_model

                                                                save

                                                                Passed to nlmixr::nlmixr

                                                                envir

                                                                Passed to nlmixr::nlmixr

                                                                x

                                                                An nlmixr.mmkin object to print

                                                                digits

                                                                Number of digits to use for printing

                                                                degparms_start

                                                                Parameter values given as a named numeric vector will +be used to override the starting values obtained from the 'mmkin' object.

                                                                add_attributes

                                                                Should the starting values used for degradation model +parameters and their distribution and for the error model parameters +be returned as attributes?

                                                                + +

                                                                Value

                                                                + +

                                                                An S3 object of class 'nlmixr.mmkin', containing the fitted +nlmixr::nlmixr object as a list component named 'nm'. The +object also inherits from 'mixed.mmkin'.

                                                                +

                                                                An function defining a model suitable for fitting with nlmixr::nlmixr.

                                                                +

                                                                An dataframe suitable for use with nlmixr::nlmixr

                                                                +

                                                                Details

                                                                + +

                                                                An mmkin row object is essentially a list of mkinfit objects that have been +obtained by fitting the same model to a list of datasets using mkinfit.

                                                                +

                                                                See also

                                                                + +

                                                                summary.nlmixr.mmkin plot.mixed.mmkin

                                                                + +

                                                                Examples

                                                                + 
                                                                # \dontrun{
+ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")]))
+names(ds) <- paste("Dataset", 6:10)
+
+f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1)
+f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc",
+  cores = 1, quiet = TRUE)
+
+f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> RxODE 1.1.0 using 8 threads (see ?getRxThreads)
+#>   no cache: create with `rxCreateCache()`
                                                                #> 1:    86.5083   -3.1968    4.1673    1.7173   48.7028
+#> 2:    87.3628   -3.1468    3.9589    1.6315   45.1225
+#> 3:    86.8866   -3.2249    3.7610    1.8212   43.0034
+#> 4:    85.9210   -3.2427    3.5729    1.7302   39.4197
+#> 5:    85.8539   -3.2018    3.3943    1.7234   38.2933
+#> 6:    85.6934   -3.2262    3.2246    1.6843   39.0348
+#> 7:    85.7421   -3.2696    4.1298    1.7086   39.8152
+#> 8:    85.1605   -3.2190    3.9234    1.7588   41.7476
+#> 9:    84.7745   -3.2389    3.7361    1.6708   41.8512
+#> 10:    84.6549   -3.2078    3.5493    1.6489   41.6110
+#> 11:    84.4739   -3.2788    3.3718    1.5664   42.0076
+#> 12:    84.7871   -3.2674    3.4931    1.6097   40.9060
+#> 13:    84.5267   -3.2635    3.3185    1.6352   39.6914
+#> 14:    84.9806   -3.2353    3.1525    1.6470   39.2556
+#> 15:    84.9752   -3.2566    2.9949    1.6756   39.6152
+#> 16:    85.6293   -3.2232    2.8452    1.7076   39.4391
+#> 17:    85.9944   -3.2268    2.7029    1.6702   40.2731
+#> 18:    86.2811   -3.2260    2.5678    1.7100   41.4854
+#> 19:    86.2617   -3.2476    2.4489    1.7051   41.3066
+#> 20:    85.7552   -3.2032    3.3323    1.8885   42.2273
+#> 21:    85.6493   -3.2685    3.2317    1.7941   39.4198
+#> 22:    86.0133   -3.2457    4.0910    1.7044   39.0319
+#> 23:    86.1636   -3.2528    4.9399    1.6571   38.6728
+#> 24:    86.3086   -3.1708    7.0791    1.8182   39.6791
+#> 25:    85.7316   -3.2203    6.7252    1.7369   38.3546
+#> 26:    85.3476   -3.2341    6.3889    1.6864   38.0521
+#> 27:    85.6328   -3.2543    6.0695    1.6945   37.7990
+#> 28:    85.1715   -3.2191    5.7660    1.7898   38.5662
+#> 29:    85.4945   -3.2264    5.4777    1.7007   40.1659
+#> 30:    85.0864   -3.2463    5.2038    1.6156   39.0718
+#> 31:    85.8220   -3.2347    4.9436    1.6115   39.2011
+#> 32:    85.9869   -3.2400    4.6964    1.6818   41.2956
+#> 33:    85.9899   -3.2041    4.4616    1.6606   40.6657
+#> 34:    85.8353   -3.2065    4.2385    1.6868   41.5006
+#> 35:    85.8113   -3.2366    4.0266    1.8261   41.0403
+#> 36:    85.5233   -3.2389    3.8253    1.7348   39.5202
+#> 37:    85.1751   -3.2657    3.6340    1.6948   39.6097
+#> 38:    85.2768   -3.2380    3.4887    1.6820   38.7641
+#> 39:    84.8240   -3.2264    3.3143    1.5979   39.8074
+#> 40:    85.3754   -3.2147    3.1485    1.5810   39.1710
+#> 41:    85.0277   -3.2347    2.9911    1.7061   39.9948
+#> 42:    85.0113   -3.2651    3.1969    1.6208   39.7266
+#> 43:    85.0772   -3.2729    3.0371    1.6160   40.2919
+#> 44:    85.0769   -3.2272    3.3310    1.7321   38.5229
+#> 45:    85.1638   -3.2546    3.1644    1.6968   40.2382
+#> 46:    84.7966   -3.2597    5.0694    1.6816   38.7996
+#> 47:    85.0588   -3.2247    5.9549    1.7452   39.6569
+#> 48:    85.1769   -3.2557    5.6572    1.7441   37.9050
+#> 49:    84.9296   -3.2425    5.3743    1.6729   37.7885
+#> 50:    85.3414   -3.2421    5.1056    1.6646   38.2243
+#> 51:    84.9127   -3.2674    5.8827    1.7180   40.2859
+#> 52:    85.2014   -3.2471    5.5885    1.7318   39.1745
+#> 53:    85.9330   -3.2228    7.2369    1.8328   39.0461
+#> 54:    86.9718   -3.1447    6.9332    1.8404   39.3098
+#> 55:    87.2708   -3.1595    6.6308    1.8049   39.1338
+#> 56:    87.2006   -3.1746    6.2993    1.7541   38.2780
+#> 57:    87.8013   -3.2306    5.9843    1.6664   40.4876
+#> 58:    87.7294   -3.2120    5.6851    1.5831   41.5056
+#> 59:    87.4898   -3.2207    5.4008    1.5039   41.4401
+#> 60:    86.9156   -3.1861    5.1308    1.6408   39.8972
+#> 61:    86.4508   -3.1870    4.8742    1.5935   39.6871
+#> 62:    86.4028   -3.2191    4.6305    1.6267   39.2092
+#> 63:    86.2536   -3.2491    4.5199    1.5617   39.7603
+#> 64:    85.9775   -3.2650    4.2939    1.6077   39.1909
+#> 65:    85.8907   -3.2430    4.0792    1.6729   37.9420
+#> 66:    85.3450   -3.2888    3.8753    1.6201   40.8998
+#> 67:    85.1869   -3.2940    3.6815    1.6157   40.5107
+#> 68:    84.8029   -3.2830    3.4974    1.6040   40.6254
+#> 69:    85.3549   -3.2425    4.4768    1.5238   40.2418
+#> 70:    85.7957   -3.2296    4.2529    1.7175   40.8618
+#> 71:    85.4200   -3.2381    4.0403    1.6695   41.5731
+#> 72:    85.2950   -3.2566    3.8383    1.5998   40.6494
+#> 73:    85.0683   -3.2464    3.6464    1.5576   39.8095
+#> 74:    85.1667   -3.2436    3.4641    1.6383   39.4925
+#> 75:    84.6547   -3.2300    3.7226    1.6656   40.4684
+#> 76:    84.4882   -3.2521    3.6468    1.6035   40.1800
+#> 77:    84.5250   -3.2398    4.1501    1.6827   40.5269
+#> 78:    84.5191   -3.2372    5.5482    1.6309   41.1739
+#> 79:    84.7471   -3.2581    6.0637    1.6259   41.1003
+#> 80:    85.0581   -3.2680    5.7605    1.6841   40.8918
+#> 81:    84.8468   -3.2564    5.4725    1.6475   39.3456
+#> 82:    84.7614   -3.2385    5.1988    1.7550   38.7275
+#> 83:    85.2921   -3.2657    5.9253    1.6672   39.2423
+#> 84:    85.5760   -3.2261    5.6290    1.7505   39.5500
+#> 85:    85.3215   -3.2277    5.5987    1.8027   39.3145
+#> 86:    85.2656   -3.2023    5.3188    1.8024   40.3098
+#> 87:    84.8950   -3.2551    5.0528    1.7123   39.3470
+#> 88:    84.3157   -3.2661    4.8002    1.6267   38.7095
+#> 89:    84.5442   -3.2870    4.5602    1.5892   39.1735
+#> 90:    85.0956   -3.2195    4.8385    1.5796   39.5164
+#> 91:    84.8619   -3.2621    4.5966    1.6889   39.5512
+#> 92:    84.4901   -3.2735    6.1405    1.6704   39.3358
+#> 93:    84.0819   -3.2609    5.8335    1.6130   38.8618
+#> 94:    84.7585   -3.2336    5.5418    1.6301   38.6591
+#> 95:    85.2669   -3.2358    5.2647    1.6619   38.9136
+#> 96:    85.4955   -3.2064    5.0015    1.7673   39.0495
+#> 97:    85.6591   -3.2016    4.7514    1.7046   40.7861
+#> 98:    86.2097   -3.2833    7.4722    1.6413   42.2938
+#> 99:    85.9645   -3.2570    7.7124    1.5592   41.7216
+#> 100:    85.7018   -3.2605    8.2687    1.6798   40.6639
+#> 101:    85.9905   -3.1956   11.0194    1.7017   39.4324
+#> 102:    87.2679   -3.1741   10.4684    1.7063   38.6812
+#> 103:    86.1910   -3.1709    9.9450    1.7151   38.5198
+#> 104:    86.4413   -3.1544    9.4478    1.7123   38.7428
+#> 105:    85.9840   -3.1921   10.6297    1.8135   38.7775
+#> 106:    85.9926   -3.1839   10.0982    1.7228   40.3136
+#> 107:    85.1792   -3.2343    9.5933    1.6367   40.2709
+#> 108:    84.7583   -3.2332    9.1136    1.6907   41.2122
+#> 109:    85.3756   -3.2311    8.6579    1.7307   39.9303
+#> 110:    84.9686   -3.2365    8.2250    1.7221   40.0379
+#> 111:    84.8527   -3.2448    7.8138    1.6775   39.6794
+#> 112:    84.6271   -3.2609    7.4231    1.7321   41.5666
+#> 113:    84.8515   -3.3056    7.2514    1.7001   41.9758
+#> 114:    84.5991   -3.2319    7.8463    1.7690   41.1386
+#> 115:    85.0535   -3.2864    7.4540    1.7282   40.3883
+#> 116:    85.8661   -3.2355    7.0813    1.7801   39.3078
+#> 117:    85.9911   -3.2357    6.7272    1.6911   38.3913
+#> 118:    86.1894   -3.2424    6.3909    1.6701   38.1915
+#> 119:    85.5637   -3.1992    6.0713    1.7360   38.9386
+#> 120:    86.0733   -3.2069    5.7677    1.7185   36.5189
+#> 121:    86.0168   -3.2181    5.4794    1.7135   38.4044
+#> 122:    86.7470   -3.2319    6.1989    1.6840   38.2615
+#> 123:    86.2918   -3.2089    5.8890    1.6656   38.8486
+#> 124:    85.9387   -3.2124    5.5945    1.6334   37.9425
+#> 125:    86.1519   -3.2717    5.3148    1.7094   38.9708
+#> 126:    85.5194   -3.2391    5.4217    1.6799   39.4876
+#> 127:    85.9691   -3.2205    5.8051    1.6436   40.0593
+#> 128:    85.6171   -3.2309    5.5148    1.6852   39.5398
+#> 129:    84.9252   -3.2495    5.2391    1.7154   40.4020
+#> 130:    85.1496   -3.2882    5.0538    1.7189   40.0908
+#> 131:    85.8552   -3.2474    7.1203    1.6329   39.0547
+#> 132:    86.4666   -3.2151    6.7643    1.7342   38.6596
+#> 133:    86.1550   -3.1895    6.4261    1.7904   38.6211
+#> 134:    86.5040   -3.1785    6.1048    1.7180   39.0804
+#> 135:    85.9752   -3.2116    5.7996    1.6979   38.1745
+#> 136:    86.2161   -3.2075    5.5096    1.7408   38.9002
+#> 137:    85.8408   -3.2604    6.9319    1.7616   39.1657
+#> 138:    86.1261   -3.2179    7.0802    1.8115   37.6614
+#> 139:    85.9082   -3.2374    6.7262    1.7209   38.1986
+#> 140:    85.9556   -3.2641    6.3899    1.8300   39.2071
+#> 141:    86.2052   -3.1928    6.0704    1.7385   38.1745
+#> 142:    86.4062   -3.2076    5.8348    1.6693   38.0271
+#> 143:    86.0680   -3.2372    5.5431    1.7259   39.3885
+#> 144:    86.2001   -3.2040    5.2659    1.6803   38.1606
+#> 145:    86.5820   -3.2306    5.0026    1.6063   38.7208
+#> 146:    86.4522   -3.2072    4.7525    1.6572   37.5206
+#> 147:    85.8311   -3.2320    4.5149    1.7043   39.6955
+#> 148:    86.0754   -3.2072    5.4070    1.6707   38.8858
+#> 149:    87.0038   -3.1954    5.1367    1.7361   37.9862
+#> 150:    86.8647   -3.1903    4.8798    1.7995   39.6906
+#> 151:    86.4913   -3.2101    4.6358    1.7618   39.2462
+#> 152:    86.4667   -3.2254    4.6929    1.7762   38.0665
+#> 153:    86.0176   -3.2241    4.4586    1.7708   37.6367
+#> 154:    85.8680   -3.2359    5.2401    1.7272   37.7322
+#> 155:    85.6560   -3.2147    3.3340    1.7833   38.4605
+#> 156:    85.6927   -3.1987    1.9644    1.8176   39.4958
+#> 157:    86.3686   -3.2294    3.4959    1.6556   39.7058
+#> 158:    86.7614   -3.2051    2.3005    1.6413   40.3968
+#> 159:    86.6393   -3.2243    1.7824    1.6521   40.0846
+#> 160:    86.8686   -3.1850    1.6490    1.7211   39.6362
+#> 161:    86.7853   -3.2071    1.1720    1.6132   39.6921
+#> 162:    86.7337   -3.1825    1.0646    1.5897   41.1027
+#> 163:    86.9192   -3.1365    1.0339    1.6656   40.2410
+#> 164:    86.6652   -3.2052    0.9750    1.5817   40.6189
+#> 165:    86.6154   -3.1870    1.2602    1.6559   40.1832
+#> 166:    86.7300   -3.2096    1.2144    1.6571   39.8989
+#> 167:    86.4536   -3.2135    0.5155    1.7436   39.6313
+#> 168:    86.4848   -3.2315    0.5060    1.6681   39.1479
+#> 169:    86.2641   -3.2444    0.3935    1.6781   40.2903
+#> 170:    86.2482   -3.2628    0.3342    1.6177   40.2600
+#> 171:    86.2833   -3.2338    0.1701    1.6698   39.8946
+#> 172:    86.2155   -3.2175    0.1858    1.6090   39.9709
+#> 173:    86.2916   -3.2313    0.2088    1.6918   41.4421
+#> 174:    86.1920   -3.2050    0.2067    1.7521   40.7724
+#> 175:    86.2771   -3.2071    0.2213    1.5502   40.5055
+#> 176:    86.2589   -3.1867    0.2010    1.5814   40.0963
+#> 177:    86.2740   -3.2209    0.2679    1.6774   40.9479
+#> 178:    86.2210   -3.1896    0.4420    1.5512   40.3238
+#> 179:    86.1769   -3.2036    0.5592    1.6008   40.3873
+#> 180:    85.9366   -3.2046    0.5056    1.6948   41.4254
+#> 181:    85.9173   -3.2167    0.6033    1.6886   39.5784
+#> 182:    85.7077   -3.2508    0.5008    1.7501   40.4224
+#> 183:    85.8084   -3.2743    0.5737    1.7174   40.0576
+#> 184:    85.7776   -3.2518    0.7164    1.7495   39.8748
+#> 185:    85.6192   -3.2378    1.1401    1.7562   39.9841
+#> 186:    85.6951   -3.2460    1.5642    1.7330   39.1282
+#> 187:    85.5281   -3.2309    1.5452    1.7900   38.4833
+#> 188:    85.3476   -3.2018    1.1385    1.8106   39.2842
+#> 189:    85.1914   -3.2180    1.0465    1.7562   40.0715
+#> 190:    85.2759   -3.2275    1.0437    1.7160   39.9928
+#> 191:    85.3630   -3.2728    1.5672    1.7394   39.4749
+#> 192:    85.1334   -3.2467    0.9598    1.6243   39.7385
+#> 193:    84.9313   -3.2401    0.6441    1.6518   39.5447
+#> 194:    84.9097   -3.2361    0.4275    1.6509   40.3383
+#> 195:    84.9131   -3.2241    0.3344    1.5868   39.1438
+#> 196:    84.9117   -3.2419    0.2435    1.6882   40.1132
+#> 197:    84.9569   -3.2776    0.2352    1.6351   40.1070
+#> 198:    84.9113   -3.2334    0.2133    1.6282   39.9988
+#> 199:    84.9028   -3.2637    0.1859    1.6127   38.8695
+#> 200:    84.9020   -3.2456    0.2429    1.6172   40.2644
+#> 201:    84.9327   -3.2292    0.1787    1.6720   40.5826
+#> 202:    84.9313   -3.2363    0.1487    1.6641   40.1952
+#> 203:    84.9208   -3.2350    0.1445    1.6449   40.0176
+#> 204:    84.9312   -3.2296    0.1488    1.6292   40.1353
+#> 205:    84.9302   -3.2277    0.1454    1.6167   40.4137
+#> 206:    84.9378   -3.2314    0.1474    1.6263   40.2241
+#> 207:    84.9190   -3.2369    0.1454    1.6374   40.1459
+#> 208:    84.9085   -3.2385    0.1527    1.6439   40.1931
+#> 209:    84.8920   -3.2411    0.1566    1.6396   40.1558
+#> 210:    84.8787   -3.2435    0.1574    1.6381   40.1872
+#> 211:    84.8784   -3.2460    0.1528    1.6407   40.1825
+#> 212:    84.8745   -3.2469    0.1474    1.6439   40.0865
+#> 213:    84.8702   -3.2474    0.1429    1.6459   40.0164
+#> 214:    84.8592   -3.2476    0.1421    1.6506   39.9852
+#> 215:    84.8558   -3.2479    0.1389    1.6549   39.9882
+#> 216:    84.8542   -3.2488    0.1365    1.6625   39.9461
+#> 217:    84.8594   -3.2488    0.1354    1.6691   39.9751
+#> 218:    84.8634   -3.2487    0.1335    1.6751   39.9844
+#> 219:    84.8653   -3.2485    0.1298    1.6759   39.9263
+#> 220:    84.8722   -3.2496    0.1267    1.6748   39.8897
+#> 221:    84.8782   -3.2496    0.1267    1.6757   39.8504
+#> 222:    84.8772   -3.2483    0.1278    1.6761   39.8406
+#> 223:    84.8765   -3.2490    0.1296    1.6785   39.8138
+#> 224:    84.8750   -3.2492    0.1274    1.6772   39.8278
+#> 225:    84.8767   -3.2493    0.1266    1.6727   39.8642
+#> 226:    84.8741   -3.2495    0.1251    1.6711   39.8208
+#> 227:    84.8678   -3.2502    0.1234    1.6680   39.8193
+#> 228:    84.8618   -3.2509    0.1217    1.6660   39.7846
+#> 229:    84.8567   -3.2504    0.1208    1.6640   39.7538
+#> 230:    84.8559   -3.2503    0.1215    1.6624   39.7184
+#> 231:    84.8548   -3.2501    0.1203    1.6596   39.6840
+#> 232:    84.8528   -3.2505    0.1206    1.6550   39.6882
+#> 233:    84.8510   -3.2499    0.1229    1.6560   39.7083
+#> 234:    84.8479   -3.2502    0.1243    1.6568   39.7116
+#> 235:    84.8443   -3.2509    0.1244    1.6571   39.7504
+#> 236:    84.8391   -3.2515    0.1253    1.6584   39.7761
+#> 237:    84.8390   -3.2522    0.1246    1.6595   39.8188
+#> 238:    84.8433   -3.2520    0.1240    1.6606   39.8393
+#> 239:    84.8453   -3.2517    0.1233    1.6604   39.8360
+#> 240:    84.8439   -3.2519    0.1225    1.6597   39.8355
+#> 241:    84.8423   -3.2516    0.1215    1.6591   39.8154
+#> 242:    84.8403   -3.2521    0.1208    1.6572   39.7956
+#> 243:    84.8378   -3.2514    0.1199    1.6579   39.7842
+#> 244:    84.8375   -3.2501    0.1191    1.6582   39.7851
+#> 245:    84.8367   -3.2497    0.1200    1.6571   39.7873
+#> 246:    84.8348   -3.2499    0.1200    1.6561   39.7972
+#> 247:    84.8344   -3.2490    0.1196    1.6546   39.8425
+#> 248:    84.8320   -3.2485    0.1197    1.6551   39.8607
+#> 249:    84.8330   -3.2477    0.1212    1.6550   39.8643
+#> 250:    84.8348   -3.2481    0.1217    1.6561   39.8570
+#> 251:    84.8384   -3.2483    0.1214    1.6569   39.8535
+#> 252:    84.8394   -3.2487    0.1218    1.6578   39.8584
+#> 253:    84.8408   -3.2490    0.1229    1.6586   39.9146
+#> 254:    84.8414   -3.2497    0.1232    1.6602   39.9561
+#> 255:    84.8424   -3.2502    0.1229    1.6617   39.9734
+#> 256:    84.8428   -3.2506    0.1230    1.6609   39.9959
+#> 257:    84.8425   -3.2507    0.1221    1.6600   40.0029
+#> 258:    84.8420   -3.2513    0.1213    1.6585   40.0135
+#> 259:    84.8411   -3.2512    0.1212    1.6576   40.0261
+#> 260:    84.8404   -3.2513    0.1219    1.6562   40.0238
+#> 261:    84.8382   -3.2514    0.1226    1.6553   40.0140
+#> 262:    84.8358   -3.2511    0.1226    1.6547   40.0022
+#> 263:    84.8337   -3.2513    0.1224    1.6539   40.0037
+#> 264:    84.8318   -3.2511    0.1223    1.6531   39.9986
+#> 265:    84.8316   -3.2504    0.1213    1.6533   40.0094
+#> 266:    84.8325   -3.2503    0.1202    1.6549   40.0179
+#> 267:    84.8328   -3.2501    0.1189    1.6547   40.0438
+#> 268:    84.8324   -3.2505    0.1183    1.6532   40.0734
+#> 269:    84.8315   -3.2505    0.1177    1.6545   40.0714
+#> 270:    84.8304   -3.2508    0.1175    1.6545   40.0698
+#> 271:    84.8293   -3.2512    0.1173    1.6542   40.0623
+#> 272:    84.8279   -3.2512    0.1165    1.6537   40.0659
+#> 273:    84.8260   -3.2512    0.1171    1.6536   40.0580
+#> 274:    84.8241   -3.2512    0.1172    1.6523   40.0540
+#> 275:    84.8245   -3.2508    0.1171    1.6529   40.0513
+#> 276:    84.8240   -3.2510    0.1165    1.6523   40.0407
+#> 277:    84.8240   -3.2509    0.1160    1.6516   40.0290
+#> 278:    84.8250   -3.2507    0.1156    1.6505   40.0255
+#> 279:    84.8253   -3.2507    0.1147    1.6509   40.0301
+#> 280:    84.8252   -3.2507    0.1140    1.6503   40.0278
+#> 281:    84.8255   -3.2508    0.1135    1.6504   40.0238
+#> 282:    84.8246   -3.2506    0.1128    1.6505   40.0212
+#> 283:    84.8237   -3.2508    0.1120    1.6509   40.0206
+#> 284:    84.8235   -3.2507    0.1121    1.6518   40.0316
+#> 285:    84.8236   -3.2499    0.1121    1.6523   40.0330
+#> 286:    84.8230   -3.2490    0.1118    1.6530   40.0435
+#> 287:    84.8222   -3.2485    0.1119    1.6526   40.0428
+#> 288:    84.8211   -3.2486    0.1120    1.6512   40.0446
+#> 289:    84.8196   -3.2490    0.1121    1.6508   40.0355
+#> 290:    84.8189   -3.2494    0.1121    1.6503   40.0319
+#> 291:    84.8183   -3.2495    0.1126    1.6501   40.0263
+#> 292:    84.8174   -3.2496    0.1127    1.6495   40.0226
+#> 293:    84.8163   -3.2499    0.1126    1.6488   40.0255
+#> 294:    84.8165   -3.2499    0.1125    1.6479   40.0207
+#> 295:    84.8165   -3.2502    0.1130    1.6466   40.0406
+#> 296:    84.8158   -3.2508    0.1131    1.6464   40.0428
+#> 297:    84.8162   -3.2506    0.1129    1.6465   40.0432
+#> 298:    84.8166   -3.2501    0.1131    1.6460   40.0415
+#> 299:    84.8184   -3.2499    0.1138    1.6451   40.0513
+#> 300:    84.8205   -3.2499    0.1144    1.6450   40.0615
+#> 301:    84.8216   -3.2496    0.1156    1.6450   40.0591
+#> 302:    84.8225   -3.2498    0.1161    1.6448   40.0618
+#> 303:    84.8232   -3.2493    0.1163    1.6451   40.0612
+#> 304:    84.8233   -3.2488    0.1166    1.6450   40.0669
+#> 305:    84.8230   -3.2485    0.1163    1.6439   40.0714
+#> 306:    84.8221   -3.2482    0.1158    1.6440   40.0838
+#> 307:    84.8217   -3.2479    0.1154    1.6445   40.0835
+#> 308:    84.8219   -3.2477    0.1156    1.6450   40.0829
+#> 309:    84.8224   -3.2477    0.1152    1.6450   40.0836
+#> 310:    84.8224   -3.2480    0.1148    1.6457   40.0873
+#> 311:    84.8225   -3.2480    0.1143    1.6459   40.0894
+#> 312:    84.8219   -3.2482    0.1136    1.6460   40.0835
+#> 313:    84.8214   -3.2484    0.1131    1.6462   40.0810
+#> 314:    84.8208   -3.2485    0.1130    1.6471   40.0786
+#> 315:    84.8211   -3.2485    0.1128    1.6470   40.0707
+#> 316:    84.8211   -3.2483    0.1127    1.6469   40.0628
+#> 317:    84.8210   -3.2482    0.1124    1.6472   40.0580
+#> 318:    84.8201   -3.2484    0.1122    1.6472   40.0602
+#> 319:    84.8196   -3.2484    0.1117    1.6479   40.0555
+#> 320:    84.8183   -3.2480    0.1119    1.6486   40.0659
+#> 321:    84.8173   -3.2479    0.1122    1.6489   40.0713
+#> 322:    84.8164   -3.2479    0.1129    1.6491   40.0781
+#> 323:    84.8159   -3.2480    0.1136    1.6489   40.0790
+#> 324:    84.8158   -3.2480    0.1140    1.6489   40.0746
+#> 325:    84.8158   -3.2480    0.1138    1.6484   40.0845
+#> 326:    84.8157   -3.2482    0.1137    1.6482   40.0953
+#> 327:    84.8155   -3.2482    0.1134    1.6482   40.0955
+#> 328:    84.8156   -3.2482    0.1133    1.6471   40.1167
+#> 329:    84.8152   -3.2483    0.1129    1.6466   40.1195
+#> 330:    84.8152   -3.2482    0.1124    1.6459   40.1280
+#> 331:    84.8151   -3.2478    0.1120    1.6467   40.1282
+#> 332:    84.8147   -3.2477    0.1115    1.6471   40.1265
+#> 333:    84.8145   -3.2477    0.1110    1.6470   40.1333
+#> 334:    84.8144   -3.2479    0.1108    1.6468   40.1474
+#> 335:    84.8141   -3.2481    0.1106    1.6475   40.1549
+#> 336:    84.8135   -3.2481    0.1103    1.6481   40.1664
+#> 337:    84.8134   -3.2481    0.1106    1.6476   40.1837
+#> 338:    84.8129   -3.2479    0.1109    1.6482   40.1855
+#> 339:    84.8126   -3.2478    0.1107    1.6478   40.1830
+#> 340:    84.8120   -3.2482    0.1106    1.6471   40.1893
+#> 341:    84.8120   -3.2482    0.1106    1.6467   40.1931
+#> 342:    84.8119   -3.2482    0.1106    1.6473   40.2091
+#> 343:    84.8135   -3.2483    0.1109    1.6475   40.2113
+#> 344:    84.8153   -3.2483    0.1114    1.6472   40.2116
+#> 345:    84.8165   -3.2484    0.1119    1.6465   40.2110
+#> 346:    84.8171   -3.2481    0.1121    1.6462   40.2099
+#> 347:    84.8184   -3.2483    0.1126    1.6459   40.2120
+#> 348:    84.8189   -3.2483    0.1127    1.6455   40.2115
+#> 349:    84.8198   -3.2483    0.1127    1.6450   40.2087
+#> 350:    84.8202   -3.2482    0.1125    1.6454   40.2118
+#> 351:    84.8208   -3.2483    0.1120    1.6447   40.2094
+#> 352:    84.8213   -3.2483    0.1118    1.6444   40.2070
+#> 353:    84.8218   -3.2481    0.1115    1.6445   40.2077
+#> 354:    84.8226   -3.2482    0.1114    1.6439   40.2077
+#> 355:    84.8230   -3.2481    0.1113    1.6439   40.2072
+#> 356:    84.8232   -3.2479    0.1111    1.6439   40.2075
+#> 357:    84.8239   -3.2477    0.1109    1.6441   40.2021
+#> 358:    84.8245   -3.2476    0.1107    1.6445   40.2028
+#> 359:    84.8251   -3.2476    0.1107    1.6452   40.2032
+#> 360:    84.8252   -3.2474    0.1110    1.6462   40.2012
+#> 361:    84.8258   -3.2473    0.1108    1.6469   40.2043
+#> 362:    84.8260   -3.2475    0.1107    1.6467   40.2056
+#> 363:    84.8262   -3.2474    0.1106    1.6469   40.2028
+#> 364:    84.8266   -3.2472    0.1104    1.6473   40.1979
+#> 365:    84.8270   -3.2469    0.1102    1.6479   40.1923
+#> 366:    84.8273   -3.2469    0.1100    1.6482   40.1872
+#> 367:    84.8267   -3.2468    0.1099    1.6483   40.1836
+#> 368:    84.8263   -3.2470    0.1099    1.6483   40.1850
+#> 369:    84.8269   -3.2471    0.1098    1.6484   40.1864
+#> 370:    84.8274   -3.2472    0.1098    1.6484   40.1856
+#> 371:    84.8282   -3.2471    0.1101    1.6489   40.1839
+#> 372:    84.8288   -3.2469    0.1099    1.6492   40.1804
+#> 373:    84.8294   -3.2467    0.1098    1.6494   40.1806
+#> 374:    84.8301   -3.2466    0.1096    1.6491   40.1855
+#> 375:    84.8301   -3.2467    0.1093    1.6488   40.1951
+#> 376:    84.8302   -3.2467    0.1092    1.6484   40.1921
+#> 377:    84.8302   -3.2467    0.1092    1.6486   40.1842
+#> 378:    84.8300   -3.2467    0.1095    1.6485   40.1760
+#> 379:    84.8296   -3.2468    0.1094    1.6483   40.1701
+#> 380:    84.8297   -3.2469    0.1094    1.6483   40.1738
+#> 381:    84.8299   -3.2469    0.1093    1.6485   40.1801
+#> 382:    84.8302   -3.2470    0.1092    1.6488   40.1857
+#> 383:    84.8299   -3.2469    0.1090    1.6491   40.1859
+#> 384:    84.8297   -3.2470    0.1090    1.6488   40.1903
+#> 385:    84.8289   -3.2469    0.1095    1.6487   40.1978
+#> 386:    84.8282   -3.2470    0.1098    1.6487   40.1976
+#> 387:    84.8277   -3.2471    0.1101    1.6488   40.1910
+#> 388:    84.8270   -3.2471    0.1104    1.6486   40.1863
+#> 389:    84.8263   -3.2471    0.1108    1.6486   40.1837
+#> 390:    84.8259   -3.2472    0.1109    1.6491   40.1881
+#> 391:    84.8250   -3.2472    0.1111    1.6499   40.1919
+#> 392:    84.8248   -3.2471    0.1113    1.6501   40.1961
+#> 393:    84.8247   -3.2471    0.1113    1.6503   40.1941
+#> 394:    84.8241   -3.2470    0.1114    1.6508   40.1933
+#> 395:    84.8239   -3.2469    0.1115    1.6510   40.1916
+#> 396:    84.8239   -3.2468    0.1115    1.6515   40.1946
+#> 397:    84.8239   -3.2466    0.1113    1.6517   40.1979
+#> 398:    84.8241   -3.2467    0.1112    1.6519   40.1966
+#> 399:    84.8244   -3.2466    0.1112    1.6522   40.1975
+#> 400:    84.8248   -3.2466    0.1111    1.6523   40.1919
+#> 401:    84.8255   -3.2466    0.1109    1.6523   40.1889
+#> 402:    84.8259   -3.2468    0.1108    1.6523   40.1836
+#> 403:    84.8257   -3.2470    0.1109    1.6524   40.1787
+#> 404:    84.8251   -3.2470    0.1111    1.6528   40.1788
+#> 405:    84.8244   -3.2472    0.1113    1.6530   40.1761
+#> 406:    84.8235   -3.2472    0.1113    1.6529   40.1763
+#> 407:    84.8231   -3.2471    0.1112    1.6531   40.1742
+#> 408:    84.8229   -3.2471    0.1110    1.6530   40.1728
+#> 409:    84.8229   -3.2471    0.1109    1.6528   40.1698
+#> 410:    84.8233   -3.2473    0.1109    1.6524   40.1701
+#> 411:    84.8235   -3.2474    0.1109    1.6522   40.1714
+#> 412:    84.8236   -3.2474    0.1110    1.6517   40.1716
+#> 413:    84.8241   -3.2474    0.1111    1.6512   40.1741
+#> 414:    84.8238   -3.2476    0.1108    1.6508   40.1809
+#> 415:    84.8238   -3.2477    0.1108    1.6505   40.1803
+#> 416:    84.8234   -3.2475    0.1110    1.6504   40.1880
+#> 417:    84.8232   -3.2475    0.1112    1.6510   40.1938
+#> 418:    84.8232   -3.2475    0.1112    1.6511   40.1944
+#> 419:    84.8231   -3.2476    0.1114    1.6513   40.1921
+#> 420:    84.8226   -3.2477    0.1113    1.6511   40.1880
+#> 421:    84.8220   -3.2478    0.1111    1.6508   40.1859
+#> 422:    84.8213   -3.2478    0.1110    1.6503   40.1897
+#> 423:    84.8207   -3.2479    0.1110    1.6499   40.1876
+#> 424:    84.8203   -3.2479    0.1111    1.6498   40.1860
+#> 425:    84.8198   -3.2479    0.1111    1.6498   40.1817
+#> 426:    84.8191   -3.2479    0.1113    1.6498   40.1796
+#> 427:    84.8186   -3.2478    0.1112    1.6498   40.1781
+#> 428:    84.8183   -3.2478    0.1114    1.6496   40.1738
+#> 429:    84.8177   -3.2477    0.1116    1.6495   40.1695
+#> 430:    84.8172   -3.2477    0.1119    1.6496   40.1739
+#> 431:    84.8169   -3.2478    0.1120    1.6494   40.1741
+#> 432:    84.8169   -3.2479    0.1121    1.6490   40.1758
+#> 433:    84.8170   -3.2479    0.1121    1.6491   40.1793
+#> 434:    84.8171   -3.2480    0.1122    1.6488   40.1808
+#> 435:    84.8173   -3.2481    0.1123    1.6487   40.1845
+#> 436:    84.8176   -3.2481    0.1123    1.6489   40.1866
+#> 437:    84.8178   -3.2480    0.1122    1.6496   40.1872
+#> 438:    84.8183   -3.2480    0.1121    1.6502   40.1869
+#> 439:    84.8185   -3.2481    0.1119    1.6504   40.1834
+#> 440:    84.8185   -3.2480    0.1118    1.6506   40.1831
+#> 441:    84.8188   -3.2480    0.1120    1.6502   40.1893
+#> 442:    84.8192   -3.2480    0.1120    1.6501   40.1930
+#> 443:    84.8196   -3.2480    0.1120    1.6499   40.1917
+#> 444:    84.8202   -3.2478    0.1122    1.6498   40.1966
+#> 445:    84.8207   -3.2476    0.1124    1.6499   40.1977
+#> 446:    84.8210   -3.2473    0.1123    1.6496   40.2017
+#> 447:    84.8217   -3.2472    0.1123    1.6491   40.2030
+#> 448:    84.8221   -3.2473    0.1122    1.6488   40.2025
+#> 449:    84.8225   -3.2474    0.1121    1.6485   40.2069
+#> 450:    84.8224   -3.2473    0.1119    1.6484   40.2078
+#> 451:    84.8221   -3.2473    0.1118    1.6483   40.2032
+#> 452:    84.8220   -3.2472    0.1117    1.6484   40.1989
+#> 453:    84.8220   -3.2472    0.1117    1.6483   40.1953
+#> 454:    84.8220   -3.2473    0.1122    1.6483   40.1942
+#> 455:    84.8220   -3.2472    0.1124    1.6484   40.1932
+#> 456:    84.8220   -3.2470    0.1124    1.6478   40.1972
+#> 457:    84.8222   -3.2469    0.1125    1.6476   40.1989
+#> 458:    84.8226   -3.2468    0.1125    1.6479   40.1989
+#> 459:    84.8228   -3.2467    0.1126    1.6480   40.2035
+#> 460:    84.8231   -3.2467    0.1124    1.6479   40.2032
+#> 461:    84.8236   -3.2466    0.1126    1.6482   40.2030
+#> 462:    84.8238   -3.2466    0.1124    1.6481   40.2052
+#> 463:    84.8238   -3.2467    0.1123    1.6479   40.2023
+#> 464:    84.8233   -3.2467    0.1123    1.6479   40.2004
+#> 465:    84.8230   -3.2468    0.1123    1.6482   40.2043
+#> 466:    84.8233   -3.2469    0.1123    1.6480   40.2062
+#> 467:    84.8236   -3.2468    0.1121    1.6480   40.2026
+#> 468:    84.8238   -3.2468    0.1120    1.6477   40.2034
+#> 469:    84.8239   -3.2468    0.1119    1.6474   40.2035
+#> 470:    84.8241   -3.2469    0.1116    1.6473   40.2015
+#> 471:    84.8241   -3.2470    0.1116    1.6476   40.1993
+#> 472:    84.8240   -3.2469    0.1117    1.6478   40.1977
+#> 473:    84.8239   -3.2468    0.1119    1.6479   40.1949
+#> 474:    84.8239   -3.2466    0.1118    1.6480   40.1946
+#> 475:    84.8239   -3.2464    0.1119    1.6483   40.1941
+#> 476:    84.8237   -3.2462    0.1121    1.6488   40.1930
+#> 477:    84.8235   -3.2462    0.1122    1.6488   40.1901
+#> 478:    84.8235   -3.2462    0.1125    1.6488   40.1837
+#> 479:    84.8238   -3.2463    0.1128    1.6486   40.1814
+#> 480:    84.8238   -3.2464    0.1129    1.6484   40.1794
+#> 481:    84.8239   -3.2464    0.1129    1.6483   40.1783
+#> 482:    84.8237   -3.2465    0.1130    1.6482   40.1784
+#> 483:    84.8234   -3.2465    0.1130    1.6483   40.1764
+#> 484:    84.8227   -3.2465    0.1132    1.6482   40.1775
+#> 485:    84.8223   -3.2465    0.1133    1.6483   40.1764
+#> 486:    84.8219   -3.2465    0.1135    1.6484   40.1781
+#> 487:    84.8215   -3.2465    0.1136    1.6487   40.1770
+#> 488:    84.8214   -3.2466    0.1136    1.6486   40.1796
+#> 489:    84.8214   -3.2466    0.1134    1.6489   40.1801
+#> 490:    84.8214   -3.2466    0.1132    1.6490   40.1786
+#> 491:    84.8218   -3.2466    0.1131    1.6494   40.1805
+#> 492:    84.8220   -3.2465    0.1133    1.6495   40.1805
+#> 493:    84.8223   -3.2465    0.1137    1.6493   40.1791
+#> 494:    84.8223   -3.2465    0.1140    1.6494   40.1774
+#> 495:    84.8224   -3.2465    0.1142    1.6491   40.1764
+#> 496:    84.8225   -3.2465    0.1142    1.6491   40.1750
+#> 497:    84.8229   -3.2465    0.1142    1.6487   40.1742
+#> 498:    84.8230   -3.2466    0.1140    1.6485   40.1712
+#> 499:    84.8229   -3.2466    0.1137    1.6485   40.1688
+#> 500:    84.8228   -3.2468    0.1134    1.6488   40.1690
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |log_k_parent |     sigma |        o1 |
+#> |.....................|        o2 |...........|...........|...........|
+#> |    1|     451.22394 |     1.000 |    -1.000 |   -0.7995 |   -0.9125 |
+#> |.....................|   -0.9081 |...........|...........|...........|
+#> |    U|     451.22394 |     86.39 |    -3.215 |     5.768 |    0.7049 |
+#> |.....................|    0.9021 |...........|...........|...........|
+#> |    X|     451.22394 |     86.39 |   0.04015 |     5.768 |    0.7049 |
+#> |.....................|    0.9021 |...........|...........|...........|
+#> |    G|    Gill Diff. |     52.79 |   0.01520 |    -15.05 |    0.6163 |
+#> |.....................|     2.488 |...........|...........|...........|
+#> |    2|     3099.6543 |   0.03939 |    -1.000 |   -0.5255 |   -0.9237 |
+#> |.....................|   -0.9534 |...........|...........|...........|
+#> |    U|     3099.6543 |     3.403 |    -3.215 |     6.558 |    0.6970 |
+#> |.....................|    0.8613 |...........|...........|...........|
+#> |    X|     3099.6543 |     3.403 |   0.04014 |     6.558 |    0.6970 |
+#> |.....................|    0.8613 |...........|...........|...........|
+#> |    3|     473.10068 |    0.9039 |    -1.000 |   -0.7721 |   -0.9136 |
+#> |.....................|   -0.9126 |...........|...........|...........|
+#> |    U|     473.10068 |     78.09 |    -3.215 |     5.847 |    0.7041 |
+#> |.....................|    0.8980 |...........|...........|...........|
+#> |    X|     473.10068 |     78.09 |   0.04015 |     5.847 |    0.7041 |
+#> |.....................|    0.8980 |...........|...........|...........|
+#> |    4|     450.95086 |    0.9904 |    -1.000 |   -0.7967 |   -0.9126 |
+#> |.....................|   -0.9086 |...........|...........|...........|
+#> |    U|     450.95086 |     85.56 |    -3.215 |     5.776 |    0.7048 |
+#> |.....................|    0.9017 |...........|...........|...........|
+#> |    X|     450.95086 |     85.56 |   0.04015 |     5.776 |    0.7048 |
+#> |.....................|    0.9017 |...........|...........|...........|
+#> |    F| Forward Diff. |    -4.520 |   0.09729 |    -14.85 |   -0.2941 |
+#> |.....................|     2.449 |...........|...........|...........|
+#> |    5|     450.82239 |    0.9932 |    -1.000 |   -0.7873 |   -0.9124 |
+#> |.....................|   -0.9101 |...........|...........|...........|
+#> |    U|     450.82239 |     85.81 |    -3.215 |     5.804 |    0.7049 |
+#> |.....................|    0.9003 |...........|...........|...........|
+#> |    X|     450.82239 |     85.81 |   0.04015 |     5.804 |    0.7049 |
+#> |.....................|    0.9003 |...........|...........|...........|
+#> |    6|     450.73959 |    0.9981 |    -1.000 |   -0.7712 |   -0.9121 |
+#> |.....................|   -0.9128 |...........|...........|...........|
+#> |    U|     450.73959 |     86.23 |    -3.215 |     5.850 |    0.7051 |
+#> |.....................|    0.8979 |...........|...........|...........|
+#> |    X|     450.73959 |     86.23 |   0.04015 |     5.850 |    0.7051 |
+#> |.....................|    0.8979 |...........|...........|...........|
+#> |    F| Forward Diff. |     41.55 |   0.02901 |    -12.22 |    0.2553 |
+#> |.....................|     2.069 |...........|...........|...........|
+#> |    7|     450.34694 |    0.9875 |    -1.000 |   -0.7467 |   -0.9114 |
+#> |.....................|   -0.9169 |...........|...........|...........|
+#> |    U|     450.34694 |     85.32 |    -3.215 |     5.921 |    0.7056 |
+#> |.....................|    0.8942 |...........|...........|...........|
+#> |    X|     450.34694 |     85.32 |   0.04014 |     5.921 |    0.7056 |
+#> |.....................|    0.8942 |...........|...........|...........|
+#> |    F| Forward Diff. |    -19.58 |    0.1161 |    -10.02 |   -0.6042 |
+#> |.....................|     1.700 |...........|...........|...........|
+#> |    8|     450.09191 |    0.9931 |    -1.001 |   -0.7208 |   -0.9093 |
+#> |.....................|   -0.9217 |...........|...........|...........|
+#> |    U|     450.09191 |     85.80 |    -3.216 |     5.995 |    0.7071 |
+#> |.....................|    0.8899 |...........|...........|...........|
+#> |    X|     450.09191 |     85.80 |   0.04012 |     5.995 |    0.7071 |
+#> |.....................|    0.8899 |...........|...........|...........|
+#> |    F| Forward Diff. |     13.00 |   0.06566 |    -7.570 |   -0.3896 |
+#> |.....................|     1.273 |...........|...........|...........|
+#> |    9|     449.93949 |    0.9873 |    -1.002 |   -0.6965 |   -0.8998 |
+#> |.....................|   -0.9259 |...........|...........|...........|
+#> |    U|     449.93949 |     85.30 |    -3.217 |     6.065 |    0.7138 |
+#> |.....................|    0.8861 |...........|...........|...........|
+#> |    X|     449.93949 |     85.30 |   0.04009 |     6.065 |    0.7138 |
+#> |.....................|    0.8861 |...........|...........|...........|
+#> |    F| Forward Diff. |    -18.86 |    0.1073 |    -5.670 |   -0.6860 |
+#> |.....................|    0.8878 |...........|...........|...........|
+#> |   10|     449.82026 |    0.9918 |    -1.004 |   -0.6799 |   -0.8791 |
+#> |.....................|   -0.9254 |...........|...........|...........|
+#> |    U|     449.82026 |     85.69 |    -3.219 |     6.113 |    0.7284 |
+#> |.....................|    0.8865 |...........|...........|...........|
+#> |    X|     449.82026 |     85.69 |   0.04000 |     6.113 |    0.7284 |
+#> |.....................|    0.8865 |...........|...........|...........|
+#> |    F| Forward Diff. |     8.164 |   0.05669 |    -4.296 |   -0.3775 |
+#> |.....................|    0.8823 |...........|...........|...........|
+#> |   11|     449.76996 |    0.9897 |    -1.006 |   -0.6720 |   -0.8560 |
+#> |.....................|   -0.9364 |...........|...........|...........|
+#> |    U|     449.76996 |     85.50 |    -3.221 |     6.136 |    0.7447 |
+#> |.....................|    0.8766 |...........|...........|...........|
+#> |    X|     449.76996 |     85.50 |   0.03990 |     6.136 |    0.7447 |
+#> |.....................|    0.8766 |...........|...........|...........|
+#> |    F| Forward Diff. |    -2.743 |   0.05613 |    -3.782 |   -0.3486 |
+#> |.....................|  -0.07732 |...........|...........|...........|
+#> |   12|     449.73800 |    0.9901 |    -1.008 |   -0.6600 |   -0.8416 |
+#> |.....................|   -0.9169 |...........|...........|...........|
+#> |    U|       449.738 |     85.54 |    -3.223 |     6.170 |    0.7549 |
+#> |.....................|    0.8942 |...........|...........|...........|
+#> |    X|       449.738 |     85.54 |   0.03983 |     6.170 |    0.7549 |
+#> |.....................|    0.8942 |...........|...........|...........|
+#> |    F| Forward Diff. |    0.5907 |   0.04688 |    -2.910 |   -0.3174 |
+#> |.....................|     1.529 |...........|...........|...........|
+#> |   13|     449.73838 |    0.9854 |    -1.008 |   -0.6366 |   -0.8390 |
+#> |.....................|   -0.9292 |...........|...........|...........|
+#> |    U|     449.73838 |     85.13 |    -3.224 |     6.238 |    0.7567 |
+#> |.....................|    0.8831 |...........|...........|...........|
+#> |    X|     449.73838 |     85.13 |   0.03981 |     6.238 |    0.7567 |
+#> |.....................|    0.8831 |...........|...........|...........|
+#> |   14|     449.71577 |    0.9877 |    -1.008 |   -0.6484 |   -0.8403 |
+#> |.....................|   -0.9231 |...........|...........|...........|
+#> |    U|     449.71577 |     85.33 |    -3.223 |     6.204 |    0.7558 |
+#> |.....................|    0.8886 |...........|...........|...........|
+#> |    X|     449.71577 |     85.33 |   0.03982 |     6.204 |    0.7558 |
+#> |.....................|    0.8886 |...........|...........|...........|
+#> |    F| Forward Diff. |    -13.00 |   0.06593 |    -2.084 |   -0.4341 |
+#> |.....................|     1.007 |...........|...........|...........|
+#> |   15|     449.68436 |    0.9912 |    -1.009 |   -0.6401 |   -0.8344 |
+#> |.....................|   -0.9311 |...........|...........|...........|
+#> |    U|     449.68436 |     85.64 |    -3.224 |     6.228 |    0.7599 |
+#> |.....................|    0.8814 |...........|...........|...........|
+#> |    X|     449.68436 |     85.64 |   0.03979 |     6.228 |    0.7599 |
+#> |.....................|    0.8814 |...........|...........|...........|
+#> |    F| Forward Diff. |     7.939 |   0.02803 |    -1.419 |   -0.2659 |
+#> |.....................|    0.3125 |...........|...........|...........|
+#> |   16|     449.66988 |    0.9896 |    -1.010 |   -0.6363 |   -0.8221 |
+#> |.....................|   -0.9344 |...........|...........|...........|
+#> |    U|     449.66988 |     85.50 |    -3.226 |     6.239 |    0.7686 |
+#> |.....................|    0.8784 |...........|...........|...........|
+#> |    X|     449.66988 |     85.50 |   0.03973 |     6.239 |    0.7686 |
+#> |.....................|    0.8784 |...........|...........|...........|
+#> |    F| Forward Diff. |   -0.8695 |   0.03361 |    -1.202 |   -0.2917 |
+#> |.....................|   0.02327 |...........|...........|...........|
+#> |   17|     449.66421 |    0.9900 |    -1.012 |   -0.6343 |   -0.8088 |
+#> |.....................|   -0.9351 |...........|...........|...........|
+#> |    U|     449.66421 |     85.53 |    -3.227 |     6.245 |    0.7779 |
+#> |.....................|    0.8778 |...........|...........|...........|
+#> |    X|     449.66421 |     85.53 |   0.03969 |     6.245 |    0.7779 |
+#> |.....................|    0.8778 |...........|...........|...........|
+#> |   18|     449.65407 |    0.9895 |    -1.015 |   -0.6307 |   -0.7728 |
+#> |.....................|   -0.9370 |...........|...........|...........|
+#> |    U|     449.65407 |     85.49 |    -3.230 |     6.255 |    0.8033 |
+#> |.....................|    0.8761 |...........|...........|...........|
+#> |    X|     449.65407 |     85.49 |   0.03957 |     6.255 |    0.8033 |
+#> |.....................|    0.8761 |...........|...........|...........|
+#> |    F| Forward Diff. |    0.6836 |  0.009868 |   -0.9456 |   -0.1262 |
+#> |.....................|   -0.2597 |...........|...........|...........|
+#> |   19|     449.64227 |    0.9890 |    -1.006 |   -0.6121 |   -0.7274 |
+#> |.....................|   -0.9339 |...........|...........|...........|
+#> |    U|     449.64227 |     85.45 |    -3.222 |     6.309 |    0.8353 |
+#> |.....................|    0.8789 |...........|...........|...........|
+#> |    X|     449.64227 |     85.45 |   0.03989 |     6.309 |    0.8353 |
+#> |.....................|    0.8789 |...........|...........|...........|
+#> |    F| Forward Diff. |   -0.4372 |   0.06357 |    0.2445 |  -0.08318 |
+#> |.....................|  -0.05696 |...........|...........|...........|
+#> |   20|     449.64227 |    0.9890 |    -1.006 |   -0.6121 |   -0.7274 |
+#> |.....................|   -0.9339 |...........|...........|...........|
+#> |    U|     449.64227 |     85.45 |    -3.222 |     6.309 |    0.8353 |
+#> |.....................|    0.8789 |...........|...........|...........|
+#> |    X|     449.64227 |     85.45 |   0.03989 |     6.309 |    0.8353 |
+#> |.....................|    0.8789 |...........|...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                
+f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1:    93.6754   -0.2977    2.0254    2.7655    0.7032    0.5111   15.3443
+#> 2:    93.8828   -0.2006    2.0786    2.6886    0.6681    0.4855    7.5256
+#> 3:    94.0494   -0.2006    2.0891    2.9975    0.6347    0.4612    7.0501
+#> 4:    94.1641   -0.2446    2.0103    3.6008    0.6029    0.4382    6.2482
+#> 5:    93.8983   -0.2562    1.9851    4.5637    0.5728    0.4163    6.1507
+#> 6:    93.9311   -0.2542    1.9733    5.7516    0.5441    0.3954    6.1445
+#> 7:    93.8631   -0.2535    1.9876    5.4640    0.5169    0.3757    5.9234
+#> 8:    94.2851   -0.2327    1.9851    5.7884    0.4943    0.3569    5.9887
+#> 9:    94.2114   -0.2348    2.0169    5.4990    0.4733    0.3390    5.9730
+#> 10:    94.0782   -0.1951    2.0678    5.2240    0.4969    0.3221    5.7694
+#> 11:    94.0527   -0.1898    2.0988    4.9628    0.4924    0.3060    5.6429
+#> 12:    93.9845   -0.1795    2.1168    4.7147    0.4748    0.2907    5.4764
+#> 13:    93.9424   -0.1958    2.0924    4.4790    0.4551    0.2762    5.5598
+#> 14:    94.2255   -0.2005    2.0963    4.2910    0.4552    0.2623    5.4520
+#> 15:    94.6065   -0.1964    2.0794    4.0765    0.4516    0.2492    5.5275
+#> 16:    94.8393   -0.1872    2.0825    4.7814    0.4714    0.2368    5.4708
+#> 17:    94.5489   -0.1873    2.0822    5.3772    0.4714    0.2249    5.5790
+#> 18:    94.5797   -0.1994    2.0702    5.1083    0.4563    0.2137    5.5962
+#> 19:    94.7205   -0.1987    2.0942    5.1405    0.4580    0.2030    5.8328
+#> 20:    94.2162   -0.1961    2.0955    7.2352    0.4578    0.2081    5.5730
+#> 21:    94.2688   -0.1935    2.0980    6.8735    0.4539    0.2199    5.6561
+#> 22:    94.4008   -0.2294    2.0430    6.5298    0.4312    0.2528    5.4970
+#> 23:    93.8617   -0.2126    2.0861    6.2033    0.4420    0.2401    5.3679
+#> 24:    93.9223   -0.2173    2.0786    5.8931    0.4419    0.2281    5.4475
+#> 25:    94.1259   -0.2199    2.0790    5.5985    0.4429    0.2167    5.2610
+#> 26:    93.5597   -0.1966    2.1115    5.3186    0.4521    0.2059    5.0971
+#> 27:    93.5468   -0.2077    2.1016    5.0526    0.4458    0.2090    5.2223
+#> 28:    93.6901   -0.2106    2.0884    4.8000    0.4439    0.2114    5.1693
+#> 29:    93.4521   -0.1991    2.1349    4.5600    0.4236    0.2248    5.1834
+#> 30:    93.7678   -0.1998    2.1267    5.5252    0.4212    0.2297    5.0549
+#> 31:    93.5695   -0.2039    2.1244    5.2489    0.4165    0.2334    5.0965
+#> 32:    93.8288   -0.1855    2.1392    5.1872    0.4401    0.2286    5.0321
+#> 33:    93.9053   -0.1827    2.1426    4.9278    0.4479    0.2171    5.0706
+#> 34:    94.0876   -0.1871    2.1151    4.6814    0.4613    0.2063    5.1438
+#> 35:    94.5298   -0.1845    2.1221    4.4474    0.4586    0.2006    5.1897
+#> 36:    94.3221   -0.1765    2.1144    5.3164    0.4401    0.2193    5.0921
+#> 37:    94.3600   -0.1842    2.1021    5.3586    0.4507    0.2210    5.0926
+#> 38:    94.3734   -0.1790    2.1261    5.0907    0.4494    0.2100    5.1494
+#> 39:    94.5052   -0.1806    2.1319    4.8362    0.4514    0.1995    5.0177
+#> 40:    94.1042   -0.1906    2.0983    4.5944    0.4360    0.1984    5.2507
+#> 41:    94.1815   -0.1914    2.1166    4.3646    0.4385    0.1977    5.1065
+#> 42:    93.9837   -0.2144    2.0673    4.1464    0.4378    0.1878    5.1603
+#> 43:    93.8806   -0.2107    2.0840    3.9642    0.4456    0.1848    5.0904
+#> 44:    94.1765   -0.2107    2.0722    3.7660    0.4456    0.1881    5.1562
+#> 45:    94.2089   -0.2018    2.0874    3.5777    0.4482    0.1787    5.1219
+#> 46:    93.8851   -0.2111    2.0869    3.9421    0.4462    0.1697    5.0752
+#> 47:    94.1372   -0.2192    2.0731    3.7450    0.4517    0.1733    5.1784
+#> 48:    94.0436   -0.2157    2.0730    3.5578    0.4577    0.1854    5.1957
+#> 49:    93.9915   -0.2122    2.0740    3.3799    0.4450    0.1829    5.1116
+#> 50:    94.0579   -0.2233    2.0633    3.2109    0.4453    0.1964    5.0295
+#> 51:    94.0044   -0.2283    2.0544    3.9314    0.4563    0.2118    5.0457
+#> 52:    94.1080   -0.2174    2.0551    4.8914    0.4548    0.2182    5.0504
+#> 53:    94.3715   -0.2134    2.0598    6.2569    0.4509    0.2162    4.9574
+#> 54:    94.7344   -0.2119    2.0459    5.9440    0.4563    0.2121    5.1069
+#> 55:    94.2730   -0.2055    2.0625    5.6468    0.4758    0.2125    5.2656
+#> 56:    94.0206   -0.2017    2.0715    5.3645    0.4719    0.2045    5.1400
+#> 57:    94.0409   -0.1986    2.0837    5.0963    0.4801    0.2068    5.0902
+#> 58:    94.2392   -0.2122    2.0652    4.8415    0.4560    0.2334    5.1883
+#> 59:    93.9996   -0.1962    2.0764    4.5994    0.4686    0.2417    5.1242
+#> 60:    94.1448   -0.1840    2.1016    4.3694    0.4916    0.2296    5.0867
+#> 61:    94.4861   -0.1840    2.1239    4.3846    0.4916    0.2181    5.3979
+#> 62:    93.9892   -0.1781    2.1083    5.1623    0.5216    0.2072    5.0944
+#> 63:    94.0641   -0.1822    2.1129    4.9628    0.5123    0.1969    5.4228
+#> 64:    94.1414   -0.1733    2.1343    6.7238    0.5220    0.1879    5.3546
+#> 65:    94.0908   -0.1754    2.1160    8.4197    0.5165    0.1852    5.0873
+#> 66:    94.1490   -0.1753    2.1054    7.9987    0.5183    0.1857    5.0777
+#> 67:    93.8958   -0.1613    2.1295    7.5988    0.5004    0.2102    5.0641
+#> 68:    94.0579   -0.1683    2.1511    7.2188    0.5083    0.2110    5.3362
+#> 69:    94.0001   -0.1581    2.1629    6.8579    0.5225    0.2272    5.4399
+#> 70:    93.9712   -0.1733    2.1393    6.5150    0.5153    0.2403    5.5011
+#> 71:    94.3143   -0.1758    2.0989    6.1893    0.5043    0.2713    5.5366
+#> 72:    94.2138   -0.1842    2.1003    5.8798    0.5130    0.2578    5.2964
+#> 73:    94.1742   -0.1951    2.0773    5.5858    0.5165    0.2449    5.1986
+#> 74:    94.1287   -0.2003    2.0606    5.3065    0.5115    0.2326    4.8815
+#> 75:    94.4113   -0.1918    2.0811    5.6717    0.5153    0.2210    4.8370
+#> 76:    94.5175   -0.1940    2.0773    5.3881    0.5127    0.2127    4.9333
+#> 77:    94.4157   -0.1882    2.0714    5.1187    0.5189    0.2021    5.0162
+#> 78:    94.6190   -0.2000    2.0529    4.8628    0.5057    0.2064    4.9436
+#> 79:    94.8081   -0.2006    2.0458    4.6196    0.5053    0.2177    5.0159
+#> 80:    94.7817   -0.1943    2.0547    4.3886    0.5076    0.2099    5.1427
+#> 81:    94.5410   -0.1990    2.0686    4.8770    0.5032    0.2092    5.1192
+#> 82:    94.9536   -0.1936    2.0879    6.9870    0.4781    0.2068    5.1053
+#> 83:    94.7923   -0.1936    2.0777    6.6377    0.4734    0.2120    5.1233
+#> 84:    94.9314   -0.1881    2.0981    6.3058    0.4701    0.2088    5.2821
+#> 85:    94.8024   -0.1866    2.0975    5.9905    0.4684    0.2150    5.2088
+#> 86:    94.6506   -0.2019    2.0677    5.6910    0.4510    0.2043    5.2488
+#> 87:    94.9460   -0.1868    2.0823    5.4064    0.4625    0.2089    5.2663
+#> 88:    94.6365   -0.1901    2.0791    5.3471    0.4509    0.2203    5.2214
+#> 89:    94.5943   -0.2135    2.0521    5.0798    0.4585    0.2093    5.0161
+#> 90:    94.7957   -0.2131    2.0545    4.8258    0.4502    0.2026    5.1344
+#> 91:    94.6308   -0.2096    2.0565    4.5845    0.4566    0.2108    5.0403
+#> 92:    94.3521   -0.2059    2.0557    4.3553    0.4925    0.2072    5.3715
+#> 93:    94.5188   -0.2130    2.0646    4.1375    0.4980    0.1996    5.5624
+#> 94:    94.5995   -0.2056    2.0593    3.9306    0.4995    0.2167    5.3581
+#> 95:    94.7276   -0.1868    2.0922    3.7341    0.4863    0.2059    5.3610
+#> 96:    94.5986   -0.1900    2.0771    3.5474    0.4998    0.1956    5.2070
+#> 97:    94.2586   -0.1881    2.1051    3.9558    0.4757    0.1858    5.1561
+#> 98:    94.0716   -0.2098    2.0698    5.6441    0.4539    0.2044    5.1802
+#> 99:    94.2657   -0.2065    2.0679    5.6964    0.4679    0.2190    5.3608
+#> 100:    94.2331   -0.2203    2.0679    5.4116    0.4445    0.2256    5.4031
+#> 101:    93.8634   -0.2222    2.0720    5.1410    0.4279    0.2341    5.3774
+#> 102:    93.7675   -0.2496    2.0232    4.8839    0.4103    0.2224    5.1238
+#> 103:    93.9534   -0.2416    2.0249    4.6397    0.4144    0.2113    5.0031
+#> 104:    94.0631   -0.2442    2.0216    4.8203    0.4119    0.2007    5.1163
+#> 105:    94.0324   -0.2464    2.0092    4.5793    0.4135    0.2047    5.1666
+#> 106:    93.9954   -0.2482    2.0256    4.9167    0.4083    0.2052    5.2515
+#> 107:    94.2189   -0.2507    2.0121    4.6709    0.4072    0.2087    5.3430
+#> 108:    94.3707   -0.2448    2.0215    4.4373    0.4119    0.1996    5.1549
+#> 109:    94.1518   -0.2428    2.0197    4.2155    0.4155    0.1958    5.5480
+#> 110:    93.9287   -0.2571    2.0275    4.0047    0.4152    0.1931    5.8482
+#> 111:    93.9743   -0.2488    2.0202    3.8045    0.4171    0.2084    5.9798
+#> 112:    93.6245   -0.2350    2.0346    3.6142    0.4397    0.1980    6.0270
+#> 113:    94.5370   -0.2330    2.0593    3.9090    0.4422    0.1881    5.4431
+#> 114:    94.5052   -0.2289    2.0555    3.7135    0.4391    0.1787    5.5970
+#> 115:    94.5963   -0.2216    2.0579    3.5279    0.4446    0.1727    5.3901
+#> 116:    94.5059   -0.2293    2.0459    3.3515    0.4407    0.1705    5.2788
+#> 117:    94.6315   -0.2211    2.0564    3.1839    0.4279    0.1689    5.3258
+#> 118:    94.4868   -0.2194    2.0508    4.6523    0.4275    0.1604    5.1421
+#> 119:    94.1809   -0.2232    2.0444    7.0101    0.4302    0.1612    5.3468
+#> 120:    94.0950   -0.2231    2.0482    7.2110    0.4304    0.1625    5.1691
+#> 121:    94.1525   -0.2059    2.0682    6.8504    0.4474    0.1875    5.2811
+#> 122:    94.7122   -0.2154    2.0692    6.6747    0.4366    0.1906    5.3851
+#> 123:    94.2915   -0.2311    2.0431    6.9655    0.4351    0.2021    5.2103
+#> 124:    93.9984   -0.2310    2.0401    6.6173    0.4396    0.2091    5.0920
+#> 125:    94.3668   -0.2068    2.0505    6.2864    0.4983    0.1987    5.3263
+#> 126:    94.3570   -0.2043    2.0525    5.9721    0.5006    0.1887    5.3281
+#> 127:    94.7086   -0.2177    2.0377    5.6735    0.4762    0.1958    5.4003
+#> 128:    94.3565   -0.2173    2.0432    5.3898    0.4754    0.2055    5.5196
+#> 129:    94.4862   -0.2066    2.0639    5.1203    0.4807    0.1952    5.4783
+#> 130:    94.6107   -0.2026    2.0908    4.8643    0.4579    0.1855    5.6186
+#> 131:    94.6831   -0.1907    2.0920    4.6211    0.4710    0.1762    5.4859
+#> 132:    94.7035   -0.2052    2.0733    4.6333    0.4492    0.1723    5.2721
+#> 133:    94.1511   -0.2192    2.0615    5.7533    0.4362    0.1905    5.5019
+#> 134:    94.2758   -0.2101    2.0624    5.4656    0.4356    0.1810    5.3233
+#> 135:    94.6546   -0.1960    2.0826    5.1923    0.4281    0.1980    5.2515
+#> 136:    94.0322   -0.2100    2.0770    4.9327    0.4156    0.2103    5.3514
+#> 137:    94.0915   -0.2096    2.0859    5.6044    0.4159    0.2008    5.2755
+#> 138:    94.2452   -0.1983    2.1055    6.0837    0.4213    0.2185    5.0580
+#> 139:    94.5460   -0.1876    2.1093    6.8410    0.4301    0.2288    5.0840
+#> 140:    94.6905   -0.1863    2.1167    7.4689    0.4313    0.2173    5.0868
+#> 141:    94.6425   -0.1703    2.1240    7.0955    0.4522    0.2065    4.9715
+#> 142:    94.2538   -0.1632    2.1514    6.7407    0.4499    0.2059    5.0853
+#> 143:    94.3098   -0.1625    2.1567    6.4037    0.4499    0.2115    5.5860
+#> 144:    94.2802   -0.1716    2.1510    6.0835    0.4535    0.2081    5.1989
+#> 145:    94.1169   -0.1707    2.1523    5.7793    0.4531    0.2109    5.1407
+#> 146:    94.2558   -0.1579    2.1623    5.4903    0.4654    0.2427    5.2652
+#> 147:    93.9440   -0.1587    2.1673    5.2158    0.4611    0.2537    5.2699
+#> 148:    94.4271   -0.1587    2.1586    4.9550    0.4611    0.2595    5.1280
+#> 149:    94.2734   -0.1768    2.1160    4.7073    0.4809    0.2802    4.9251
+#> 150:    94.2406   -0.1928    2.0941    5.4176    0.4626    0.2662    5.0837
+#> 151:    94.4217   -0.1884    2.0965    5.1467    0.4677    0.2538    5.1728
+#> 152:    94.4856   -0.1826    2.1127    5.6736    0.4646    0.2373    5.1522
+#> 153:    94.3458   -0.1686    2.1381    6.3603    0.4760    0.2028    5.2197
+#> 154:    94.3945   -0.1633    2.1370    5.1586    0.4402    0.1955    5.3770
+#> 155:    94.6367   -0.1520    2.1596    6.4738    0.4533    0.1882    5.3345
+#> 156:    94.9050   -0.1521    2.1417    6.8382    0.4532    0.1729    5.2770
+#> 157:    94.5823   -0.1540    2.1326    5.5745    0.4487    0.1813    5.2760
+#> 158:    94.8355   -0.1691    2.1357    5.2979    0.4296    0.1990    5.3177
+#> 159:    94.7330   -0.1740    2.1148    4.0960    0.4476    0.1820    5.3001
+#> 160:    94.4926   -0.1731    2.1123    4.3550    0.4666    0.1817    5.1849
+#> 161:    94.4953   -0.1758    2.1063    4.0311    0.4698    0.1929    5.1371
+#> 162:    94.5639   -0.1753    2.1064    4.3044    0.4692    0.1911    5.1437
+#> 163:    94.5477   -0.1798    2.1041    4.1393    0.4804    0.2002    5.3561
+#> 164:    94.3812   -0.1934    2.1019    3.5760    0.4689    0.1908    5.3231
+#> 165:    94.0978   -0.1924    2.0973    2.2052    0.4743    0.1962    5.2813
+#> 166:    94.1374   -0.2043    2.0834    2.5477    0.4639    0.1904    5.3277
+#> 167:    94.1587   -0.2036    2.0797    2.7035    0.4561    0.1951    5.3106
+#> 168:    94.1518   -0.2166    2.0654    2.4969    0.4405    0.2090    5.3148
+#> 169:    94.3328   -0.2164    2.0652    2.3067    0.4455    0.1993    5.2385
+#> 170:    94.6029   -0.2176    2.0456    1.7913    0.4478    0.2085    5.4589
+#> 171:    94.2690   -0.2189    2.0635    1.8133    0.4496    0.1999    5.4918
+#> 172:    94.3227   -0.2120    2.0643    1.7763    0.4337    0.2063    5.4992
+#> 173:    94.3099   -0.2039    2.0892    1.1103    0.4350    0.2201    5.5148
+#> 174:    94.3192   -0.1895    2.1140    0.9817    0.4454    0.2078    5.5249
+#> 175:    94.2327   -0.1967    2.0939    0.9890    0.4361    0.1876    5.6321
+#> 176:    94.2707   -0.1989    2.0958    1.3001    0.4405    0.1790    5.6494
+#> 177:    94.0762   -0.2024    2.0908    0.9179    0.4426    0.1778    5.7085
+#> 178:    94.1807   -0.2074    2.0761    1.2663    0.4237    0.2064    5.5157
+#> 179:    94.2221   -0.2029    2.1083    2.0148    0.4270    0.2023    5.6770
+#> 180:    94.5889   -0.1975    2.0974    1.5302    0.4223    0.1778    5.7495
+#> 181:    94.4280   -0.2163    2.0648    1.8829    0.3908    0.1994    5.3948
+#> 182:    94.7076   -0.2247    2.0340    2.1148    0.4238    0.2062    5.4167
+#> 183:    94.5127   -0.2292    2.0317    3.0950    0.4302    0.2160    5.5009
+#> 184:    94.2522   -0.2335    2.0515    2.8900    0.4265    0.2038    5.2995
+#> 185:    94.2331   -0.2330    2.0431    3.3282    0.4276    0.2044    5.2220
+#> 186:    94.2207   -0.2259    2.0512    4.0568    0.4253    0.2008    5.2307
+#> 187:    94.5124   -0.2188    2.0603    3.0941    0.4381    0.1962    5.6927
+#> 188:    94.7691   -0.2454    2.0193    3.1090    0.4409    0.2012    5.5051
+#> 189:    94.5693   -0.2399    2.0169    3.1069    0.4292    0.1883    5.4354
+#> 190:    94.5742   -0.2318    2.0256    4.4216    0.4200    0.1932    5.3851
+#> 191:    94.3882   -0.2475    1.9949    4.5490    0.4366    0.1972    5.2470
+#> 192:    94.4267   -0.2478    1.9943    4.3327    0.4281    0.1995    5.2792
+#> 193:    94.6313   -0.2522    1.9703    3.5911    0.4321    0.1944    5.6218
+#> 194:    94.4345   -0.2616    1.9704    3.2209    0.4260    0.1925    5.5199
+#> 195:    94.6135   -0.2614    1.9622    2.1481    0.4264    0.1879    5.5750
+#> 196:    94.7574   -0.2324    2.0049    1.3351    0.4661    0.1738    5.6590
+#> 197:    94.8293   -0.2064    2.0452    1.6807    0.4904    0.1600    5.7639
+#> 198:    94.6372   -0.2157    2.0307    1.6350    0.5008    0.1524    5.6539
+#> 199:    94.5600   -0.2145    2.0318    1.5133    0.4982    0.1604    5.7178
+#> 200:    94.6945   -0.2100    2.0475    1.4526    0.5066    0.1649    5.6094
+#> 201:    94.5335   -0.2025    2.0594    1.3754    0.5066    0.1681    5.6560
+#> 202:    94.4663   -0.1992    2.0657    1.3622    0.5074    0.1665    5.6522
+#> 203:    94.4750   -0.1956    2.0762    1.3218    0.5051    0.1648    5.5985
+#> 204:    94.4206   -0.1916    2.0795    1.3219    0.5066    0.1593    5.5864
+#> 205:    94.4408   -0.1891    2.0816    1.2934    0.5089    0.1553    5.5967
+#> 206:    94.4631   -0.1863    2.0859    1.2768    0.5108    0.1522    5.6212
+#> 207:    94.4742   -0.1825    2.0912    1.3219    0.5122    0.1479    5.6704
+#> 208:    94.4802   -0.1789    2.0950    1.3488    0.5137    0.1450    5.7072
+#> 209:    94.4734   -0.1756    2.1019    1.3165    0.5155    0.1423    5.7458
+#> 210:    94.4589   -0.1742    2.1056    1.3379    0.5156    0.1409    5.7722
+#> 211:    94.4513   -0.1727    2.1083    1.3395    0.5192    0.1395    5.7707
+#> 212:    94.4422   -0.1718    2.1096    1.3506    0.5219    0.1384    5.7602
+#> 213:    94.4503   -0.1704    2.1112    1.3519    0.5233    0.1377    5.7705
+#> 214:    94.4387   -0.1688    2.1143    1.3620    0.5238    0.1374    5.7627
+#> 215:    94.4468   -0.1677    2.1171    1.3815    0.5236    0.1366    5.7552
+#> 216:    94.4314   -0.1671    2.1191    1.4034    0.5217    0.1362    5.7279
+#> 217:    94.4134   -0.1669    2.1206    1.4118    0.5197    0.1363    5.7109
+#> 218:    94.3896   -0.1665    2.1219    1.3959    0.5181    0.1381    5.6979
+#> 219:    94.3836   -0.1667    2.1226    1.3965    0.5160    0.1402    5.6829
+#> 220:    94.3740   -0.1674    2.1219    1.4130    0.5144    0.1419    5.6839
+#> 221:    94.3663   -0.1677    2.1216    1.4134    0.5131    0.1436    5.6717
+#> 222:    94.3498   -0.1683    2.1212    1.4170    0.5117    0.1453    5.6595
+#> 223:    94.3416   -0.1687    2.1219    1.4195    0.5105    0.1467    5.6587
+#> 224:    94.3412   -0.1687    2.1222    1.4245    0.5097    0.1474    5.6517
+#> 225:    94.3323   -0.1685    2.1235    1.4231    0.5093    0.1484    5.6419
+#> 226:    94.3228   -0.1686    2.1239    1.4167    0.5088    0.1493    5.6305
+#> 227:    94.3135   -0.1688    2.1241    1.4162    0.5084    0.1502    5.6197
+#> 228:    94.3088   -0.1686    2.1251    1.4170    0.5088    0.1515    5.6124
+#> 229:    94.2995   -0.1685    2.1257    1.4316    0.5092    0.1527    5.6079
+#> 230:    94.2864   -0.1690    2.1256    1.4492    0.5088    0.1534    5.6042
+#> 231:    94.2783   -0.1688    2.1260    1.4606    0.5085    0.1548    5.6037
+#> 232:    94.2725   -0.1687    2.1267    1.4571    0.5083    0.1557    5.6020
+#> 233:    94.2692   -0.1682    2.1279    1.4649    0.5076    0.1570    5.6027
+#> 234:    94.2697   -0.1678    2.1292    1.4540    0.5070    0.1584    5.5990
+#> 235:    94.2623   -0.1673    2.1302    1.4424    0.5064    0.1593    5.5919
+#> 236:    94.2610   -0.1667    2.1313    1.4255    0.5055    0.1599    5.5953
+#> 237:    94.2660   -0.1663    2.1322    1.4242    0.5053    0.1605    5.5922
+#> 238:    94.2753   -0.1666    2.1320    1.4370    0.5044    0.1611    5.5891
+#> 239:    94.2821   -0.1662    2.1326    1.4395    0.5036    0.1629    5.5864
+#> 240:    94.2886   -0.1661    2.1330    1.4375    0.5028    0.1644    5.5815
+#> 241:    94.2934   -0.1664    2.1329    1.4276    0.5020    0.1661    5.5777
+#> 242:    94.2963   -0.1664    2.1329    1.4247    0.5012    0.1677    5.5704
+#> 243:    94.2931   -0.1666    2.1328    1.4269    0.5008    0.1690    5.5631
+#> 244:    94.2919   -0.1667    2.1326    1.4279    0.5003    0.1701    5.5610
+#> 245:    94.2959   -0.1675    2.1316    1.4289    0.4993    0.1705    5.5524
+#> 246:    94.2992   -0.1683    2.1305    1.4378    0.4986    0.1706    5.5436
+#> 247:    94.2997   -0.1689    2.1296    1.4461    0.4977    0.1707    5.5383
+#> 248:    94.2978   -0.1693    2.1290    1.4430    0.4970    0.1714    5.5362
+#> 249:    94.2991   -0.1697    2.1285    1.4495    0.4963    0.1720    5.5379
+#> 250:    94.3068   -0.1702    2.1279    1.4556    0.4954    0.1723    5.5390
+#> 251:    94.3097   -0.1707    2.1272    1.4588    0.4936    0.1729    5.5342
+#> 252:    94.3104   -0.1711    2.1267    1.4582    0.4919    0.1739    5.5310
+#> 253:    94.3099   -0.1715    2.1262    1.4551    0.4903    0.1746    5.5279
+#> 254:    94.3110   -0.1721    2.1255    1.4592    0.4886    0.1758    5.5223
+#> 255:    94.3111   -0.1731    2.1236    1.4755    0.4878    0.1775    5.5175
+#> 256:    94.3096   -0.1735    2.1227    1.4971    0.4875    0.1784    5.5162
+#> 257:    94.3079   -0.1738    2.1222    1.5277    0.4874    0.1795    5.5132
+#> 258:    94.3103   -0.1741    2.1217    1.5521    0.4872    0.1806    5.5112
+#> 259:    94.3148   -0.1745    2.1212    1.5788    0.4868    0.1817    5.5066
+#> 260:    94.3170   -0.1750    2.1205    1.6038    0.4863    0.1832    5.5007
+#> 261:    94.3158   -0.1756    2.1197    1.6324    0.4857    0.1849    5.4968
+#> 262:    94.3141   -0.1763    2.1186    1.6503    0.4850    0.1866    5.4918
+#> 263:    94.3135   -0.1764    2.1184    1.6658    0.4849    0.1879    5.4910
+#> 264:    94.3121   -0.1767    2.1183    1.6841    0.4848    0.1893    5.4875
+#> 265:    94.3098   -0.1769    2.1184    1.7115    0.4847    0.1903    5.4832
+#> 266:    94.3087   -0.1768    2.1188    1.7162    0.4845    0.1911    5.4783
+#> 267:    94.3082   -0.1767    2.1191    1.7209    0.4842    0.1920    5.4735
+#> 268:    94.3094   -0.1764    2.1198    1.7314    0.4837    0.1926    5.4720
+#> 269:    94.3074   -0.1764    2.1199    1.7340    0.4831    0.1938    5.4718
+#> 270:    94.3025   -0.1764    2.1200    1.7440    0.4832    0.1949    5.4720
+#> 271:    94.3025   -0.1769    2.1194    1.7538    0.4829    0.1958    5.4748
+#> 272:    94.3039   -0.1772    2.1191    1.7664    0.4829    0.1966    5.4773
+#> 273:    94.3046   -0.1773    2.1192    1.7820    0.4826    0.1976    5.4754
+#> 274:    94.3051   -0.1774    2.1193    1.7895    0.4823    0.1988    5.4735
+#> 275:    94.3026   -0.1773    2.1193    1.7891    0.4819    0.1998    5.4749
+#> 276:    94.3034   -0.1771    2.1195    1.7875    0.4812    0.2010    5.4829
+#> 277:    94.3047   -0.1771    2.1197    1.7843    0.4805    0.2026    5.4878
+#> 278:    94.3067   -0.1771    2.1197    1.7747    0.4799    0.2039    5.4888
+#> 279:    94.3066   -0.1768    2.1202    1.7772    0.4795    0.2049    5.4889
+#> 280:    94.3035   -0.1768    2.1203    1.7797    0.4788    0.2062    5.4888
+#> 281:    94.2961   -0.1771    2.1203    1.7789    0.4782    0.2068    5.4874
+#> 282:    94.2893   -0.1772    2.1203    1.7797    0.4777    0.2072    5.4865
+#> 283:    94.2880   -0.1776    2.1198    1.7743    0.4772    0.2074    5.4856
+#> 284:    94.2897   -0.1779    2.1195    1.7717    0.4768    0.2076    5.4836
+#> 285:    94.2922   -0.1781    2.1194    1.7756    0.4765    0.2075    5.4818
+#> 286:    94.2964   -0.1783    2.1190    1.7759    0.4763    0.2074    5.4798
+#> 287:    94.2991   -0.1787    2.1181    1.7884    0.4761    0.2075    5.4769
+#> 288:    94.2980   -0.1793    2.1171    1.7901    0.4756    0.2077    5.4772
+#> 289:    94.2948   -0.1797    2.1166    1.7957    0.4752    0.2077    5.4763
+#> 290:    94.2922   -0.1801    2.1161    1.8012    0.4749    0.2074    5.4752
+#> 291:    94.2891   -0.1803    2.1157    1.8016    0.4747    0.2073    5.4743
+#> 292:    94.2890   -0.1805    2.1155    1.8012    0.4746    0.2072    5.4743
+#> 293:    94.2874   -0.1808    2.1152    1.8012    0.4743    0.2073    5.4743
+#> 294:    94.2841   -0.1811    2.1148    1.8003    0.4740    0.2075    5.4758
+#> 295:    94.2834   -0.1813    2.1143    1.7982    0.4743    0.2075    5.4766
+#> 296:    94.2817   -0.1816    2.1138    1.7997    0.4745    0.2074    5.4756
+#> 297:    94.2772   -0.1820    2.1131    1.8025    0.4747    0.2074    5.4778
+#> 298:    94.2759   -0.1822    2.1125    1.8097    0.4747    0.2073    5.4781
+#> 299:    94.2752   -0.1825    2.1120    1.8176    0.4748    0.2071    5.4784
+#> 300:    94.2758   -0.1828    2.1115    1.8353    0.4750    0.2069    5.4771
+#> 301:    94.2789   -0.1829    2.1113    1.8511    0.4749    0.2066    5.4767
+#> 302:    94.2808   -0.1833    2.1107    1.8541    0.4747    0.2065    5.4785
+#> 303:    94.2832   -0.1836    2.1103    1.8571    0.4745    0.2064    5.4789
+#> 304:    94.2838   -0.1840    2.1097    1.8584    0.4743    0.2064    5.4792
+#> 305:    94.2835   -0.1843    2.1090    1.8633    0.4741    0.2066    5.4790
+#> 306:    94.2868   -0.1847    2.1083    1.8633    0.4738    0.2069    5.4802
+#> 307:    94.2909   -0.1851    2.1076    1.8702    0.4737    0.2072    5.4787
+#> 308:    94.2916   -0.1857    2.1067    1.8754    0.4735    0.2075    5.4773
+#> 309:    94.2889   -0.1860    2.1062    1.8785    0.4732    0.2078    5.4774
+#> 310:    94.2875   -0.1863    2.1059    1.8854    0.4727    0.2082    5.4763
+#> 311:    94.2889   -0.1867    2.1053    1.8873    0.4722    0.2087    5.4746
+#> 312:    94.2889   -0.1870    2.1047    1.8956    0.4717    0.2090    5.4748
+#> 313:    94.2836   -0.1873    2.1044    1.8980    0.4711    0.2093    5.4721
+#> 314:    94.2801   -0.1876    2.1041    1.8924    0.4706    0.2096    5.4718
+#> 315:    94.2768   -0.1880    2.1038    1.8875    0.4701    0.2096    5.4727
+#> 316:    94.2766   -0.1883    2.1035    1.8854    0.4697    0.2097    5.4730
+#> 317:    94.2779   -0.1886    2.1030    1.8808    0.4693    0.2099    5.4725
+#> 318:    94.2806   -0.1889    2.1024    1.8789    0.4688    0.2101    5.4713
+#> 319:    94.2853   -0.1891    2.1018    1.8852    0.4684    0.2104    5.4690
+#> 320:    94.2867   -0.1894    2.1016    1.8898    0.4680    0.2106    5.4677
+#> 321:    94.2883   -0.1897    2.1013    1.8975    0.4676    0.2108    5.4656
+#> 322:    94.2864   -0.1899    2.1011    1.9078    0.4672    0.2109    5.4622
+#> 323:    94.2831   -0.1902    2.1009    1.9181    0.4668    0.2109    5.4593
+#> 324:    94.2799   -0.1904    2.1008    1.9355    0.4665    0.2109    5.4599
+#> 325:    94.2802   -0.1905    2.1007    1.9474    0.4660    0.2112    5.4608
+#> 326:    94.2808   -0.1907    2.1006    1.9656    0.4654    0.2114    5.4606
+#> 327:    94.2815   -0.1907    2.1006    1.9851    0.4649    0.2118    5.4596
+#> 328:    94.2805   -0.1908    2.1007    2.0051    0.4644    0.2120    5.4584
+#> 329:    94.2810   -0.1909    2.1004    2.0162    0.4638    0.2124    5.4566
+#> 330:    94.2812   -0.1912    2.0999    2.0210    0.4632    0.2131    5.4548
+#> 331:    94.2830   -0.1915    2.0994    2.0253    0.4625    0.2136    5.4520
+#> 332:    94.2835   -0.1920    2.0987    2.0288    0.4619    0.2142    5.4493
+#> 333:    94.2832   -0.1924    2.0981    2.0365    0.4615    0.2148    5.4463
+#> 334:    94.2845   -0.1928    2.0976    2.0433    0.4611    0.2153    5.4436
+#> 335:    94.2856   -0.1931    2.0971    2.0423    0.4607    0.2158    5.4405
+#> 336:    94.2886   -0.1936    2.0963    2.0400    0.4606    0.2165    5.4386
+#> 337:    94.2888   -0.1939    2.0957    2.0352    0.4604    0.2171    5.4376
+#> 338:    94.2879   -0.1944    2.0950    2.0360    0.4600    0.2179    5.4361
+#> 339:    94.2860   -0.1947    2.0946    2.0418    0.4599    0.2186    5.4342
+#> 340:    94.2842   -0.1951    2.0940    2.0455    0.4597    0.2192    5.4324
+#> 341:    94.2804   -0.1954    2.0934    2.0535    0.4596    0.2199    5.4310
+#> 342:    94.2772   -0.1958    2.0928    2.0586    0.4594    0.2204    5.4310
+#> 343:    94.2753   -0.1962    2.0921    2.0604    0.4592    0.2209    5.4304
+#> 344:    94.2749   -0.1965    2.0916    2.0591    0.4589    0.2214    5.4305
+#> 345:    94.2757   -0.1969    2.0911    2.0582    0.4586    0.2220    5.4302
+#> 346:    94.2774   -0.1972    2.0906    2.0554    0.4584    0.2225    5.4301
+#> 347:    94.2772   -0.1974    2.0901    2.0533    0.4583    0.2230    5.4298
+#> 348:    94.2769   -0.1977    2.0895    2.0497    0.4581    0.2235    5.4302
+#> 349:    94.2792   -0.1980    2.0890    2.0439    0.4579    0.2241    5.4327
+#> 350:    94.2825   -0.1983    2.0884    2.0391    0.4577    0.2245    5.4358
+#> 351:    94.2849   -0.1985    2.0879    2.0352    0.4576    0.2251    5.4399
+#> 352:    94.2871   -0.1988    2.0874    2.0396    0.4576    0.2257    5.4414
+#> 353:    94.2888   -0.1991    2.0869    2.0407    0.4573    0.2262    5.4417
+#> 354:    94.2914   -0.1994    2.0863    2.0383    0.4571    0.2268    5.4417
+#> 355:    94.2933   -0.1996    2.0859    2.0385    0.4570    0.2275    5.4418
+#> 356:    94.2932   -0.1999    2.0853    2.0377    0.4569    0.2284    5.4426
+#> 357:    94.2944   -0.2001    2.0850    2.0362    0.4566    0.2292    5.4423
+#> 358:    94.2948   -0.2003    2.0847    2.0415    0.4562    0.2299    5.4409
+#> 359:    94.2950   -0.2005    2.0843    2.0452    0.4558    0.2304    5.4393
+#> 360:    94.2967   -0.2008    2.0840    2.0514    0.4554    0.2307    5.4385
+#> 361:    94.2983   -0.2009    2.0839    2.0676    0.4551    0.2308    5.4386
+#> 362:    94.2992   -0.2009    2.0840    2.0770    0.4549    0.2307    5.4370
+#> 363:    94.2991   -0.2008    2.0841    2.0831    0.4550    0.2306    5.4348
+#> 364:    94.2982   -0.2007    2.0843    2.0892    0.4549    0.2304    5.4348
+#> 365:    94.2951   -0.2005    2.0847    2.1002    0.4551    0.2302    5.4347
+#> 366:    94.2938   -0.2004    2.0850    2.1176    0.4553    0.2300    5.4343
+#> 367:    94.2945   -0.2003    2.0850    2.1310    0.4553    0.2298    5.4346
+#> 368:    94.2956   -0.2003    2.0851    2.1436    0.4554    0.2295    5.4323
+#> 369:    94.2960   -0.2003    2.0850    2.1526    0.4555    0.2293    5.4309
+#> 370:    94.2964   -0.2003    2.0848    2.1577    0.4555    0.2292    5.4295
+#> 371:    94.2965   -0.2004    2.0847    2.1621    0.4555    0.2290    5.4278
+#> 372:    94.2972   -0.2004    2.0847    2.1635    0.4556    0.2285    5.4275
+#> 373:    94.2975   -0.2003    2.0848    2.1643    0.4556    0.2282    5.4275
+#> 374:    94.2985   -0.2004    2.0847    2.1648    0.4556    0.2277    5.4270
+#> 375:    94.3001   -0.2004    2.0846    2.1682    0.4555    0.2273    5.4255
+#> 376:    94.3024   -0.2005    2.0845    2.1692    0.4555    0.2268    5.4246
+#> 377:    94.3050   -0.2005    2.0843    2.1700    0.4555    0.2264    5.4239
+#> 378:    94.3041   -0.2005    2.0843    2.1680    0.4555    0.2258    5.4242
+#> 379:    94.3034   -0.2006    2.0842    2.1688    0.4554    0.2255    5.4233
+#> 380:    94.3027   -0.2007    2.0840    2.1754    0.4554    0.2250    5.4222
+#> 381:    94.3015   -0.2008    2.0839    2.1806    0.4553    0.2246    5.4205
+#> 382:    94.3006   -0.2009    2.0837    2.1812    0.4552    0.2242    5.4194
+#> 383:    94.3004   -0.2010    2.0835    2.1835    0.4551    0.2236    5.4178
+#> 384:    94.3001   -0.2011    2.0834    2.1895    0.4550    0.2232    5.4159
+#> 385:    94.3005   -0.2012    2.0834    2.1910    0.4547    0.2228    5.4148
+#> 386:    94.2993   -0.2013    2.0834    2.1926    0.4545    0.2224    5.4139
+#> 387:    94.2974   -0.2014    2.0834    2.1956    0.4543    0.2221    5.4135
+#> 388:    94.2964   -0.2014    2.0835    2.1979    0.4541    0.2218    5.4124
+#> 389:    94.2956   -0.2013    2.0837    2.1974    0.4540    0.2215    5.4117
+#> 390:    94.2962   -0.2013    2.0838    2.1995    0.4538    0.2213    5.4115
+#> 391:    94.2962   -0.2013    2.0838    2.1987    0.4537    0.2211    5.4116
+#> 392:    94.2956   -0.2013    2.0839    2.2007    0.4536    0.2209    5.4111
+#> 393:    94.2954   -0.2012    2.0839    2.2041    0.4535    0.2207    5.4106
+#> 394:    94.2953   -0.2012    2.0840    2.2033    0.4535    0.2205    5.4103
+#> 395:    94.2964   -0.2012    2.0841    2.2052    0.4533    0.2203    5.4098
+#> 396:    94.2950   -0.2012    2.0841    2.2123    0.4532    0.2202    5.4081
+#> 397:    94.2940   -0.2011    2.0843    2.2227    0.4533    0.2201    5.4070
+#> 398:    94.2938   -0.2011    2.0842    2.2283    0.4534    0.2201    5.4065
+#> 399:    94.2930   -0.2012    2.0842    2.2296    0.4535    0.2201    5.4066
+#> 400:    94.2931   -0.2011    2.0844    2.2345    0.4537    0.2199    5.4071
+#> 401:    94.2926   -0.2009    2.0846    2.2414    0.4539    0.2198    5.4067
+#> 402:    94.2916   -0.2008    2.0848    2.2478    0.4541    0.2196    5.4070
+#> 403:    94.2902   -0.2007    2.0849    2.2543    0.4544    0.2194    5.4071
+#> 404:    94.2895   -0.2007    2.0851    2.2578    0.4546    0.2192    5.4079
+#> 405:    94.2896   -0.2006    2.0853    2.2600    0.4548    0.2190    5.4082
+#> 406:    94.2897   -0.2004    2.0855    2.2636    0.4550    0.2188    5.4086
+#> 407:    94.2880   -0.2002    2.0859    2.2670    0.4554    0.2188    5.4079
+#> 408:    94.2883   -0.1999    2.0861    2.2735    0.4556    0.2189    5.4076
+#> 409:    94.2874   -0.1997    2.0865    2.2822    0.4559    0.2190    5.4073
+#> 410:    94.2861   -0.1995    2.0867    2.2861    0.4563    0.2190    5.4062
+#> 411:    94.2861   -0.1993    2.0869    2.2883    0.4566    0.2190    5.4049
+#> 412:    94.2869   -0.1991    2.0872    2.2926    0.4570    0.2190    5.4039
+#> 413:    94.2874   -0.1990    2.0873    2.2936    0.4574    0.2190    5.4031
+#> 414:    94.2881   -0.1988    2.0874    2.2972    0.4577    0.2189    5.4019
+#> 415:    94.2895   -0.1987    2.0876    2.2999    0.4580    0.2188    5.4004
+#> 416:    94.2900   -0.1985    2.0878    2.3003    0.4582    0.2186    5.3997
+#> 417:    94.2917   -0.1984    2.0880    2.2986    0.4583    0.2185    5.3993
+#> 418:    94.2937   -0.1982    2.0882    2.2986    0.4584    0.2183    5.3995
+#> 419:    94.2947   -0.1981    2.0885    2.2993    0.4584    0.2182    5.3995
+#> 420:    94.2954   -0.1979    2.0886    2.2993    0.4585    0.2180    5.3996
+#> 421:    94.2963   -0.1978    2.0888    2.3029    0.4587    0.2180    5.3992
+#> 422:    94.2982   -0.1976    2.0890    2.3074    0.4588    0.2178    5.4000
+#> 423:    94.3001   -0.1975    2.0891    2.3099    0.4589    0.2178    5.3999
+#> 424:    94.3007   -0.1974    2.0891    2.3106    0.4589    0.2177    5.4001
+#> 425:    94.3016   -0.1973    2.0893    2.3107    0.4589    0.2176    5.3997
+#> 426:    94.3021   -0.1972    2.0894    2.3119    0.4590    0.2175    5.3990
+#> 427:    94.3009   -0.1972    2.0894    2.3100    0.4590    0.2175    5.3971
+#> 428:    94.2998   -0.1972    2.0895    2.3070    0.4590    0.2175    5.3966
+#> 429:    94.2988   -0.1973    2.0895    2.3033    0.4590    0.2175    5.3958
+#> 430:    94.2968   -0.1973    2.0895    2.3028    0.4590    0.2174    5.3955
+#> 431:    94.2950   -0.1973    2.0895    2.3004    0.4589    0.2174    5.3954
+#> 432:    94.2944   -0.1973    2.0896    2.2966    0.4589    0.2174    5.3956
+#> 433:    94.2950   -0.1972    2.0897    2.2942    0.4589    0.2176    5.3959
+#> 434:    94.2949   -0.1972    2.0898    2.2911    0.4589    0.2177    5.3955
+#> 435:    94.2943   -0.1971    2.0900    2.2914    0.4588    0.2179    5.3943
+#> 436:    94.2943   -0.1970    2.0902    2.2895    0.4586    0.2180    5.3948
+#> 437:    94.2955   -0.1970    2.0903    2.2890    0.4585    0.2181    5.3954
+#> 438:    94.2961   -0.1969    2.0905    2.2918    0.4584    0.2183    5.3958
+#> 439:    94.2954   -0.1968    2.0906    2.2943    0.4583    0.2185    5.3953
+#> 440:    94.2944   -0.1968    2.0906    2.2977    0.4581    0.2187    5.3949
+#> 441:    94.2931   -0.1968    2.0907    2.2991    0.4578    0.2188    5.3952
+#> 442:    94.2926   -0.1968    2.0908    2.2990    0.4575    0.2188    5.3951
+#> 443:    94.2922   -0.1968    2.0909    2.2990    0.4573    0.2188    5.3938
+#> 444:    94.2917   -0.1969    2.0909    2.2995    0.4571    0.2188    5.3927
+#> 445:    94.2901   -0.1969    2.0910    2.3067    0.4568    0.2187    5.3911
+#> 446:    94.2898   -0.1969    2.0910    2.3082    0.4566    0.2187    5.3891
+#> 447:    94.2897   -0.1969    2.0910    2.3121    0.4564    0.2187    5.3871
+#> 448:    94.2883   -0.1970    2.0911    2.3180    0.4562    0.2188    5.3858
+#> 449:    94.2879   -0.1970    2.0912    2.3210    0.4561    0.2188    5.3851
+#> 450:    94.2874   -0.1970    2.0914    2.3243    0.4559    0.2188    5.3841
+#> 451:    94.2873   -0.1969    2.0915    2.3247    0.4557    0.2188    5.3834
+#> 452:    94.2873   -0.1969    2.0917    2.3249    0.4555    0.2187    5.3839
+#> 453:    94.2868   -0.1968    2.0920    2.3257    0.4554    0.2187    5.3831
+#> 454:    94.2857   -0.1967    2.0922    2.3240    0.4552    0.2187    5.3824
+#> 455:    94.2848   -0.1965    2.0925    2.3214    0.4551    0.2186    5.3822
+#> 456:    94.2838   -0.1964    2.0929    2.3204    0.4550    0.2185    5.3822
+#> 457:    94.2831   -0.1962    2.0932    2.3202    0.4549    0.2184    5.3819
+#> 458:    94.2831   -0.1961    2.0935    2.3174    0.4548    0.2183    5.3810
+#> 459:    94.2829   -0.1960    2.0938    2.3183    0.4546    0.2183    5.3807
+#> 460:    94.2818   -0.1958    2.0941    2.3213    0.4545    0.2183    5.3802
+#> 461:    94.2812   -0.1956    2.0945    2.3292    0.4544    0.2182    5.3785
+#> 462:    94.2813   -0.1955    2.0948    2.3328    0.4544    0.2182    5.3778
+#> 463:    94.2816   -0.1953    2.0951    2.3364    0.4543    0.2181    5.3770
+#> 464:    94.2810   -0.1952    2.0954    2.3365    0.4542    0.2180    5.3764
+#> 465:    94.2797   -0.1950    2.0957    2.3341    0.4541    0.2179    5.3756
+#> 466:    94.2777   -0.1949    2.0960    2.3368    0.4541    0.2178    5.3750
+#> 467:    94.2755   -0.1949    2.0962    2.3417    0.4539    0.2178    5.3738
+#> 468:    94.2741   -0.1948    2.0965    2.3426    0.4537    0.2177    5.3731
+#> 469:    94.2735   -0.1947    2.0967    2.3410    0.4535    0.2175    5.3729
+#> 470:    94.2731   -0.1946    2.0970    2.3440    0.4534    0.2173    5.3733
+#> 471:    94.2727   -0.1945    2.0972    2.3505    0.4533    0.2171    5.3724
+#> 472:    94.2734   -0.1944    2.0973    2.3550    0.4533    0.2169    5.3719
+#> 473:    94.2745   -0.1944    2.0974    2.3593    0.4533    0.2167    5.3715
+#> 474:    94.2746   -0.1944    2.0975    2.3622    0.4533    0.2166    5.3708
+#> 475:    94.2753   -0.1943    2.0975    2.3673    0.4533    0.2165    5.3701
+#> 476:    94.2760   -0.1943    2.0976    2.3745    0.4534    0.2166    5.3698
+#> 477:    94.2771   -0.1942    2.0978    2.3812    0.4535    0.2166    5.3695
+#> 478:    94.2767   -0.1941    2.0981    2.3891    0.4535    0.2166    5.3691
+#> 479:    94.2762   -0.1940    2.0984    2.3931    0.4534    0.2166    5.3691
+#> 480:    94.2754   -0.1939    2.0986    2.3958    0.4533    0.2166    5.3685
+#> 481:    94.2743   -0.1938    2.0987    2.3990    0.4532    0.2165    5.3677
+#> 482:    94.2733   -0.1937    2.0988    2.3996    0.4531    0.2164    5.3670
+#> 483:    94.2724   -0.1937    2.0989    2.4031    0.4531    0.2163    5.3659
+#> 484:    94.2726   -0.1937    2.0989    2.4035    0.4530    0.2162    5.3651
+#> 485:    94.2722   -0.1937    2.0989    2.4033    0.4530    0.2162    5.3649
+#> 486:    94.2716   -0.1936    2.0991    2.4046    0.4529    0.2163    5.3645
+#> 487:    94.2710   -0.1936    2.0992    2.4078    0.4527    0.2165    5.3643
+#> 488:    94.2693   -0.1936    2.0992    2.4088    0.4525    0.2167    5.3653
+#> 489:    94.2689   -0.1936    2.0993    2.4116    0.4523    0.2170    5.3645
+#> 490:    94.2686   -0.1936    2.0993    2.4105    0.4520    0.2172    5.3644
+#> 491:    94.2685   -0.1935    2.0994    2.4097    0.4518    0.2174    5.3651
+#> 492:    94.2677   -0.1935    2.0995    2.4103    0.4517    0.2175    5.3657
+#> 493:    94.2670   -0.1935    2.0996    2.4112    0.4515    0.2177    5.3661
+#> 494:    94.2668   -0.1935    2.0996    2.4140    0.4514    0.2178    5.3662
+#> 495:    94.2667   -0.1936    2.0996    2.4157    0.4513    0.2179    5.3660
+#> 496:    94.2670   -0.1936    2.0996    2.4163    0.4511    0.2180    5.3668
+#> 497:    94.2664   -0.1936    2.0996    2.4170    0.4510    0.2181    5.3676
+#> 498:    94.2654   -0.1937    2.0996    2.4128    0.4509    0.2181    5.3683
+#> 499:    94.2643   -0.1937    2.0996    2.4109    0.4508    0.2181    5.3679
+#> 500:    94.2635   -0.1938    2.0995    2.4122    0.4508    0.2181    5.3682
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 | log_alpha |  log_beta |     sigma |
+#> |.....................|        o1 |        o2 |        o3 |...........|
+#> |    1|     296.31831 |     1.000 |    -1.000 |   -0.9520 |   -0.9547 |
+#> |.....................|   -0.9791 |   -0.9725 |   -0.9706 |...........|
+#> |    U|     296.31831 |     94.44 |   -0.2226 |     2.048 |     1.920 |
+#> |.....................|    0.7656 |     1.078 |     1.168 |...........|
+#> |    X|     296.31831 |     94.44 |    0.8004 |     7.754 |     1.920 |
+#> |.....................|    0.7656 |     1.078 |     1.168 |...........|
+#> |    G|    Gill Diff. |     9.126 |  0.009097 |  -0.01177 |    -32.33 |
+#> |.....................|     6.099 |    -8.436 |    -11.35 |...........|
+#> |    2|     594.25462 |    0.7531 |    -1.000 |   -0.9517 |  -0.07988 |
+#> |.....................|    -1.144 |   -0.7442 |   -0.6636 |...........|
+#> |    U|     594.25462 |     71.12 |   -0.2229 |     2.049 |     2.760 |
+#> |.....................|    0.6392 |     1.324 |     1.526 |...........|
+#> |    X|     594.25462 |     71.12 |    0.8002 |     7.756 |     2.760 |
+#> |.....................|    0.6392 |     1.324 |     1.526 |...........|
+#> |    3|     298.71818 |    0.9753 |    -1.000 |   -0.9520 |   -0.8672 |
+#> |.....................|   -0.9956 |   -0.9497 |   -0.9399 |...........|
+#> |    U|     298.71818 |     92.11 |   -0.2226 |     2.048 |     2.004 |
+#> |.....................|    0.7529 |     1.103 |     1.204 |...........|
+#> |    X|     298.71818 |     92.11 |    0.8004 |     7.754 |     2.004 |
+#> |.....................|    0.7529 |     1.103 |     1.204 |...........|
+#> |    4|     295.79061 |    0.9925 |    -1.000 |   -0.9520 |   -0.9282 |
+#> |.....................|   -0.9841 |   -0.9656 |   -0.9613 |...........|
+#> |    U|     295.79061 |     93.73 |   -0.2226 |     2.048 |     1.945 |
+#> |.....................|    0.7617 |     1.086 |     1.179 |...........|
+#> |    X|     295.79061 |     93.73 |    0.8004 |     7.754 |     1.945 |
+#> |.....................|    0.7617 |     1.086 |     1.179 |...........|
+#> |    F| Forward Diff. |    -134.6 |  -0.07715 |   -0.3541 |    -29.37 |
+#> |.....................|     6.863 |    -7.752 |    -10.79 |...........|
+#> |    5|     294.44078 |     1.001 |    -1.000 |   -0.9520 |   -0.9020 |
+#> |.....................|   -0.9892 |   -0.9588 |   -0.9521 |...........|
+#> |    U|     294.44078 |     94.55 |   -0.2226 |     2.048 |     1.970 |
+#> |.....................|    0.7578 |     1.093 |     1.189 |...........|
+#> |    X|     294.44078 |     94.55 |    0.8004 |     7.754 |     1.970 |
+#> |.....................|    0.7578 |     1.093 |     1.189 |...........|
+#> |    F| Forward Diff. |     30.39 |   0.01643 |   0.02646 |    -26.06 |
+#> |.....................|     5.336 |    -7.397 |    -10.44 |...........|
+#> |    6|     293.62741 |    0.9971 |    -1.000 |   -0.9519 |   -0.8750 |
+#> |.....................|   -0.9945 |   -0.9516 |   -0.9423 |...........|
+#> |    U|     293.62741 |     94.17 |   -0.2226 |     2.048 |     1.996 |
+#> |.....................|    0.7538 |     1.101 |     1.201 |...........|
+#> |    X|     293.62741 |     94.17 |    0.8004 |     7.754 |     1.996 |
+#> |.....................|    0.7538 |     1.101 |     1.201 |...........|
+#> |    7|     292.50099 |    0.9961 |    -1.000 |   -0.9519 |   -0.8316 |
+#> |.....................|    -1.003 |   -0.9401 |   -0.9267 |...........|
+#> |    U|     292.50099 |     94.07 |   -0.2226 |     2.048 |     2.038 |
+#> |.....................|    0.7474 |     1.113 |     1.219 |...........|
+#> |    X|     292.50099 |     94.07 |    0.8004 |     7.755 |     2.038 |
+#> |.....................|    0.7474 |     1.113 |     1.219 |...........|
+#> |    8|     290.76125 |    0.9939 |    -1.000 |   -0.9518 |   -0.7361 |
+#> |.....................|    -1.021 |   -0.9149 |   -0.8925 |...........|
+#> |    U|     290.76125 |     93.87 |   -0.2226 |     2.048 |     2.130 |
+#> |.....................|    0.7332 |     1.140 |     1.259 |...........|
+#> |    X|     290.76125 |     93.87 |    0.8004 |     7.756 |     2.130 |
+#> |.....................|    0.7332 |     1.140 |     1.259 |...........|
+#> |    F| Forward Diff. |    -91.20 |  -0.08176 |   -0.4010 |    -10.74 |
+#> |.....................|     3.658 |    -4.872 |    -7.770 |...........|
+#> |    9|     293.40175 |     1.024 |   -0.9990 |   -0.9455 |   -0.7012 |
+#> |.....................|    -1.060 |   -0.8302 |   -0.7398 |...........|
+#> |    U|     293.40175 |     96.67 |   -0.2216 |     2.055 |     2.163 |
+#> |.....................|    0.7035 |     1.231 |     1.437 |...........|
+#> |    X|     293.40175 |     96.67 |    0.8012 |     7.804 |     2.163 |
+#> |.....................|    0.7035 |     1.231 |     1.437 |...........|
+#> |   10|     292.85583 |     1.019 |   -0.9997 |   -0.9499 |   -0.7242 |
+#> |.....................|    -1.033 |   -0.8898 |   -0.8474 |...........|
+#> |    U|     292.85583 |     96.21 |   -0.2223 |     2.050 |     2.141 |
+#> |.....................|    0.7242 |     1.167 |     1.312 |...........|
+#> |    X|     292.85583 |     96.21 |    0.8007 |     7.770 |     2.141 |
+#> |.....................|    0.7242 |     1.167 |     1.312 |...........|
+#> |   11|     291.55187 |     1.011 |    -1.000 |   -0.9517 |   -0.7341 |
+#> |.....................|    -1.022 |   -0.9140 |   -0.8910 |...........|
+#> |    U|     291.55187 |     95.48 |   -0.2226 |     2.048 |     2.132 |
+#> |.....................|    0.7326 |     1.141 |     1.261 |...........|
+#> |    X|     291.55187 |     95.48 |    0.8004 |     7.756 |     2.132 |
+#> |.....................|    0.7326 |     1.141 |     1.261 |...........|
+#> |   12|     290.49268 |    0.9997 |    -1.000 |   -0.9518 |   -0.7354 |
+#> |.....................|    -1.022 |   -0.9146 |   -0.8920 |...........|
+#> |    U|     290.49268 |     94.41 |   -0.2226 |     2.048 |     2.130 |
+#> |.....................|    0.7330 |     1.141 |     1.259 |...........|
+#> |    X|     290.49268 |     94.41 |    0.8004 |     7.756 |     2.130 |
+#> |.....................|    0.7330 |     1.141 |     1.259 |...........|
+#> |    F| Forward Diff. |     2.619 | -0.007793 |  -0.07320 |    -10.57 |
+#> |.....................|     3.077 |    -4.876 |    -7.795 |...........|
+#> |   13|     290.41825 |    0.9986 |    -1.000 |   -0.9517 |   -0.7312 |
+#> |.....................|    -1.023 |   -0.9126 |   -0.8889 |...........|
+#> |    U|     290.41825 |     94.31 |   -0.2226 |     2.048 |     2.134 |
+#> |.....................|    0.7321 |     1.143 |     1.263 |...........|
+#> |    X|     290.41825 |     94.31 |    0.8004 |     7.756 |     2.134 |
+#> |.....................|    0.7321 |     1.143 |     1.263 |...........|
+#> |   14|     290.31205 |    0.9955 |    -1.000 |   -0.9517 |   -0.7186 |
+#> |.....................|    -1.027 |   -0.9068 |   -0.8796 |...........|
+#> |    U|     290.31205 |     94.01 |   -0.2226 |     2.049 |     2.146 |
+#> |.....................|    0.7292 |     1.149 |     1.274 |...........|
+#> |    X|     290.31205 |     94.01 |    0.8004 |     7.757 |     2.146 |
+#> |.....................|    0.7292 |     1.149 |     1.274 |...........|
+#> |    F| Forward Diff. |    -64.45 |  -0.06351 |   -0.3251 |    -9.485 |
+#> |.....................|     2.861 |    -4.414 |    -7.225 |...........|
+#> |   15|     290.00198 |     1.000 |   -0.9999 |   -0.9510 |   -0.7191 |
+#> |.....................|    -1.030 |   -0.8965 |   -0.8595 |...........|
+#> |    U|     290.00198 |     94.46 |   -0.2225 |     2.049 |     2.146 |
+#> |.....................|    0.7268 |     1.160 |     1.297 |...........|
+#> |    X|     290.00198 |     94.46 |    0.8005 |     7.762 |     2.146 |
+#> |.....................|    0.7268 |     1.160 |     1.297 |...........|
+#> |    F| Forward Diff. |     11.27 | -0.003123 |  -0.03408 |    -9.156 |
+#> |.....................|     2.235 |    -3.823 |    -6.423 |...........|
+#> |   16|     289.83558 |    0.9983 |   -0.9998 |   -0.9502 |   -0.7180 |
+#> |.....................|    -1.031 |   -0.8872 |   -0.8384 |...........|
+#> |    U|     289.83558 |     94.28 |   -0.2224 |     2.050 |     2.147 |
+#> |.....................|    0.7259 |     1.170 |     1.322 |...........|
+#> |    X|     289.83558 |     94.28 |    0.8006 |     7.768 |     2.147 |
+#> |.....................|    0.7259 |     1.170 |     1.322 |...........|
+#> |   17|     289.63307 |    0.9979 |   -0.9995 |   -0.9489 |   -0.7184 |
+#> |.....................|    -1.032 |   -0.8720 |   -0.8037 |...........|
+#> |    U|     289.63307 |     94.24 |   -0.2221 |     2.051 |     2.147 |
+#> |.....................|    0.7248 |     1.186 |     1.363 |...........|
+#> |    X|     289.63307 |     94.24 |    0.8008 |     7.778 |     2.147 |
+#> |.....................|    0.7248 |     1.186 |     1.363 |...........|
+#> |   18|     289.44450 |    0.9972 |   -0.9991 |   -0.9468 |   -0.7190 |
+#> |.....................|    -1.035 |   -0.8473 |   -0.7469 |...........|
+#> |    U|      289.4445 |     94.18 |   -0.2217 |     2.053 |     2.146 |
+#> |.....................|    0.7231 |     1.213 |     1.429 |...........|
+#> |    X|      289.4445 |     94.18 |    0.8011 |     7.794 |     2.146 |
+#> |.....................|    0.7231 |     1.213 |     1.429 |...........|
+#> |    F| Forward Diff. |    -36.76 |  -0.05208 |   -0.1861 |    -9.057 |
+#> |.....................|     2.429 |   -0.6853 |    -1.924 |...........|
+#> |   19|     288.93351 |    0.9984 |   -0.9961 |   -0.9370 |   -0.6306 |
+#> |.....................|    -1.080 |   -0.9120 |   -0.7149 |...........|
+#> |    U|     288.93351 |     94.29 |   -0.2187 |     2.063 |     2.231 |
+#> |.....................|    0.6885 |     1.143 |     1.466 |...........|
+#> |    X|     288.93351 |     94.29 |    0.8035 |     7.871 |     2.231 |
+#> |.....................|    0.6885 |     1.143 |     1.466 |...........|
+#> |    F| Forward Diff. |    -14.48 |  -0.02726 |    0.2181 |    -3.062 |
+#> |.....................|   -0.1976 |    -4.306 |   -0.8806 |...........|
+#> |   20|     288.85238 |     1.002 |   -0.9934 |   -0.9444 |   -0.5654 |
+#> |.....................|    -1.062 |   -0.8288 |   -0.7747 |...........|
+#> |    U|     288.85238 |     94.67 |   -0.2160 |     2.056 |     2.293 |
+#> |.....................|    0.7024 |     1.233 |     1.396 |...........|
+#> |    X|     288.85238 |     94.67 |    0.8057 |     7.813 |     2.293 |
+#> |.....................|    0.7024 |     1.233 |     1.396 |...........|
+#> |    F| Forward Diff. |     40.49 |    0.1537 |    0.2940 |    0.6524 |
+#> |.....................|    0.4942 |    0.3489 |    -3.099 |...........|
+#> |   21|     289.09335 |    0.9960 |    -1.025 |    -1.050 |   -0.5645 |
+#> |.....................|    -1.111 |   -0.8117 |   -0.7552 |...........|
+#> |    U|     289.09335 |     94.07 |   -0.2476 |     1.951 |     2.294 |
+#> |.....................|    0.6648 |     1.251 |     1.419 |...........|
+#> |    X|     289.09335 |     94.07 |    0.7806 |     7.034 |     2.294 |
+#> |.....................|    0.6648 |     1.251 |     1.419 |...........|
+#> |   22|     288.97418 |    0.9945 |    -1.003 |   -0.9755 |   -0.5652 |
+#> |.....................|    -1.076 |   -0.8238 |   -0.7685 |...........|
+#> |    U|     288.97418 |     93.92 |   -0.2254 |     2.025 |     2.294 |
+#> |.....................|    0.6912 |     1.238 |     1.404 |...........|
+#> |    X|     288.97418 |     93.92 |    0.7982 |     7.574 |     2.294 |
+#> |.....................|    0.6912 |     1.238 |     1.404 |...........|
+#> |   23|     288.99640 |    0.9941 |   -0.9963 |   -0.9538 |   -0.5655 |
+#> |.....................|    -1.066 |   -0.8273 |   -0.7723 |...........|
+#> |    U|      288.9964 |     93.88 |   -0.2189 |     2.046 |     2.293 |
+#> |.....................|    0.6990 |     1.235 |     1.399 |...........|
+#> |    X|      288.9964 |     93.88 |    0.8034 |     7.740 |     2.293 |
+#> |.....................|    0.6990 |     1.235 |     1.399 |...........|
+#> |   24|     288.82158 |    0.9975 |   -0.9934 |   -0.9445 |   -0.5655 |
+#> |.....................|    -1.062 |   -0.8288 |   -0.7743 |...........|
+#> |    U|     288.82158 |     94.20 |   -0.2160 |     2.056 |     2.293 |
+#> |.....................|    0.7023 |     1.233 |     1.397 |...........|
+#> |    X|     288.82158 |     94.20 |    0.8057 |     7.813 |     2.293 |
+#> |.....................|    0.7023 |     1.233 |     1.397 |...........|
+#> |    F| Forward Diff. |    -27.98 |   0.07663 |  -0.09902 |    0.6250 |
+#> |.....................|    0.3387 |    0.3777 |    -3.049 |...........|
+#> |   25|     288.78525 |    0.9995 |   -0.9943 |   -0.9465 |   -0.5657 |
+#> |.....................|    -1.059 |   -0.8303 |   -0.7716 |...........|
+#> |    U|     288.78525 |     94.39 |   -0.2169 |     2.054 |     2.293 |
+#> |.....................|    0.7042 |     1.231 |     1.400 |...........|
+#> |    X|     288.78525 |     94.39 |    0.8050 |     7.797 |     2.293 |
+#> |.....................|    0.7042 |     1.231 |     1.400 |...........|
+#> |    F| Forward Diff. |   -0.7037 |   0.08814 | -0.009566 |    0.5597 |
+#> |.....................|    0.2999 |    0.2778 |    -2.968 |...........|
+#> |   26|     288.77680 |     1.000 |   -0.9946 |   -0.9467 |   -0.5664 |
+#> |.....................|    -1.059 |   -0.8311 |   -0.7670 |...........|
+#> |    U|      288.7768 |     94.48 |   -0.2172 |     2.053 |     2.292 |
+#> |.....................|    0.7047 |     1.231 |     1.405 |...........|
+#> |    X|      288.7768 |     94.48 |    0.8048 |     7.795 |     2.292 |
+#> |.....................|    0.7047 |     1.231 |     1.405 |...........|
+#> |    F| Forward Diff. |     12.46 |   0.09472 |   0.05753 |    0.4960 |
+#> |.....................|    0.3156 |    0.2411 |    -2.796 |...........|
+#> |   27|     288.76499 |    0.9995 |   -0.9954 |   -0.9482 |   -0.5665 |
+#> |.....................|    -1.055 |   -0.8326 |   -0.7642 |...........|
+#> |    U|     288.76499 |     94.39 |   -0.2180 |     2.052 |     2.292 |
+#> |.....................|    0.7071 |     1.229 |     1.409 |...........|
+#> |    X|     288.76499 |     94.39 |    0.8042 |     7.783 |     2.292 |
+#> |.....................|    0.7071 |     1.229 |     1.409 |...........|
+#> |    F| Forward Diff. |   -0.8358 |   0.06465 |  -0.06858 |    0.5747 |
+#> |.....................|    0.6430 |    0.1630 |    -2.683 |...........|
+#> |   28|     288.75697 |     1.000 |   -0.9957 |   -0.9484 |   -0.5681 |
+#> |.....................|    -1.059 |   -0.8325 |   -0.7609 |...........|
+#> |    U|     288.75697 |     94.45 |   -0.2183 |     2.052 |     2.291 |
+#> |.....................|    0.7046 |     1.229 |     1.413 |...........|
+#> |    X|     288.75697 |     94.45 |    0.8039 |     7.782 |     2.291 |
+#> |.....................|    0.7046 |     1.229 |     1.413 |...........|
+#> |    F| Forward Diff. |     8.673 |   0.06496 |  -0.02049 |    0.4885 |
+#> |.....................|    0.5066 |    0.1747 |    -2.560 |...........|
+#> |   29|     288.75050 |    0.9994 |   -0.9958 |   -0.9480 |   -0.5696 |
+#> |.....................|    -1.063 |   -0.8317 |   -0.7600 |...........|
+#> |    U|      288.7505 |     94.38 |   -0.2184 |     2.052 |     2.289 |
+#> |.....................|    0.7012 |     1.230 |     1.414 |...........|
+#> |    X|      288.7505 |     94.38 |    0.8038 |     7.785 |     2.289 |
+#> |.....................|    0.7012 |     1.230 |     1.414 |...........|
+#> |    F| Forward Diff. |    -2.463 |   0.04955 |  -0.07455 |    0.3979 |
+#> |.....................|    0.1788 |    0.2263 |    -2.511 |...........|
+#> |   30|     288.74110 |    0.9997 |   -0.9954 |   -0.9459 |   -0.5705 |
+#> |.....................|    -1.061 |   -0.8331 |   -0.7562 |...........|
+#> |    U|      288.7411 |     94.41 |   -0.2180 |     2.054 |     2.289 |
+#> |.....................|    0.7025 |     1.228 |     1.418 |...........|
+#> |    X|      288.7411 |     94.41 |    0.8041 |     7.801 |     2.289 |
+#> |.....................|    0.7025 |     1.228 |     1.418 |...........|
+#> |   31|     288.72064 |    0.9993 |   -0.9939 |   -0.9392 |   -0.5730 |
+#> |.....................|    -1.056 |   -0.8374 |   -0.7455 |...........|
+#> |    U|     288.72064 |     94.37 |   -0.2166 |     2.061 |     2.286 |
+#> |.....................|    0.7068 |     1.224 |     1.431 |...........|
+#> |    X|     288.72064 |     94.37 |    0.8053 |     7.854 |     2.286 |
+#> |.....................|    0.7068 |     1.224 |     1.431 |...........|
+#> |   32|     288.70690 |    0.9989 |   -0.9915 |   -0.9277 |   -0.5774 |
+#> |.....................|    -1.046 |   -0.8449 |   -0.7267 |...........|
+#> |    U|      288.7069 |     94.33 |   -0.2141 |     2.072 |     2.282 |
+#> |.....................|    0.7141 |     1.216 |     1.453 |...........|
+#> |    X|      288.7069 |     94.33 |    0.8073 |     7.944 |     2.282 |
+#> |.....................|    0.7141 |     1.216 |     1.453 |...........|
+#> |    F| Forward Diff. |    -8.246 |   0.08782 |    0.6230 |   -0.2261 |
+#> |.....................|    0.9054 |   -0.5290 |    -1.268 |...........|
+#> |   33|     288.68146 |     1.000 |   -0.9932 |   -0.9567 |   -0.5899 |
+#> |.....................|    -1.067 |   -0.8479 |   -0.7019 |...........|
+#> |    U|     288.68146 |     94.46 |   -0.2158 |     2.043 |     2.270 |
+#> |.....................|    0.6982 |     1.212 |     1.481 |...........|
+#> |    X|     288.68146 |     94.46 |    0.8059 |     7.717 |     2.270 |
+#> |.....................|    0.6982 |     1.212 |     1.481 |...........|
+#> |    F| Forward Diff. |     8.603 |    0.1068 |   -0.4021 |   -0.6499 |
+#> |.....................|    0.1745 |   -0.5873 |   -0.4459 |...........|
+#> |   34|     288.70236 |     1.001 |    -1.018 |   -0.9264 |   -0.5930 |
+#> |.....................|    -1.088 |   -0.8392 |   -0.6985 |...........|
+#> |    U|     288.70236 |     94.50 |   -0.2403 |     2.074 |     2.267 |
+#> |.....................|    0.6822 |     1.222 |     1.485 |...........|
+#> |    X|     288.70236 |     94.50 |    0.7864 |     7.955 |     2.267 |
+#> |.....................|    0.6822 |     1.222 |     1.485 |...........|
+#> |   35|     288.67546 |    0.9997 |   -0.9992 |   -0.9493 |   -0.5906 |
+#> |.....................|    -1.072 |   -0.8457 |   -0.7010 |...........|
+#> |    U|     288.67546 |     94.41 |   -0.2218 |     2.051 |     2.269 |
+#> |.....................|    0.6943 |     1.215 |     1.482 |...........|
+#> |    X|     288.67546 |     94.41 |    0.8011 |     7.775 |     2.269 |
+#> |.....................|    0.6943 |     1.215 |     1.482 |...........|
+#> |    F| Forward Diff. |     1.309 |  -0.03968 |   -0.1448 |   -0.6596 |
+#> |.....................|   0.05856 |   -0.4617 |   -0.3123 |...........|
+#> |   36|     288.67323 |    0.9995 |   -0.9891 |   -0.9462 |   -0.5890 |
+#> |.....................|    -1.074 |   -0.8436 |   -0.6999 |...........|
+#> |    U|     288.67323 |     94.40 |   -0.2117 |     2.054 |     2.271 |
+#> |.....................|    0.6929 |     1.217 |     1.484 |...........|
+#> |    X|     288.67323 |     94.40 |    0.8092 |     7.800 |     2.271 |
+#> |.....................|    0.6929 |     1.217 |     1.484 |...........|
+#> |    F| Forward Diff. |   -0.3529 |    0.1695 |  -0.04594 |   -0.6688 |
+#> |.....................|   -0.2932 |   -0.3576 |   -0.2566 |...........|
+#> |   37|     288.67323 |    0.9995 |   -0.9891 |   -0.9462 |   -0.5890 |
+#> |.....................|    -1.074 |   -0.8436 |   -0.6999 |...........|
+#> |    U|     288.67323 |     94.40 |   -0.2117 |     2.054 |     2.271 |
+#> |.....................|    0.6929 |     1.217 |     1.484 |...........|
+#> |    X|     288.67323 |     94.40 |    0.8092 |     7.800 |     2.271 |
+#> |.....................|    0.6929 |     1.217 |     1.484 |...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                
+f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1:    93.2375   -1.6690   -4.0126    0.0336    3.3441    0.9789    2.1220    0.5342   18.1447
+#> 2:    92.9778   -1.6369   -3.9297    0.0067    3.1769    1.2515    2.0460    0.5166   11.1022
+#> 3:    92.9382   -1.6747   -3.9496   -0.0050    3.0181    1.1889    1.9437    0.4908    9.5980
+#> 4:    93.4481   -1.8083   -3.9734   -0.0250    2.8672    1.1295    1.8797    0.4662    8.6240
+#> 5:    93.4584   -1.8288   -4.0221    0.0414    2.7238    1.0730    1.8467    0.5161    8.1404
+#> 6:    93.7533   -1.8675   -4.0215    0.0158    2.5876    1.0194    1.8017    0.4911    7.5848
+#> 7:    93.6006   -1.8542   -4.0241   -0.0026    2.4582    0.9684    1.7860    0.4916    7.0796
+#> 8:    93.6918   -1.8416   -3.9940    0.0121    2.3353    0.9200    1.7061    0.4681    6.9985
+#> 9:    93.4789   -1.8738   -3.9845    0.0318    3.1307    0.8740    1.7845    0.4553    6.8335
+#> 10:    93.6048   -1.8723   -4.0154    0.0112    3.1962    0.8303    1.7434    0.4325    7.0681
+#> 11:    93.5135   -1.8675   -3.9905    0.0295    3.2177    0.7888    1.6910    0.4619    6.9572
+#> 12:    93.4407   -1.8790   -3.9877    0.0509    3.4194    0.7493    1.6324    0.5060    6.5755
+#> 13:    93.5033   -1.9250   -4.0416    0.0734    3.2485    0.7295    1.7369    0.4807    6.3881
+#> 14:    93.4276   -1.9082   -4.0516    0.0558    3.0860    0.7281    1.7241    0.4567    5.9840
+#> 15:    93.3041   -1.9256   -4.0718    0.0854    3.4389    0.7293    1.7446    0.4524    5.8195
+#> 16:    93.2979   -1.9297   -4.0624    0.0730    3.2670    0.7239    1.7476    0.4298    5.7629
+#> 17:    93.3522   -1.9570   -4.0876    0.1304    3.3053    0.7020    1.7402    0.4083    5.6926
+#> 18:    93.3500   -1.9652   -4.0816    0.1350    3.1400    0.7130    1.7217    0.3879    5.5714
+#> 19:    93.3822   -1.9519   -4.0961    0.1322    2.9830    0.7087    1.7228    0.3745    5.4176
+#> 20:    93.2823   -1.9490   -4.0841    0.1238    2.8339    0.6988    1.7659    0.3753    5.5279
+#> 21:    93.5951   -1.9298   -4.0874    0.1345    2.6922    0.6665    1.7724    0.3645    5.4414
+#> 22:    93.5052   -1.9469   -4.0739    0.1260    3.1244    0.6776    1.7629    0.3618    5.5395
+#> 23:    93.4734   -1.9952   -4.0909    0.1472    3.0340    0.7225    1.8104    0.3437    5.5072
+#> 24:    93.8816   -1.9639   -4.0914    0.1511    2.8824    0.7215    1.8586    0.3324    5.6009
+#> 25:    93.5874   -1.9750   -4.1026    0.1296    2.7383    0.7178    1.8209    0.3680    5.6274
+#> 26:    93.4057   -1.9316   -4.0922    0.1224    3.8103    0.7331    1.7796    0.3639    5.6861
+#> 27:    93.5013   -1.9188   -4.0698    0.0758    3.7127    0.7670    1.8750    0.3457    5.6624
+#> 28:    93.5703   -1.9523   -4.0758    0.0731    4.6390    0.7489    1.8583    0.3445    5.8077
+#> 29:    93.4694   -1.9559   -4.0566    0.0444    5.1290    0.8062    1.9344    0.3273    5.8688
+#> 30:    93.2290   -1.9824   -4.0475    0.0674    4.8726    0.8702    2.0343    0.3109    5.7579
+#> 31:    93.8652   -1.9771   -4.0510    0.0679    4.6289    0.8565    2.0529    0.2954    5.5526
+#> 32:    93.5854   -1.9573   -4.0510    0.0643    5.1320    0.8417    2.0138    0.2806    5.4199
+#> 33:    93.9870   -1.9503   -4.0513    0.0542    4.8754    0.8412    2.0433    0.2666    5.6945
+#> 34:    93.6884   -1.9172   -4.0633    0.0556    4.6317    0.8847    2.0861    0.2702    5.2687
+#> 35:    94.0375   -1.9365   -4.0576    0.0753    5.2320    0.8404    2.0791    0.2582    5.2760
+#> 36:    94.1588   -1.9423   -4.0499    0.0792    4.9704    0.8221    2.1145    0.2669    5.2050
+#> 37:    93.8626   -1.9356   -4.0538    0.0591    5.2723    0.8360    2.1407    0.2536    5.3218
+#> 38:    93.7237   -1.9357   -4.0611    0.0543    5.0087    0.8361    2.0788    0.2710    5.2866
+#> 39:    93.6513   -1.9327   -4.0408    0.0712    4.7582    0.8408    2.0051    0.2899    5.4693
+#> 40:    93.4619   -1.9634   -4.0360    0.1232    4.5203    0.8317    2.0367    0.3288    5.4324
+#> 41:    93.4809   -1.9601   -4.0351    0.1261    4.2943    0.8424    2.0081    0.3306    5.4573
+#> 42:    93.5851   -1.9745   -4.0428    0.1250    4.9744    0.8003    1.9818    0.3141    5.5168
+#> 43:    93.7820   -1.9597   -4.0401    0.1305    5.9118    0.7603    2.1332    0.2984    5.4899
+#> 44:    93.7419   -1.9509   -4.0495    0.1345    5.6162    0.7743    2.0459    0.2998    5.5344
+#> 45:    93.6967   -1.9366   -4.0522    0.1215    5.3354    0.7968    2.0566    0.2848    5.7738
+#> 46:    93.3665   -1.9553   -4.0018    0.0951    5.0686    0.7583    2.1124    0.2706    5.3850
+#> 47:    93.2974   -1.9332   -4.0091    0.0869    5.2792    0.8149    2.1009    0.2597    5.6743
+#> 48:    93.3967   -1.9540   -4.0218    0.0623    5.0152    0.8006    2.1538    0.2467    5.5889
+#> 49:    93.1652   -1.9724   -4.0350    0.0506    4.7645    0.8055    2.1445    0.2344    5.3586
+#> 50:    93.1464   -1.9377   -4.0185    0.0591    5.3658    0.8149    2.1523    0.2226    5.2483
+#> 51:    93.5217   -1.9246   -4.0272    0.0423    5.8579    0.8368    2.1596    0.2115    5.2746
+#> 52:    93.5512   -1.9257   -4.0204    0.0307    7.2345    0.8463    2.1903    0.2065    5.2405
+#> 53:    93.5400   -1.9428   -4.0300    0.0572    6.8728    0.8268    2.0807    0.2139    5.4127
+#> 54:    93.9868   -1.9502   -4.0129    0.0282    9.6651    0.8468    2.0823    0.2032    5.0396
+#> 55:    94.0505   -1.9393   -4.0073    0.0390   10.0994    0.8375    2.1018    0.2016    4.9147
+#> 56:    93.8010   -1.9493   -4.0026    0.0415   10.1741    0.8816    2.1117    0.2207    5.0723
+#> 57:    93.7596   -1.9762   -4.0154    0.0651    9.6654    0.8952    2.1662    0.2096    5.2311
+#> 58:    94.3399   -1.9353   -4.0095    0.0446    9.1821    0.9498    2.2103    0.1991    5.1009
+#> 59:    94.4036   -1.9283   -4.0279    0.0475    8.7230    0.9480    2.3209    0.1892    4.9930
+#> 60:    94.6395   -1.9260   -4.0348    0.0457    8.8651    0.9006    2.2565    0.1797    5.1751
+#> 61:    94.6499   -1.9291   -4.0216    0.0297    8.4218    0.9206    2.2220    0.1843    5.1124
+#> 62:    94.3847   -1.9010   -4.0300    0.0257    9.0591    0.9331    2.2795    0.1816    5.0834
+#> 63:    94.5510   -1.9120   -4.0116    0.0179    8.6061    0.9256    2.1791    0.1736    5.1513
+#> 64:    94.2510   -1.9213   -4.0184    0.0204    8.1758    0.9124    2.2131    0.1682    5.0698
+#> 65:    94.1173   -1.9044   -4.0279    0.0286    8.6773    0.9211    2.2202    0.1598    5.1120
+#> 66:    94.2093   -1.9098   -4.0206    0.0160    8.2435    0.9230    2.2475    0.1750    5.0175
+#> 67:    94.2814   -1.9339   -4.0041    0.0146    7.8313    0.9377    2.2350    0.1709    5.1478
+#> 68:    94.3001   -1.9079   -4.0127   -0.0103    7.4397    0.9163    2.2245    0.1640    5.2529
+#> 69:    94.3820   -1.9167   -4.0176    0.0296    7.0678    0.8704    2.2236    0.1888    5.2574
+#> 70:    94.2691   -1.9037   -4.0156    0.0388    6.7144    0.8601    2.1833    0.2128    5.0230
+#> 71:    94.3827   -1.9183   -4.0056    0.0485    6.3786    0.8491    2.2147    0.2345    5.1212
+#> 72:    94.3104   -1.9291   -4.0099    0.0330    6.0597    0.9007    2.2316    0.2255    5.3748
+#> 73:    94.1778   -1.9238   -4.0054    0.0222    5.7567    0.9479    2.2969    0.2142    5.2827
+#> 74:    94.1022   -1.9149   -4.0017    0.0497    5.4689    0.9305    2.3058    0.2035    5.3117
+#> 75:    94.2343   -1.9045   -4.0141    0.0189    5.1954    0.9141    2.3227    0.1933    5.1047
+#> 76:    94.0905   -1.9019   -4.0166    0.0170    5.5411    0.8978    2.3315    0.1836    5.1233
+#> 77:    94.2772   -1.9117   -4.0053    0.0584    5.2641    0.9238    2.3678    0.1866    4.9803
+#> 78:    94.7235   -1.9141   -4.0464    0.0758    5.0735    0.9308    2.3720    0.2062    5.0544
+#> 79:    94.4674   -1.9287   -4.0494    0.0724    5.7355    0.9063    2.3680    0.1959    5.0910
+#> 80:    93.9895   -1.9271   -4.0456    0.0366    7.2150    0.8857    2.4000    0.1861    5.0612
+#> 81:    94.3190   -1.9358   -4.0402    0.0506    7.5591    0.8891    2.3317    0.1814    4.8617
+#> 82:    94.1898   -1.9126   -4.0552    0.0595    7.6462    0.9157    2.3848    0.1854    4.7335
+#> 83:    94.2044   -1.9145   -4.0359    0.0295    7.8610    0.9451    2.4305    0.1871    4.9258
+#> 84:    93.8197   -1.9058   -3.9879   -0.0409   10.4218    0.9604    2.3848    0.2177    5.0619
+#> 85:    94.0219   -1.8957   -3.9753   -0.0441    9.9007    0.9637    2.4476    0.2219    5.0532
+#> 86:    94.0737   -1.8889   -3.9753   -0.0220    9.4056    0.9675    2.4476    0.2284    5.2694
+#> 87:    93.8548   -1.8755   -3.9707   -0.0024    8.9354    1.0066    2.4895    0.2340    5.4019
+#> 88:    93.7578   -1.9046   -3.9804   -0.0042    8.4886    0.9656    2.5006    0.2271    5.3724
+#> 89:    93.6848   -1.8936   -3.9689   -0.0396   10.6813    0.9805    2.4561    0.2254    5.2615
+#> 90:    93.3617   -1.9167   -3.9801   -0.0221   10.1472    1.0147    2.3589    0.2141    5.4193
+#> 91:    93.7419   -1.8964   -3.9888   -0.0363    9.6398    1.0077    2.3748    0.2066    5.3463
+#> 92:    93.8635   -1.8994   -3.9783   -0.0625    9.1578    1.0028    2.3282    0.2239    5.3026
+#> 93:    94.0864   -1.8648   -3.9426   -0.0813    8.8693    1.0348    2.3654    0.2127    5.2637
+#> 94:    93.9789   -1.8949   -3.9840   -0.0549   10.0871    1.0752    2.4551    0.2021    5.4225
+#> 95:    93.9008   -1.9141   -4.0080   -0.0644    9.8584    1.1599    2.4184    0.1920    5.2179
+#> 96:    93.6926   -2.0270   -3.8911   -0.0777   10.3968    1.1019    3.0518    0.1824    5.3163
+#> 97:    93.2478   -2.0074   -3.9034   -0.0427   10.7200    1.0468    2.9960    0.1732    5.5172
+#> 98:    93.4556   -2.0118   -3.9034   -0.0294   10.1840    1.0007    2.9960    0.1646    5.5887
+#> 99:    93.7548   -2.0076   -3.8894   -0.0157    9.7519    0.9507    3.0357    0.1569    5.7139
+#> 100:    93.8962   -2.0112   -3.8887   -0.0406    9.2643    0.9048    3.0369    0.1491    5.6145
+#> 101:    94.0889   -2.0221   -3.8612   -0.0145    8.8011    0.8728    3.1466    0.1499    5.4224
+#> 102:    94.5428   -2.0206   -3.8489    0.0115    8.3611    0.8292    3.1577    0.1473    5.6634
+#> 103:    94.4882   -2.0447   -3.8594    0.0514    7.9430    0.8202    3.1812    0.1562    5.5136
+#> 104:    94.3185   -2.0389   -3.8584    0.0258    8.3364    0.8346    3.1801    0.1484    5.3612
+#> 105:    94.2858   -2.0345   -3.8738   -0.0001   10.6008    0.8415    3.2251    0.1410    5.3359
+#> 106:    94.1264   -2.0415   -3.8756    0.0411   10.0707    0.8554    3.2182    0.1658    5.2280
+#> 107:    93.9801   -2.0574   -3.8674    0.0403   10.0269    0.8807    3.2628    0.1744    5.0299
+#> 108:    93.6911   -2.0295   -3.8693    0.0355    9.5255    0.8683    3.2708    0.1803    5.1880
+#> 109:    94.0646   -2.0260   -3.8806    0.0506    9.0493    0.8729    3.3140    0.1759    5.1927
+#> 110:    94.4591   -2.0378   -3.8962    0.0360    8.5968    0.8890    3.3076    0.1675    4.8961
+#> 111:    94.3748   -2.0319   -3.9053    0.0397    8.1670    0.8995    3.3254    0.1591    4.8066
+#> 112:    94.2370   -2.0338   -3.9017    0.0603    7.7586    0.8545    3.2484    0.1512    4.8856
+#> 113:    94.1242   -2.0237   -3.8954    0.0795    7.3707    0.8980    3.2127    0.1530    5.1859
+#> 114:    94.1452   -2.0298   -3.9197    0.0530    7.0021    0.8771    3.0744    0.1628    5.1303
+#> 115:    94.1403   -2.0410   -3.9093    0.0476    6.9173    0.9383    3.0223    0.1621    5.2563
+#> 116:    94.1612   -2.0424   -3.9063    0.0593    7.6367    0.8914    3.0420    0.1856    5.1566
+#> 117:    94.2018   -2.0488   -3.9041    0.0539    7.2549    0.8549    3.0204    0.1796    5.2119
+#> 118:    94.1315   -2.0579   -3.9139    0.0564    6.8922    0.8121    3.0180    0.1948    5.0302
+#> 119:    93.7398   -2.0747   -3.9202    0.0570    6.7510    0.7838    3.0084    0.1906    5.0863
+#> 120:    93.5945   -2.0511   -3.9388    0.0534    6.4134    0.7885    3.0100    0.2128    5.0597
+#> 121:    93.9845   -2.0613   -3.9338    0.0568    6.0928    0.7793    2.9944    0.2022    5.3179
+#> 122:    93.7779   -2.0831   -3.9338    0.0630    5.7881    0.7778    2.9944    0.1921    5.2399
+#> 123:    93.9128   -2.0623   -3.9135    0.0493    5.4987    0.8329    2.9729    0.1825    5.0752
+#> 124:    93.5190   -2.0804   -3.9315    0.0538    5.2238    0.8581    3.0220    0.1733    4.9713
+#> 125:    93.7427   -2.0649   -3.9309    0.0499    4.9626    0.8431    3.0260    0.1882    5.0718
+#> 126:    9.3540e+01  -2.0238e+00  -3.9006e+00  -6.8989e-05   4.7145e+00   8.3548e-01   2.9498e+00   1.9993e-01   5.2080e+00
+#> 127:    93.4310   -2.0496   -3.8898   -0.0173    4.4788    0.8864    2.9614    0.2302    5.6432
+#> 128:    93.7512   -2.0285   -3.9180   -0.0096    4.2548    0.8653    3.0768    0.2312    5.3906
+#> 129:    93.6908   -2.0718   -3.9113   -0.0194    4.0421    0.9022    3.0506    0.2386    5.3278
+#> 130:    93.5805   -1.9753   -4.0480    0.0065    3.8400    1.0388    2.8980    0.2276    5.2583
+#> 131:    93.8050   -1.9501   -4.0447    0.0040    3.8738    1.0957    2.7531    0.2162    5.3026
+#> 132:    93.6470   -1.9322   -4.0411    0.0048    3.6801    1.0618    2.6155    0.2159    5.2552
+#> 133:    94.2927   -1.9445   -4.0067   -0.0040    5.6903    1.0378    2.5425    0.2094    5.2430
+#> 134:    94.2814   -1.9286   -4.0021   -0.0144    6.9123    1.1316    2.5172    0.1990    5.3877
+#> 135:    94.0440   -1.9285   -4.0415    0.0254    6.5667    1.1416    2.4394    0.1975    5.3248
+#> 136:    94.0122   -1.9256   -4.0542    0.0156    6.6147    1.1511    2.4728    0.1954    5.2109
+#> 137:    93.8613   -1.9095   -4.0629   -0.0007    6.2840    1.1789    2.5078    0.2045    5.2876
+#> 138:    93.7410   -1.9345   -4.0788   -0.0005    6.0718    1.1507    2.5026    0.2086    5.3284
+#> 139:    93.6437   -1.9499   -4.0788    0.0159    5.7682    1.0932    2.5026    0.1982    5.4211
+#> 140:    93.4066   -1.9591   -4.0720    0.0472    6.9432    1.0883    2.4756    0.1947    5.4439
+#> 141:    93.6086   -1.9625   -4.1026    0.0785    7.7204    1.1027    2.3974    0.2084    5.6595
+#> 142:    93.8693   -1.9640   -4.1003    0.0670   10.1206    1.1196    2.2775    0.1980    5.4918
+#> 143:    93.6954   -1.9890   -4.0792    0.0824    9.6146    1.0636    2.3366    0.1881    5.2818
+#> 144:    93.5119   -1.9888   -4.0603    0.0645    9.1339    1.0557    2.3380    0.1787    5.3491
+#> 145:    93.3539   -1.9874   -4.0563    0.0764    8.6772    1.0340    2.3573    0.1697    5.4214
+#> 146:    93.2812   -1.9734   -4.0620    0.0674    8.4698    1.0504    2.3604    0.1641    5.5968
+#> 147:    93.8919   -1.9657   -4.0863    0.0596    8.0463    1.0288    2.4569    0.1668    5.3476
+#> 148:    93.7841   -1.9719   -4.0688    0.0880    9.4571    1.0719    2.4020    0.1692    5.1664
+#> 149:    93.6361   -1.9912   -4.0523    0.0895    8.9842    1.0183    2.5236    0.1671    5.5060
+#> 150:    93.6402   -1.9940   -4.0365    0.0730    9.1100    0.9674    2.3974    0.1669    5.5402
+#> 151:    93.4283   -1.9861   -4.0594    0.0805    8.6545    0.9567    2.4304    0.1652    5.3571
+#> 152:    93.7431   -1.9444   -4.0833    0.0612    9.2738    0.9789    2.3602    0.1571    5.0632
+#> 153:    93.7239   -1.9307   -4.0780    0.0780    8.9915    0.9995    2.3398    0.1600    5.1077
+#> 154:    94.0115   -1.9655   -4.0978    0.0859    9.0507    0.9894    2.3313    0.1628    5.2272
+#> 155:    94.3207   -1.9792   -4.0905    0.1114    8.5756    0.9988    2.3790    0.1873    5.0916
+#> 156:    94.3160   -1.9811   -4.0894    0.0906    5.0717    0.9968    2.3662    0.2003    4.9973
+#> 157:    94.3042   -1.9641   -4.1031    0.0966    5.1875    0.9911    2.3908    0.1943    4.9993
+#> 158:    94.0102   -1.9635   -4.1047    0.1003    5.2398    0.9834    2.3905    0.1809    5.2765
+#> 159:    94.5686   -2.0012   -4.1459    0.1212    6.8800    1.0317    2.5969    0.1215    5.3943
+#> 160:    94.2433   -1.9673   -4.1420    0.1165    8.0930    1.0286    2.5827    0.1092    5.2904
+#> 161:    94.1327   -1.9644   -4.1595    0.1196    9.5810    1.0786    2.7063    0.1123    5.1723
+#> 162:    94.0779   -1.9525   -4.1608    0.1103    6.6456    1.0562    2.7111    0.1277    5.0224
+#> 163:    94.0995   -1.9687   -4.1910    0.1320    8.2582    1.0701    2.8394    0.1232    5.1593
+#> 164:    94.4575   -1.9800   -4.1936    0.1208    6.4860    1.1603    2.8332    0.1254    5.1325
+#> 165:    94.3298   -1.9968   -4.1963    0.1506    5.7592    1.1484    2.9143    0.1196    5.3059
+#> 166:    94.2531   -1.9977   -4.1748    0.1566    5.3810    1.1262    2.8044    0.1142    5.2569
+#> 167:    94.4593   -1.9985   -4.1758    0.1435    7.0082    1.1247    2.8542    0.1125    5.4332
+#> 168:    94.0868   -2.0117   -4.2259    0.1345    7.5364    1.1395    3.0314    0.1137    5.2790
+#> 169:    93.7927   -2.0072   -4.2177    0.1276    6.7023    1.1292    3.0535    0.1135    5.1357
+#> 170:    93.8094   -2.0309   -4.2244    0.1298    6.7343    1.0975    3.2542    0.1065    5.2372
+#> 171:    93.7263   -2.0349   -4.2115    0.1204    8.2555    1.0626    3.2292    0.1020    5.4467
+#> 172:    93.3380   -2.0022   -4.2262    0.1193    6.6891    1.0822    3.2762    0.0989    5.3641
+#> 173:    93.5334   -2.0224   -4.2488    0.1145    6.0685    1.0328    3.4694    0.0978    5.4780
+#> 174:    93.1805   -2.0207   -4.2344    0.1025    6.1648    1.0612    3.2079    0.0976    5.2570
+#> 175:    93.3423   -2.0255   -4.1644    0.1070    5.9418    1.0701    2.8555    0.1059    5.3415
+#> 176:    93.3387   -2.0192   -4.1473    0.0786    4.7649    1.0508    2.9102    0.1101    5.3381
+#> 177:    93.4640   -2.0177   -4.1504    0.0709    4.5672    1.0590    2.9447    0.1103    5.3245
+#> 178:    93.4930   -2.0147   -4.1568    0.0777    4.5325    1.1063    2.7902    0.1247    5.2036
+#> 179:    93.7455   -2.0101   -4.1580    0.0823    4.2094    1.1020    2.8075    0.1246    5.1184
+#> 180:    93.4838   -1.9989   -4.1631    0.0868    3.6999    1.0782    2.8790    0.1280    5.2677
+#> 181:    93.5207   -1.9975   -4.1926    0.1013    4.5693    1.0706    2.9216    0.1375    5.3783
+#> 182:    93.6695   -2.0251   -4.1717    0.0809    3.8373    1.0341    2.9954    0.1328    5.3774
+#> 183:    93.7238   -2.0095   -4.1222    0.0861    3.8354    1.0138    2.7536    0.1512    5.2600
+#> 184:    93.7106   -2.0032   -4.1244    0.0853    4.1968    1.0250    2.6849    0.1590    5.1996
+#> 185:    93.2862   -2.0028   -4.1628    0.0743    5.4347    1.0373    2.6528    0.1640    5.3269
+#> 186:    93.5567   -2.0040   -4.1438    0.0807    6.5150    1.0562    2.6486    0.1589    5.4158
+#> 187:    93.7894   -2.0023   -4.1137    0.1288    5.1401    1.0207    2.5217    0.1745    5.6484
+#> 188:    93.4911   -1.8872   -4.2405    0.1324    4.3165    0.8176    2.2483    0.1870    5.5214
+#> 189:    93.9184   -1.8982   -4.2936    0.1606    3.7995    0.8383    2.2555    0.1766    5.6320
+#> 190:    93.7487   -1.8878   -4.2872    0.1651    3.6764    0.8860    2.2088    0.1748    5.4829
+#> 191:    93.8940   -1.8715   -4.3244    0.1650    2.8119    0.9024    2.1141    0.1903    5.7768
+#> 192:    93.9378   -1.9105   -4.3010    0.1954    2.5239    0.8232    2.1331    0.1831    5.8507
+#> 193:    94.5609   -1.8766   -4.3303    0.2042    3.9595    0.8413    2.0662    0.2095    5.6119
+#> 194:    94.7465   -1.9036   -4.3363    0.2112    5.0784    0.8176    2.1071    0.2149    5.6051
+#> 195:    94.4761   -1.8852   -4.3375    0.2021    4.7026    0.7615    2.0556    0.2333    5.3997
+#> 196:    93.7678   -1.9037   -4.3676    0.2273    5.6976    0.7824    2.1487    0.2478    5.2531
+#> 197:    94.0788   -1.9208   -4.3670    0.2203    3.8352    0.7644    2.0893    0.2354    5.2196
+#> 198:    94.3424   -1.8825   -4.3288    0.2075    4.9447    0.7304    1.9525    0.2502    5.1387
+#> 199:    94.0613   -1.9911   -4.1676    0.2379    3.6248    0.6126    2.8184    0.2801    5.3421
+#> 200:    94.4814   -2.0045   -4.1782    0.2245    3.5637    0.6427    2.7132    0.3014    5.3984
+#> 201:    94.3903   -1.9973   -4.1773    0.2165    3.4686    0.6525    2.7040    0.2901    5.4178
+#> 202:    94.1840   -1.9928   -4.1742    0.2117    3.6920    0.6576    2.7046    0.2870    5.3743
+#> 203:    94.1832   -1.9865   -4.1670    0.2025    3.8180    0.6618    2.7097    0.2758    5.3389
+#> 204:    94.1550   -1.9832   -4.1631    0.1955    3.9449    0.6613    2.6998    0.2691    5.2948
+#> 205:    94.1853   -1.9824   -4.1602    0.1948    4.1753    0.6598    2.6909    0.2695    5.2556
+#> 206:    94.1775   -1.9800   -4.1564    0.1918    4.1962    0.6581    2.6778    0.2678    5.2316
+#> 207:    94.1754   -1.9736   -4.1532    0.1864    4.2107    0.6580    2.6645    0.2694    5.2553
+#> 208:    94.1591   -1.9695   -4.1498    0.1811    4.2621    0.6596    2.6537    0.2712    5.2543
+#> 209:    94.1225   -1.9675   -4.1454    0.1744    4.1977    0.6651    2.6519    0.2687    5.3075
+#> 210:    94.1047   -1.9628   -4.1424    0.1666    4.1981    0.6663    2.6570    0.2717    5.3160
+#> 211:    94.1161   -1.9587   -4.1398    0.1600    4.1858    0.6674    2.6614    0.2728    5.3307
+#> 212:    94.0976   -1.9551   -4.1379    0.1529    4.2002    0.6693    2.6737    0.2709    5.3288
+#> 213:    94.0845   -1.9511   -4.1365    0.1449    4.1381    0.6710    2.6727    0.2680    5.3322
+#> 214:    94.0582   -1.9493   -4.1351    0.1394    4.0630    0.6733    2.6729    0.2663    5.3504
+#> 215:    94.0449   -1.9493   -4.1338    0.1340    3.9607    0.6733    2.6719    0.2641    5.3681
+#> 216:    94.0030   -1.9496   -4.1321    0.1299    4.0200    0.6742    2.6727    0.2622    5.3619
+#> 217:    93.9560   -1.9514   -4.1315    0.1267    4.0642    0.6778    2.6764    0.2612    5.3584
+#> 218:    93.9485   -1.9520   -4.1297    0.1235    4.1822    0.6795    2.6745    0.2599    5.3471
+#> 219:    93.9650   -1.9523   -4.1289    0.1211    4.3244    0.6807    2.6851    0.2591    5.3531
+#> 220:    93.9961   -1.9519   -4.1284    0.1193    4.4276    0.6837    2.6936    0.2577    5.3528
+#> 221:    94.0080   -1.9517   -4.1275    0.1183    4.5303    0.6866    2.6979    0.2578    5.3538
+#> 222:    94.0143   -1.9505   -4.1272    0.1159    4.5882    0.6887    2.7039    0.2570    5.3489
+#> 223:    94.0189   -1.9491   -4.1269    0.1138    4.5674    0.6910    2.7092    0.2562    5.3424
+#> 224:    94.0136   -1.9464   -4.1270    0.1126    4.5582    0.6923    2.7161    0.2548    5.3421
+#> 225:    94.0118   -1.9444   -4.1276    0.1112    4.6000    0.6929    2.7269    0.2533    5.3525
+#> 226:    93.9884   -1.9428   -4.1260    0.1099    4.6720    0.6935    2.7428    0.2530    5.3427
+#> 227:    93.9657   -1.9416   -4.1247    0.1097    4.7197    0.6937    2.7581    0.2529    5.3455
+#> 228:    93.9586   -1.9410   -4.1234    0.1105    4.7731    0.6945    2.7801    0.2528    5.3408
+#> 229:    93.9574   -1.9409   -4.1215    0.1102    4.7898    0.6963    2.7970    0.2518    5.3366
+#> 230:    93.9495   -1.9410   -4.1201    0.1096    4.7966    0.6982    2.8117    0.2505    5.3301
+#> 231:    93.9378   -1.9416   -4.1193    0.1093    4.7947    0.6993    2.8274    0.2492    5.3270
+#> 232:    93.9362   -1.9421   -4.1184    0.1086    4.8132    0.7011    2.8411    0.2477    5.3191
+#> 233:    93.9412   -1.9424   -4.1167    0.1074    4.8188    0.7028    2.8514    0.2459    5.3134
+#> 234:    93.9436   -1.9424   -4.1152    0.1061    4.7865    0.7040    2.8618    0.2440    5.3153
+#> 235:    93.9413   -1.9425   -4.1134    0.1051    4.8017    0.7062    2.8679    0.2426    5.3137
+#> 236:    93.9480   -1.9423   -4.1119    0.1033    4.8537    0.7085    2.8730    0.2416    5.3089
+#> 237:    93.9560   -1.9408   -4.1105    0.1020    4.9091    0.7098    2.8777    0.2411    5.2970
+#> 238:    93.9610   -1.9393   -4.1091    0.1003    4.9394    0.7113    2.8824    0.2409    5.2902
+#> 239:    93.9634   -1.9378   -4.1080    0.0993    4.9304    0.7121    2.8875    0.2407    5.2868
+#> 240:    93.9727   -1.9360   -4.1063    0.0980    4.9651    0.7128    2.8918    0.2404    5.2825
+#> 241:    93.9736   -1.9348   -4.1045    0.0969    5.0080    0.7139    2.8917    0.2395    5.2751
+#> 242:    93.9779   -1.9334   -4.1030    0.0959    5.0856    0.7150    2.8923    0.2389    5.2656
+#> 243:    93.9807   -1.9322   -4.1015    0.0953    5.1490    0.7158    2.8929    0.2385    5.2560
+#> 244:    93.9858   -1.9317   -4.0998    0.0942    5.2172    0.7171    2.8922    0.2380    5.2514
+#> 245:    93.9798   -1.9309   -4.0984    0.0920    5.2903    0.7172    2.8892    0.2383    5.2502
+#> 246:    93.9782   -1.9296   -4.0971    0.0903    5.3132    0.7180    2.8866    0.2384    5.2482
+#> 247:    93.9809   -1.9290   -4.0958    0.0886    5.3342    0.7188    2.8839    0.2386    5.2466
+#> 248:    93.9731   -1.9281   -4.0944    0.0873    5.3438    0.7187    2.8812    0.2393    5.2480
+#> 249:    93.9594   -1.9273   -4.0932    0.0852    5.3449    0.7181    2.8781    0.2401    5.2489
+#> 250:    93.9508   -1.9261   -4.0919    0.0835    5.3194    0.7173    2.8752    0.2406    5.2495
+#> 251:    93.9421   -1.9248   -4.0903    0.0812    5.3051    0.7180    2.8714    0.2410    5.2480
+#> 252:    93.9291   -1.9240   -4.0888    0.0793    5.3122    0.7175    2.8681    0.2415    5.2447
+#> 253:    93.9233   -1.9232   -4.0876    0.0777    5.3289    0.7170    2.8636    0.2420    5.2423
+#> 254:    93.9189   -1.9217   -4.0863    0.0760    5.3708    0.7165    2.8593    0.2425    5.2395
+#> 255:    93.9130   -1.9205   -4.0850    0.0743    5.4093    0.7157    2.8548    0.2428    5.2393
+#> 256:    93.9031   -1.9195   -4.0837    0.0731    5.4400    0.7153    2.8501    0.2432    5.2417
+#> 257:    93.9079   -1.9183   -4.0821    0.0720    5.4612    0.7138    2.8454    0.2434    5.2469
+#> 258:    93.9117   -1.9173   -4.0807    0.0711    5.4979    0.7126    2.8412    0.2439    5.2491
+#> 259:    93.9199   -1.9164   -4.0797    0.0708    5.5145    0.7107    2.8364    0.2449    5.2481
+#> 260:    93.9300   -1.9150   -4.0782    0.0699    5.5067    0.7086    2.8316    0.2453    5.2501
+#> 261:    93.9382   -1.9140   -4.0768    0.0689    5.5191    0.7070    2.8271    0.2455    5.2518
+#> 262:    93.9467   -1.9126   -4.0755    0.0681    5.5261    0.7049    2.8227    0.2454    5.2564
+#> 263:    93.9594   -1.9110   -4.0739    0.0667    5.5365    0.7039    2.8196    0.2455    5.2613
+#> 264:    93.9697   -1.9096   -4.0718    0.0650    5.5589    0.7033    2.8174    0.2459    5.2628
+#> 265:    93.9784   -1.9080   -4.0698    0.0631    5.5668    0.7025    2.8153    0.2458    5.2627
+#> 266:    93.9865   -1.9068   -4.0686    0.0615    5.5819    0.7012    2.8114    0.2456    5.2638
+#> 267:    93.9940   -1.9055   -4.0673    0.0599    5.5887    0.7000    2.8076    0.2452    5.2644
+#> 268:    93.9991   -1.9045   -4.0660    0.0584    5.5989    0.6986    2.8039    0.2453    5.2657
+#> 269:    94.0034   -1.9036   -4.0649    0.0573    5.6276    0.6972    2.7990    0.2453    5.2648
+#> 270:    94.0104   -1.9028   -4.0639    0.0561    5.6456    0.6959    2.7945    0.2453    5.2614
+#> 271:    94.0190   -1.9022   -4.0629    0.0550    5.6409    0.6950    2.7900    0.2451    5.2606
+#> 272:    94.0244   -1.9017   -4.0623    0.0542    5.6452    0.6944    2.7863    0.2449    5.2626
+#> 273:    94.0312   -1.9010   -4.0620    0.0531    5.6581    0.6939    2.7821    0.2450    5.2620
+#> 274:    94.0387   -1.9004   -4.0615    0.0520    5.6569    0.6932    2.7774    0.2456    5.2657
+#> 275:    94.0381   -1.9000   -4.0611    0.0510    5.6525    0.6938    2.7727    0.2463    5.2662
+#> 276:    94.0426   -1.8994   -4.0606    0.0498    5.6664    0.6955    2.7682    0.2472    5.2687
+#> 277:    94.0437   -1.8988   -4.0604    0.0486    5.6705    0.6969    2.7646    0.2479    5.2699
+#> 278:    94.0470   -1.8982   -4.0606    0.0476    5.6495    0.6983    2.7620    0.2487    5.2741
+#> 279:    94.0475   -1.8980   -4.0608    0.0470    5.6561    0.6990    2.7590    0.2494    5.2749
+#> 280:    94.0485   -1.8977   -4.0609    0.0462    5.6510    0.6997    2.7565    0.2501    5.2755
+#> 281:    94.0473   -1.8975   -4.0609    0.0456    5.6493    0.6998    2.7529    0.2504    5.2764
+#> 282:    94.0448   -1.8972   -4.0608    0.0448    5.6523    0.7003    2.7495    0.2506    5.2773
+#> 283:    94.0392   -1.8975   -4.0608    0.0440    5.6543    0.7011    2.7463    0.2507    5.2772
+#> 284:    94.0315   -1.8976   -4.0609    0.0432    5.6575    0.7017    2.7431    0.2506    5.2792
+#> 285:    94.0262   -1.8980   -4.0611    0.0427    5.6632    0.7018    2.7402    0.2505    5.2805
+#> 286:    94.0255   -1.8986   -4.0615    0.0427    5.6683    0.7018    2.7371    0.2507    5.2795
+#> 287:    94.0234   -1.8992   -4.0619    0.0427    5.6533    0.7014    2.7340    0.2513    5.2803
+#> 288:    94.0227   -1.9000   -4.0631    0.0431    5.6485    0.7016    2.7352    0.2517    5.2802
+#> 289:    94.0179   -1.9008   -4.0641    0.0433    5.6553    0.7016    2.7358    0.2523    5.2808
+#> 290:    94.0135   -1.9017   -4.0650    0.0435    5.6776    0.7015    2.7363    0.2528    5.2839
+#> 291:    94.0101   -1.9025   -4.0660    0.0440    5.7028    0.7012    2.7372    0.2531    5.2883
+#> 292:    94.0066   -1.9034   -4.0672    0.0442    5.7277    0.7007    2.7369    0.2536    5.2890
+#> 293:    94.0002   -1.9042   -4.0681    0.0441    5.7462    0.7004    2.7366    0.2538    5.2906
+#> 294:    93.9917   -1.9049   -4.0690    0.0440    5.7707    0.7001    2.7363    0.2539    5.2927
+#> 295:    93.9864   -1.9055   -4.0703    0.0440    5.7816    0.7001    2.7362    0.2542    5.2950
+#> 296:    93.9807   -1.9060   -4.0716    0.0441    5.7884    0.7000    2.7362    0.2545    5.2974
+#> 297:    93.9749   -1.9063   -4.0729    0.0442    5.7926    0.7005    2.7362    0.2548    5.3032
+#> 298:    93.9700   -1.9070   -4.0735    0.0442    5.7850    0.7005    2.7323    0.2553    5.3067
+#> 299:    93.9668   -1.9075   -4.0740    0.0442    5.7688    0.7000    2.7293    0.2558    5.3100
+#> 300:    93.9654   -1.9080   -4.0742    0.0441    5.7541    0.6993    2.7260    0.2563    5.3123
+#> 301:    93.9678   -1.9082   -4.0744    0.0439    5.7383    0.6980    2.7217    0.2568    5.3165
+#> 302:    93.9687   -1.9087   -4.0747    0.0435    5.7262    0.6977    2.7175    0.2574    5.3179
+#> 303:    93.9675   -1.9090   -4.0751    0.0430    5.7050    0.6966    2.7137    0.2580    5.3197
+#> 304:    93.9641   -1.9092   -4.0755    0.0428    5.6977    0.6954    2.7097    0.2583    5.3215
+#> 305:    93.9624   -1.9095   -4.0759    0.0427    5.6986    0.6947    2.7061    0.2585    5.3200
+#> 306:    93.9623   -1.9098   -4.0763    0.0428    5.7065    0.6941    2.7025    0.2587    5.3174
+#> 307:    93.9635   -1.9105   -4.0767    0.0430    5.7229    0.6938    2.6992    0.2585    5.3153
+#> 308:    93.9658   -1.9112   -4.0778    0.0435    5.7340    0.6935    2.6992    0.2580    5.3131
+#> 309:    93.9671   -1.9119   -4.0784    0.0440    5.7510    0.6929    2.6990    0.2576    5.3113
+#> 310:    93.9669   -1.9124   -4.0791    0.0441    5.7560    0.6926    2.6988    0.2569    5.3128
+#> 311:    93.9670   -1.9129   -4.0795    0.0443    5.7557    0.6922    2.6972    0.2563    5.3134
+#> 312:    93.9689   -1.9132   -4.0799    0.0446    5.7554    0.6921    2.6959    0.2559    5.3125
+#> 313:    93.9685   -1.9136   -4.0806    0.0448    5.7489    0.6921    2.6960    0.2553    5.3110
+#> 314:    93.9673   -1.9138   -4.0812    0.0447    5.7562    0.6925    2.6964    0.2545    5.3107
+#> 315:    93.9635   -1.9139   -4.0818    0.0447    5.7392    0.6931    2.6971    0.2539    5.3127
+#> 316:    93.9581   -1.9139   -4.0823    0.0442    5.7376    0.6937    2.6974    0.2532    5.3140
+#> 317:    93.9541   -1.9140   -4.0826    0.0437    5.7426    0.6946    2.6968    0.2526    5.3155
+#> 318:    93.9521   -1.9141   -4.0829    0.0432    5.7378    0.6951    2.6970    0.2521    5.3158
+#> 319:    93.9520   -1.9139   -4.0829    0.0423    5.7366    0.6959    2.6977    0.2516    5.3138
+#> 320:    93.9538   -1.9136   -4.0828    0.0414    5.7416    0.6964    2.6980    0.2510    5.3135
+#> 321:    93.9557   -1.9132   -4.0827    0.0406    5.7539    0.6969    2.6983    0.2503    5.3141
+#> 322:    93.9568   -1.9130   -4.0825    0.0399    5.7460    0.6971    2.6988    0.2497    5.3155
+#> 323:    93.9594   -1.9125   -4.0824    0.0393    5.7274    0.6972    2.6993    0.2492    5.3166
+#> 324:    93.9608   -1.9122   -4.0823    0.0386    5.7161    0.6973    2.7006    0.2487    5.3156
+#> 325:    93.9601   -1.9120   -4.0822    0.0379    5.7036    0.6973    2.7019    0.2483    5.3161
+#> 326:    93.9602   -1.9118   -4.0822    0.0372    5.6817    0.6977    2.7023    0.2480    5.3182
+#> 327:    93.9615   -1.9115   -4.0820    0.0364    5.6682    0.6986    2.7024    0.2476    5.3203
+#> 328:    93.9601   -1.9114   -4.0814    0.0355    5.6746    0.6999    2.7012    0.2472    5.3224
+#> 329:    93.9580   -1.9112   -4.0809    0.0348    5.6670    0.7014    2.7003    0.2469    5.3229
+#> 330:    93.9577   -1.9111   -4.0808    0.0341    5.6613    0.7023    2.7007    0.2466    5.3224
+#> 331:    93.9570   -1.9109   -4.0808    0.0334    5.6607    0.7029    2.7020    0.2463    5.3223
+#> 332:    93.9599   -1.9106   -4.0806    0.0328    5.6610    0.7037    2.7023    0.2459    5.3212
+#> 333:    93.9638   -1.9102   -4.0806    0.0320    5.6751    0.7043    2.7029    0.2458    5.3187
+#> 334:    93.9672   -1.9096   -4.0805    0.0311    5.6801    0.7051    2.7033    0.2456    5.3168
+#> 335:    93.9714   -1.9093   -4.0805    0.0302    5.6855    0.7058    2.7038    0.2453    5.3156
+#> 336:    93.9755   -1.9090   -4.0804    0.0294    5.6979    0.7062    2.7040    0.2452    5.3158
+#> 337:    93.9796   -1.9088   -4.0803    0.0286    5.7025    0.7069    2.7038    0.2447    5.3159
+#> 338:    93.9845   -1.9087   -4.0803    0.0278    5.7100    0.7074    2.7042    0.2443    5.3166
+#> 339:    93.9889   -1.9084   -4.0803    0.0273    5.7123    0.7080    2.7045    0.2438    5.3165
+#> 340:    93.9916   -1.9082   -4.0801    0.0267    5.7289    0.7086    2.7045    0.2434    5.3167
+#> 341:    93.9938   -1.9080   -4.0800    0.0263    5.7602    0.7091    2.7048    0.2430    5.3173
+#> 342:    93.9971   -1.9076   -4.0799    0.0257    5.7951    0.7096    2.7046    0.2427    5.3171
+#> 343:    93.9979   -1.9073   -4.0794    0.0251    5.8156    0.7101    2.7044    0.2424    5.3157
+#> 344:    94.0015   -1.9070   -4.0792    0.0246    5.8378    0.7105    2.7047    0.2420    5.3153
+#> 345:    94.0040   -1.9067   -4.0789    0.0241    5.8559    0.7111    2.7046    0.2414    5.3149
+#> 346:    94.0073   -1.9066   -4.0787    0.0237    5.8810    0.7119    2.7045    0.2409    5.3131
+#> 347:    94.0084   -1.9066   -4.0785    0.0232    5.8815    0.7127    2.7044    0.2406    5.3125
+#> 348:    94.0084   -1.9067   -4.0785    0.0229    5.8870    0.7132    2.7051    0.2403    5.3110
+#> 349:    94.0079   -1.9068   -4.0785    0.0225    5.8882    0.7136    2.7048    0.2401    5.3127
+#> 350:    94.0075   -1.9067   -4.0785    0.0220    5.8857    0.7137    2.7045    0.2396    5.3133
+#> 351:    94.0068   -1.9068   -4.0786    0.0218    5.8849    0.7140    2.7041    0.2393    5.3135
+#> 352:    94.0059   -1.9067   -4.0788    0.0216    5.8778    0.7141    2.7039    0.2390    5.3139
+#> 353:    94.0073   -1.9067   -4.0792    0.0215    5.8709    0.7140    2.7047    0.2388    5.3129
+#> 354:    94.0078   -1.9065   -4.0795    0.0214    5.8623    0.7139    2.7054    0.2386    5.3135
+#> 355:    94.0065   -1.9064   -4.0795    0.0211    5.8637    0.7137    2.7048    0.2383    5.3122
+#> 356:    94.0080   -1.9063   -4.0796    0.0209    5.8613    0.7134    2.7041    0.2380    5.3121
+#> 357:    94.0105   -1.9061   -4.0797    0.0206    5.8613    0.7132    2.7036    0.2379    5.3119
+#> 358:    94.0114   -1.9059   -4.0798    0.0205    5.8539    0.7130    2.7029    0.2377    5.3107
+#> 359:    94.0154   -1.9058   -4.0799    0.0203    5.8559    0.7126    2.7024    0.2374    5.3112
+#> 360:    94.0165   -1.9057   -4.0800    0.0201    5.8544    0.7124    2.7020    0.2372    5.3099
+#> 361:    94.0198   -1.9056   -4.0802    0.0199    5.8511    0.7121    2.7018    0.2370    5.3089
+#> 362:    94.0224   -1.9054   -4.0811    0.0198    5.8509    0.7122    2.7071    0.2368    5.3077
+#> 363:    94.0241   -1.9053   -4.0821    0.0197    5.8582    0.7121    2.7135    0.2366    5.3073
+#> 364:    94.0254   -1.9052   -4.0824    0.0195    5.8606    0.7122    2.7147    0.2362    5.3079
+#> 365:    94.0276   -1.9052   -4.0831    0.0195    5.8668    0.7119    2.7197    0.2359    5.3081
+#> 366:    94.0276   -1.9052   -4.0836    0.0195    5.8765    0.7121    2.7217    0.2357    5.3074
+#> 367:    94.0276   -1.9051   -4.0842    0.0194    5.8627    0.7120    2.7240    0.2354    5.3083
+#> 368:    94.0292   -1.9050   -4.0847    0.0195    5.8579    0.7120    2.7254    0.2352    5.3096
+#> 369:    94.0289   -1.9049   -4.0852    0.0195    5.8590    0.7122    2.7271    0.2350    5.3095
+#> 370:    94.0300   -1.9049   -4.0855    0.0194    5.8712    0.7123    2.7284    0.2348    5.3094
+#> 371:    94.0309   -1.9050   -4.0858    0.0194    5.8766    0.7122    2.7295    0.2346    5.3095
+#> 372:    94.0306   -1.9050   -4.0860    0.0196    5.8800    0.7121    2.7306    0.2344    5.3101
+#> 373:    94.0315   -1.9051   -4.0861    0.0196    5.8840    0.7120    2.7305    0.2341    5.3091
+#> 374:    94.0323   -1.9052   -4.0862    0.0194    5.8755    0.7120    2.7301    0.2337    5.3101
+#> 375:    94.0344   -1.9055   -4.0863    0.0193    5.8744    0.7122    2.7308    0.2333    5.3121
+#> 376:    94.0341   -1.9056   -4.0865    0.0191    5.8738    0.7122    2.7311    0.2327    5.3136
+#> 377:    94.0320   -1.9055   -4.0868    0.0188    5.8703    0.7121    2.7311    0.2322    5.3161
+#> 378:    94.0291   -1.9058   -4.0869    0.0186    5.8771    0.7124    2.7311    0.2317    5.3187
+#> 379:    94.0273   -1.9062   -4.0872    0.0184    5.8829    0.7127    2.7316    0.2312    5.3206
+#> 380:    94.0259   -1.9067   -4.0875    0.0181    5.8786    0.7130    2.7321    0.2306    5.3235
+#> 381:    94.0231   -1.9068   -4.0877    0.0178    5.8716    0.7132    2.7331    0.2300    5.3231
+#> 382:    94.0210   -1.9069   -4.0879    0.0172    5.8636    0.7134    2.7340    0.2294    5.3240
+#> 383:    94.0189   -1.9070   -4.0880    0.0167    5.8596    0.7140    2.7351    0.2287    5.3246
+#> 384:    94.0171   -1.9070   -4.0882    0.0161    5.8588    0.7147    2.7365    0.2281    5.3251
+#> 385:    94.0141   -1.9070   -4.0880    0.0154    5.8659    0.7152    2.7365    0.2276    5.3263
+#> 386:    94.0116   -1.9070   -4.0879    0.0148    5.8785    0.7158    2.7364    0.2270    5.3272
+#> 387:    94.0090   -1.9070   -4.0877    0.0142    5.8874    0.7164    2.7363    0.2264    5.3286
+#> 388:    94.0068   -1.9069   -4.0875    0.0136    5.9016    0.7169    2.7364    0.2258    5.3299
+#> 389:    94.0063   -1.9067   -4.0873    0.0131    5.9114    0.7175    2.7363    0.2253    5.3332
+#> 390:    94.0074   -1.9064   -4.0872    0.0126    5.9258    0.7175    2.7362    0.2249    5.3353
+#> 391:    94.0092   -1.9061   -4.0870    0.0121    5.9426    0.7174    2.7359    0.2245    5.3370
+#> 392:    94.0112   -1.9060   -4.0870    0.0119    5.9499    0.7175    2.7358    0.2242    5.3375
+#> 393:    94.0120   -1.9058   -4.0869    0.0116    5.9514    0.7177    2.7351    0.2237    5.3364
+#> 394:    94.0137   -1.9056   -4.0867    0.0112    5.9560    0.7179    2.7342    0.2234    5.3371
+#> 395:    94.0150   -1.9054   -4.0866    0.0109    5.9566    0.7184    2.7340    0.2229    5.3376
+#> 396:    94.0175   -1.9054   -4.0866    0.0106    5.9564    0.7189    2.7341    0.2226    5.3370
+#> 397:    94.0195   -1.9055   -4.0866    0.0104    5.9447    0.7193    2.7344    0.2223    5.3378
+#> 398:    94.0201   -1.9056   -4.0867    0.0102    5.9353    0.7197    2.7348    0.2220    5.3380
+#> 399:    94.0204   -1.9056   -4.0868    0.0101    5.9282    0.7201    2.7350    0.2217    5.3387
+#> 400:    94.0198   -1.9058   -4.0867    0.0099    5.9243    0.7206    2.7348    0.2214    5.3383
+#> 401:    94.0194   -1.9059   -4.0867    0.0097    5.9225    0.7210    2.7345    0.2211    5.3379
+#> 402:    94.0176   -1.9060   -4.0868    0.0096    5.9237    0.7215    2.7342    0.2209    5.3370
+#> 403:    94.0172   -1.9061   -4.0869    0.0095    5.9259    0.7220    2.7337    0.2206    5.3371
+#> 404:    94.0147   -1.9062   -4.0870    0.0093    5.9322    0.7226    2.7330    0.2203    5.3382
+#> 405:    94.0131   -1.9065   -4.0872    0.0092    5.9354    0.7232    2.7326    0.2202    5.3385
+#> 406:    94.0117   -1.9066   -4.0872    0.0091    5.9399    0.7237    2.7318    0.2200    5.3388
+#> 407:    94.0114   -1.9069   -4.0871    0.0090    5.9495    0.7238    2.7314    0.2199    5.3397
+#> 408:    94.0133   -1.9071   -4.0870    0.0089    5.9505    0.7238    2.7310    0.2197    5.3401
+#> 409:    94.0159   -1.9074   -4.0868    0.0090    5.9523    0.7237    2.7309    0.2196    5.3417
+#> 410:    94.0171   -1.9076   -4.0864    0.0087    5.9503    0.7235    2.7307    0.2195    5.3449
+#> 411:    94.0193   -1.9078   -4.0862    0.0086    5.9528    0.7234    2.7304    0.2194    5.3476
+#> 412:    94.0193   -1.9082   -4.0860    0.0088    5.9516    0.7236    2.7303    0.2195    5.3509
+#> 413:    94.0192   -1.9085   -4.0859    0.0087    5.9491    0.7235    2.7302    0.2195    5.3517
+#> 414:    94.0175   -1.9086   -4.0860    0.0087    5.9453    0.7237    2.7297    0.2196    5.3523
+#> 415:    94.0156   -1.9088   -4.0861    0.0088    5.9408    0.7238    2.7289    0.2196    5.3528
+#> 416:    94.0145   -1.9090   -4.0861    0.0088    5.9442    0.7236    2.7281    0.2197    5.3540
+#> 417:    94.0140   -1.9093   -4.0862    0.0092    5.9459    0.7235    2.7274    0.2198    5.3549
+#> 418:    94.0144   -1.9097   -4.0864    0.0095    5.9495    0.7233    2.7269    0.2199    5.3551
+#> 419:    94.0142   -1.9102   -4.0866    0.0099    5.9425    0.7233    2.7265    0.2200    5.3555
+#> 420:    94.0134   -1.9107   -4.0867    0.0102    5.9338    0.7234    2.7260    0.2200    5.3563
+#> 421:    94.0096   -1.9113   -4.0869    0.0105    5.9272    0.7236    2.7260    0.2200    5.3571
+#> 422:    94.0069   -1.9118   -4.0872    0.0108    5.9238    0.7238    2.7261    0.2200    5.3576
+#> 423:    94.0034   -1.9124   -4.0874    0.0111    5.9217    0.7240    2.7260    0.2200    5.3579
+#> 424:    94.0009   -1.9129   -4.0876    0.0114    5.9258    0.7240    2.7259    0.2200    5.3578
+#> 425:    94.0000   -1.9134   -4.0879    0.0119    5.9330    0.7240    2.7258    0.2199    5.3572
+#> 426:    93.9991   -1.9138   -4.0881    0.0122    5.9526    0.7243    2.7256    0.2198    5.3572
+#> 427:    93.9969   -1.9140   -4.0882    0.0124    5.9692    0.7247    2.7258    0.2196    5.3587
+#> 428:    93.9940   -1.9143   -4.0883    0.0124    5.9777    0.7247    2.7259    0.2194    5.3591
+#> 429:    93.9935   -1.9145   -4.0882    0.0123    5.9781    0.7247    2.7260    0.2192    5.3601
+#> 430:    93.9925   -1.9147   -4.0881    0.0122    5.9772    0.7247    2.7260    0.2190    5.3606
+#> 431:    93.9928   -1.9150   -4.0879    0.0120    5.9824    0.7249    2.7262    0.2189    5.3616
+#> 432:    93.9930   -1.9152   -4.0879    0.0120    5.9797    0.7251    2.7267    0.2188    5.3618
+#> 433:    93.9930   -1.9154   -4.0878    0.0119    5.9785    0.7254    2.7271    0.2187    5.3626
+#> 434:    93.9930   -1.9156   -4.0878    0.0120    5.9711    0.7255    2.7273    0.2186    5.3638
+#> 435:    93.9935   -1.9157   -4.0878    0.0120    5.9659    0.7255    2.7269    0.2186    5.3643
+#> 436:    93.9951   -1.9158   -4.0876    0.0120    5.9570    0.7253    2.7263    0.2184    5.3667
+#> 437:    93.9980   -1.9158   -4.0874    0.0119    5.9492    0.7252    2.7259    0.2182    5.3680
+#> 438:    93.9999   -1.9158   -4.0872    0.0117    5.9361    0.7250    2.7255    0.2179    5.3700
+#> 439:    93.9990   -1.9159   -4.0868    0.0115    5.9312    0.7249    2.7247    0.2177    5.3700
+#> 440:    93.9986   -1.9160   -4.0865    0.0114    5.9280    0.7248    2.7235    0.2175    5.3698
+#> 441:    93.9996   -1.9160   -4.0863    0.0114    5.9248    0.7246    2.7222    0.2173    5.3696
+#> 442:    94.0001   -1.9160   -4.0861    0.0114    5.9266    0.7243    2.7213    0.2171    5.3702
+#> 443:    94.0004   -1.9159   -4.0859    0.0113    5.9228    0.7241    2.7202    0.2169    5.3707
+#> 444:    93.9989   -1.9161   -4.0858    0.0113    5.9200    0.7239    2.7194    0.2166    5.3722
+#> 445:    93.9971   -1.9162   -4.0857    0.0114    5.9257    0.7238    2.7182    0.2165    5.3736
+#> 446:    93.9970   -1.9164   -4.0858    0.0114    5.9286    0.7238    2.7177    0.2164    5.3738
+#> 447:    93.9959   -1.9163   -4.0858    0.0113    5.9407    0.7237    2.7166    0.2165    5.3731
+#> 448:    93.9947   -1.9163   -4.0856    0.0113    5.9442    0.7237    2.7159    0.2167    5.3723
+#> 449:    93.9948   -1.9164   -4.0854    0.0114    5.9386    0.7234    2.7151    0.2170    5.3730
+#> 450:    93.9937   -1.9164   -4.0853    0.0115    5.9368    0.7231    2.7142    0.2172    5.3732
+#> 451:    93.9929   -1.9164   -4.0851    0.0114    5.9312    0.7229    2.7135    0.2173    5.3735
+#> 452:    93.9923   -1.9163   -4.0850    0.0112    5.9288    0.7227    2.7121    0.2175    5.3747
+#> 453:    93.9918   -1.9162   -4.0849    0.0111    5.9339    0.7225    2.7112    0.2178    5.3759
+#> 454:    93.9912   -1.9164   -4.0849    0.0111    5.9355    0.7224    2.7103    0.2181    5.3777
+#> 455:    93.9902   -1.9164   -4.0849    0.0111    5.9412    0.7223    2.7097    0.2183    5.3784
+#> 456:    93.9894   -1.9164   -4.0848    0.0110    5.9554    0.7223    2.7076    0.2186    5.3801
+#> 457:    93.9902   -1.9161   -4.0846    0.0110    5.9675    0.7219    2.7054    0.2188    5.3807
+#> 458:    93.9907   -1.9159   -4.0845    0.0109    5.9710    0.7216    2.7032    0.2191    5.3815
+#> 459:    93.9926   -1.9157   -4.0844    0.0108    5.9751    0.7213    2.7011    0.2193    5.3817
+#> 460:    93.9930   -1.9155   -4.0845    0.0107    5.9788    0.7210    2.6985    0.2197    5.3818
+#> 461:    93.9933   -1.9153   -4.0845    0.0106    5.9809    0.7208    2.6959    0.2200    5.3822
+#> 462:    93.9941   -1.9153   -4.0845    0.0105    5.9904    0.7205    2.6935    0.2203    5.3820
+#> 463:    93.9945   -1.9152   -4.0844    0.0105    5.9971    0.7201    2.6913    0.2206    5.3817
+#> 464:    93.9942   -1.9151   -4.0844    0.0104    6.0010    0.7198    2.6892    0.2209    5.3818
+#> 465:    93.9931   -1.9152   -4.0843    0.0103    6.0113    0.7193    2.6872    0.2212    5.3823
+#> 466:    93.9937   -1.9152   -4.0840    0.0101    6.0145    0.7188    2.6853    0.2215    5.3828
+#> 467:    93.9939   -1.9152   -4.0838    0.0099    6.0189    0.7182    2.6835    0.2218    5.3832
+#> 468:    93.9933   -1.9153   -4.0835    0.0097    6.0247    0.7177    2.6818    0.2221    5.3830
+#> 469:    93.9933   -1.9153   -4.0832    0.0095    6.0251    0.7173    2.6801    0.2224    5.3822
+#> 470:    93.9914   -1.9153   -4.0829    0.0092    6.0332    0.7169    2.6785    0.2226    5.3823
+#> 471:    93.9894   -1.9153   -4.0826    0.0089    6.0455    0.7165    2.6769    0.2230    5.3822
+#> 472:    93.9869   -1.9152   -4.0824    0.0086    6.0454    0.7161    2.6754    0.2232    5.3836
+#> 473:    93.9852   -1.9152   -4.0822    0.0084    6.0501    0.7159    2.6740    0.2234    5.3832
+#> 474:    93.9829   -1.9152   -4.0821    0.0082    6.0579    0.7155    2.6725    0.2235    5.3831
+#> 475:    93.9826   -1.9152   -4.0819    0.0082    6.0661    0.7150    2.6711    0.2238    5.3829
+#> 476:    93.9837   -1.9152   -4.0819    0.0082    6.0774    0.7147    2.6696    0.2241    5.3824
+#> 477:    93.9852   -1.9151   -4.0819    0.0081    6.0890    0.7145    2.6681    0.2244    5.3817
+#> 478:    93.9851   -1.9151   -4.0820    0.0080    6.0957    0.7144    2.6665    0.2246    5.3827
+#> 479:    93.9857   -1.9150   -4.0820    0.0079    6.0981    0.7144    2.6651    0.2250    5.3838
+#> 480:    93.9856   -1.9151   -4.0821    0.0080    6.0944    0.7144    2.6638    0.2255    5.3854
+#> 481:    93.9864   -1.9152   -4.0823    0.0081    6.0912    0.7144    2.6624    0.2258    5.3865
+#> 482:    93.9870   -1.9153   -4.0825    0.0081    6.0954    0.7142    2.6613    0.2262    5.3864
+#> 483:    93.9888   -1.9153   -4.0826    0.0081    6.0888    0.7141    2.6602    0.2267    5.3870
+#> 484:    93.9903   -1.9154   -4.0828    0.0082    6.0848    0.7139    2.6592    0.2272    5.3861
+#> 485:    93.9914   -1.9154   -4.0831    0.0085    6.0851    0.7138    2.6586    0.2275    5.3858
+#> 486:    93.9909   -1.9154   -4.0834    0.0088    6.0824    0.7137    2.6581    0.2278    5.3850
+#> 487:    93.9899   -1.9155   -4.0838    0.0091    6.0870    0.7137    2.6577    0.2281    5.3838
+#> 488:    93.9882   -1.9156   -4.0842    0.0095    6.0877    0.7135    2.6574    0.2284    5.3835
+#> 489:    93.9865   -1.9163   -4.0841    0.0099    6.0839    0.7139    2.6581    0.2287    5.3835
+#> 490:    93.9859   -1.9170   -4.0841    0.0104    6.0783    0.7143    2.6587    0.2290    5.3830
+#> 491:    93.9847   -1.9177   -4.0838    0.0108    6.0773    0.7148    2.6596    0.2293    5.3824
+#> 492:    93.9840   -1.9183   -4.0836    0.0110    6.0833    0.7152    2.6606    0.2295    5.3817
+#> 493:    93.9832   -1.9188   -4.0834    0.0113    6.0832    0.7157    2.6613    0.2297    5.3814
+#> 494:    93.9824   -1.9195   -4.0832    0.0115    6.0859    0.7163    2.6620    0.2299    5.3819
+#> 495:    93.9813   -1.9200   -4.0830    0.0117    6.0878    0.7169    2.6633    0.2300    5.3820
+#> 496:    93.9798   -1.9206   -4.0827    0.0118    6.0871    0.7173    2.6644    0.2302    5.3825
+#> 497:    93.9787   -1.9213   -4.0824    0.0120    6.0856    0.7178    2.6653    0.2304    5.3834
+#> 498:    93.9771   -1.9220   -4.0822    0.0123    6.0759    0.7181    2.6660    0.2308    5.3850
+#> 499:    93.9744   -1.9225   -4.0819    0.0125    6.0692    0.7183    2.6666    0.2311    5.3868
+#> 500:    93.9728   -1.9229   -4.0816    0.0129    6.0609    0.7184    2.6675    0.2314    5.3884
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |    log_k1 |    log_k2 |  g_qlogis |
+#> |.....................|     sigma |        o1 |        o2 |        o3 |
+#> |.....................|        o4 |...........|...........|...........|
+#> |    1|     319.20504 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     319.20504 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     319.20504 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    G|    Gill Diff. |     17.25 |  -0.06517 |   -0.2231 |   0.05323 |
+#> |.....................|    -31.06 |     10.54 |    -5.521 |     3.149 |
+#> |.....................|    -10.19 |...........|...........|...........|
+#> |    2|     930.59637 |    0.5572 |   -0.9500 |   -0.9943 |   -0.9135 |
+#> |.....................|  -0.07749 |    -1.170 |   -0.7520 |   -0.9767 |
+#> |.....................|   -0.6292 |...........|...........|...........|
+#> |    U|     930.59637 |     52.42 |    -1.832 |    -4.205 |    0.1099 |
+#> |.....................|     2.723 |    0.5378 |     1.159 |    0.8352 |
+#> |.....................|     1.457 |...........|...........|...........|
+#> |    X|     930.59637 |     52.42 |    0.1600 |   0.01492 |    0.5274 |
+#> |.....................|     2.723 |    0.5378 |     1.159 |    0.8352 |
+#> |.....................|     1.457 |...........|...........|...........|
+#> |    3|     366.81009 |    0.9557 |   -0.9515 |   -0.9994 |   -0.9122 |
+#> |.....................|   -0.7950 |   -0.9264 |   -0.8795 |   -0.9039 |
+#> |.....................|   -0.8647 |...........|...........|...........|
+#> |    U|     366.81009 |     89.92 |    -1.834 |    -4.210 |    0.1100 |
+#> |.....................|     2.024 |    0.7174 |     1.030 |    0.9013 |
+#> |.....................|     1.185 |...........|...........|...........|
+#> |    X|     366.81009 |     89.92 |    0.1598 |   0.01484 |    0.5275 |
+#> |.....................|     2.024 |    0.7174 |     1.030 |    0.9013 |
+#> |.....................|     1.185 |...........|...........|...........|
+#> |    4|     354.05577 |    0.9956 |   -0.9516 |   -0.9999 |   -0.9121 |
+#> |.....................|   -0.8667 |   -0.9020 |   -0.8922 |   -0.8966 |
+#> |.....................|   -0.8882 |...........|...........|...........|
+#> |    U|     354.05577 |     93.67 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.954 |    0.7353 |     1.017 |    0.9079 |
+#> |.....................|     1.158 |...........|...........|...........|
+#> |    X|     354.05577 |     93.67 |    0.1597 |   0.01484 |    0.5275 |
+#> |.....................|     1.954 |    0.7353 |     1.017 |    0.9079 |
+#> |.....................|     1.158 |...........|...........|...........|
+#> |    5|     354.18966 |    0.9996 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8739 |   -0.8996 |   -0.8935 |   -0.8959 |
+#> |.....................|   -0.8906 |...........|...........|...........|
+#> |    U|     354.18966 |     94.04 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7371 |     1.015 |    0.9086 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.18966 |     94.04 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7371 |     1.015 |    0.9086 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    6|     354.21855 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8746 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.21855 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.21855 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    7|     354.22159 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.22159 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.22159 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    8|     354.22201 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.22201 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.22201 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    9|     354.22204 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.22204 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.22204 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |   10|     354.22204 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.22204 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.22204 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |   11|     354.22204 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.22204 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.22204 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |   12|     354.22204 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.22204 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.22204 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |   13|     354.22204 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.22204 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.22204 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |   14|     354.22204 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|     354.22204 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|     354.22204 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |   15|     354.22200 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|       354.222 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|       354.222 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |   16|     354.22200 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|       354.222 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|       354.222 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |   17|     354.22200 |     1.000 |   -0.9516 |    -1.000 |   -0.9121 |
+#> |.....................|   -0.8747 |   -0.8993 |   -0.8937 |   -0.8958 |
+#> |.....................|   -0.8908 |...........|...........|...........|
+#> |    U|       354.222 |     94.08 |    -1.834 |    -4.211 |    0.1100 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> |    X|       354.222 |     94.08 |    0.1597 |   0.01483 |    0.5275 |
+#> |.....................|     1.947 |    0.7373 |     1.015 |    0.9087 |
+#> |.....................|     1.155 |...........|...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                
+f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1:    93.5894   -2.4029   -3.9815    2.0318    3.0448    0.8581    1.0844    0.3182   21.0327
+#> 2:    93.5363   -2.3652   -3.9374    1.9473    2.8925    0.8152    1.0302    0.3023   14.7642
+#> 3:    93.3061   -2.3950   -3.8630    1.9537    2.7479    0.7744    1.0729    0.2872   12.2332
+#> 4:    93.4757   -2.3967   -3.8509    1.9504    2.6105    0.7357    1.1580    0.2729   11.6140
+#> 5:    93.6045   -2.3957   -3.8593    1.9732    2.4800    0.6989    1.1001    0.2592   11.0776
+#> 6:    93.6138   -2.4089   -3.9577    1.9557    2.8119    0.6640    1.0451    0.2463   11.5001
+#> 7:    93.4125   -2.3879   -3.8924    1.9950    3.1015    0.6308    1.0649    0.2339   10.6133
+#> 8:    93.5798   -2.3850   -3.9314    1.9888    3.0019    0.5992    1.0116    0.2222   10.4278
+#> 9:    93.1493   -2.3918   -3.9011    2.0040    4.3802    0.5693    1.0723    0.2111   10.2172
+#> 10:    93.5411   -2.3906   -3.8778    1.9664    4.5606    0.5408    1.0616    0.2006   10.1244
+#> 11:    93.3749   -2.4011   -3.8586    1.9682    4.3326    0.5138    1.0696    0.1905   10.1145
+#> 12:    93.0136   -2.3943   -3.8530    1.9633    4.1160    0.4881    1.0606    0.1810   10.0091
+#> 13:    93.1809   -2.4059   -3.9088    1.9821    3.9102    0.5448    1.0076    0.1720    9.8058
+#> 14:    93.3891   -2.4107   -3.9285    1.9894    3.7147    0.5504    0.9810    0.1634   10.2784
+#> 15:    93.4041   -2.4114   -3.9711    2.0216    4.4250    0.6070    0.9495    0.1552    9.4036
+#> 16:    93.4244   -2.4191   -4.0366    2.0511    4.2037    0.6035    0.9020    0.1474   10.0835
+#> 17:    93.6295   -2.4103   -4.0143    2.0509    4.0926    0.5997    0.8599    0.1401    9.7686
+#> 18:    93.6653   -2.4165   -3.9724    2.0405    3.8880    0.5979    0.9046    0.1331    9.6299
+#> 19:    93.6510   -2.4088   -3.9969    2.0328    3.6936    0.5934    0.9181    0.1264    9.3236
+#> 20:    93.6048   -2.4117   -3.9552    2.0268    3.9084    0.5879    1.0078    0.1201    9.6618
+#> 21:    94.0961   -2.4193   -3.9812    2.0552    3.7456    0.5743    0.9574    0.1141    9.6510
+#> 22:    93.9157   -2.4202   -3.9102    2.0263    5.0447    0.6198    0.9742    0.1294    9.6463
+#> 23:    94.1580   -2.4286   -3.9223    2.0441    4.7925    0.5981    0.9312    0.1230    9.8346
+#> 24:    94.4405   -2.4141   -3.9564    2.0383    4.5529    0.5925    0.9173    0.1168   10.6161
+#> 25:    93.8846   -2.3958   -4.0122    2.0053    4.9956    0.5677    0.8715    0.1173   10.3823
+#> 26:    93.6815   -2.3835   -3.9801    1.9872    5.6625    0.5514    0.8368    0.1114    9.8283
+#> 27:    93.6463   -2.3779   -3.9731    1.9833    5.3794    0.5566    0.8650    0.1059    9.5439
+#> 28:    93.7974   -2.3980   -3.9583    1.9657    6.4804    0.5366    0.8756    0.1006    9.7998
+#> 29:    93.6921   -2.4221   -3.8982    1.9701    6.1564    0.6070    0.9713    0.0955    9.2988
+#> 30:    93.3112   -2.4200   -3.8916    1.9702    6.6968    0.6110    0.9538    0.0908    9.1812
+#> 31:    93.9900   -2.4282   -3.9448    2.0257    6.3620    0.6071    0.9061    0.0862    9.4865
+#> 32:    93.8014   -2.4241   -3.9364    2.0053    6.8497    0.6173    0.8608    0.0819    9.5589
+#> 33:    94.0330   -2.4215   -3.9888    2.0034    6.5072    0.6142    0.8178    0.0778   10.2023
+#> 34:    93.5811   -2.4215   -3.9917    2.0170    6.1819    0.5907    0.8314    0.0842   10.2204
+#> 35:    93.9308   -2.4210   -3.8798    2.0046    6.7593    0.5877    1.1132    0.0800    9.2384
+#> 36:    94.0000   -2.4325   -3.8970    2.0457    6.4213    0.5886    1.0731    0.0835    8.8987
+#> 37:    93.4010   -2.4325   -3.9306    2.0550    7.2268    0.5886    1.0220    0.0969    9.1261
+#> 38:    93.3896   -2.4291   -3.9250    2.0148    6.8655    0.5885    0.9709    0.1039    9.2989
+#> 39:    93.3821   -2.4349   -3.9148    2.0368    6.5222    0.6059    0.9647    0.1095    9.2864
+#> 40:    93.1382   -2.4685   -3.9384    2.1083    8.6249    0.6287    1.0265    0.1066    9.6411
+#> 41:    92.7963   -2.4643   -3.8992    2.0585    8.1937    0.6376    1.1117    0.1234    9.4738
+#> 42:    92.7160   -2.4545   -3.9652    2.0680    7.7840    0.6068    1.0561    0.1173    9.4776
+#> 43:    93.0070   -2.4360   -4.0223    2.0624    7.9556    0.5840    1.0033    0.1114    9.7197
+#> 44:    93.3836   -2.4207   -4.0739    2.0872    7.5578    0.5788    0.9531    0.1058   10.3515
+#> 45:    93.3240   -2.4382   -4.0210    2.1103    7.1799    0.6211    0.9055    0.1165   10.5050
+#> 46:    93.1921   -2.4438   -4.0330    2.0842    7.3884    0.6159    0.8602    0.1107   10.7251
+#> 47:    92.9710   -2.4351   -4.0155    2.1117    7.0189    0.5998    0.8519    0.1091   10.2972
+#> 48:    93.0129   -2.4395   -3.9677    2.0986    6.6680    0.5804    0.8775    0.1058   10.8515
+#> 49:    92.6562   -2.4474   -4.0295    2.0877    6.3346    0.6338    0.8723    0.1155   10.0641
+#> 50:    92.5101   -2.4612   -4.0295    2.0845    6.0179    0.6197    0.8742    0.1097    9.9048
+#> 51:    92.9446   -2.4615   -3.9927    2.1199    5.7170    0.6165    0.9311    0.1042    9.8383
+#> 52:    92.8362   -2.4525   -3.9682    2.0787    5.4311    0.6329    0.9647    0.0990    9.0726
+#> 53:    92.8579   -2.4598   -3.9324    2.0529    5.1596    0.6057    0.9192    0.0940    9.5677
+#> 54:    92.8667   -2.4858   -3.9104    2.0454    5.0661    0.6304    1.0025    0.0893    9.0977
+#> 55:    93.2327   -2.4650   -3.8323    2.0628    6.8188    0.6499    1.1366    0.0852    8.5677
+#> 56:    92.9319   -2.4794   -3.8376    2.0490    6.4778    0.6635    1.1141    0.1064    9.0723
+#> 57:    93.1126   -2.5128   -3.8223    2.0834    6.1539    0.6637    1.1361    0.1010    9.2678
+#> 58:    93.5085   -2.4894   -3.8723    2.0650    5.8462    0.6745    1.0793    0.0960    9.0367
+#> 59:    93.7882   -2.4614   -3.9241    2.0707    5.5539    0.6898    1.0254    0.0912    8.7466
+#> 60:    94.1492   -2.4386   -3.9415    2.0599    5.2762    0.6711    0.9741    0.0932    8.4466
+#> 61:    94.4215   -2.4272   -3.9647    2.0482    5.0124    0.6549    0.9254    0.0911    8.7870
+#> 62:    94.3607   -2.4053   -3.9633    1.9966    4.7618    0.6534    0.8878    0.1221    9.0404
+#> 63:    94.3958   -2.4179   -3.9386    2.0041    4.5237    0.6462    0.9360    0.1245    9.0491
+#> 64:    94.5204   -2.4175   -3.9411    2.0106    4.2975    0.6532    0.9657    0.1183    8.9115
+#> 65:    94.5674   -2.4117   -3.9701    2.0546    4.0826    0.6438    0.9238    0.1247    8.7293
+#> 66:    94.2199   -2.4337   -3.9298    2.0287    4.7686    0.6582    0.9262    0.1185    9.0519
+#> 67:    94.2756   -2.4305   -3.9706    2.0782    4.5301    0.6512    0.8799    0.1126    9.1397
+#> 68:    94.4195   -2.4193   -4.0049    2.0643    4.3036    0.6804    0.8359    0.1220    9.5306
+#> 69:    94.5255   -2.4183   -4.0119    2.0733    4.0884    0.6784    0.8577    0.1297    9.4535
+#> 70:    94.5668   -2.4117   -3.9662    2.0762    4.2149    0.6511    0.9325    0.1475    9.1637
+#> 71:    94.7464   -2.4147   -3.9937    2.0942    4.2418    0.6571    0.9524    0.1540    9.6576
+#> 72:    94.4869   -2.4160   -4.0050    2.1075    4.8520    0.6687    1.0119    0.1488    9.4234
+#> 73:    94.3747   -2.4423   -4.0072    2.1484    6.4364    0.6948    1.0011    0.1438    9.1490
+#> 74:    94.3997   -2.4464   -4.0147    2.1965    6.1146    0.7030    1.0566    0.1521    9.0697
+#> 75:    94.4187   -2.4566   -3.9611    2.1337    5.8089    0.6866    1.1666    0.1656    8.9436
+#> 76:    94.4381   -2.4502   -3.9816    2.1209    5.6488    0.7266    1.1449    0.1573    8.9289
+#> 77:    94.6421   -2.4446   -3.9603    2.1544    5.3663    0.6968    1.2087    0.1662    8.5186
+#> 78:    94.8397   -2.4420   -3.9690    2.1380    5.0980    0.6969    1.1833    0.1578    8.9071
+#> 79:    94.4296   -2.4547   -3.9576    2.1569    6.3095    0.6829    1.1850    0.1544    9.1345
+#> 80:    93.9628   -2.4530   -3.9312    2.0956    8.5844    0.6880    1.2548    0.1835    8.6936
+#> 81:    94.2900   -2.4687   -3.8570    2.0779    9.0596    0.6993    1.2012    0.1743    8.9092
+#> 82:    93.9652   -2.4742   -3.9261    2.0913    8.6066    0.6970    1.1667    0.1656    8.4359
+#> 83:    94.0828   -2.4739   -3.8603    2.0587    8.1763    0.7123    1.2575    0.1638    8.5431
+#> 84:    93.5926   -2.4645   -3.8993    2.0391    9.8721    0.7178    1.1947    0.1556    8.5623
+#> 85:    93.7052   -2.4692   -3.8411    2.0448    9.3785    0.7251    1.1349    0.1478    8.5558
+#> 86:    93.8043   -2.4726   -3.9028    2.0745    8.9096    0.7064    1.0782    0.1404    9.1308
+#> 87:    93.5704   -2.4836   -3.8694    2.0999   12.3224    0.7284    1.0922    0.1334    8.8645
+#> 88:    93.5715   -2.4827   -3.9202    2.0861   11.7063    0.7541    1.0376    0.1267    9.2433
+#> 89:    93.6894   -2.4720   -3.8964    2.1093   12.4610    0.7727    1.0218    0.1325    9.0321
+#> 90:    93.2881   -2.4787   -3.9464    2.1137   11.8380    0.7850    0.9707    0.1258    8.8265
+#> 91:    93.8454   -2.4626   -3.9566    2.1181   11.2461    0.7620    0.9579    0.1396    8.8279
+#> 92:    93.8268   -2.4639   -3.8951    2.0936   10.6838    0.7618    1.1083    0.1553    8.4609
+#> 93:    94.0622   -2.4853   -3.8531    2.0740   10.1496    0.7493    1.1237    0.1596    8.2057
+#> 94:    93.6190   -2.4843   -3.8857    2.0625    9.6421    0.7596    1.1104    0.1686    8.3522
+#> 95:    93.6352   -2.4725   -3.9243    2.0582    9.1600    0.7732    1.0549    0.1694    8.3993
+#> 96:    93.5291   -2.4707   -3.9318    2.0612    8.7020    0.7853    1.0639    0.1609    8.2908
+#> 97:    93.0626   -2.4639   -3.9255    2.0887    8.4092    0.7717    1.1477    0.1685    8.2710
+#> 98:    93.3712   -2.4677   -3.9642    2.1350    7.9888    0.7703    1.0903    0.1921    8.5468
+#> 99:    93.7108   -2.4848   -3.9775    2.1733    7.5893    0.7490    1.0367    0.1825    8.5629
+#> 100:    94.1114   -2.4867   -4.0111    2.1705    7.2099    0.7446    0.9849    0.1832    8.6964
+#> 101:    93.7547   -2.4897   -3.9793    2.1817    7.1755    0.7513    0.9899    0.1774    8.5077
+#> 102:    93.8818   -2.5029   -3.9929    2.2028    6.8167    0.7137    1.0045    0.1685    8.3706
+#> 103:    94.0026   -2.5094   -3.9680    2.2059    6.4759    0.7073    1.0498    0.1601    8.3087
+#> 104:    93.5946   -2.5260   -3.9640    2.2209    6.2674    0.7688    1.0548    0.1531    8.3444
+#> 105:    93.3863   -2.5431   -4.0087    2.2211    7.1040    0.7987    1.0020    0.1454    8.2210
+#> 106:    93.1536   -2.5365   -4.0243    2.2457    6.7488    0.7909    0.9519    0.1389    8.0950
+#> 107:    93.2220   -2.5446   -4.0016    2.2508    6.4114    0.8108    0.9483    0.1364    8.5629
+#> 108:    93.0778   -2.5470   -3.9678    2.2329    6.4774    0.8077    1.0081    0.1850    9.2740
+#> 109:    93.8925   -2.5453   -3.9560    2.2193    6.1535    0.8079    1.0608    0.2111    9.2651
+#> 110:    94.3171   -2.5179   -4.0040    2.2145    5.8458    0.7874    1.0520    0.2135    8.9788
+#> 111:    94.0655   -2.5069   -3.9752    2.2009    5.5536    0.8056    1.1206    0.2192    8.9410
+#> 112:    93.8552   -2.4994   -3.9791    2.1597    5.2759    0.8012    1.0646    0.2365    8.9570
+#> 113:    93.5190   -2.5053   -3.9760    2.1727    5.0121    0.8326    1.0114    0.2246    9.2154
+#> 114:    93.5531   -2.5083   -3.9569    2.1636    4.7615    0.8255    0.9879    0.2134    9.1197
+#> 115:    93.4780   -2.5217   -3.9467    2.1529    4.5234    0.8314    1.0392    0.2027    8.7850
+#> 116:    93.5707   -2.5216   -3.9098    2.1667    4.2972    0.8261    1.1213    0.1926    9.2991
+#> 117:    93.6610   -2.5445   -3.8775    2.1473    4.0824    0.8122    1.1232    0.1830    9.2054
+#> 118:    93.4315   -2.5251   -3.9166    2.1365    4.6012    0.7933    1.0690    0.1738    8.8061
+#> 119:    93.2491   -2.5265   -3.9236    2.1671    5.0672    0.8046    1.0711    0.1709    8.2293
+#> 120:    93.2605   -2.5327   -3.9714    2.1984    4.8138    0.8025    1.0176    0.1623    7.9088
+#> 121:    93.5831   -2.5448   -3.9669    2.2195    4.5731    0.8079    0.9921    0.1542    8.2211
+#> 122:    93.3408   -2.5460   -3.9710    2.2235    4.6838    0.8053    1.0377    0.1658    8.2934
+#> 123:    93.4581   -2.5395   -3.9487    2.2279    4.4496    0.8298    1.0338    0.1732    8.2859
+#> 124:    93.0562   -2.5565   -3.9587    2.2299    4.2272    0.8590    1.0531    0.1964    8.1244
+#> 125:    93.0576   -2.5660   -3.9434    2.2457    4.0158    0.8564    1.0768    0.1866    8.3730
+#> 126:    92.8366   -2.5571   -3.9463    2.2096    3.8150    0.8551    1.0476    0.1773    8.3820
+#> 127:    92.9607   -2.5595   -3.9773    2.2325    3.6243    0.8497    0.9952    0.1684    9.2276
+#> 128:    93.0655   -2.5463   -3.9731    2.1901    3.4430    0.8903    0.9454    0.1600    8.8096
+#> 129:    93.0669   -2.5467   -3.9713    2.2204    3.2709    0.8905    0.9234    0.1520    8.8686
+#> 130:    93.2036   -2.5524   -3.9702    2.2070    3.1073    0.8719    0.9514    0.1578    8.8433
+#> 131:    93.3565   -2.5544   -3.9809    2.1654    2.9520    0.8777    0.9117    0.1764    8.9770
+#> 132:    93.0371   -2.5364   -3.9250    2.1761    2.8044    0.8338    1.0518    0.1731    8.5405
+#> 133:    93.5727   -2.5388   -3.8759    2.1580    3.6769    0.8616    1.0981    0.1858    8.5303
+#> 134:    93.4962   -2.5341   -3.9006    2.1394    4.3695    0.8904    1.0432    0.1765    8.7067
+#> 135:    93.3219   -2.5413   -3.8922    2.1888    4.1510    0.8971    1.0435    0.1857    8.4977
+#> 136:    93.3582   -2.5477   -3.8412    2.1957    3.9435    0.8816    1.1954    0.2102    8.1330
+#> 137:    93.2791   -2.5313   -3.8936    2.1570    3.7463    0.8875    1.1356    0.1997    8.3094
+#> 138:    93.0890   -2.5428   -3.8910    2.1414    3.5590    0.8826    1.1008    0.2120    8.2653
+#> 139:    93.2404   -2.5407   -3.8926    2.1727    3.3810    0.8829    1.1068    0.2014    8.3739
+#> 140:    93.0870   -2.5514   -3.9131    2.2182    3.2120    0.8712    1.0870    0.1914    8.6179
+#> 141:    93.2715   -2.5499   -3.9460    2.2216    3.4383    0.8470    1.0662    0.1900    8.4034
+#> 142:    93.1915   -2.5583   -3.9990    2.2475    5.2653    0.8607    1.0129    0.2061    7.9891
+#> 143:    93.3709   -2.5650   -3.9422    2.2369    5.0020    0.8748    1.2043    0.2248    8.0084
+#> 144:    93.2092   -2.5706   -3.9016    2.1930    4.7519    0.8667    1.1977    0.2179    8.2733
+#> 145:    92.6640   -2.5733   -3.9225    2.1859    4.5143    0.8636    1.1695    0.2070    8.6212
+#> 146:    92.7581   -2.5695   -3.9055    2.1801    5.4209    0.8589    1.1678    0.1967    8.9378
+#> 147:    93.1089   -2.5707   -3.9825    2.2113    7.6640    0.8710    1.1094    0.1934    9.0543
+#> 148:    93.0803   -2.5672   -3.9461    2.2066    9.9043    0.8648    1.1043    0.1863    8.6209
+#> 149:    92.6332   -2.5468   -3.9425    2.1881    9.4091    0.8278    1.1313    0.1769    8.4652
+#> 150:    92.9068   -2.5440   -3.9531    2.2005    8.9386    0.8189    1.1104    0.1681    8.4196
+#> 151:    92.7324   -2.5497   -3.9648    2.2387    8.4917    0.8205    1.1421    0.1597    8.4228
+#> 152:    93.0394   -2.5282   -3.9916    2.2251    3.9029    0.8190    1.0320    0.1612    8.3453
+#> 153:    93.3137   -2.5268   -3.9993    2.2294    3.7951    0.8187    1.0311    0.1780    8.4258
+#> 154:    93.6677   -2.5264   -3.9756    2.2615    4.8704    0.8177    1.1355    0.1799    8.7204
+#> 155:    94.0822   -2.5409   -4.0456    2.2507    5.1202    0.8032    0.9930    0.1613    8.8844
+#> 156:    93.6289   -2.5388   -4.1150    2.2777    4.6367    0.8080    0.8336    0.1817    8.4370
+#> 157:    93.9171   -2.5327   -4.0218    2.2696    3.1121    0.8069    1.0394    0.1800    8.5006
+#> 158:    94.0010   -2.5357   -4.0036    2.2695    3.1485    0.8087    1.1132    0.2048    8.7160
+#> 159:    94.1277   -2.5541   -3.9717    2.2773    5.1432    0.8088    1.0732    0.1980    8.5378
+#> 160:    94.0075   -2.5436   -3.9550    2.2796    4.7826    0.8286    1.0820    0.1953    8.3885
+#> 161:    93.6793   -2.5471   -3.9675    2.2713    3.9366    0.8603    1.0682    0.1972    8.3026
+#> 162:    93.2649   -2.5429   -3.9564    2.2406    2.7349    0.8469    1.0889    0.1929    8.3765
+#> 163:    93.2072   -2.5519   -3.9786    2.2535    3.1500    0.8361    1.1240    0.1997    8.4527
+#> 164:    93.4059   -2.5471   -4.0398    2.2257    2.8708    0.8284    1.0541    0.2105    8.4984
+#> 165:    93.2579   -2.5407   -3.9665    2.2305    2.7397    0.8251    1.1355    0.2302    7.9794
+#> 166:    93.4900   -2.5465   -3.9565    2.2316    1.9775    0.8359    1.0939    0.2243    8.1279
+#> 167:    93.3825   -2.5567   -3.9784    2.2276    2.3737    0.8251    1.0894    0.2254    8.6657
+#> 168:    93.2568   -2.5681   -3.9993    2.2818    2.6721    0.8237    1.1398    0.2207    8.4894
+#> 169:    93.0484   -2.5468   -3.9693    2.2586    1.9105    0.8518    1.1911    0.1917    8.5627
+#> 170:    93.2703   -2.5730   -3.9059    2.2512    2.1481    0.8068    1.3267    0.2198    8.2260
+#> 171:    93.2041   -2.5720   -3.8992    2.2227    2.7790    0.8045    1.2387    0.2059    8.1401
+#> 172:    92.7596   -2.5722   -3.8802    2.2537    2.9977    0.8049    1.2807    0.1831    8.3375
+#> 173:    92.7734   -2.5716   -3.8811    2.1987    3.0176    0.8063    1.3070    0.2285    8.5061
+#> 174:    92.5561   -2.5700   -3.9236    2.2351    3.0286    0.8250    1.2000    0.2200    8.0725
+#> 175:    92.5072   -2.5724   -3.9968    2.2479    2.4287    0.8333    1.0169    0.2235    8.2600
+#> 176:    92.3531   -2.5787   -3.9977    2.2407    2.9999    0.8167    0.9813    0.2451    8.7505
+#> 177:    92.4672   -2.5746   -4.0095    2.2733    2.8040    0.8361    0.9794    0.2363    8.5176
+#> 178:    92.5747   -2.5981   -3.9921    2.2835    1.8203    0.8411    0.9795    0.2112    8.8034
+#> 179:    92.7101   -2.5766   -3.9697    2.2337    1.7808    0.8348    1.0402    0.2247    8.3952
+#> 180:    92.5348   -2.5714   -3.9595    2.2236    1.2661    0.8361    1.0107    0.2375    8.7156
+#> 181:    92.7241   -2.5730   -3.9205    2.2162    1.1047    0.8321    1.1192    0.2147    8.8821
+#> 182:    92.9177   -2.5864   -3.9351    2.2280    1.2069    0.8108    1.1022    0.2163    8.5703
+#> 183:    92.8646   -2.5704   -3.9755    2.2192    1.5680    0.8232    0.9400    0.1848    8.6586
+#> 184:    92.8081   -2.5759   -3.9981    2.2411    1.7739    0.8394    0.8711    0.1788    8.6327
+#> 185:    92.6830   -2.5700   -4.0110    2.2360    1.5375    0.8093    0.9114    0.1782    8.6703
+#> 186:    92.7691   -2.5764   -3.9671    2.2148    1.8813    0.8117    0.9794    0.1901    8.4813
+#> 187:    92.7540   -2.5659   -3.9695    2.2543    1.3755    0.8130    1.0332    0.1960    8.5371
+#> 188:    92.5722   -2.5650   -3.9527    2.2552    1.4000    0.8142    1.1013    0.1881    8.2025
+#> 189:    92.9404   -2.5644   -3.9446    2.2579    1.3589    0.8157    1.1262    0.1741    8.2347
+#> 190:    92.8142   -2.5628   -3.9397    2.2549    1.1871    0.8241    1.1571    0.1728    8.1590
+#> 191:    92.7352   -2.5682   -3.9476    2.2502    0.8302    0.7954    1.1448    0.1859    8.6148
+#> 192:    92.7380   -2.5574   -3.9273    2.2318    0.6692    0.8185    1.1124    0.1932    8.5279
+#> 193:    92.9199   -2.5652   -3.9586    2.2184    0.9877    0.8097    1.1689    0.1709    8.7071
+#> 194:    93.0042   -2.5651   -3.9699    2.2302    1.3311    0.8135    1.1202    0.1832    8.8051
+#> 195:    92.8090   -2.5890   -3.9799    2.2360    0.9251    0.8313    1.0192    0.1806    9.3110
+#> 196:    92.5114   -2.5894   -3.9883    2.2553    0.8504    0.8299    1.0665    0.1855    8.9668
+#> 197:    92.6704   -2.5845   -3.9577    2.2490    0.3567    0.8365    1.0893    0.1896    8.5856
+#> 198:    92.7249   -2.5753   -3.9775    2.2327    0.4282    0.8506    1.0736    0.2003    8.7110
+#> 199:    92.5538   -2.5696   -3.9550    2.2382    0.3177    0.8550    1.1060    0.2132    8.5431
+#> 200:    92.6352   -2.5716   -3.9921    2.2372    0.2500    0.8592    1.0083    0.2057    8.5811
+#> 201:    92.6440   -2.5663   -3.9931    2.2219    0.2611    0.8647    1.0130    0.1931    8.6428
+#> 202:    92.6090   -2.5633   -3.9837    2.2198    0.2389    0.8680    1.0373    0.1958    8.6818
+#> 203:    92.6180   -2.5627   -3.9823    2.2185    0.2315    0.8627    1.0398    0.1939    8.6310
+#> 204:    92.6140   -2.5628   -3.9783    2.2176    0.2289    0.8588    1.0462    0.1923    8.5391
+#> 205:    92.6337   -2.5619   -3.9802    2.2190    0.2227    0.8579    1.0407    0.1965    8.5514
+#> 206:    92.6373   -2.5615   -3.9835    2.2175    0.2313    0.8580    1.0330    0.2006    8.5635
+#> 207:    92.6403   -2.5594   -3.9836    2.2189    0.2365    0.8608    1.0282    0.2017    8.5721
+#> 208:    92.6415   -2.5587   -3.9862    2.2192    0.2480    0.8615    1.0221    0.2001    8.5738
+#> 209:    92.6303   -2.5586   -3.9872    2.2180    0.2544    0.8608    1.0127    0.1966    8.6159
+#> 210:    92.6278   -2.5584   -3.9829    2.2178    0.2577    0.8576    1.0149    0.1932    8.6336
+#> 211:    92.6320   -2.5580   -3.9844    2.2163    0.2614    0.8544    1.0057    0.1902    8.6594
+#> 212:    92.6266   -2.5576   -3.9802    2.2140    0.2554    0.8515    1.0125    0.1891    8.6549
+#> 213:    92.6226   -2.5570   -3.9771    2.2114    0.2491    0.8468    1.0201    0.1879    8.6612
+#> 214:    92.6217   -2.5570   -3.9759    2.2119    0.2430    0.8429    1.0289    0.1859    8.6700
+#> 215:    92.6212   -2.5573   -3.9743    2.2121    0.2354    0.8394    1.0383    0.1853    8.6796
+#> 216:    92.6151   -2.5566   -3.9736    2.2125    0.2329    0.8378    1.0446    0.1850    8.7036
+#> 217:    92.6073   -2.5558   -3.9759    2.2133    0.2311    0.8373    1.0459    0.1854    8.7185
+#> 218:    92.6090   -2.5556   -3.9771    2.2142    0.2312    0.8373    1.0499    0.1866    8.7181
+#> 219:    92.6166   -2.5553   -3.9764    2.2142    0.2358    0.8376    1.0624    0.1882    8.7228
+#> 220:    92.6268   -2.5549   -3.9770    2.2150    0.2404    0.8395    1.0671    0.1899    8.7325
+#> 221:    92.6337   -2.5548   -3.9765    2.2172    0.2460    0.8412    1.0713    0.1900    8.7409
+#> 222:    92.6383   -2.5563   -3.9796    2.2211    0.2499    0.8412    1.0667    0.1898    8.7456
+#> 223:    92.6399   -2.5575   -3.9806    2.2259    0.2494    0.8406    1.0665    0.1898    8.7564
+#> 224:    92.6424   -2.5589   -3.9840    2.2296    0.2451    0.8412    1.0624    0.1894    8.7571
+#> 225:    92.6431   -2.5599   -3.9883    2.2336    0.2427    0.8423    1.0555    0.1885    8.7754
+#> 226:    92.6393   -2.5612   -3.9919    2.2371    0.2384    0.8431    1.0488    0.1886    8.7904
+#> 227:    92.6354   -2.5630   -3.9918    2.2406    0.2361    0.8432    1.0501    0.1892    8.8070
+#> 228:    92.6328   -2.5650   -3.9926    2.2437    0.2336    0.8434    1.0524    0.1908    8.8133
+#> 229:    92.6328   -2.5672   -3.9913    2.2462    0.2318    0.8439    1.0578    0.1926    8.8314
+#> 230:    92.6322   -2.5684   -3.9911    2.2482    0.2269    0.8426    1.0621    0.1952    8.8464
+#> 231:    92.6263   -2.5698   -3.9910    2.2500    0.2240    0.8418    1.0628    0.1963    8.8734
+#> 232:    92.6228   -2.5710   -3.9908    2.2515    0.2218    0.8411    1.0644    0.1977    8.9056
+#> 233:    92.6235   -2.5721   -3.9919    2.2545    0.2192    0.8409    1.0649    0.1983    8.9192
+#> 234:    92.6232   -2.5727   -3.9927    2.2551    0.2171    0.8397    1.0649    0.1981    8.9294
+#> 235:    92.6219   -2.5733   -3.9924    2.2562    0.2155    0.8390    1.0646    0.1978    8.9242
+#> 236:    92.6212   -2.5737   -3.9924    2.2574    0.2145    0.8384    1.0639    0.1975    8.9292
+#> 237:    92.6211   -2.5738   -3.9938    2.2588    0.2142    0.8379    1.0607    0.1970    8.9400
+#> 238:    92.6194   -2.5735   -3.9931    2.2589    0.2155    0.8373    1.0630    0.1969    8.9371
+#> 239:    92.6175   -2.5734   -3.9928    2.2593    0.2155    0.8371    1.0648    0.1967    8.9315
+#> 240:    92.6175   -2.5729   -3.9923    2.2593    0.2143    0.8367    1.0673    0.1963    8.9180
+#> 241:    92.6155   -2.5728   -3.9917    2.2591    0.2133    0.8372    1.0695    0.1960    8.9139
+#> 242:    92.6135   -2.5726   -3.9923    2.2588    0.2136    0.8375    1.0681    0.1965    8.9191
+#> 243:    92.6115   -2.5726   -3.9930    2.2592    0.2127    0.8375    1.0683    0.1969    8.9117
+#> 244:    92.6106   -2.5726   -3.9925    2.2588    0.2123    0.8381    1.0704    0.1975    8.9124
+#> 245:    92.6065   -2.5730   -3.9930    2.2586    0.2127    0.8388    1.0691    0.1982    8.9140
+#> 246:    92.6046   -2.5734   -3.9931    2.2588    0.2109    0.8397    1.0701    0.1986    8.9132
+#> 247:    92.6048   -2.5737   -3.9938    2.2597    0.2081    0.8404    1.0708    0.1989    8.9224
+#> 248:    92.6029   -2.5739   -3.9932    2.2599    0.2056    0.8410    1.0718    0.1993    8.9198
+#> 249:    92.6006   -2.5743   -3.9934    2.2598    0.2052    0.8419    1.0705    0.1996    8.9244
+#> 250:    92.5984   -2.5740   -3.9930    2.2595    0.2037    0.8417    1.0709    0.1997    8.9208
+#> 251:    92.5967   -2.5739   -3.9932    2.2595    0.2018    0.8418    1.0700    0.1996    8.9143
+#> 252:    92.5943   -2.5737   -3.9920    2.2594    0.2009    0.8412    1.0734    0.1992    8.9090
+#> 253:    92.5944   -2.5736   -3.9904    2.2588    0.1997    0.8405    1.0769    0.1995    8.9035
+#> 254:    92.5941   -2.5732   -3.9896    2.2582    0.1987    0.8394    1.0788    0.1993    8.8940
+#> 255:    92.5916   -2.5728   -3.9892    2.2571    0.1983    0.8387    1.0794    0.1988    8.8894
+#> 256:    92.5889   -2.5724   -3.9880    2.2562    0.1988    0.8382    1.0813    0.1992    8.8834
+#> 257:    92.5889   -2.5719   -3.9872    2.2557    0.2003    0.8378    1.0831    0.1995    8.8806
+#> 258:    92.5889   -2.5717   -3.9866    2.2556    0.2021    0.8377    1.0858    0.1995    8.8792
+#> 259:    92.5898   -2.5715   -3.9867    2.2556    0.2033    0.8373    1.0884    0.1999    8.8785
+#> 260:    92.5924   -2.5709   -3.9868    2.2556    0.2033    0.8367    1.0891    0.2006    8.8743
+#> 261:    92.5956   -2.5703   -3.9866    2.2552    0.2045    0.8360    1.0908    0.2014    8.8635
+#> 262:    92.5985   -2.5698   -3.9859    2.2546    0.2054    0.8354    1.0940    0.2022    8.8551
+#> 263:    92.6014   -2.5694   -3.9857    2.2544    0.2067    0.8347    1.0964    0.2028    8.8479
+#> 264:    92.6041   -2.5690   -3.9858    2.2543    0.2069    0.8338    1.0977    0.2028    8.8421
+#> 265:    92.6063   -2.5687   -3.9861    2.2541    0.2079    0.8327    1.0976    0.2029    8.8394
+#> 266:    92.6087   -2.5684   -3.9867    2.2540    0.2107    0.8318    1.0968    0.2027    8.8351
+#> 267:    92.6108   -2.5682   -3.9863    2.2534    0.2118    0.8314    1.0970    0.2032    8.8283
+#> 268:    92.6130   -2.5680   -3.9860    2.2530    0.2131    0.8309    1.0970    0.2034    8.8263
+#> 269:    92.6139   -2.5678   -3.9851    2.2526    0.2155    0.8306    1.0979    0.2040    8.8240
+#> 270:    92.6144   -2.5676   -3.9851    2.2521    0.2176    0.8303    1.0972    0.2044    8.8283
+#> 271:    92.6153   -2.5675   -3.9855    2.2518    0.2190    0.8305    1.0961    0.2049    8.8310
+#> 272:    92.6163   -2.5674   -3.9859    2.2521    0.2196    0.8305    1.0965    0.2051    8.8378
+#> 273:    92.6178   -2.5672   -3.9862    2.2520    0.2198    0.8302    1.0959    0.2051    8.8421
+#> 274:    92.6193   -2.5670   -3.9870    2.2524    0.2195    0.8299    1.0955    0.2051    8.8441
+#> 275:    92.6197   -2.5669   -3.9874    2.2526    0.2194    0.8295    1.0947    0.2052    8.8477
+#> 276:    92.6215   -2.5665   -3.9875    2.2523    0.2206    0.8288    1.0956    0.2058    8.8462
+#> 277:    92.6225   -2.5660   -3.9878    2.2522    0.2231    0.8282    1.0974    0.2064    8.8450
+#> 278:    92.6237   -2.5655   -3.9883    2.2522    0.2240    0.8277    1.0988    0.2074    8.8501
+#> 279:    92.6249   -2.5651   -3.9888    2.2524    0.2244    0.8274    1.0994    0.2083    8.8504
+#> 280:    92.6259   -2.5647   -3.9891    2.2523    0.2235    0.8270    1.0992    0.2087    8.8514
+#> 281:    92.6264   -2.5643   -3.9889    2.2522    0.2225    0.8262    1.1001    0.2090    8.8559
+#> 282:    92.6270   -2.5639   -3.9889    2.2516    0.2223    0.8255    1.0997    0.2090    8.8593
+#> 283:    92.6280   -2.5633   -3.9885    2.2503    0.2214    0.8248    1.0999    0.2101    8.8586
+#> 284:    92.6281   -2.5627   -3.9883    2.2491    0.2212    0.8241    1.0993    0.2110    8.8580
+#> 285:    92.6283   -2.5621   -3.9881    2.2481    0.2213    0.8235    1.0986    0.2118    8.8590
+#> 286:    92.6288   -2.5615   -3.9886    2.2475    0.2219    0.8231    1.0973    0.2123    8.8602
+#> 287:    92.6291   -2.5611   -3.9890    2.2470    0.2217    0.8230    1.0961    0.2133    8.8577
+#> 288:    92.6292   -2.5607   -3.9893    2.2468    0.2202    0.8229    1.0960    0.2142    8.8570
+#> 289:    92.6275   -2.5602   -3.9895    2.2464    0.2192    0.8226    1.0964    0.2151    8.8554
+#> 290:    92.6262   -2.5598   -3.9892    2.2457    0.2189    0.8223    1.0977    0.2161    8.8578
+#> 291:    92.6246   -2.5596   -3.9890    2.2454    0.2183    0.8218    1.0999    0.2165    8.8596
+#> 292:    92.6223   -2.5593   -3.9892    2.2451    0.2183    0.8213    1.1003    0.2173    8.8575
+#> 293:    92.6201   -2.5590   -3.9896    2.2447    0.2193    0.8209    1.1003    0.2175    8.8569
+#> 294:    92.6169   -2.5587   -3.9902    2.2445    0.2202    0.8204    1.0998    0.2176    8.8568
+#> 295:    92.6144   -2.5584   -3.9906    2.2442    0.2217    0.8197    1.0994    0.2176    8.8565
+#> 296:    92.6126   -2.5581   -3.9913    2.2441    0.2223    0.8188    1.0983    0.2175    8.8585
+#> 297:    92.6112   -2.5576   -3.9920    2.2439    0.2235    0.8182    1.0969    0.2175    8.8600
+#> 298:    92.6108   -2.5572   -3.9921    2.2433    0.2250    0.8174    1.0964    0.2177    8.8612
+#> 299:    92.6101   -2.5567   -3.9919    2.2425    0.2254    0.8169    1.0960    0.2178    8.8626
+#> 300:    92.6097   -2.5562   -3.9913    2.2415    0.2257    0.8163    1.0974    0.2182    8.8632
+#> 301:    92.6102   -2.5556   -3.9913    2.2407    0.2255    0.8156    1.0972    0.2183    8.8600
+#> 302:    92.6102   -2.5551   -3.9916    2.2400    0.2252    0.8156    1.0966    0.2186    8.8586
+#> 303:    92.6099   -2.5546   -3.9915    2.2391    0.2250    0.8152    1.0978    0.2189    8.8589
+#> 304:    92.6096   -2.5541   -3.9913    2.2387    0.2242    0.8149    1.0987    0.2194    8.8570
+#> 305:    92.6100   -2.5538   -3.9914    2.2383    0.2247    0.8144    1.0995    0.2202    8.8553
+#> 306:    92.6109   -2.5533   -3.9915    2.2378    0.2255    0.8144    1.1001    0.2212    8.8531
+#> 307:    92.6119   -2.5529   -3.9913    2.2371    0.2252    0.8143    1.1007    0.2217    8.8498
+#> 308:    92.6128   -2.5525   -3.9912    2.2366    0.2249    0.8142    1.1012    0.2219    8.8490
+#> 309:    92.6143   -2.5519   -3.9905    2.2357    0.2251    0.8138    1.1018    0.2224    8.8449
+#> 310:    92.6160   -2.5513   -3.9900    2.2346    0.2255    0.8136    1.1020    0.2230    8.8403
+#> 311:    92.6177   -2.5506   -3.9891    2.2333    0.2258    0.8132    1.1031    0.2236    8.8392
+#> 312:    92.6190   -2.5499   -3.9881    2.2319    0.2267    0.8130    1.1047    0.2242    8.8382
+#> 313:    92.6192   -2.5493   -3.9872    2.2305    0.2273    0.8127    1.1057    0.2249    8.8350
+#> 314:    92.6196   -2.5490   -3.9864    2.2300    0.2279    0.8129    1.1067    0.2257    8.8315
+#> 315:    92.6197   -2.5488   -3.9858    2.2295    0.2277    0.8132    1.1072    0.2266    8.8285
+#> 316:    92.6192   -2.5485   -3.9850    2.2284    0.2276    0.8133    1.1087    0.2275    8.8278
+#> 317:    92.6190   -2.5482   -3.9840    2.2275    0.2278    0.8135    1.1105    0.2282    8.8296
+#> 318:    92.6193   -2.5480   -3.9833    2.2266    0.2274    0.8133    1.1120    0.2289    8.8313
+#> 319:    92.6200   -2.5476   -3.9827    2.2257    0.2265    0.8129    1.1133    0.2297    8.8326
+#> 320:    92.6211   -2.5472   -3.9820    2.2250    0.2260    0.8124    1.1150    0.2302    8.8359
+#> 321:    92.6226   -2.5468   -3.9816    2.2246    0.2254    0.8118    1.1158    0.2308    8.8396
+#> 322:    92.6238   -2.5464   -3.9808    2.2238    0.2249    0.8114    1.1169    0.2316    8.8424
+#> 323:    92.6248   -2.5461   -3.9805    2.2231    0.2241    0.8109    1.1173    0.2320    8.8458
+#> 324:    92.6252   -2.5458   -3.9801    2.2224    0.2233    0.8103    1.1182    0.2324    8.8474
+#> 325:    92.6248   -2.5455   -3.9799    2.2216    0.2225    0.8096    1.1192    0.2328    8.8507
+#> 326:    92.6247   -2.5451   -3.9802    2.2209    0.2216    0.8091    1.1186    0.2331    8.8519
+#> 327:    92.6248   -2.5446   -3.9806    2.2203    0.2203    0.8088    1.1179    0.2335    8.8535
+#> 328:    92.6242   -2.5442   -3.9808    2.2198    0.2196    0.8084    1.1175    0.2339    8.8533
+#> 329:    92.6234   -2.5437   -3.9809    2.2192    0.2188    0.8081    1.1176    0.2342    8.8550
+#> 330:    92.6229   -2.5433   -3.9806    2.2187    0.2182    0.8078    1.1187    0.2346    8.8574
+#> 331:    92.6220   -2.5429   -3.9801    2.2181    0.2183    0.8075    1.1210    0.2352    8.8599
+#> 332:    92.6214   -2.5425   -3.9796    2.2175    0.2185    0.8072    1.1235    0.2360    8.8612
+#> 333:    92.6215   -2.5421   -3.9794    2.2170    0.2184    0.8068    1.1248    0.2365    8.8660
+#> 334:    92.6218   -2.5417   -3.9795    2.2168    0.2180    0.8065    1.1257    0.2369    8.8675
+#> 335:    92.6220   -2.5413   -3.9793    2.2164    0.2177    0.8062    1.1269    0.2374    8.8683
+#> 336:    92.6228   -2.5410   -3.9792    2.2159    0.2173    0.8059    1.1275    0.2378    8.8707
+#> 337:    92.6244   -2.5405   -3.9792    2.2153    0.2175    0.8057    1.1278    0.2387    8.8734
+#> 338:    92.6266   -2.5401   -3.9792    2.2146    0.2184    0.8057    1.1283    0.2396    8.8757
+#> 339:    92.6290   -2.5398   -3.9790    2.2144    0.2191    0.8060    1.1294    0.2403    8.8770
+#> 340:    92.6309   -2.5396   -3.9790    2.2142    0.2200    0.8061    1.1295    0.2405    8.8766
+#> 341:    92.6328   -2.5394   -3.9788    2.2142    0.2211    0.8061    1.1300    0.2406    8.8752
+#> 342:    92.6347   -2.5392   -3.9788    2.2140    0.2223    0.8062    1.1291    0.2405    8.8744
+#> 343:    92.6365   -2.5390   -3.9787    2.2139    0.2233    0.8063    1.1288    0.2405    8.8732
+#> 344:    92.6383   -2.5388   -3.9785    2.2136    0.2242    0.8060    1.1295    0.2404    8.8730
+#> 345:    92.6392   -2.5386   -3.9781    2.2133    0.2248    0.8055    1.1303    0.2401    8.8737
+#> 346:    92.6401   -2.5384   -3.9780    2.2129    0.2249    0.8051    1.1302    0.2399    8.8739
+#> 347:    92.6411   -2.5381   -3.9777    2.2124    0.2248    0.8049    1.1302    0.2399    8.8794
+#> 348:    92.6427   -2.5380   -3.9777    2.2122    0.2251    0.8047    1.1306    0.2398    8.8814
+#> 349:    92.6444   -2.5378   -3.9777    2.2119    0.2252    0.8047    1.1304    0.2397    8.8834
+#> 350:    92.6462   -2.5376   -3.9776    2.2115    0.2260    0.8043    1.1300    0.2395    8.8859
+#> 351:    92.6470   -2.5375   -3.9772    2.2110    0.2265    0.8041    1.1303    0.2392    8.8883
+#> 352:    92.6478   -2.5373   -3.9772    2.2106    0.2266    0.8037    1.1293    0.2386    8.8926
+#> 353:    92.6493   -2.5373   -3.9772    2.2103    0.2268    0.8032    1.1285    0.2381    8.8928
+#> 354:    92.6504   -2.5372   -3.9772    2.2100    0.2264    0.8028    1.1274    0.2376    8.8946
+#> 355:    92.6512   -2.5370   -3.9771    2.2096    0.2267    0.8023    1.1273    0.2373    8.8951
+#> 356:    92.6522   -2.5367   -3.9767    2.2089    0.2275    0.8018    1.1272    0.2370    8.8947
+#> 357:    92.6534   -2.5364   -3.9765    2.2080    0.2290    0.8015    1.1268    0.2369    8.8932
+#> 358:    92.6545   -2.5362   -3.9761    2.2072    0.2301    0.8011    1.1270    0.2368    8.8919
+#> 359:    92.6566   -2.5360   -3.9757    2.2064    0.2310    0.8008    1.1269    0.2369    8.8928
+#> 360:    92.6584   -2.5358   -3.9751    2.2059    0.2311    0.8005    1.1272    0.2368    8.8924
+#> 361:    92.6611   -2.5356   -3.9744    2.2051    0.2317    0.8004    1.1280    0.2369    8.8932
+#> 362:    92.6639   -2.5353   -3.9740    2.2043    0.2321    0.8003    1.1284    0.2370    8.8914
+#> 363:    92.6662   -2.5349   -3.9733    2.2033    0.2328    0.8001    1.1289    0.2371    8.8902
+#> 364:    92.6679   -2.5345   -3.9729    2.2025    0.2325    0.7998    1.1292    0.2372    8.8883
+#> 365:    92.6695   -2.5341   -3.9725    2.2019    0.2321    0.7994    1.1297    0.2373    8.8865
+#> 366:    92.6709   -2.5337   -3.9722    2.2011    0.2321    0.7990    1.1297    0.2373    8.8860
+#> 367:    92.6724   -2.5334   -3.9720    2.2005    0.2317    0.7987    1.1295    0.2372    8.8848
+#> 368:    92.6736   -2.5330   -3.9719    2.1999    0.2314    0.7985    1.1288    0.2371    8.8844
+#> 369:    92.6745   -2.5326   -3.9717    2.1994    0.2310    0.7982    1.1283    0.2371    8.8840
+#> 370:    92.6758   -2.5323   -3.9714    2.1990    0.2312    0.7980    1.1283    0.2370    8.8854
+#> 371:    92.6776   -2.5321   -3.9708    2.1984    0.2313    0.7977    1.1286    0.2369    8.8852
+#> 372:    92.6791   -2.5317   -3.9704    2.1978    0.2311    0.7973    1.1282    0.2367    8.8865
+#> 373:    92.6804   -2.5312   -3.9701    2.1969    0.2308    0.7969    1.1280    0.2366    8.8884
+#> 374:    92.6814   -2.5308   -3.9699    2.1962    0.2305    0.7965    1.1279    0.2364    8.8898
+#> 375:    92.6827   -2.5304   -3.9698    2.1954    0.2305    0.7961    1.1271    0.2360    8.8938
+#> 376:    92.6832   -2.5301   -3.9695    2.1947    0.2301    0.7957    1.1268    0.2359    8.8930
+#> 377:    92.6835   -2.5298   -3.9692    2.1941    0.2300    0.7953    1.1269    0.2357    8.8933
+#> 378:    92.6831   -2.5295   -3.9693    2.1935    0.2303    0.7950    1.1266    0.2357    8.8990
+#> 379:    92.6827   -2.5293   -3.9694    2.1933    0.2307    0.7948    1.1265    0.2356    8.9027
+#> 380:    92.6826   -2.5291   -3.9695    2.1931    0.2307    0.7947    1.1262    0.2356    8.9045
+#> 381:    92.6822   -2.5290   -3.9695    2.1929    0.2307    0.7945    1.1259    0.2355    8.9040
+#> 382:    92.6817   -2.5289   -3.9694    2.1925    0.2305    0.7943    1.1258    0.2357    8.9033
+#> 383:    92.6812   -2.5288   -3.9695    2.1922    0.2305    0.7942    1.1255    0.2358    8.9045
+#> 384:    92.6810   -2.5288   -3.9695    2.1920    0.2302    0.7940    1.1253    0.2360    8.9058
+#> 385:    92.6806   -2.5287   -3.9694    2.1918    0.2301    0.7938    1.1254    0.2361    8.9052
+#> 386:    92.6801   -2.5286   -3.9692    2.1914    0.2298    0.7936    1.1256    0.2362    8.9039
+#> 387:    92.6800   -2.5285   -3.9687    2.1914    0.2294    0.7934    1.1261    0.2361    8.9032
+#> 388:    92.6801   -2.5284   -3.9683    2.1913    0.2293    0.7931    1.1267    0.2360    8.9027
+#> 389:    92.6802   -2.5283   -3.9684    2.1912    0.2288    0.7928    1.1261    0.2360    8.9028
+#> 390:    92.6805   -2.5281   -3.9684    2.1910    0.2283    0.7925    1.1258    0.2360    8.9044
+#> 391:    92.6806   -2.5280   -3.9685    2.1908    0.2285    0.7921    1.1254    0.2360    8.9047
+#> 392:    92.6810   -2.5278   -3.9682    2.1907    0.2288    0.7918    1.1257    0.2360    8.9057
+#> 393:    92.6810   -2.5275   -3.9681    2.1906    0.2290    0.7916    1.1257    0.2360    8.9049
+#> 394:    92.6811   -2.5272   -3.9682    2.1904    0.2292    0.7913    1.1253    0.2360    8.9056
+#> 395:    92.6812   -2.5269   -3.9682    2.1900    0.2295    0.7911    1.1251    0.2362    8.9044
+#> 396:    92.6817   -2.5269   -3.9683    2.1901    0.2292    0.7911    1.1251    0.2361    8.9031
+#> 397:    92.6824   -2.5269   -3.9686    2.1903    0.2292    0.7911    1.1250    0.2361    8.9043
+#> 398:    92.6828   -2.5270   -3.9688    2.1907    0.2291    0.7913    1.1248    0.2359    8.9035
+#> 399:    92.6829   -2.5271   -3.9689    2.1909    0.2292    0.7916    1.1248    0.2358    8.9043
+#> 400:    92.6829   -2.5273   -3.9688    2.1909    0.2295    0.7919    1.1250    0.2356    8.9037
+#> 401:    92.6827   -2.5274   -3.9687    2.1910    0.2299    0.7922    1.1249    0.2356    8.9035
+#> 402:    92.6822   -2.5276   -3.9687    2.1911    0.2303    0.7926    1.1248    0.2355    8.9033
+#> 403:    92.6821   -2.5277   -3.9686    2.1913    0.2307    0.7929    1.1250    0.2354    8.9009
+#> 404:    92.6817   -2.5279   -3.9684    2.1914    0.2314    0.7930    1.1249    0.2352    8.9012
+#> 405:    92.6813   -2.5281   -3.9683    2.1915    0.2318    0.7930    1.1252    0.2349    8.9015
+#> 406:    92.6811   -2.5283   -3.9680    2.1916    0.2321    0.7930    1.1255    0.2345    8.9019
+#> 407:    92.6817   -2.5285   -3.9677    2.1918    0.2329    0.7930    1.1255    0.2343    8.9014
+#> 408:    92.6824   -2.5287   -3.9675    2.1919    0.2330    0.7930    1.1253    0.2341    8.9019
+#> 409:    92.6833   -2.5289   -3.9674    2.1922    0.2331    0.7931    1.1249    0.2338    8.9031
+#> 410:    92.6840   -2.5291   -3.9673    2.1924    0.2331    0.7930    1.1245    0.2335    8.9054
+#> 411:    92.6848   -2.5292   -3.9672    2.1926    0.2333    0.7929    1.1243    0.2333    8.9083
+#> 412:    92.6852   -2.5293   -3.9671    2.1928    0.2333    0.7931    1.1243    0.2333    8.9107
+#> 413:    92.6858   -2.5293   -3.9671    2.1929    0.2332    0.7932    1.1246    0.2332    8.9119
+#> 414:    92.6863   -2.5293   -3.9671    2.1928    0.2332    0.7934    1.1252    0.2333    8.9112
+#> 415:    92.6868   -2.5293   -3.9671    2.1928    0.2330    0.7935    1.1253    0.2332    8.9109
+#> 416:    92.6872   -2.5293   -3.9672    2.1928    0.2327    0.7935    1.1247    0.2330    8.9101
+#> 417:    92.6875   -2.5293   -3.9674    2.1929    0.2324    0.7934    1.1241    0.2330    8.9126
+#> 418:    92.6881   -2.5294   -3.9675    2.1929    0.2322    0.7935    1.1238    0.2332    8.9148
+#> 419:    92.6885   -2.5295   -3.9674    2.1929    0.2322    0.7936    1.1236    0.2331    8.9179
+#> 420:    92.6890   -2.5297   -3.9674    2.1929    0.2322    0.7936    1.1235    0.2331    8.9194
+#> 421:    92.6891   -2.5299   -3.9672    2.1930    0.2318    0.7937    1.1236    0.2330    8.9192
+#> 422:    92.6894   -2.5301   -3.9670    2.1930    0.2318    0.7937    1.1239    0.2329    8.9183
+#> 423:    92.6898   -2.5302   -3.9667    2.1931    0.2318    0.7937    1.1242    0.2327    8.9190
+#> 424:    92.6905   -2.5304   -3.9667    2.1931    0.2316    0.7937    1.1243    0.2326    8.9190
+#> 425:    92.6910   -2.5305   -3.9667    2.1932    0.2316    0.7936    1.1240    0.2327    8.9203
+#> 426:    92.6917   -2.5306   -3.9668    2.1935    0.2318    0.7937    1.1237    0.2326    8.9200
+#> 427:    92.6918   -2.5308   -3.9671    2.1939    0.2322    0.7938    1.1227    0.2326    8.9224
+#> 428:    92.6912   -2.5310   -3.9670    2.1941    0.2319    0.7939    1.1225    0.2325    8.9268
+#> 429:    92.6912   -2.5312   -3.9670    2.1944    0.2316    0.7939    1.1225    0.2324    8.9301
+#> 430:    92.6910   -2.5314   -3.9674    2.1948    0.2314    0.7940    1.1217    0.2322    8.9314
+#> 431:    92.6911   -2.5315   -3.9675    2.1950    0.2314    0.7942    1.1210    0.2320    8.9320
+#> 432:    92.6911   -2.5316   -3.9677    2.1953    0.2312    0.7944    1.1204    0.2320    8.9327
+#> 433:    92.6910   -2.5317   -3.9681    2.1955    0.2309    0.7946    1.1196    0.2320    8.9336
+#> 434:    92.6910   -2.5318   -3.9683    2.1957    0.2306    0.7949    1.1188    0.2320    8.9337
+#> 435:    92.6912   -2.5319   -3.9687    2.1960    0.2302    0.7951    1.1178    0.2319    8.9343
+#> 436:    92.6914   -2.5320   -3.9688    2.1961    0.2300    0.7953    1.1173    0.2319    8.9345
+#> 437:    92.6919   -2.5321   -3.9688    2.1962    0.2299    0.7955    1.1168    0.2318    8.9335
+#> 438:    92.6920   -2.5323   -3.9688    2.1964    0.2296    0.7957    1.1164    0.2318    8.9334
+#> 439:    92.6917   -2.5324   -3.9689    2.1965    0.2292    0.7959    1.1165    0.2317    8.9322
+#> 440:    92.6910   -2.5326   -3.9688    2.1969    0.2289    0.7960    1.1170    0.2316    8.9319
+#> 441:    92.6907   -2.5328   -3.9688    2.1973    0.2283    0.7961    1.1175    0.2316    8.9317
+#> 442:    92.6909   -2.5330   -3.9689    2.1976    0.2280    0.7962    1.1174    0.2315    8.9326
+#> 443:    92.6911   -2.5332   -3.9689    2.1980    0.2277    0.7963    1.1180    0.2315    8.9338
+#> 444:    92.6906   -2.5332   -3.9690    2.1981    0.2275    0.7963    1.1181    0.2315    8.9354
+#> 445:    92.6897   -2.5333   -3.9691    2.1982    0.2276    0.7962    1.1181    0.2315    8.9364
+#> 446:    92.6896   -2.5333   -3.9692    2.1982    0.2272    0.7962    1.1176    0.2314    8.9363
+#> 447:    92.6893   -2.5334   -3.9693    2.1982    0.2272    0.7961    1.1173    0.2313    8.9365
+#> 448:    92.6890   -2.5334   -3.9693    2.1982    0.2271    0.7961    1.1173    0.2313    8.9364
+#> 449:    92.6888   -2.5335   -3.9693    2.1982    0.2267    0.7961    1.1170    0.2313    8.9372
+#> 450:    92.6884   -2.5335   -3.9693    2.1982    0.2262    0.7959    1.1166    0.2312    8.9364
+#> 451:    92.6885   -2.5335   -3.9691    2.1981    0.2261    0.7958    1.1167    0.2312    8.9350
+#> 452:    92.6887   -2.5335   -3.9691    2.1980    0.2260    0.7957    1.1164    0.2311    8.9349
+#> 453:    92.6888   -2.5335   -3.9691    2.1979    0.2258    0.7957    1.1163    0.2310    8.9375
+#> 454:    92.6890   -2.5335   -3.9689    2.1977    0.2258    0.7957    1.1160    0.2308    8.9385
+#> 455:    92.6894   -2.5334   -3.9687    2.1975    0.2259    0.7956    1.1158    0.2307    8.9382
+#> 456:    92.6898   -2.5334   -3.9685    2.1974    0.2261    0.7957    1.1154    0.2306    8.9380
+#> 457:    92.6904   -2.5334   -3.9685    2.1975    0.2265    0.7956    1.1146    0.2304    8.9391
+#> 458:    92.6908   -2.5334   -3.9687    2.1975    0.2266    0.7956    1.1137    0.2303    8.9418
+#> 459:    92.6911   -2.5335   -3.9689    2.1975    0.2270    0.7956    1.1129    0.2303    8.9442
+#> 460:    92.6912   -2.5335   -3.9687    2.1976    0.2274    0.7957    1.1126    0.2301    8.9461
+#> 461:    92.6913   -2.5336   -3.9687    2.1975    0.2276    0.7958    1.1125    0.2300    8.9463
+#> 462:    92.6914   -2.5336   -3.9686    2.1974    0.2280    0.7959    1.1126    0.2299    8.9456
+#> 463:    92.6917   -2.5336   -3.9684    2.1973    0.2280    0.7960    1.1127    0.2297    8.9452
+#> 464:    92.6918   -2.5336   -3.9683    2.1970    0.2280    0.7961    1.1127    0.2295    8.9444
+#> 465:    92.6921   -2.5336   -3.9682    2.1967    0.2277    0.7962    1.1127    0.2294    8.9447
+#> 466:    92.6924   -2.5336   -3.9679    2.1967    0.2275    0.7964    1.1127    0.2291    8.9454
+#> 467:    92.6930   -2.5336   -3.9677    2.1966    0.2273    0.7967    1.1128    0.2290    8.9453
+#> 468:    92.6935   -2.5337   -3.9675    2.1966    0.2275    0.7970    1.1128    0.2289    8.9458
+#> 469:    92.6937   -2.5338   -3.9676    2.1967    0.2278    0.7972    1.1123    0.2287    8.9455
+#> 470:    92.6938   -2.5338   -3.9677    2.1967    0.2283    0.7974    1.1122    0.2285    8.9451
+#> 471:    92.6940   -2.5339   -3.9676    2.1969    0.2290    0.7976    1.1124    0.2283    8.9448
+#> 472:    92.6938   -2.5339   -3.9676    2.1972    0.2293    0.7977    1.1125    0.2281    8.9460
+#> 473:    92.6937   -2.5340   -3.9676    2.1972    0.2298    0.7978    1.1121    0.2278    8.9461
+#> 474:    92.6934   -2.5341   -3.9677    2.1974    0.2308    0.7978    1.1118    0.2276    8.9470
+#> 475:    92.6936   -2.5342   -3.9677    2.1978    0.2316    0.7979    1.1113    0.2273    8.9486
+#> 476:    92.6940   -2.5345   -3.9679    2.1983    0.2324    0.7981    1.1106    0.2271    8.9491
+#> 477:    92.6945   -2.5347   -3.9681    2.1989    0.2332    0.7983    1.1099    0.2269    8.9502
+#> 478:    92.6951   -2.5349   -3.9682    2.1992    0.2344    0.7986    1.1093    0.2267    8.9502
+#> 479:    92.6958   -2.5352   -3.9683    2.1995    0.2357    0.7987    1.1088    0.2266    8.9521
+#> 480:    92.6967   -2.5354   -3.9684    2.1998    0.2370    0.7988    1.1083    0.2265    8.9524
+#> 481:    92.6977   -2.5355   -3.9685    2.2001    0.2383    0.7990    1.1079    0.2263    8.9521
+#> 482:    92.6985   -2.5357   -3.9687    2.2004    0.2395    0.7992    1.1073    0.2262    8.9518
+#> 483:    92.6992   -2.5359   -3.9690    2.2008    0.2403    0.7995    1.1066    0.2262    8.9524
+#> 484:    92.7000   -2.5361   -3.9691    2.2010    0.2406    0.7998    1.1061    0.2260    8.9516
+#> 485:    92.7009   -2.5362   -3.9693    2.2015    0.2410    0.8001    1.1057    0.2261    8.9508
+#> 486:    92.7010   -2.5363   -3.9695    2.2019    0.2412    0.8004    1.1051    0.2261    8.9502
+#> 487:    92.7008   -2.5365   -3.9698    2.2023    0.2413    0.8009    1.1048    0.2260    8.9502
+#> 488:    92.7006   -2.5366   -3.9700    2.2026    0.2411    0.8012    1.1044    0.2260    8.9501
+#> 489:    92.7006   -2.5367   -3.9701    2.2029    0.2410    0.8015    1.1041    0.2261    8.9504
+#> 490:    92.7006   -2.5368   -3.9702    2.2031    0.2407    0.8015    1.1043    0.2260    8.9498
+#> 491:    92.7007   -2.5369   -3.9701    2.2034    0.2405    0.8016    1.1047    0.2261    8.9484
+#> 492:    92.7008   -2.5370   -3.9702    2.2035    0.2406    0.8017    1.1046    0.2261    8.9473
+#> 493:    92.7010   -2.5370   -3.9704    2.2037    0.2406    0.8018    1.1044    0.2261    8.9469
+#> 494:    92.7015   -2.5371   -3.9707    2.2038    0.2408    0.8019    1.1040    0.2261    8.9453
+#> 495:    92.7017   -2.5371   -3.9708    2.2039    0.2407    0.8021    1.1042    0.2262    8.9447
+#> 496:    92.7016   -2.5371   -3.9708    2.2039    0.2407    0.8022    1.1042    0.2262    8.9433
+#> 497:    92.7015   -2.5371   -3.9709    2.2039    0.2408    0.8023    1.1044    0.2262    8.9431
+#> 498:    92.7013   -2.5371   -3.9709    2.2040    0.2409    0.8024    1.1047    0.2262    8.9452
+#> 499:    92.7011   -2.5371   -3.9710    2.2039    0.2409    0.8023    1.1049    0.2261    8.9481
+#> 500:    92.7010   -2.5371   -3.9712    2.2040    0.2412    0.8022    1.1049    0.2260    8.9498
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |    log_k1 |    log_k2 |    log_tb |
+#> |.....................|     sigma |        o1 |        o2 |        o3 |
+#> |.....................|        o4 |...........|...........|...........|
+#> |    1|     360.27275 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     360.27275 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     360.27275 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    G|    Gill Diff. |     106.2 |    0.7918 |   0.06750 |     10.50 |
+#> |.....................|    -26.04 |     2.358 |    -5.196 |    -2.491 |
+#> |.....................|    -12.13 |...........|...........|...........|
+#> |    2|     7055.7467 |   0.04059 |   -0.9733 |    -1.001 |   -0.9739 |
+#> |.....................|   -0.6317 |   -0.9263 |   -0.8528 |   -0.8784 |
+#> |.....................|   -0.7843 |...........|...........|...........|
+#> |    U|     7055.7467 |     3.818 |    -2.236 |    -3.887 |     1.944 |
+#> |.....................|     2.941 |    0.7466 |     1.074 |    0.9892 |
+#> |.....................|     1.458 |...........|...........|...........|
+#> |    X|     7055.7467 |     3.818 |    0.1069 |   0.02050 |     6.988 |
+#> |.....................|     2.941 |    0.7466 |     1.074 |    0.9892 |
+#> |.....................|     1.458 |...........|...........|...........|
+#> |    3|     499.76989 |    0.9041 |   -0.9669 |    -1.000 |   -0.8885 |
+#> |.....................|   -0.8434 |   -0.9072 |   -0.8950 |   -0.8986 |
+#> |.....................|   -0.8828 |...........|...........|...........|
+#> |    U|     499.76989 |     85.03 |    -2.229 |    -3.887 |     2.030 |
+#> |.....................|     2.663 |    0.7612 |     1.031 |    0.9696 |
+#> |.....................|     1.329 |...........|...........|...........|
+#> |    X|     499.76989 |     85.03 |    0.1076 |   0.02051 |     7.611 |
+#> |.....................|     2.663 |    0.7612 |     1.031 |    0.9696 |
+#> |.....................|     1.329 |...........|...........|...........|
+#> |    4|     360.48011 |    0.9904 |   -0.9662 |    -1.000 |   -0.8799 |
+#> |.....................|   -0.8645 |   -0.9053 |   -0.8992 |   -0.9007 |
+#> |.....................|   -0.8927 |...........|...........|...........|
+#> |    U|     360.48011 |     93.15 |    -2.229 |    -3.887 |     2.038 |
+#> |.....................|     2.635 |    0.7627 |     1.026 |    0.9677 |
+#> |.....................|     1.316 |...........|...........|...........|
+#> |    X|     360.48011 |     93.15 |    0.1077 |   0.02051 |     7.676 |
+#> |.....................|     2.635 |    0.7627 |     1.026 |    0.9677 |
+#> |.....................|     1.316 |...........|...........|...........|
+#> |    5|     360.80998 |    0.9960 |   -0.9662 |    -1.000 |   -0.8794 |
+#> |.....................|   -0.8659 |   -0.9051 |   -0.8995 |   -0.9008 |
+#> |.....................|   -0.8933 |...........|...........|...........|
+#> |    U|     360.80998 |     93.68 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.633 |    0.7628 |     1.026 |    0.9676 |
+#> |.....................|     1.315 |...........|...........|...........|
+#> |    X|     360.80998 |     93.68 |    0.1077 |   0.02051 |     7.680 |
+#> |.....................|     2.633 |    0.7628 |     1.026 |    0.9676 |
+#> |.....................|     1.315 |...........|...........|...........|
+#> |    6|     361.20154 |    0.9991 |   -0.9661 |    -1.000 |   -0.8791 |
+#> |.....................|   -0.8667 |   -0.9051 |   -0.8996 |   -0.9009 |
+#> |.....................|   -0.8937 |...........|...........|...........|
+#> |    U|     361.20154 |     93.97 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.315 |...........|...........|...........|
+#> |    X|     361.20154 |     93.97 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.315 |...........|...........|...........|
+#> |    7|     361.33469 |    0.9999 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.33469 |     94.05 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.33469 |     94.05 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    8|     361.34878 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.34878 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.34878 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    9|     361.35091 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35091 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35091 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   10|     361.35004 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35004 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35004 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   11|     361.35006 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35006 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35006 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   12|     361.35006 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35006 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35006 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   13|     361.35006 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35006 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35006 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   14|     361.35006 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35006 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35006 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   15|     361.35006 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35006 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35006 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   16|     361.35006 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35006 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35006 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   17|     361.35006 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35006 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35006 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |   18|     361.35006 |     1.000 |   -0.9661 |    -1.000 |   -0.8790 |
+#> |.....................|   -0.8669 |   -0.9051 |   -0.8997 |   -0.9009 |
+#> |.....................|   -0.8938 |...........|...........|...........|
+#> |    U|     361.35006 |     94.06 |    -2.229 |    -3.887 |     2.039 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> |    X|     361.35006 |     94.06 |    0.1077 |   0.02051 |     7.683 |
+#> |.....................|     2.632 |    0.7629 |     1.026 |    0.9675 |
+#> |.....................|     1.314 |...........|...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                
+f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> 1:    92.2167    0.0936    1.9256    3.3974    0.7958    0.7197   11.8539    0.0004
+#> 2:    92.5446    0.0892    2.4952    3.3516    0.8528    0.6837    4.5197    0.0001
+#> 3:    9.2720e+01   1.3849e-01   2.5917e+00   3.9204e+00   9.5883e-01   6.4953e-01   4.0268e+00   5.9554e-05
+#> 4:    92.6098    0.1052    2.5494    5.0533    1.0968    0.6171    3.2396    0.0200
+#> 5:    92.6795    0.0406    2.4151    5.6729    1.0420    0.5862    3.1558    0.0183
+#> 6:    92.6580    0.0258    2.3640    5.7014    0.9899    0.5569    3.0212    0.0140
+#> 7:    93.0532   -0.0754    2.2262    7.3582    0.9404    0.5291    2.5591    0.0180
+#> 8:    92.8372   -0.0760    2.2080    6.9903    0.8934    0.5026    2.5653    0.0187
+#> 9:    93.0757   -0.1322    2.1663    6.6408    0.8487    0.4775    2.4943    0.0182
+#> 10:    93.0704   -0.1520    2.1410    6.3087    0.8063    0.4536    2.4004    0.0225
+#> 11:    93.1611   -0.1366    2.1740    5.9933    0.7659    0.4309    2.4242    0.0199
+#> 12:    92.7195   -0.0787    2.2947    5.6936    0.7277    0.4094    2.4532    0.0205
+#> 13:    92.6573   -0.1543    2.1929    5.4089    0.6913    0.3889    2.3750    0.0244
+#> 14:    93.1138   -0.1547    2.1924    5.1385    0.6567    0.3695    2.3590    0.0187
+#> 15:    93.5083   -0.1625    2.1831    4.8816    0.6239    0.3510    2.4420    0.0125
+#> 16:    93.2086   -0.1667    2.1516    4.6375    0.5927    0.3334    2.4527    0.0004
+#> 17:    93.3988   -0.1766    2.1521    4.4056    0.5630    0.3168    2.4527    0.0004
+#> 18:    93.4526   -0.1748    2.1461    4.1853    0.5349    0.3009    2.3775    0.0116
+#> 19:    93.5953   -0.1963    2.1167    3.9761    0.5081    0.2859    2.4693    0.0031
+#> 20:    9.3404e+01  -2.4408e-01   2.0453e+00   3.7773e+00   4.8274e-01   2.7158e-01   2.4789e+00   2.0760e-05
+#> 21:    9.3624e+01  -2.4691e-01   2.0524e+00   3.5884e+00   4.5860e-01   2.5800e-01   2.4789e+00   2.0760e-05
+#> 22:    9.3821e+01  -2.5932e-01   2.0021e+00   3.4090e+00   4.3567e-01   2.8670e-01   2.4182e+00   9.3297e-06
+#> 23:    9.3572e+01  -2.3703e-01   2.0725e+00   3.2385e+00   4.4889e-01   2.7237e-01   2.4525e+00   1.5592e-06
+#> 24:    9.3496e+01  -2.2704e-01   2.0746e+00   3.0766e+00   4.3674e-01   2.5875e-01   2.4569e+00   6.1365e-05
+#> 25:    9.3772e+01  -2.2211e-01   2.0762e+00   2.9228e+00   4.4843e-01   2.6194e-01   2.4015e+00   8.3288e-05
+#> 26:    9.3266e+01  -1.9408e-01   2.1345e+00   2.7766e+00   4.6952e-01   2.4885e-01   2.3827e+00   2.9029e-05
+#> 27:    9.3472e+01  -1.9793e-01   2.1141e+00   3.5922e+00   4.7687e-01   2.3640e-01   2.3827e+00   2.9029e-05
+#> 28:    9.3411e+01  -1.7721e-01   2.1334e+00   3.4125e+00   4.8209e-01   2.2458e-01   2.3864e+00   6.5503e-06
+#> 29:    93.6868   -0.1863    2.1258    4.5379    0.4744    0.2134    2.3001    0.0045
+#> 30:    9.4054e+01  -1.8122e-01   2.1287e+00   4.9729e+00   4.7945e-01   2.0269e-01   2.2979e+00   5.8327e-05
+#> 31:    9.3955e+01  -1.9131e-01   2.1202e+00   5.6375e+00   4.7965e-01   1.9255e-01   2.2671e+00   1.6931e-05
+#> 32:    9.4376e+01  -1.6810e-01   2.1287e+00   5.3556e+00   4.7567e-01   1.8972e-01   2.2483e+00   1.1778e-05
+#> 33:    9.4067e+01  -1.5819e-01   2.1656e+00   5.0878e+00   4.5710e-01   1.9389e-01   2.2696e+00   2.4282e-05
+#> 34:    9.4526e+01  -1.6367e-01   2.1473e+00   4.8334e+00   4.7085e-01   1.8419e-01   2.2919e+00   8.8644e-06
+#> 35:    9.4972e+01  -1.6784e-01   2.1353e+00   4.5917e+00   4.7510e-01   1.7498e-01   2.3129e+00   2.2851e-05
+#> 36:    9.4744e+01  -1.5973e-01   2.1281e+00   5.2356e+00   4.5695e-01   1.8499e-01   2.2896e+00   6.8824e-05
+#> 37:    9.4721e+01  -1.6756e-01   2.1168e+00   5.1111e+00   4.6804e-01   1.8407e-01   2.3035e+00   1.5534e-06
+#> 38:    9.4613e+01  -1.5952e-01   2.1385e+00   4.8555e+00   4.6107e-01   1.7720e-01   2.2650e+00   1.2489e-05
+#> 39:    9.4787e+01  -1.6113e-01   2.1458e+00   4.6128e+00   4.6317e-01   1.8378e-01   2.2831e+00   1.3668e-05
+#> 40:    94.5315   -0.1765    2.1186    4.3821    0.4428    0.1902    2.3132    0.0001
+#> 41:    9.4336e+01  -1.8333e-01   2.1285e+00   4.1630e+00   4.4521e-01   1.9913e-01   2.3092e+00   1.3482e-05
+#> 42:    94.0780   -0.2031    2.0724    3.9549    0.4405    0.1892    2.2704    0.0056
+#> 43:    93.9276   -0.1896    2.1191    3.7571    0.4590    0.1797    2.2396    0.0080
+#> 44:    94.2491   -0.1896    2.1006    3.8473    0.4590    0.1764    2.2774    0.0083
+#> 45:    94.4073   -0.1811    2.1156    3.6550    0.4519    0.1676    2.2682    0.0078
+#> 46:    93.9736   -0.1882    2.1196    3.4722    0.4545    0.1633    2.2775    0.0035
+#> 47:    94.1930   -0.1965    2.1102    3.2986    0.4599    0.1664    2.3243    0.0005
+#> 48:    9.4147e+01  -1.9494e-01   2.1118e+00   3.3188e+00   4.7005e-01   1.7181e-01   2.3345e+00   1.1669e-05
+#> 49:    9.4139e+01  -1.7920e-01   2.1199e+00   3.1528e+00   4.7417e-01   1.6322e-01   2.2794e+00   3.5582e-05
+#> 50:    9.4031e+01  -1.9074e-01   2.1098e+00   2.9952e+00   4.7498e-01   1.6695e-01   2.2574e+00   2.7302e-06
+#> 51:    9.3982e+01  -1.9369e-01   2.1058e+00   2.8848e+00   4.8158e-01   1.8408e-01   2.2447e+00   1.8188e-05
+#> 52:    9.3924e+01  -2.0726e-01   2.0809e+00   3.6064e+00   4.7008e-01   2.0029e-01   2.2319e+00   6.8301e-06
+#> 53:    9.4094e+01  -1.9609e-01   2.0780e+00   4.4341e+00   4.7556e-01   1.9742e-01   2.2701e+00   2.1343e-06
+#> 54:    9.4351e+01  -1.9839e-01   2.0746e+00   4.2124e+00   4.7456e-01   1.8866e-01   2.2778e+00   4.8058e-06
+#> 55:    93.9450   -0.1876    2.1059    4.0017    0.4892    0.1792    2.2720    0.0001
+#> 56:    9.3741e+01  -1.8208e-01   2.1172e+00   3.8017e+00   4.7696e-01   1.7027e-01   2.2332e+00   2.1237e-05
+#> 57:    9.3668e+01  -1.8580e-01   2.1181e+00   3.9224e+00   4.7704e-01   1.7425e-01   2.2512e+00   2.2766e-05
+#> 58:    9.3811e+01  -1.8324e-01   2.1178e+00   3.7263e+00   4.7945e-01   1.7939e-01   2.2512e+00   2.2766e-05
+#> 59:    9.3800e+01  -1.6691e-01   2.1250e+00   3.8213e+00   4.9353e-01   1.8464e-01   2.2763e+00   4.6129e-06
+#> 60:    9.3997e+01  -1.5920e-01   2.1489e+00   3.6303e+00   5.0788e-01   1.7541e-01   2.2466e+00   4.1975e-06
+#> 61:    9.4215e+01  -1.6445e-01   2.1482e+00   3.9303e+00   5.1966e-01   1.6664e-01   2.3053e+00   5.8982e-07
+#> 62:    9.3936e+01  -1.6721e-01   2.1376e+00   4.0316e+00   5.3719e-01   1.7228e-01   2.2841e+00   7.8603e-05
+#> 63:    9.3832e+01  -1.6209e-01   2.1334e+00   3.8698e+00   5.4370e-01   1.7064e-01   2.3046e+00   6.4415e-07
+#> 64:    93.9042   -0.1617    2.1563    5.5384    0.5430    0.1622    2.2988    0.0002
+#> 65:    93.8613   -0.1723    2.1239    6.2143    0.5304    0.1541    2.2949    0.0001
+#> 66:    9.4113e+01  -1.9168e-01   2.1019e+00   7.3588e+00   5.1287e-01   1.4641e-01   2.3164e+00   1.2580e-05
+#> 67:    9.3954e+01  -1.8141e-01   2.1199e+00   6.9909e+00   5.0278e-01   1.4993e-01   2.2676e+00   1.1126e-05
+#> 68:    93.8741   -0.1852    2.1343    6.6414    0.4997    0.1493    2.2706    0.0001
+#> 69:    9.3657e+01  -1.8345e-01   2.1375e+00   6.3093e+00   5.0292e-01   1.6326e-01   2.2809e+00   1.7299e-07
+#> 70:    9.3762e+01  -1.7493e-01   2.1512e+00   5.9938e+00   5.1042e-01   1.5509e-01   2.2837e+00   4.5745e-05
+#> 71:    9.4060e+01  -1.6516e-01   2.1440e+00   5.6941e+00   5.1615e-01   1.4734e-01   2.3001e+00   3.9993e-07
+#> 72:    9.3927e+01  -1.7365e-01   2.1347e+00   5.4094e+00   5.2582e-01   1.3997e-01   2.3075e+00   6.3748e-06
+#> 73:    9.4049e+01  -1.8080e-01   2.1254e+00   5.1390e+00   5.1154e-01   1.3297e-01   2.3042e+00   9.5858e-06
+#> 74:    9.3917e+01  -1.9083e-01   2.1051e+00   4.8820e+00   5.0104e-01   1.4605e-01   2.2733e+00   7.4923e-05
+#> 75:    9.4271e+01  -1.8281e-01   2.1059e+00   5.0872e+00   5.0773e-01   1.5322e-01   2.2387e+00   1.4240e-05
+#> 76:    9.4205e+01  -1.8352e-01   2.1160e+00   4.8328e+00   5.0684e-01   1.5669e-01   2.2708e+00   3.6346e-05
+#> 77:    9.4480e+01  -1.8352e-01   2.0942e+00   4.9009e+00   5.0684e-01   1.4885e-01   2.3098e+00   1.8186e-06
+#> 78:    9.4699e+01  -1.9686e-01   2.0671e+00   4.6559e+00   4.9182e-01   1.4503e-01   2.2806e+00   7.9443e-08
+#> 79:    9.4785e+01  -2.0649e-01   2.0500e+00   6.0608e+00   4.6723e-01   1.6185e-01   2.2607e+00   2.1557e-07
+#> 80:    9.4782e+01  -2.0045e-01   2.0680e+00   5.7578e+00   4.5759e-01   1.5747e-01   2.2926e+00   8.6381e-06
+#> 81:    9.4339e+01  -2.0435e-01   2.0885e+00   6.9051e+00   4.5054e-01   1.7410e-01   2.2796e+00   1.5517e-05
+#> 82:    9.4805e+01  -2.1032e-01   2.0658e+00   7.1580e+00   4.7091e-01   1.6539e-01   2.3013e+00   1.3893e-05
+#> 83:    9.4650e+01  -2.0507e-01   2.0485e+00   6.8001e+00   4.7624e-01   1.5938e-01   2.3104e+00   6.6569e-06
+#> 84:    9.4766e+01  -1.9959e-01   2.0667e+00   6.4601e+00   4.7322e-01   1.5619e-01   2.3359e+00   1.8890e-09
+#> 85:    9.4714e+01  -1.9959e-01   2.0756e+00   6.1371e+00   4.7322e-01   1.6894e-01   2.2738e+00   4.9578e-06
+#> 86:    9.4466e+01  -2.0544e-01   2.0626e+00   5.8302e+00   4.6340e-01   1.6050e-01   2.2773e+00   3.6221e-07
+#> 87:    9.4786e+01  -1.9292e-01   2.0703e+00   5.5387e+00   4.6881e-01   1.5641e-01   2.2746e+00   2.3326e-05
+#> 88:    9.4573e+01  -1.9488e-01   2.0597e+00   5.2618e+00   4.6538e-01   1.5079e-01   2.3225e+00   4.7054e-05
+#> 89:    94.8466   -0.2040    2.0603    4.9987    0.4620    0.1456    2.2807    0.0002
+#> 90:    9.4839e+01  -2.0359e-01   2.0673e+00   4.7488e+00   4.5379e-01   1.4729e-01   2.3099e+00   2.7922e-05
+#> 91:    9.4897e+01  -2.0635e-01   2.0496e+00   4.5113e+00   4.4018e-01   1.3993e-01   2.2924e+00   1.7074e-05
+#> 92:    9.4740e+01  -2.0567e-01   2.0518e+00   4.2858e+00   4.6190e-01   1.3293e-01   2.3396e+00   9.0471e-05
+#> 93:    94.9558   -0.2033    2.0824    4.0715    0.4877    0.1263    2.3785    0.0082
+#> 94:    95.1673   -0.1801    2.0900    3.8679    0.5150    0.1200    2.4128    0.0106
+#> 95:    95.3129   -0.1686    2.1057    3.6745    0.4892    0.1140    2.4135    0.0147
+#> 96:    9.5235e+01  -1.6834e-01   2.1069e+00   3.4908e+00   4.9584e-01   1.0827e-01   2.4408e+00   2.5829e-06
+#> 97:    9.4892e+01  -1.5911e-01   2.1277e+00   3.3162e+00   4.7518e-01   1.0286e-01   2.4658e+00   1.8589e-05
+#> 98:    9.4749e+01  -1.6133e-01   2.1204e+00   4.5926e+00   4.6435e-01   1.0192e-01   2.4716e+00   5.8808e-09
+#> 99:    9.4546e+01  -1.5627e-01   2.1358e+00   5.5648e+00   4.8843e-01   1.0047e-01   2.5033e+00   2.5865e-05
+#> 100:    9.4544e+01  -1.6341e-01   2.1317e+00   5.2974e+00   4.7076e-01   1.1065e-01   2.4711e+00   4.4438e-05
+#> 101:    94.2461   -0.1640    2.1458    5.0325    0.4750    0.1107    2.5297    0.0002
+#> 102:    9.4039e+01  -1.6946e-01   2.1490e+00   4.9929e+00   4.7265e-01   1.2109e-01   2.3907e+00   2.3093e-05
+#> 103:    9.4132e+01  -1.6840e-01   2.1250e+00   5.3879e+00   4.7062e-01   1.2389e-01   2.3401e+00   5.4840e-07
+#> 104:    9.4376e+01  -1.6842e-01   2.1239e+00   7.9826e+00   4.7053e-01   1.1769e-01   2.3663e+00   1.9617e-05
+#> 105:    9.4370e+01  -1.7024e-01   2.1187e+00   7.5834e+00   4.6738e-01   1.1683e-01   2.3471e+00   1.4035e-05
+#> 106:    9.4462e+01  -1.6562e-01   2.1406e+00   7.5466e+00   4.6364e-01   1.2640e-01   2.3140e+00   4.7933e-05
+#> 107:    9.4541e+01  -1.6582e-01   2.1308e+00   7.1692e+00   4.6457e-01   1.2964e-01   2.3395e+00   1.8489e-05
+#> 108:    9.4709e+01  -1.6157e-01   2.1484e+00   6.8108e+00   4.5925e-01   1.3393e-01   2.3340e+00   1.5230e-06
+#> 109:    9.4450e+01  -1.8900e-01   2.0799e+00   6.4702e+00   4.4801e-01   1.4728e-01   2.3847e+00   3.2613e-05
+#> 110:    9.4180e+01  -1.9104e-01   2.1172e+00   6.1467e+00   4.5389e-01   1.4273e-01   2.3775e+00   6.0285e-05
+#> 111:    9.4366e+01  -1.8908e-01   2.1031e+00   5.8394e+00   4.4875e-01   1.4983e-01   2.3898e+00   7.2653e-05
+#> 112:    9.4008e+01  -1.8144e-01   2.1008e+00   5.5474e+00   4.6433e-01   1.4234e-01   2.3705e+00   9.9395e-06
+#> 113:    9.4372e+01  -1.8885e-01   2.1154e+00   5.2700e+00   4.7983e-01   1.3522e-01   2.3641e+00   1.8643e-05
+#> 114:    94.1292   -0.1872    2.1134    5.0065    0.4824    0.1285    2.3163    0.0001
+#> 115:    9.4510e+01  -1.7805e-01   2.1185e+00   4.7562e+00   4.8451e-01   1.2204e-01   2.3568e+00   1.6277e-07
+#> 116:    9.4234e+01  -1.8613e-01   2.1214e+00   4.5184e+00   4.7967e-01   1.2275e-01   2.3388e+00   3.4361e-05
+#> 117:    9.4438e+01  -1.7276e-01   2.1218e+00   4.2925e+00   4.6200e-01   1.1662e-01   2.3686e+00   5.6594e-06
+#> 118:    9.4498e+01  -1.7628e-01   2.1143e+00   6.2395e+00   4.5445e-01   1.2378e-01   2.3303e+00   6.5645e-05
+#> 119:    9.4303e+01  -1.8107e-01   2.1120e+00   7.5774e+00   4.6102e-01   1.2829e-01   2.3595e+00   9.4057e-06
+#> 120:    9.4022e+01  -1.7626e-01   2.1258e+00   9.9044e+00   4.6505e-01   1.5188e-01   2.3582e+00   3.6907e-05
+#> 121:    9.4103e+01  -1.5976e-01   2.1354e+00   9.4091e+00   4.7792e-01   1.5429e-01   2.3729e+00   4.6749e-05
+#> 122:    9.4727e+01  -1.9092e-01   2.0956e+00   8.9387e+00   4.5402e-01   1.6371e-01   2.3118e+00   6.8573e-06
+#> 123:    9.4447e+01  -1.9139e-01   2.0898e+00   8.4918e+00   4.3317e-01   1.7569e-01   2.3083e+00   4.4068e-05
+#> 124:    9.4422e+01  -1.9130e-01   2.0920e+00   8.0672e+00   4.3952e-01   1.7160e-01   2.3003e+00   1.7162e-05
+#> 125:    9.4608e+01  -1.7777e-01   2.1007e+00   7.6638e+00   4.8253e-01   1.6302e-01   2.3244e+00   1.9896e-05
+#> 126:    9.4512e+01  -1.6596e-01   2.1139e+00   7.2806e+00   4.7648e-01   1.7588e-01   2.2913e+00   4.9747e-05
+#> 127:    9.4983e+01  -1.6562e-01   2.1290e+00   6.9166e+00   4.7028e-01   1.6709e-01   2.3141e+00   3.3357e-05
+#> 128:    94.3910   -0.1728    2.1159    6.5708    0.4914    0.1850    2.3173    0.0001
+#> 129:    9.4578e+01  -1.7211e-01   2.1177e+00   6.2422e+00   4.8295e-01   1.7709e-01   2.2815e+00   4.8158e-05
+#> 130:    9.4646e+01  -1.6785e-01   2.1333e+00   5.9301e+00   4.6360e-01   1.6823e-01   2.3140e+00   2.1204e-05
+#> 131:    9.4670e+01  -1.4897e-01   2.1480e+00   5.6336e+00   4.8826e-01   1.5982e-01   2.3436e+00   1.3221e-05
+#> 132:    9.4625e+01  -1.6160e-01   2.1599e+00   5.3519e+00   4.6385e-01   1.6125e-01   2.2830e+00   9.6815e-06
+#> 133:    9.3985e+01  -1.7636e-01   2.1299e+00   5.8178e+00   4.4885e-01   1.5389e-01   2.2810e+00   6.7789e-06
+#> 134:    9.4105e+01  -1.7389e-01   2.1199e+00   5.5269e+00   4.5848e-01   1.4628e-01   2.2992e+00   7.6542e-06
+#> 135:    9.4387e+01  -1.5936e-01   2.1418e+00   5.2506e+00   4.5002e-01   1.6349e-01   2.3403e+00   7.6250e-05
+#> 136:    94.3595   -0.1696    2.1493    4.9880    0.4407    0.1722    2.3121    0.0001
+#> 137:    9.4056e+01  -1.6030e-01   2.1720e+00   5.5600e+00   4.4954e-01   1.8233e-01   2.3099e+00   2.1195e-05
+#> 138:    9.4043e+01  -1.4848e-01   2.1876e+00   5.2820e+00   4.5696e-01   1.8542e-01   2.2876e+00   8.7271e-06
+#> 139:    94.3020   -0.1374    2.1965    5.6428    0.4668    0.1927    2.3341    0.0001
+#> 140:    9.4260e+01  -1.3603e-01   2.2014e+00   5.7727e+00   4.6823e-01   1.8302e-01   2.3248e+00   1.4731e-06
+#> 141:    9.4302e+01  -1.2134e-01   2.1992e+00   5.4841e+00   4.8967e-01   1.7947e-01   2.3212e+00   1.6339e-05
+#> 142:    94.0970   -0.1143    2.2570    5.4173    0.4766    0.1726    2.3581    0.0077
+#> 143:    94.2078   -0.1162    2.2460    5.1464    0.4745    0.1874    2.3551    0.0152
+#> 144:    94.2085   -0.0953    2.2685    4.8891    0.5010    0.1780    2.3881    0.0095
+#> 145:    94.1483   -0.0906    2.2751    5.0705    0.4959    0.1770    2.3103    0.0143
+#> 146:    94.4257   -0.0859    2.2735    5.0201    0.5331    0.2050    2.3104    0.0160
+#> 147:    93.8072   -0.0887    2.2766    4.7691    0.5253    0.2200    2.2903    0.0180
+#> 148:    94.4354   -0.0901    2.2770    4.5306    0.5237    0.2147    2.3108    0.0150
+#> 149:    94.1171   -0.1126    2.2342    4.3041    0.5412    0.2300    2.3454    0.0126
+#> 150:    94.0704   -0.1267    2.2071    4.0889    0.5324    0.2185    2.3673    0.0097
+#> 151:    93.9860   -0.1480    2.1852    3.8844    0.5101    0.2529    2.3280    0.0056
+#> 152:    93.9500   -0.1419    2.1940    4.4687    0.5066    0.2371    2.3617    0.0002
+#> 153:    93.8058   -0.1368    2.1917    4.2493    0.5068    0.2057    2.3481    0.0002
+#> 154:    9.4043e+01  -1.3331e-01   2.1972e+00   4.3921e+00   4.7605e-01   1.9689e-01   2.3952e+00   9.5657e-05
+#> 155:    94.2500   -0.1223    2.2260    5.7786    0.4848    0.1829    2.3361    0.0075
+#> 156:    94.5035   -0.1223    2.2091    6.1344    0.4848    0.1558    2.3951    0.0048
+#> 157:    9.4448e+01  -1.3268e-01   2.1797e+00   6.4746e+00   4.6934e-01   1.6035e-01   2.3496e+00   5.2771e-05
+#> 158:    94.7438   -0.1401    2.1904    6.0162    0.4589    0.1692    2.3444    0.0001
+#> 159:    94.2681   -0.1430    2.1852    4.6165    0.4774    0.1617    2.3601    0.0001
+#> 160:    9.3911e+01  -1.1659e-01   2.2267e+00   4.9756e+00   4.9349e-01   1.7150e-01   2.3500e+00   9.0374e-06
+#> 161:    9.3914e+01  -1.1938e-01   2.2233e+00   4.8238e+00   4.9674e-01   1.8358e-01   2.3536e+00   1.1877e-06
+#> 162:    93.9974   -0.1188    2.2349    5.1092    0.4967    0.1714    2.3237    0.0041
+#> 163:    9.3939e+01  -1.2170e-01   2.2147e+00   4.8315e+00   5.0622e-01   1.8195e-01   2.3823e+00   2.4030e-05
+#> 164:    93.8015   -0.1362    2.2166    3.9112    0.4958    0.1684    2.3488    0.0072
+#> 165:    93.4082   -0.1398    2.2132    3.2992    0.5087    0.1734    2.2861    0.0125
+#> 166:    93.4680   -0.1421    2.2077    3.3232    0.5075    0.1643    2.2792    0.0149
+#> 167:    93.5455   -0.1443    2.2080    3.7465    0.4972    0.1685    2.2194    0.0191
+#> 168:    93.5603   -0.1711    2.1421    3.2407    0.5201    0.1940    2.3029    0.0198
+#> 169:    93.7281   -0.1578    2.1553    2.5110    0.4988    0.1836    2.3343    0.0134
+#> 170:    93.9675   -0.1564    2.1532    2.2507    0.5049    0.1753    2.3089    0.0110
+#> 171:    93.8255   -0.1543    2.1647    2.7302    0.5114    0.1691    2.2959    0.0113
+#> 172:    93.8071   -0.1536    2.1689    2.5849    0.5069    0.1751    2.3047    0.0099
+#> 173:    93.7137   -0.1403    2.2096    1.5160    0.5204    0.1622    2.3452    0.0155
+#> 174:    93.7182   -0.1376    2.1975    1.3366    0.5222    0.1700    2.3311    0.0149
+#> 175:    93.5957   -0.1587    2.1613    1.3539    0.5321    0.1470    2.3893    0.0156
+#> 176:    93.6058   -0.1587    2.1602    1.4588    0.5321    0.1412    2.4323    0.0116
+#> 177:    93.4496   -0.1858    2.1323    1.2423    0.4987    0.1460    2.3491    0.0167
+#> 178:    93.5894   -0.1935    2.1217    1.7812    0.4776    0.1643    2.3046    0.0168
+#> 179:    93.6386   -0.1887    2.1445    2.8813    0.4808    0.1585    2.2689    0.0192
+#> 180:    93.9288   -0.1950    2.1015    2.0905    0.4681    0.1557    2.2783    0.0170
+#> 181:    93.8165   -0.1950    2.0840    2.6302    0.4681    0.1592    2.3643    0.0173
+#> 182:    94.2132   -0.1936    2.0866    3.0185    0.5131    0.1712    2.3164    0.0147
+#> 183:    94.0929   -0.1896    2.0782    3.0716    0.5288    0.1644    2.5169    0.0066
+#> 184:    93.8694   -0.1968    2.0946    2.4734    0.5121    0.1709    2.3795    0.0071
+#> 185:    93.8138   -0.1970    2.0987    2.9707    0.4957    0.1500    2.3995    0.0034
+#> 186:    9.4047e+01  -2.1045e-01   2.0791e+00   3.6686e+00   4.8764e-01   1.4347e-01   2.3654e+00   3.8127e-05
+#> 187:    9.4498e+01  -1.9649e-01   2.0949e+00   2.0912e+00   4.7479e-01   1.6122e-01   2.3873e+00   4.7739e-06
+#> 188:    9.4650e+01  -1.8508e-01   2.1132e+00   2.1529e+00   4.6244e-01   1.4403e-01   2.3367e+00   3.5345e-06
+#> 189:    9.4301e+01  -1.8137e-01   2.1132e+00   2.6433e+00   4.4894e-01   1.3537e-01   2.3145e+00   9.6836e-06
+#> 190:    9.4501e+01  -1.8209e-01   2.0962e+00   3.1460e+00   4.4908e-01   1.2006e-01   2.3563e+00   3.3387e-05
+#> 191:    9.4156e+01  -2.0214e-01   2.0803e+00   3.2334e+00   4.7635e-01   1.1917e-01   2.3782e+00   6.6641e-06
+#> 192:    9.3981e+01  -2.1562e-01   2.0492e+00   3.0526e+00   5.0505e-01   1.3669e-01   2.3412e+00   7.3871e-05
+#> 193:    9.4085e+01  -2.2693e-01   2.0318e+00   2.9855e+00   4.9563e-01   1.4371e-01   2.3727e+00   8.6443e-05
+#> 194:    9.3922e+01  -2.3089e-01   2.0323e+00   2.9709e+00   4.9151e-01   1.4470e-01   2.3667e+00   3.5941e-05
+#> 195:    9.4180e+01  -2.2865e-01   2.0284e+00   2.2426e+00   4.8793e-01   1.5283e-01   2.3442e+00   1.8882e-05
+#> 196:    9.4259e+01  -2.0053e-01   2.0541e+00   1.5155e+00   5.1571e-01   1.5596e-01   2.3638e+00   2.9015e-05
+#> 197:    9.4225e+01  -2.0144e-01   2.0551e+00   1.6032e+00   5.0920e-01   1.6454e-01   2.3564e+00   2.7823e-05
+#> 198:    9.4166e+01  -1.9411e-01   2.0602e+00   1.8793e+00   5.5190e-01   1.8338e-01   2.3611e+00   1.6669e-05
+#> 199:    9.4230e+01  -1.9621e-01   2.0737e+00   1.8847e+00   5.4082e-01   1.7340e-01   2.3488e+00   5.8282e-07
+#> 200:    9.4215e+01  -1.9629e-01   2.0888e+00   1.9185e+00   5.4293e-01   1.7502e-01   2.3563e+00   5.7303e-06
+#> 201:    94.0654   -0.1931    2.0901    1.8074    0.5373    0.1886    2.3869    0.0025
+#> 202:    93.9801   -0.1898    2.0990    1.6823    0.5318    0.1841    2.4043    0.0016
+#> 203:    94.0246   -0.1893    2.1004    1.6503    0.5286    0.1855    2.3971    0.0012
+#> 204:    93.9893   -0.1870    2.1014    1.6166    0.5276    0.1846    2.3900    0.0010
+#> 205:    94.0154   -0.1854    2.1006    1.5294    0.5286    0.1828    2.3939    0.0009
+#> 206:    94.0468   -0.1833    2.1024    1.5102    0.5295    0.1807    2.3967    0.0007
+#> 207:    94.0641   -0.1810    2.1049    1.5136    0.5289    0.1798    2.4037    0.0008
+#> 208:    94.0794   -0.1790    2.1062    1.5078    0.5286    0.1790    2.4139    0.0007
+#> 209:    94.0892   -0.1799    2.1049    1.4549    0.5261    0.1793    2.4144    0.0006
+#> 210:    94.0911   -0.1817    2.1042    1.4537    0.5217    0.1810    2.4069    0.0012
+#> 211:    94.1011   -0.1828    2.1016    1.4582    0.5235    0.1825    2.4049    0.0011
+#> 212:    94.1081   -0.1839    2.0989    1.4657    0.5255    0.1838    2.4031    0.0010
+#> 213:    94.1264   -0.1842    2.0973    1.4527    0.5263    0.1851    2.4026    0.0010
+#> 214:    94.1287   -0.1844    2.0974    1.4405    0.5270    0.1869    2.4006    0.0009
+#> 215:    94.1440   -0.1850    2.0973    1.4556    0.5269    0.1876    2.3985    0.0009
+#> 216:    94.1352   -0.1863    2.0970    1.4698    0.5258    0.1885    2.3977    0.0008
+#> 217:    94.1261   -0.1868    2.0962    1.4850    0.5244    0.1897    2.3946    0.0008
+#> 218:    94.1100   -0.1858    2.0987    1.4673    0.5230    0.1934    2.3924    0.0007
+#> 219:    94.1073   -0.1845    2.1013    1.4630    0.5218    0.1993    2.3890    0.0011
+#> 220:    94.1026   -0.1836    2.1030    1.4705    0.5205    0.2028    2.3904    0.0010
+#> 221:    94.0972   -0.1824    2.1046    1.4732    0.5198    0.2065    2.3907    0.0010
+#> 222:    94.0898   -0.1824    2.1052    1.4952    0.5180    0.2083    2.3892    0.0010
+#> 223:    94.0975   -0.1830    2.1050    1.5035    0.5161    0.2107    2.3888    0.0011
+#> 224:    94.1027   -0.1831    2.1050    1.5196    0.5148    0.2124    2.3878    0.0011
+#> 225:    94.0977   -0.1828    2.1065    1.5153    0.5142    0.2141    2.3856    0.0013
+#> 226:    94.0907   -0.1831    2.1066    1.5287    0.5130    0.2151    2.3828    0.0014
+#> 227:    94.0831   -0.1833    2.1065    1.5535    0.5119    0.2159    2.3814    0.0014
+#> 228:    94.0834   -0.1832    2.1072    1.5713    0.5114    0.2174    2.3813    0.0014
+#> 229:    94.0793   -0.1832    2.1076    1.6041    0.5111    0.2184    2.3811    0.0015
+#> 230:    94.0701   -0.1843    2.1064    1.6177    0.5096    0.2181    2.3803    0.0017
+#> 231:    94.0598   -0.1853    2.1052    1.6254    0.5085    0.2180    2.3818    0.0016
+#> 232:    94.0539   -0.1862    2.1045    1.6254    0.5074    0.2175    2.3824    0.0017
+#> 233:    94.0498   -0.1869    2.1034    1.6380    0.5065    0.2169    2.3826    0.0017
+#> 234:    94.0514   -0.1872    2.1035    1.6300    0.5050    0.2160    2.3829    0.0017
+#> 235:    94.0521   -0.1876    2.1026    1.6263    0.5041    0.2148    2.3825    0.0018
+#> 236:    94.0587   -0.1876    2.1024    1.6277    0.5023    0.2134    2.3834    0.0020
+#> 237:    94.0741   -0.1873    2.1025    1.6349    0.5013    0.2120    2.3828    0.0019
+#> 238:    94.0898   -0.1876    2.1022    1.6509    0.4997    0.2107    2.3837    0.0019
+#> 239:    94.1055   -0.1880    2.1016    1.6596    0.4979    0.2098    2.3836    0.0018
+#> 240:    94.1209   -0.1885    2.1007    1.6627    0.4958    0.2092    2.3831    0.0018
+#> 241:    94.1322   -0.1893    2.0992    1.6563    0.4945    0.2085    2.3825    0.0017
+#> 242:    94.1404   -0.1904    2.0976    1.6574    0.4930    0.2082    2.3814    0.0017
+#> 243:    94.1428   -0.1914    2.0961    1.6412    0.4918    0.2078    2.3800    0.0017
+#> 244:    94.1477   -0.1923    2.0945    1.6287    0.4907    0.2071    2.3795    0.0016
+#> 245:    94.1525   -0.1931    2.0933    1.6225    0.4897    0.2064    2.3791    0.0016
+#> 246:    94.1557   -0.1938    2.0927    1.6243    0.4890    0.2048    2.3780    0.0016
+#> 247:    94.1576   -0.1943    2.0919    1.6333    0.4881    0.2034    2.3777    0.0015
+#> 248:    94.1603   -0.1951    2.0909    1.6328    0.4863    0.2026    2.3775    0.0015
+#> 249:    94.1648   -0.1957    2.0898    1.6427    0.4847    0.2018    2.3774    0.0015
+#> 250:    94.1766   -0.1963    2.0889    1.6482    0.4829    0.2012    2.3770    0.0015
+#> 251:    94.1854   -0.1971    2.0875    1.6536    0.4806    0.2011    2.3769    0.0016
+#> 252:    94.1906   -0.1980    2.0861    1.6527    0.4785    0.2013    2.3763    0.0017
+#> 253:    94.1913   -0.1982    2.0857    1.6459    0.4772    0.2014    2.3751    0.0019
+#> 254:    94.1945   -0.1985    2.0852    1.6413    0.4759    0.2019    2.3755    0.0019
+#> 255:    94.1972   -0.1989    2.0837    1.6451    0.4754    0.2027    2.3753    0.0018
+#> 256:    94.1994   -0.1989    2.0833    1.6548    0.4758    0.2024    2.3752    0.0018
+#> 257:    94.2014   -0.1987    2.0833    1.6708    0.4765    0.2024    2.3752    0.0018
+#> 258:    94.2081   -0.1984    2.0836    1.6903    0.4768    0.2023    2.3749    0.0017
+#> 259:    94.2151   -0.1982    2.0839    1.7169    0.4767    0.2023    2.3737    0.0017
+#> 260:    94.2212   -0.1980    2.0838    1.7426    0.4766    0.2031    2.3725    0.0017
+#> 261:    94.2229   -0.1981    2.0835    1.7696    0.4764    0.2038    2.3714    0.0017
+#> 262:    94.2213   -0.1983    2.0832    1.7977    0.4762    0.2045    2.3704    0.0016
+#> 263:    94.2220   -0.1984    2.0830    1.8277    0.4764    0.2051    2.3700    0.0016
+#> 264:    94.2230   -0.1983    2.0830    1.8430    0.4766    0.2057    2.3690    0.0016
+#> 265:    94.2235   -0.1983    2.0832    1.8679    0.4768    0.2060    2.3674    0.0016
+#> 266:    94.2242   -0.1982    2.0833    1.8705    0.4769    0.2064    2.3658    0.0015
+#> 267:    94.2267   -0.1982    2.0832    1.8715    0.4769    0.2070    2.3643    0.0015
+#> 268:    94.2312   -0.1980    2.0834    1.8824    0.4769    0.2074    2.3631    0.0015
+#> 269:    94.2329   -0.1981    2.0829    1.8843    0.4766    0.2084    2.3628    0.0015
+#> 270:    94.2321   -0.1982    2.0825    1.8904    0.4770    0.2093    2.3629    0.0015
+#> 271:    94.2349   -0.1985    2.0820    1.8942    0.4769    0.2101    2.3633    0.0016
+#> 272:    94.2388   -0.1989    2.0818    1.9099    0.4767    0.2110    2.3634    0.0018
+#> 273:    94.2420   -0.1992    2.0816    1.9259    0.4765    0.2118    2.3632    0.0018
+#> 274:    94.2454   -0.1994    2.0813    1.9330    0.4763    0.2128    2.3629    0.0017
+#> 275:    94.2456   -0.1997    2.0810    1.9316    0.4761    0.2138    2.3624    0.0018
+#> 276:    94.2472   -0.1999    2.0807    1.9306    0.4758    0.2146    2.3624    0.0019
+#> 277:    94.2492   -0.2001    2.0808    1.9326    0.4756    0.2153    2.3623    0.0020
+#> 278:    94.2493   -0.2003    2.0807    1.9225    0.4752    0.2163    2.3628    0.0020
+#> 279:    94.2481   -0.2002    2.0808    1.9206    0.4750    0.2168    2.3628    0.0019
+#> 280:    94.2433   -0.2002    2.0810    1.9257    0.4749    0.2173    2.3626    0.0019
+#> 281:    94.2358   -0.2004    2.0809    1.9217    0.4748    0.2173    2.3620    0.0019
+#> 282:    94.2307   -0.2005    2.0807    1.9209    0.4748    0.2172    2.3617    0.0019
+#> 283:    94.2302   -0.2008    2.0803    1.9131    0.4748    0.2172    2.3615    0.0019
+#> 284:    94.2309   -0.2009    2.0802    1.9085    0.4749    0.2171    2.3610    0.0018
+#> 285:    94.2344   -0.2010    2.0799    1.9135    0.4749    0.2170    2.3603    0.0018
+#> 286:    94.2381   -0.2013    2.0794    1.9099    0.4749    0.2167    2.3596    0.0018
+#> 287:    94.2420   -0.2016    2.0786    1.9105    0.4749    0.2164    2.3596    0.0018
+#> 288:    94.2425   -0.2020    2.0778    1.9081    0.4749    0.2161    2.3590    0.0019
+#> 289:    94.2386   -0.2023    2.0773    1.9136    0.4749    0.2158    2.3586    0.0019
+#> 290:    94.2357   -0.2026    2.0768    1.9171    0.4750    0.2154    2.3581    0.0019
+#> 291:    94.2326   -0.2026    2.0765    1.9162    0.4750    0.2150    2.3577    0.0019
+#> 292:    94.2305   -0.2026    2.0766    1.9178    0.4753    0.2144    2.3577    0.0020
+#> 293:    94.2268   -0.2023    2.0771    1.9257    0.4754    0.2138    2.3574    0.0022
+#> 294:    94.2216   -0.2023    2.0773    1.9326    0.4754    0.2132    2.3565    0.0023
+#> 295:    94.2193   -0.2024    2.0769    1.9378    0.4762    0.2125    2.3565    0.0024
+#> 296:    94.2160   -0.2025    2.0765    1.9463    0.4771    0.2117    2.3565    0.0025
+#> 297:    94.2106   -0.2026    2.0761    1.9523    0.4779    0.2109    2.3569    0.0026
+#> 298:    94.2089   -0.2028    2.0756    1.9622    0.4787    0.2099    2.3578    0.0025
+#> 299:    94.2077   -0.2029    2.0753    1.9721    0.4794    0.2090    2.3586    0.0026
+#> 300:    94.2064   -0.2030    2.0749    1.9838    0.4802    0.2080    2.3589    0.0026
+#> 301:    94.2086   -0.2029    2.0747    1.9942    0.4806    0.2071    2.3587    0.0025
+#> 302:    94.2111   -0.2031    2.0744    1.9938    0.4810    0.2063    2.3593    0.0025
+#> 303:    94.2133   -0.2031    2.0743    1.9923    0.4811    0.2056    2.3593    0.0025
+#> 304:    94.2151   -0.2032    2.0739    1.9885    0.4811    0.2049    2.3595    0.0025
+#> 305:    94.2159   -0.2035    2.0735    1.9872    0.4813    0.2044    2.3594    0.0024
+#> 306:    94.2192   -0.2038    2.0729    1.9806    0.4813    0.2041    2.3592    0.0024
+#> 307:    94.2226   -0.2040    2.0724    1.9796    0.4814    0.2038    2.3588    0.0024
+#> 308:    94.2224   -0.2042    2.0723    1.9828    0.4814    0.2036    2.3589    0.0024
+#> 309:    94.2200   -0.2043    2.0723    1.9859    0.4812    0.2034    2.3587    0.0024
+#> 310:    94.2183   -0.2044    2.0723    1.9892    0.4810    0.2034    2.3583    0.0023
+#> 311:    94.2175   -0.2044    2.0724    1.9895    0.4805    0.2033    2.3580    0.0023
+#> 312:    94.2171   -0.2044    2.0725    1.9977    0.4800    0.2032    2.3581    0.0023
+#> 313:    94.2108   -0.2044    2.0728    1.9995    0.4795    0.2030    2.3578    0.0023
+#> 314:    94.2068   -0.2045    2.0730    1.9929    0.4790    0.2029    2.3576    0.0024
+#> 315:    94.2031   -0.2047    2.0730    1.9884    0.4784    0.2028    2.3579    0.0024
+#> 316:    94.2018   -0.2048    2.0731    1.9860    0.4779    0.2026    2.3582    0.0024
+#> 317:    94.2015   -0.2050    2.0729    1.9836    0.4773    0.2025    2.3582    0.0024
+#> 318:    94.2025   -0.2052    2.0728    1.9814    0.4768    0.2024    2.3580    0.0023
+#> 319:    94.2066   -0.2053    2.0726    1.9867    0.4764    0.2024    2.3577    0.0023
+#> 320:    94.2074   -0.2055    2.0727    1.9896    0.4760    0.2024    2.3575    0.0023
+#> 321:    94.2097   -0.2055    2.0728    1.9985    0.4758    0.2026    2.3573    0.0023
+#> 322:    94.2080   -0.2054    2.0731    2.0108    0.4759    0.2028    2.3570    0.0023
+#> 323:    94.2042   -0.2054    2.0732    2.0253    0.4762    0.2030    2.3566    0.0023
+#> 324:    94.2005   -0.2054    2.0733    2.0514    0.4765    0.2032    2.3566    0.0023
+#> 325:    94.2000   -0.2053    2.0735    2.0719    0.4767    0.2034    2.3570    0.0023
+#> 326:    94.2002   -0.2052    2.0738    2.0907    0.4769    0.2034    2.3573    0.0023
+#> 327:    94.1997   -0.2051    2.0741    2.1140    0.4770    0.2035    2.3571    0.0023
+#> 328:    94.1976   -0.2050    2.0743    2.1379    0.4770    0.2035    2.3569    0.0023
+#> 329:    94.1969   -0.2051    2.0741    2.1485    0.4769    0.2038    2.3566    0.0022
+#> 330:    94.1959   -0.2053    2.0738    2.1533    0.4767    0.2042    2.3561    0.0022
+#> 331:    94.1962   -0.2055    2.0733    2.1588    0.4763    0.2046    2.3555    0.0022
+#> 332:    94.1967   -0.2059    2.0727    2.1626    0.4760    0.2051    2.3551    0.0022
+#> 333:    94.1964   -0.2062    2.0721    2.1666    0.4757    0.2056    2.3547    0.0022
+#> 334:    94.1978   -0.2064    2.0718    2.1703    0.4756    0.2063    2.3543    0.0022
+#> 335:    94.1985   -0.2066    2.0715    2.1698    0.4755    0.2068    2.3538    0.0021
+#> 336:    94.1999   -0.2068    2.0711    2.1705    0.4757    0.2075    2.3534    0.0021
+#> 337:    94.1990   -0.2069    2.0708    2.1690    0.4759    0.2080    2.3530    0.0021
+#> 338:    94.1965   -0.2071    2.0706    2.1708    0.4760    0.2085    2.3525    0.0021
+#> 339:    94.1934   -0.2071    2.0704    2.1769    0.4761    0.2088    2.3518    0.0021
+#> 340:    94.1908   -0.2072    2.0704    2.1794    0.4763    0.2091    2.3515    0.0021
+#> 341:    94.1875   -0.2072    2.0706    2.1859    0.4762    0.2092    2.3512    0.0021
+#> 342:    94.1840   -0.2071    2.0707    2.1903    0.4762    0.2093    2.3513    0.0021
+#> 343:    94.1816   -0.2072    2.0706    2.1909    0.4761    0.2093    2.3512    0.0020
+#> 344:    94.1815   -0.2070    2.0708    2.1877    0.4758    0.2091    2.3514    0.0021
+#> 345:    94.1839   -0.2070    2.0710    2.1844    0.4757    0.2090    2.3517    0.0021
+#> 346:    94.1868   -0.2068    2.0713    2.1787    0.4756    0.2088    2.3520    0.0020
+#> 347:    94.1871   -0.2066    2.0717    2.1762    0.4756    0.2086    2.3519    0.0020
+#> 348:    94.1868   -0.2064    2.0722    2.1724    0.4756    0.2084    2.3521    0.0020
+#> 349:    94.1892   -0.2062    2.0725    2.1673    0.4755    0.2080    2.3524    0.0020
+#> 350:    94.1921   -0.2060    2.0726    2.1632    0.4754    0.2076    2.3527    0.0020
+#> 351:    94.1947   -0.2060    2.0726    2.1613    0.4752    0.2073    2.3530    0.0020
+#> 352:    94.1988   -0.2060    2.0725    2.1647    0.4751    0.2069    2.3530    0.0020
+#> 353:    94.2036   -0.2061    2.0721    2.1684    0.4749    0.2067    2.3530    0.0020
+#> 354:    94.2082   -0.2061    2.0719    2.1670    0.4747    0.2063    2.3529    0.0020
+#> 355:    94.2111   -0.2061    2.0718    2.1645    0.4747    0.2062    2.3526    0.0020
+#> 356:    94.2123   -0.2061    2.0717    2.1628    0.4747    0.2063    2.3525    0.0020
+#> 357:    94.2146   -0.2062    2.0716    2.1610    0.4746    0.2064    2.3523    0.0020
+#> 358:    94.2161   -0.2062    2.0715    2.1656    0.4744    0.2065    2.3520    0.0020
+#> 359:    94.2178   -0.2063    2.0714    2.1684    0.4743    0.2065    2.3516    0.0020
+#> 360:    94.2194   -0.2063    2.0713    2.1687    0.4742    0.2065    2.3512    0.0019
+#> 361:    94.2191   -0.2064    2.0713    2.1738    0.4741    0.2065    2.3508    0.0019
+#> 362:    94.2186   -0.2064    2.0713    2.1762    0.4740    0.2065    2.3502    0.0019
+#> 363:    94.2179   -0.2064    2.0714    2.1754    0.4740    0.2065    2.3495    0.0019
+#> 364:    94.2165   -0.2063    2.0715    2.1740    0.4741    0.2064    2.3495    0.0019
+#> 365:    94.2149   -0.2063    2.0716    2.1736    0.4741    0.2062    2.3495    0.0020
+#> 366:    94.2141   -0.2062    2.0717    2.1813    0.4740    0.2064    2.3490    0.0020
+#> 367:    94.2158   -0.2062    2.0717    2.1905    0.4739    0.2063    2.3491    0.0019
+#> 368:    94.2173   -0.2062    2.0718    2.1963    0.4737    0.2063    2.3485    0.0019
+#> 369:    94.2183   -0.2062    2.0717    2.2005    0.4736    0.2064    2.3481    0.0019
+#> 370:    94.2194   -0.2062    2.0716    2.2016    0.4735    0.2063    2.3477    0.0019
+#> 371:    94.2192   -0.2063    2.0715    2.1997    0.4733    0.2064    2.3476    0.0019
+#> 372:    94.2202   -0.2062    2.0716    2.1957    0.4733    0.2065    2.3479    0.0019
+#> 373:    94.2208   -0.2061    2.0717    2.1913    0.4733    0.2065    2.3480    0.0019
+#> 374:    94.2209   -0.2061    2.0719    2.1870    0.4731    0.2065    2.3479    0.0019
+#> 375:    94.2219   -0.2061    2.0719    2.1864    0.4729    0.2064    2.3477    0.0019
+#> 376:    94.2231   -0.2061    2.0720    2.1849    0.4726    0.2063    2.3473    0.0019
+#> 377:    94.2251   -0.2061    2.0720    2.1835    0.4724    0.2063    2.3471    0.0019
+#> 378:    94.2238   -0.2062    2.0719    2.1777    0.4721    0.2062    2.3472    0.0018
+#> 379:    94.2226   -0.2064    2.0717    2.1741    0.4717    0.2063    2.3471    0.0018
+#> 380:    94.2216   -0.2066    2.0714    2.1759    0.4714    0.2063    2.3468    0.0018
+#> 381:    94.2206   -0.2068    2.0711    2.1784    0.4711    0.2063    2.3465    0.0018
+#> 382:    94.2200   -0.2071    2.0707    2.1753    0.4707    0.2062    2.3462    0.0018
+#> 383:    94.2205   -0.2073    2.0704    2.1757    0.4703    0.2061    2.3461    0.0018
+#> 384:    94.2201   -0.2076    2.0702    2.1802    0.4698    0.2060    2.3458    0.0018
+#> 385:    94.2210   -0.2078    2.0701    2.1795    0.4693    0.2061    2.3457    0.0018
+#> 386:    94.2199   -0.2079    2.0700    2.1788    0.4688    0.2061    2.3455    0.0018
+#> 387:    94.2181   -0.2081    2.0699    2.1801    0.4683    0.2061    2.3454    0.0018
+#> 388:    94.2169   -0.2082    2.0699    2.1850    0.4679    0.2061    2.3452    0.0017
+#> 389:    94.2158   -0.2083    2.0699    2.1881    0.4674    0.2063    2.3449    0.0017
+#> 390:    94.2162   -0.2084    2.0696    2.1928    0.4671    0.2064    2.3447    0.0017
+#> 391:    94.2172   -0.2085    2.0696    2.1921    0.4669    0.2063    2.3444    0.0017
+#> 392:    94.2175   -0.2085    2.0695    2.1933    0.4667    0.2063    2.3442    0.0017
+#> 393:    94.2174   -0.2086    2.0695    2.1972    0.4666    0.2062    2.3440    0.0017
+#> 394:    94.2179   -0.2087    2.0694    2.1972    0.4664    0.2061    2.3439    0.0017
+#> 395:    94.2200   -0.2087    2.0694    2.2009    0.4663    0.2059    2.3438    0.0017
+#> 396:    94.2189   -0.2086    2.0695    2.2062    0.4662    0.2058    2.3434    0.0017
+#> 397:    94.2183   -0.2085    2.0696    2.2151    0.4663    0.2056    2.3431    0.0017
+#> 398:    94.2186   -0.2085    2.0696    2.2200    0.4664    0.2056    2.3430    0.0017
+#> 399:    94.2183   -0.2084    2.0698    2.2204    0.4664    0.2056    2.3428    0.0017
+#> 400:    94.2184   -0.2082    2.0703    2.2252    0.4665    0.2054    2.3428    0.0016
+#> 401:    94.2176   -0.2080    2.0707    2.2323    0.4666    0.2052    2.3427    0.0016
+#> 402:    94.2167   -0.2078    2.0712    2.2397    0.4668    0.2050    2.3426    0.0016
+#> 403:    94.2157   -0.2075    2.0716    2.2464    0.4669    0.2049    2.3426    0.0016
+#> 404:    94.2152   -0.2074    2.0719    2.2508    0.4670    0.2047    2.3427    0.0016
+#> 405:    94.2152   -0.2072    2.0723    2.2537    0.4671    0.2046    2.3427    0.0016
+#> 406:    94.2151   -0.2070    2.0726    2.2565    0.4672    0.2044    2.3427    0.0016
+#> 407:    94.2132   -0.2067    2.0731    2.2568    0.4673    0.2044    2.3426    0.0016
+#> 408:    94.2142   -0.2065    2.0734    2.2579    0.4674    0.2046    2.3424    0.0016
+#> 409:    94.2136   -0.2063    2.0738    2.2630    0.4674    0.2046    2.3420    0.0016
+#> 410:    94.2125   -0.2062    2.0739    2.2635    0.4674    0.2047    2.3417    0.0016
+#> 411:    94.2131   -0.2061    2.0741    2.2634    0.4674    0.2048    2.3413    0.0016
+#> 412:    94.2132   -0.2060    2.0742    2.2662    0.4674    0.2048    2.3409    0.0016
+#> 413:    94.2143   -0.2059    2.0743    2.2666    0.4673    0.2048    2.3407    0.0016
+#> 414:    94.2156   -0.2058    2.0743    2.2710    0.4672    0.2048    2.3404    0.0015
+#> 415:    94.2174   -0.2057    2.0745    2.2751    0.4671    0.2049    2.3400    0.0015
+#> 416:    94.2185   -0.2057    2.0746    2.2762    0.4669    0.2049    2.3399    0.0015
+#> 417:    94.2208   -0.2056    2.0748    2.2759    0.4667    0.2049    2.3397    0.0015
+#> 418:    94.2231   -0.2054    2.0751    2.2772    0.4664    0.2050    2.3398    0.0015
+#> 419:    94.2249   -0.2053    2.0754    2.2783    0.4663    0.2050    2.3396    0.0015
+#> 420:    94.2255   -0.2052    2.0757    2.2798    0.4660    0.2050    2.3395    0.0015
+#> 421:    94.2265   -0.2051    2.0759    2.2848    0.4659    0.2050    2.3392    0.0016
+#> 422:    94.2288   -0.2049    2.0761    2.2929    0.4659    0.2050    2.3390    0.0016
+#> 423:    94.2307   -0.2048    2.0762    2.2988    0.4657    0.2051    2.3390    0.0016
+#> 424:    94.2313   -0.2047    2.0764    2.3017    0.4656    0.2051    2.3391    0.0016
+#> 425:    94.2322   -0.2046    2.0765    2.3028    0.4655    0.2050    2.3388    0.0016
+#> 426:    94.2327   -0.2046    2.0765    2.3049    0.4654    0.2050    2.3386    0.0016
+#> 427:    94.2323   -0.2045    2.0768    2.3053    0.4655    0.2049    2.3386    0.0016
+#> 428:    94.2324   -0.2044    2.0770    2.3016    0.4655    0.2048    2.3387    0.0017
+#> 429:    94.2322   -0.2043    2.0772    2.2984    0.4656    0.2047    2.3386    0.0017
+#> 430:    94.2306   -0.2042    2.0774    2.2971    0.4656    0.2046    2.3384    0.0017
+#> 431:    94.2295   -0.2042    2.0775    2.2931    0.4657    0.2044    2.3384    0.0017
+#> 432:    94.2298   -0.2040    2.0778    2.2896    0.4656    0.2044    2.3383    0.0018
+#> 433:    94.2311   -0.2039    2.0780    2.2885    0.4656    0.2044    2.3383    0.0018
+#> 434:    94.2311   -0.2037    2.0783    2.2854    0.4655    0.2044    2.3381    0.0018
+#> 435:    94.2314   -0.2036    2.0786    2.2838    0.4654    0.2044    2.3378    0.0018
+#> 436:    94.2315   -0.2035    2.0788    2.2817    0.4653    0.2044    2.3377    0.0018
+#> 437:    94.2326   -0.2034    2.0790    2.2801    0.4652    0.2044    2.3378    0.0018
+#> 438:    94.2338   -0.2034    2.0791    2.2802    0.4650    0.2046    2.3380    0.0018
+#> 439:    94.2340   -0.2033    2.0791    2.2810    0.4649    0.2046    2.3377    0.0018
+#> 440:    94.2330   -0.2034    2.0791    2.2822    0.4646    0.2046    2.3376    0.0018
+#> 441:    94.2323   -0.2035    2.0790    2.2818    0.4644    0.2046    2.3375    0.0018
+#> 442:    94.2321   -0.2034    2.0792    2.2804    0.4642    0.2043    2.3375    0.0018
+#> 443:    94.2313   -0.2033    2.0794    2.2812    0.4641    0.2041    2.3372    0.0018
+#> 444:    94.2301   -0.2032    2.0796    2.2820    0.4640    0.2040    2.3369    0.0018
+#> 445:    94.2279   -0.2031    2.0799    2.2872    0.4639    0.2039    2.3366    0.0018
+#> 446:    94.2272   -0.2030    2.0801    2.2874    0.4639    0.2037    2.3363    0.0018
+#> 447:    94.2262   -0.2029    2.0803    2.2881    0.4639    0.2036    2.3359    0.0018
+#> 448:    94.2248   -0.2028    2.0806    2.2905    0.4639    0.2036    2.3358    0.0018
+#> 449:    94.2245   -0.2027    2.0808    2.2914    0.4638    0.2035    2.3356    0.0018
+#> 450:    94.2237   -0.2027    2.0809    2.2928    0.4638    0.2035    2.3356    0.0018
+#> 451:    94.2233   -0.2025    2.0813    2.2917    0.4639    0.2033    2.3355    0.0018
+#> 452:    94.2232   -0.2023    2.0816    2.2898    0.4640    0.2031    2.3356    0.0018
+#> 453:    94.2230   -0.2021    2.0819    2.2890    0.4641    0.2030    2.3356    0.0018
+#> 454:    94.2222   -0.2020    2.0822    2.2851    0.4641    0.2029    2.3357    0.0018
+#> 455:    94.2214   -0.2018    2.0824    2.2820    0.4640    0.2028    2.3357    0.0017
+#> 456:    94.2212   -0.2017    2.0827    2.2797    0.4640    0.2026    2.3357    0.0017
+#> 457:    94.2216   -0.2016    2.0829    2.2771    0.4640    0.2024    2.3358    0.0017
+#> 458:    94.2220   -0.2015    2.0831    2.2740    0.4639    0.2022    2.3358    0.0017
+#> 459:    94.2229   -0.2013    2.0834    2.2765    0.4638    0.2021    2.3358    0.0017
+#> 460:    94.2226   -0.2012    2.0837    2.2810    0.4637    0.2020    2.3359    0.0017
+#> 461:    94.2227   -0.2009    2.0841    2.2893    0.4637    0.2018    2.3358    0.0017
+#> 462:    94.2235   -0.2007    2.0844    2.2942    0.4637    0.2016    2.3357    0.0017
+#> 463:    94.2241   -0.2005    2.0848    2.2971    0.4637    0.2014    2.3358    0.0017
+#> 464:    94.2236   -0.2002    2.0853    2.2953    0.4637    0.2012    2.3360    0.0017
+#> 465:    94.2230   -0.2000    2.0858    2.2946    0.4638    0.2010    2.3360    0.0017
+#> 466:    94.2215   -0.1997    2.0863    2.2995    0.4638    0.2009    2.3363    0.0017
+#> 467:    94.2193   -0.1995    2.0868    2.3051    0.4637    0.2008    2.3363    0.0017
+#> 468:    94.2174   -0.1992    2.0874    2.3086    0.4636    0.2006    2.3363    0.0018
+#> 469:    94.2160   -0.1989    2.0881    2.3072    0.4636    0.2006    2.3361    0.0018
+#> 470:    94.2152   -0.1985    2.0887    2.3075    0.4637    0.2005    2.3363    0.0018
+#> 471:    94.2139   -0.1982    2.0891    2.3126    0.4638    0.2004    2.3361    0.0018
+#> 472:    94.2134   -0.1980    2.0895    2.3151    0.4640    0.2002    2.3360    0.0018
+#> 473:    94.2141   -0.1979    2.0897    2.3149    0.4640    0.2001    2.3360    0.0018
+#> 474:    94.2144   -0.1978    2.0900    2.3140    0.4640    0.2001    2.3358    0.0018
+#> 475:    94.2151   -0.1977    2.0901    2.3151    0.4640    0.2000    2.3358    0.0018
+#> 476:    94.2154   -0.1975    2.0903    2.3195    0.4641    0.2001    2.3357    0.0018
+#> 477:    94.2167   -0.1974    2.0905    2.3253    0.4642    0.2002    2.3358    0.0018
+#> 478:    94.2163   -0.1972    2.0909    2.3324    0.4641    0.2004    2.3357    0.0017
+#> 479:    94.2156   -0.1970    2.0912    2.3364    0.4640    0.2006    2.3355    0.0017
+#> 480:    94.2149   -0.1969    2.0915    2.3395    0.4638    0.2007    2.3353    0.0017
+#> 481:    94.2140   -0.1968    2.0918    2.3431    0.4637    0.2008    2.3350    0.0017
+#> 482:    94.2137   -0.1967    2.0919    2.3440    0.4635    0.2010    2.3349    0.0017
+#> 483:    94.2139   -0.1966    2.0920    2.3468    0.4634    0.2011    2.3348    0.0017
+#> 484:    94.2149   -0.1966    2.0921    2.3488    0.4633    0.2012    2.3346    0.0017
+#> 485:    94.2153   -0.1966    2.0921    2.3486    0.4632    0.2012    2.3345    0.0017
+#> 486:    94.2148   -0.1965    2.0923    2.3483    0.4631    0.2015    2.3345    0.0017
+#> 487:    94.2140   -0.1965    2.0923    2.3492    0.4628    0.2018    2.3345    0.0017
+#> 488:    94.2121   -0.1965    2.0923    2.3489    0.4625    0.2020    2.3347    0.0017
+#> 489:    94.2119   -0.1966    2.0923    2.3497    0.4622    0.2023    2.3346    0.0017
+#> 490:    94.2120   -0.1966    2.0923    2.3476    0.4618    0.2025    2.3346    0.0017
+#> 491:    94.2124   -0.1966    2.0923    2.3462    0.4615    0.2028    2.3346    0.0017
+#> 492:    94.2118   -0.1966    2.0923    2.3453    0.4613    0.2029    2.3346    0.0017
+#> 493:    94.2113   -0.1967    2.0923    2.3452    0.4610    0.2030    2.3347    0.0017
+#> 494:    94.2118   -0.1968    2.0922    2.3488    0.4608    0.2030    2.3347    0.0017
+#> 495:    94.2122   -0.1969    2.0920    2.3530    0.4605    0.2029    2.3347    0.0017
+#> 496:    94.2138   -0.1969    2.0919    2.3540    0.4603    0.2028    2.3350    0.0017
+#> 497:    94.2148   -0.1970    2.0917    2.3554    0.4601    0.2029    2.3352    0.0017
+#> 498:    94.2152   -0.1971    2.0916    2.3534    0.4600    0.2029    2.3356    0.0017
+#> 499:    94.2157   -0.1972    2.0914    2.3519    0.4598    0.2029    2.3357    0.0016
+#> 500:    94.2162   -0.1973    2.0912    2.3498    0.4596    0.2030    2.3358    0.0016
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 | log_alpha |  log_beta | sigma_low |
+#> |.....................|  rsd_high |        o1 |        o2 |        o3 |
+#> |    1|     356.08238 |     1.000 |    -1.000 |   -0.9495 |   -0.9739 |
+#> |.....................|   -0.9969 |   -0.9818 |   -0.9750 |   -0.9744 |
+#> |    U|     356.08238 |     93.10 |   -0.1209 |     2.232 |     1.095 |
+#> |.....................|   0.02509 |    0.7272 |     1.045 |     1.072 |
+#> |    X|     356.08238 |     93.10 |    0.8861 |     9.321 |     1.095 |
+#> |.....................|   0.02509 |    0.7272 |     1.045 |     1.072 |
+#> |    G|    Gill Diff. |    -85.81 |    0.5929 |    0.9043 |    -97.79 |
+#> |.....................|    -28.71 |  -0.07427 |    -8.550 |    -12.99 |
+#> |    2|     1940.7752 |     1.640 |    -1.004 |   -0.9563 |   -0.2449 |
+#> |.....................|   -0.7829 |   -0.9813 |   -0.9112 |   -0.8775 |
+#> |    U|     1940.7752 |     152.7 |   -0.1253 |     2.226 |     1.495 |
+#> |.....................|   0.02778 |    0.7276 |     1.112 |     1.176 |
+#> |    X|     1940.7752 |     152.7 |    0.8822 |     9.258 |     1.495 |
+#> |.....................|   0.02778 |    0.7276 |     1.112 |     1.176 |
+#> |    3|     370.78508 |     1.064 |    -1.000 |   -0.9502 |   -0.9010 |
+#> |.....................|   -0.9755 |   -0.9817 |   -0.9686 |   -0.9647 |
+#> |    U|     370.78508 |     99.05 |   -0.1213 |     2.232 |     1.135 |
+#> |.....................|   0.02536 |    0.7272 |     1.052 |     1.082 |
+#> |    X|     370.78508 |     99.05 |    0.8857 |     9.315 |     1.135 |
+#> |.....................|   0.02536 |    0.7272 |     1.052 |     1.082 |
+#> |    4|     354.52588 |     1.015 |    -1.000 |   -0.9497 |   -0.9565 |
+#> |.....................|   -0.9918 |   -0.9818 |   -0.9735 |   -0.9721 |
+#> |    U|     354.52588 |     94.52 |   -0.1210 |     2.232 |     1.105 |
+#> |.....................|   0.02516 |    0.7272 |     1.047 |     1.074 |
+#> |    X|     354.52588 |     94.52 |    0.8860 |     9.319 |     1.105 |
+#> |.....................|   0.02516 |    0.7272 |     1.047 |     1.074 |
+#> |    F| Forward Diff. |     126.3 |    0.7329 |     1.391 |    -95.71 |
+#> |.....................|    -26.58 |    0.4812 |    -8.528 |    -12.76 |
+#> |    5|     352.43362 |    0.9998 |    -1.000 |   -0.9499 |   -0.9392 |
+#> |.....................|   -0.9869 |   -0.9819 |   -0.9719 |   -0.9698 |
+#> |    U|     352.43362 |     93.08 |   -0.1211 |     2.232 |     1.114 |
+#> |.....................|   0.02522 |    0.7271 |     1.048 |     1.077 |
+#> |    X|     352.43362 |     93.08 |    0.8859 |     9.317 |     1.114 |
+#> |.....................|   0.02522 |    0.7271 |     1.048 |     1.077 |
+#> |    F| Forward Diff. |    -88.58 |    0.5971 |    0.9141 |    -92.65 |
+#> |.....................|    -26.61 |  -0.01862 |    -8.458 |    -12.78 |
+#> |    6|     350.82994 |     1.015 |    -1.000 |   -0.9501 |   -0.9214 |
+#> |.....................|   -0.9818 |   -0.9819 |   -0.9703 |   -0.9673 |
+#> |    U|     350.82994 |     94.46 |   -0.1213 |     2.232 |     1.124 |
+#> |.....................|   0.02528 |    0.7271 |     1.050 |     1.079 |
+#> |    X|     350.82994 |     94.46 |    0.8858 |     9.315 |     1.124 |
+#> |.....................|   0.02528 |    0.7271 |     1.050 |     1.079 |
+#> |    F| Forward Diff. |     115.7 |    0.7442 |     1.407 |    -90.51 |
+#> |.....................|    -24.67 |    0.2416 |    -8.378 |    -12.59 |
+#> |    7|     348.85697 |     1.000 |    -1.000 |   -0.9503 |   -0.9035 |
+#> |.....................|   -0.9769 |   -0.9819 |   -0.9686 |   -0.9649 |
+#> |    U|     348.85697 |     93.10 |   -0.1214 |     2.231 |     1.134 |
+#> |.....................|   0.02534 |    0.7271 |     1.052 |     1.082 |
+#> |    X|     348.85697 |     93.10 |    0.8857 |     9.313 |     1.134 |
+#> |.....................|   0.02534 |    0.7271 |     1.052 |     1.082 |
+#> |    F| Forward Diff. |    -86.89 |    0.6078 |    0.9395 |    -87.49 |
+#> |.....................|    -24.70 |   -0.2033 |    -8.301 |    -12.59 |
+#> |    8|     347.23757 |     1.014 |    -1.001 |   -0.9506 |   -0.8852 |
+#> |.....................|   -0.9717 |   -0.9819 |   -0.9669 |   -0.9622 |
+#> |    U|     347.23757 |     94.41 |   -0.1215 |     2.231 |     1.144 |
+#> |.....................|   0.02541 |    0.7271 |     1.054 |     1.085 |
+#> |    X|     347.23757 |     94.41 |    0.8856 |     9.311 |     1.144 |
+#> |.....................|   0.02541 |    0.7271 |     1.054 |     1.085 |
+#> |    F| Forward Diff. |     106.0 |    0.7499 |     1.419 |    -85.67 |
+#> |.....................|    -22.89 |  -0.09812 |    -8.213 |    -12.39 |
+#> |    9|     345.37317 |     1.000 |    -1.001 |   -0.9508 |   -0.8667 |
+#> |.....................|   -0.9667 |   -0.9818 |   -0.9651 |   -0.9596 |
+#> |    U|     345.37317 |     93.12 |   -0.1217 |     2.231 |     1.154 |
+#> |.....................|   0.02547 |    0.7272 |     1.056 |     1.088 |
+#> |    X|     345.37317 |     93.12 |    0.8854 |     9.308 |     1.154 |
+#> |.....................|   0.02547 |    0.7272 |     1.056 |     1.088 |
+#> |    F| Forward Diff. |    -84.47 |    0.6193 |    0.9668 |    -82.72 |
+#> |.....................|    -22.87 |   -0.2860 |    -8.128 |    -12.38 |
+#> |   10|     343.77522 |     1.014 |    -1.001 |   -0.9511 |   -0.8479 |
+#> |.....................|   -0.9616 |   -0.9818 |   -0.9633 |   -0.9568 |
+#> |    U|     343.77522 |     94.37 |   -0.1218 |     2.231 |     1.164 |
+#> |.....................|   0.02554 |    0.7272 |     1.057 |     1.091 |
+#> |    X|     343.77522 |     94.37 |    0.8853 |     9.306 |     1.164 |
+#> |.....................|   0.02554 |    0.7272 |     1.057 |     1.091 |
+#> |    F| Forward Diff. |     98.54 |    0.7582 |     1.440 |    -80.80 |
+#> |.....................|    -21.11 |   -0.2480 |    -8.037 |    -12.18 |
+#> |   11|     342.01002 |     1.000 |    -1.001 |   -0.9514 |   -0.8290 |
+#> |.....................|   -0.9566 |   -0.9817 |   -0.9614 |   -0.9539 |
+#> |    U|     342.01002 |     93.14 |   -0.1220 |     2.230 |     1.175 |
+#> |.....................|   0.02560 |    0.7273 |     1.059 |     1.094 |
+#> |    X|     342.01002 |     93.14 |    0.8852 |     9.303 |     1.175 |
+#> |.....................|   0.02560 |    0.7273 |     1.059 |     1.094 |
+#> |    F| Forward Diff. |    -81.78 |    0.6281 |    0.9934 |    -78.17 |
+#> |.....................|    -21.11 |   -0.4903 |    -7.943 |    -12.16 |
+#> |   12|     340.43696 |     1.013 |    -1.001 |   -0.9517 |   -0.8098 |
+#> |.....................|   -0.9515 |   -0.9816 |   -0.9595 |   -0.9509 |
+#> |    U|     340.43696 |     94.32 |   -0.1222 |     2.230 |     1.185 |
+#> |.....................|   0.02566 |    0.7274 |     1.062 |     1.097 |
+#> |    X|     340.43696 |     94.32 |    0.8850 |     9.301 |     1.185 |
+#> |.....................|   0.02566 |    0.7274 |     1.062 |     1.097 |
+#> |    F| Forward Diff. |     90.87 |    0.7671 |     1.462 |    -75.86 |
+#> |.....................|    -19.30 |   -0.2119 |    -7.851 |    -11.96 |
+#> |   13|     338.78414 |     1.001 |    -1.001 |   -0.9520 |   -0.7906 |
+#> |.....................|   -0.9465 |   -0.9815 |   -0.9574 |   -0.9478 |
+#> |    U|     338.78414 |     93.15 |   -0.1223 |     2.230 |     1.196 |
+#> |.....................|   0.02572 |    0.7274 |     1.064 |     1.100 |
+#> |    X|     338.78414 |     93.15 |    0.8848 |     9.298 |     1.196 |
+#> |.....................|   0.02572 |    0.7274 |     1.064 |     1.100 |
+#> |    F| Forward Diff. |    -80.47 |    0.6431 |     1.023 |    -73.28 |
+#> |.....................|    -19.27 |   -0.2791 |    -7.739 |    -11.92 |
+#> |   14|     337.22825 |     1.013 |    -1.002 |   -0.9523 |   -0.7710 |
+#> |.....................|   -0.9415 |   -0.9814 |   -0.9553 |   -0.9445 |
+#> |    U|     337.22825 |     94.28 |   -0.1225 |     2.229 |     1.206 |
+#> |.....................|   0.02579 |    0.7275 |     1.066 |     1.104 |
+#> |    X|     337.22825 |     94.28 |    0.8847 |     9.295 |     1.206 |
+#> |.....................|   0.02579 |    0.7275 |     1.066 |     1.104 |
+#> |    F| Forward Diff. |     82.17 |    0.7754 |     1.480 |    -71.69 |
+#> |.....................|    -17.81 |   -0.5846 |    -7.635 |    -11.71 |
+#> |   15|     335.66851 |     1.001 |    -1.002 |   -0.9527 |   -0.7512 |
+#> |.....................|   -0.9367 |   -0.9812 |   -0.9531 |   -0.9411 |
+#> |    U|     335.66851 |     93.18 |   -0.1228 |     2.229 |     1.217 |
+#> |.....................|   0.02585 |    0.7276 |     1.068 |     1.108 |
+#> |    X|     335.66851 |     93.18 |    0.8845 |     9.291 |     1.217 |
+#> |.....................|   0.02585 |    0.7276 |     1.068 |     1.108 |
+#> |    F| Forward Diff. |    -77.03 |    0.6546 |     1.055 |    -69.28 |
+#> |.....................|    -17.76 |   -0.6126 |    -7.531 |    -11.66 |
+#> |   16|     334.17549 |     1.012 |    -1.002 |   -0.9531 |   -0.7314 |
+#> |.....................|   -0.9319 |   -0.9810 |   -0.9509 |   -0.9376 |
+#> |    U|     334.17549 |     94.25 |   -0.1230 |     2.229 |     1.228 |
+#> |.....................|   0.02591 |    0.7278 |     1.070 |     1.111 |
+#> |    X|     334.17549 |     94.25 |    0.8843 |     9.287 |     1.228 |
+#> |.....................|   0.02591 |    0.7278 |     1.070 |     1.111 |
+#> |    F| Forward Diff. |     77.34 |    0.7869 |     1.511 |    -67.40 |
+#> |.....................|    -16.23 |   -0.6338 |    -7.414 |    -11.45 |
+#> |   17|     332.70253 |     1.001 |    -1.002 |   -0.9536 |   -0.7113 |
+#> |.....................|   -0.9273 |   -0.9807 |   -0.9485 |   -0.9339 |
+#> |    U|     332.70253 |     93.20 |   -0.1232 |     2.228 |     1.239 |
+#> |.....................|   0.02597 |    0.7280 |     1.073 |     1.115 |
+#> |    X|     332.70253 |     93.20 |    0.8841 |     9.283 |     1.239 |
+#> |.....................|   0.02597 |    0.7280 |     1.073 |     1.115 |
+#> |    F| Forward Diff. |    -74.42 |    0.6680 |     1.089 |    -65.07 |
+#> |.....................|    -16.20 |   -0.6067 |    -7.288 |    -11.39 |
+#> |   18|     331.26057 |     1.012 |    -1.003 |   -0.9540 |   -0.6912 |
+#> |.....................|   -0.9227 |   -0.9804 |   -0.9461 |   -0.9301 |
+#> |    U|     331.26057 |     94.22 |   -0.1235 |     2.228 |     1.250 |
+#> |.....................|   0.02602 |    0.7282 |     1.076 |     1.119 |
+#> |    X|     331.26057 |     94.22 |    0.8838 |     9.279 |     1.250 |
+#> |.....................|   0.02602 |    0.7282 |     1.076 |     1.119 |
+#> |    F| Forward Diff. |     71.33 |    0.7962 |     1.537 |    -63.45 |
+#> |.....................|    -14.84 |   -0.8466 |    -7.169 |    -11.16 |
+#> |   19|     329.86877 |     1.001 |    -1.003 |   -0.9546 |   -0.6708 |
+#> |.....................|   -0.9184 |   -0.9799 |   -0.9435 |   -0.9260 |
+#> |    U|     329.86877 |     93.23 |   -0.1238 |     2.227 |     1.261 |
+#> |.....................|   0.02608 |    0.7285 |     1.078 |     1.124 |
+#> |    X|     329.86877 |     93.23 |    0.8836 |     9.273 |     1.261 |
+#> |.....................|   0.02608 |    0.7285 |     1.078 |     1.124 |
+#> |    F| Forward Diff. |    -70.96 |    0.6825 |     1.126 |    -60.92 |
+#> |.....................|    -14.66 |   -0.5289 |    -7.027 |    -11.08 |
+#> |   20|     328.50031 |     1.012 |    -1.003 |   -0.9552 |   -0.6504 |
+#> |.....................|   -0.9143 |   -0.9795 |   -0.9408 |   -0.9217 |
+#> |    U|     328.50031 |     94.20 |   -0.1241 |     2.227 |     1.272 |
+#> |.....................|   0.02613 |    0.7288 |     1.081 |     1.128 |
+#> |    X|     328.50031 |     94.20 |    0.8833 |     9.268 |     1.272 |
+#> |.....................|   0.02613 |    0.7288 |     1.081 |     1.128 |
+#> |    F| Forward Diff. |     67.86 |    0.8082 |     1.577 |    -59.49 |
+#> |.....................|    -13.42 |   -0.7986 |    -6.899 |    -10.84 |
+#> |   21|     327.16645 |     1.002 |    -1.004 |   -0.9559 |   -0.6298 |
+#> |.....................|   -0.9105 |   -0.9791 |   -0.9380 |   -0.9171 |
+#> |    U|     327.16645 |     93.27 |   -0.1245 |     2.226 |     1.284 |
+#> |.....................|   0.02618 |    0.7291 |     1.084 |     1.133 |
+#> |    X|     327.16645 |     93.27 |    0.8829 |     9.261 |     1.284 |
+#> |.....................|   0.02618 |    0.7291 |     1.084 |     1.133 |
+#> |    F| Forward Diff. |    -65.39 |    0.6978 |     1.172 |    -57.48 |
+#> |.....................|    -13.36 |   -0.7754 |    -6.743 |    -10.73 |
+#> |   22|     325.87373 |     1.012 |    -1.004 |   -0.9567 |   -0.6091 |
+#> |.....................|   -0.9070 |   -0.9785 |   -0.9351 |   -0.9123 |
+#> |    U|     325.87373 |     94.19 |   -0.1249 |     2.225 |     1.295 |
+#> |.....................|   0.02622 |    0.7296 |     1.087 |     1.138 |
+#> |    X|     325.87373 |     94.19 |    0.8826 |     9.255 |     1.295 |
+#> |.....................|   0.02622 |    0.7296 |     1.087 |     1.138 |
+#> |    F| Forward Diff. |     64.00 |    0.8187 |     1.613 |    -55.46 |
+#> |.....................|    -12.01 |   -0.6347 |    -6.615 |    -10.48 |
+#> |   23|     324.62990 |     1.002 |    -1.004 |   -0.9576 |   -0.5884 |
+#> |.....................|   -0.9040 |   -0.9780 |   -0.9320 |   -0.9071 |
+#> |    U|      324.6299 |     93.29 |   -0.1254 |     2.224 |     1.306 |
+#> |.....................|   0.02626 |    0.7300 |     1.090 |     1.144 |
+#> |    X|      324.6299 |     93.29 |    0.8822 |     9.246 |     1.306 |
+#> |.....................|   0.02626 |    0.7300 |     1.090 |     1.144 |
+#> |    F| Forward Diff. |    -64.25 |    0.7091 |     1.205 |    -53.86 |
+#> |.....................|    -12.06 |   -0.7132 |    -6.446 |    -10.35 |
+#> |   24|     323.37595 |     1.011 |    -1.005 |   -0.9586 |   -0.5676 |
+#> |.....................|   -0.9015 |   -0.9774 |   -0.9287 |   -0.9014 |
+#> |    U|     323.37595 |     94.14 |   -0.1259 |     2.223 |     1.318 |
+#> |.....................|   0.02629 |    0.7304 |     1.094 |     1.150 |
+#> |    X|     323.37595 |     94.14 |    0.8817 |     9.236 |     1.318 |
+#> |.....................|   0.02629 |    0.7304 |     1.094 |     1.150 |
+#> |    F| Forward Diff. |     56.04 |    0.8254 |     1.637 |    -52.44 |
+#> |.....................|    -10.96 |   -0.9420 |    -6.280 |    -10.07 |
+#> |   25|     322.22752 |     1.002 |    -1.006 |   -0.9598 |   -0.5467 |
+#> |.....................|   -0.8995 |   -0.9764 |   -0.9254 |   -0.8957 |
+#> |    U|     322.22752 |     93.30 |   -0.1265 |     2.222 |     1.329 |
+#> |.....................|   0.02631 |    0.7311 |     1.097 |     1.156 |
+#> |    X|     322.22752 |     93.30 |    0.8812 |     9.225 |     1.329 |
+#> |.....................|   0.02631 |    0.7311 |     1.097 |     1.156 |
+#> |    F| Forward Diff. |    -62.58 |    0.7198 |     1.238 |    -50.46 |
+#> |.....................|    -10.85 |   -0.6563 |    -6.111 |    -9.931 |
+#> |   26|     321.05050 |     1.011 |    -1.006 |   -0.9612 |   -0.5258 |
+#> |.....................|   -0.8983 |   -0.9755 |   -0.9219 |   -0.8894 |
+#> |    U|      321.0505 |     94.13 |   -0.1272 |     2.221 |     1.341 |
+#> |.....................|   0.02633 |    0.7318 |     1.101 |     1.163 |
+#> |    X|      321.0505 |     94.13 |    0.8805 |     9.213 |     1.341 |
+#> |.....................|   0.02633 |    0.7318 |     1.101 |     1.163 |
+#> |    F| Forward Diff. |     53.55 |    0.8319 |     1.674 |    -49.18 |
+#> |.....................|    -9.827 |   -0.8926 |    -5.944 |    -9.631 |
+#> |   27|     319.96320 |     1.003 |    -1.007 |   -0.9629 |   -0.5048 |
+#> |.....................|   -0.8978 |   -0.9744 |   -0.9184 |   -0.8829 |
+#> |    U|      319.9632 |     93.35 |   -0.1280 |     2.219 |     1.352 |
+#> |.....................|   0.02633 |    0.7325 |     1.104 |     1.170 |
+#> |    X|      319.9632 |     93.35 |    0.8798 |     9.197 |     1.352 |
+#> |.....................|   0.02633 |    0.7325 |     1.104 |     1.170 |
+#> |    F| Forward Diff. |    -57.14 |    0.7318 |     1.284 |    -47.52 |
+#> |.....................|    -9.778 |   -0.7040 |    -5.744 |    -9.448 |
+#> |   28|     318.87595 |     1.011 |    -1.008 |   -0.9647 |   -0.4840 |
+#> |.....................|   -0.8984 |   -0.9733 |   -0.9148 |   -0.8761 |
+#> |    U|     318.87595 |     94.12 |   -0.1289 |     2.217 |     1.364 |
+#> |.....................|   0.02633 |    0.7334 |     1.108 |     1.177 |
+#> |    X|     318.87595 |     94.12 |    0.8790 |     9.180 |     1.364 |
+#> |.....................|   0.02633 |    0.7334 |     1.108 |     1.177 |
+#> |    F| Forward Diff. |     50.84 |    0.8352 |     1.706 |    -46.29 |
+#> |.....................|    -8.837 |   -0.9158 |    -5.564 |    -9.134 |
+#> |   29|     317.86528 |     1.003 |    -1.009 |   -0.9669 |   -0.4631 |
+#> |.....................|   -0.9000 |   -0.9719 |   -0.9113 |   -0.8691 |
+#> |    U|     317.86528 |     93.39 |   -0.1300 |     2.215 |     1.375 |
+#> |.....................|   0.02631 |    0.7344 |     1.112 |     1.185 |
+#> |    X|     317.86528 |     93.39 |    0.8781 |     9.160 |     1.375 |
+#> |.....................|   0.02631 |    0.7344 |     1.112 |     1.185 |
+#> |    F| Forward Diff. |    -53.64 |    0.7337 |     1.307 |    -44.73 |
+#> |.....................|    -8.788 |   -0.7242 |    -5.380 |    -8.940 |
+#> |   30|     316.86653 |     1.011 |    -1.010 |   -0.9694 |   -0.4424 |
+#> |.....................|   -0.9029 |   -0.9703 |   -0.9078 |   -0.8619 |
+#> |    U|     316.86653 |     94.11 |   -0.1312 |     2.212 |     1.386 |
+#> |.....................|   0.02627 |    0.7355 |     1.115 |     1.192 |
+#> |    X|     316.86653 |     94.11 |    0.8771 |     9.137 |     1.386 |
+#> |.....................|   0.02627 |    0.7355 |     1.115 |     1.192 |
+#> |    F| Forward Diff. |     47.91 |    0.8298 |     1.717 |    -43.37 |
+#> |.....................|    -7.860 |   -0.7095 |    -5.221 |    -8.628 |
+#> |   31|     315.94581 |     1.003 |    -1.012 |   -0.9723 |   -0.4219 |
+#> |.....................|   -0.9070 |   -0.9693 |   -0.9044 |   -0.8547 |
+#> |    U|     315.94581 |     93.42 |   -0.1325 |     2.209 |     1.398 |
+#> |.....................|   0.02622 |    0.7363 |     1.119 |     1.200 |
+#> |    X|     315.94581 |     93.42 |    0.8759 |     9.111 |     1.398 |
+#> |.....................|   0.02622 |    0.7363 |     1.119 |     1.200 |
+#> |    F| Forward Diff. |    -50.84 |    0.7268 |     1.307 |    -41.97 |
+#> |.....................|    -7.840 |   -0.5502 |    -5.032 |    -8.421 |
+#> |   32|     315.03994 |     1.011 |    -1.013 |   -0.9754 |   -0.4018 |
+#> |.....................|   -0.9129 |   -0.9687 |   -0.9011 |   -0.8473 |
+#> |    U|     315.03994 |     94.09 |   -0.1340 |     2.206 |     1.409 |
+#> |.....................|   0.02615 |    0.7367 |     1.122 |     1.208 |
+#> |    X|     315.03994 |     94.09 |    0.8746 |     9.082 |     1.409 |
+#> |.....................|   0.02615 |    0.7367 |     1.122 |     1.208 |
+#> |    F| Forward Diff. |     43.50 |    0.8139 |     1.698 |    -41.38 |
+#> |.....................|    -7.196 |   -0.9249 |    -4.882 |    -8.108 |
+#> |   33|     314.20198 |     1.004 |    -1.015 |   -0.9788 |   -0.3816 |
+#> |.....................|   -0.9197 |   -0.9671 |   -0.8983 |   -0.8406 |
+#> |    U|     314.20198 |     93.47 |   -0.1355 |     2.203 |     1.420 |
+#> |.....................|   0.02606 |    0.7379 |     1.125 |     1.215 |
+#> |    X|     314.20198 |     93.47 |    0.8733 |     9.052 |     1.420 |
+#> |.....................|   0.02606 |    0.7379 |     1.125 |     1.215 |
+#> |    F| Forward Diff. |    -46.04 |    0.7133 |     1.286 |    -40.35 |
+#> |.....................|    -7.243 |   -0.8268 |    -4.724 |    -7.917 |
+#> |   34|     313.39087 |     1.011 |    -1.016 |   -0.9822 |   -0.3616 |
+#> |.....................|   -0.9277 |   -0.9641 |   -0.8960 |   -0.8348 |
+#> |    U|     313.39087 |     94.10 |   -0.1371 |     2.200 |     1.431 |
+#> |.....................|   0.02596 |    0.7401 |     1.128 |     1.221 |
+#> |    X|     313.39087 |     94.10 |    0.8719 |     9.021 |     1.431 |
+#> |.....................|   0.02596 |    0.7401 |     1.128 |     1.221 |
+#> |    F| Forward Diff. |     42.44 |    0.7936 |     1.657 |    -38.93 |
+#> |.....................|    -6.417 |   -0.6060 |    -4.631 |    -7.687 |
+#> |   35|     312.65204 |     1.004 |    -1.018 |   -0.9857 |   -0.3421 |
+#> |.....................|   -0.9371 |   -0.9626 |   -0.8936 |   -0.8290 |
+#> |    U|     312.65204 |     93.49 |   -0.1387 |     2.196 |     1.441 |
+#> |.....................|   0.02584 |    0.7411 |     1.130 |     1.228 |
+#> |    X|     312.65204 |     93.49 |    0.8705 |     8.989 |     1.441 |
+#> |.....................|   0.02584 |    0.7411 |     1.130 |     1.228 |
+#> |    F| Forward Diff. |    -46.74 |    0.6875 |     1.233 |    -38.07 |
+#> |.....................|    -6.520 |   -0.5247 |    -4.495 |    -7.518 |
+#> |   36|     311.92333 |     1.010 |    -1.020 |   -0.9894 |   -0.3235 |
+#> |.....................|   -0.9483 |   -0.9627 |   -0.8910 |   -0.8230 |
+#> |    U|     311.92333 |     94.07 |   -0.1404 |     2.192 |     1.452 |
+#> |.....................|   0.02570 |    0.7411 |     1.133 |     1.234 |
+#> |    X|     311.92333 |     94.07 |    0.8690 |     8.957 |     1.452 |
+#> |.....................|   0.02570 |    0.7411 |     1.133 |     1.234 |
+#> |    F| Forward Diff. |     35.63 |    0.7624 |     1.583 |    -37.23 |
+#> |.....................|    -5.893 |   -0.6222 |    -4.382 |    -7.287 |
+#> |   37|     311.27355 |     1.004 |    -1.021 |   -0.9929 |   -0.3046 |
+#> |.....................|   -0.9595 |   -0.9623 |   -0.8888 |   -0.8177 |
+#> |    U|     311.27355 |     93.51 |   -0.1420 |     2.189 |     1.462 |
+#> |.....................|   0.02556 |    0.7413 |     1.135 |     1.240 |
+#> |    X|     311.27355 |     93.51 |    0.8676 |     8.925 |     1.462 |
+#> |.....................|   0.02556 |    0.7413 |     1.135 |     1.240 |
+#> |    F| Forward Diff. |    -45.98 |    0.6631 |     1.170 |    -36.31 |
+#> |.....................|    -5.950 |   -0.4376 |    -4.255 |    -7.133 |
+#> |   38|     310.62439 |     1.010 |    -1.023 |   -0.9963 |   -0.2868 |
+#> |.....................|   -0.9728 |   -0.9625 |   -0.8869 |   -0.8128 |
+#> |    U|     310.62439 |     94.07 |   -0.1437 |     2.185 |     1.472 |
+#> |.....................|   0.02539 |    0.7412 |     1.137 |     1.245 |
+#> |    X|     310.62439 |     94.07 |    0.8661 |     8.895 |     1.472 |
+#> |.....................|   0.02539 |    0.7412 |     1.137 |     1.245 |
+#> |    F| Forward Diff. |     33.19 |    0.7369 |     1.513 |    -35.63 |
+#> |.....................|    -5.399 |   -0.5527 |    -4.174 |    -6.950 |
+#> |   39|     310.04420 |     1.005 |    -1.024 |   -0.9995 |   -0.2687 |
+#> |.....................|   -0.9859 |   -0.9628 |   -0.8850 |   -0.8081 |
+#> |    U|      310.0442 |     93.55 |   -0.1453 |     2.182 |     1.482 |
+#> |.....................|   0.02523 |    0.7410 |     1.139 |     1.250 |
+#> |    X|      310.0442 |     93.55 |    0.8648 |     8.866 |     1.482 |
+#> |.....................|   0.02523 |    0.7410 |     1.139 |     1.250 |
+#> |    F| Forward Diff. |    -43.63 |    0.6390 |     1.117 |    -34.92 |
+#> |.....................|    -5.491 |   -0.4082 |    -4.072 |    -6.814 |
+#> |   40|     309.46411 |     1.010 |    -1.026 |    -1.003 |   -0.2518 |
+#> |.....................|    -1.001 |   -0.9632 |   -0.8835 |   -0.8040 |
+#> |    U|     309.46411 |     94.07 |   -0.1468 |     2.179 |     1.491 |
+#> |.....................|   0.02504 |    0.7407 |     1.141 |     1.254 |
+#> |    X|     309.46411 |     94.07 |    0.8634 |     8.839 |     1.491 |
+#> |.....................|   0.02504 |    0.7407 |     1.141 |     1.254 |
+#> |    F| Forward Diff. |     30.94 |    0.7075 |     1.451 |    -34.14 |
+#> |.....................|    -4.970 |   -0.4915 |    -4.021 |    -6.668 |
+#> |   41|     308.94397 |     1.005 |    -1.027 |    -1.005 |   -0.2344 |
+#> |.....................|    -1.015 |   -0.9639 |   -0.8817 |   -0.7999 |
+#> |    U|     308.94397 |     93.57 |   -0.1483 |     2.176 |     1.500 |
+#> |.....................|   0.02486 |    0.7402 |     1.143 |     1.259 |
+#> |    X|     308.94397 |     93.57 |    0.8622 |     8.814 |     1.500 |
+#> |.....................|   0.02486 |    0.7402 |     1.143 |     1.259 |
+#> |    F| Forward Diff. |    -43.40 |    0.6150 |     1.062 |    -33.15 |
+#> |.....................|    -4.981 |   -0.1275 |    -3.914 |    -6.542 |
+#> |   42|     308.42636 |     1.010 |    -1.029 |    -1.008 |   -0.2188 |
+#> |.....................|    -1.031 |   -0.9663 |   -0.8797 |   -0.7956 |
+#> |    U|     308.42636 |     94.07 |   -0.1498 |     2.174 |     1.509 |
+#> |.....................|   0.02466 |    0.7384 |     1.145 |     1.264 |
+#> |    X|     308.42636 |     94.07 |    0.8609 |     8.789 |     1.509 |
+#> |.....................|   0.02466 |    0.7384 |     1.145 |     1.264 |
+#> |    F| Forward Diff. |     28.94 |    0.6832 |     1.395 |    -33.36 |
+#> |.....................|    -4.720 |   -0.6585 |    -3.841 |    -6.387 |
+#> |   43|     307.94294 |     1.006 |    -1.030 |    -1.011 |   -0.2019 |
+#> |.....................|    -1.047 |   -0.9672 |   -0.8783 |   -0.7922 |
+#> |    U|     307.94294 |     93.62 |   -0.1511 |     2.171 |     1.518 |
+#> |.....................|   0.02447 |    0.7378 |     1.146 |     1.267 |
+#> |    X|     307.94294 |     93.62 |    0.8597 |     8.766 |     1.518 |
+#> |.....................|   0.02447 |    0.7378 |     1.146 |     1.267 |
+#> |    F| Forward Diff. |    -38.44 |    0.5985 |     1.037 |    -32.41 |
+#> |.....................|    -4.734 |   -0.3663 |    -3.762 |    -6.284 |
+#> |   44|     307.46797 |     1.011 |    -1.032 |    -1.013 |   -0.1861 |
+#> |.....................|    -1.063 |   -0.9666 |   -0.8774 |   -0.7896 |
+#> |    U|     307.46797 |     94.11 |   -0.1524 |     2.169 |     1.527 |
+#> |.....................|   0.02426 |    0.7383 |     1.147 |     1.270 |
+#> |    X|     307.46797 |     94.11 |    0.8586 |     8.746 |     1.527 |
+#> |.....................|   0.02426 |    0.7383 |     1.147 |     1.270 |
+#> |    F| Forward Diff. |     31.70 |    0.6652 |     1.367 |    -32.07 |
+#> |.....................|    -4.364 |   -0.4841 |    -3.739 |    -6.200 |
+#> |   45|     307.02197 |     1.006 |    -1.033 |    -1.016 |   -0.1702 |
+#> |.....................|    -1.080 |   -0.9671 |   -0.8762 |   -0.7866 |
+#> |    U|     307.02197 |     93.66 |   -0.1537 |     2.166 |     1.536 |
+#> |.....................|   0.02405 |    0.7379 |     1.149 |     1.273 |
+#> |    X|     307.02197 |     93.66 |    0.8575 |     8.725 |     1.536 |
+#> |.....................|   0.02405 |    0.7379 |     1.149 |     1.273 |
+#> |    F| Forward Diff. |    -34.81 |    0.5817 |     1.015 |    -31.25 |
+#> |.....................|    -4.413 |   -0.2597 |    -3.670 |    -6.117 |
+#> |   46|     306.58875 |     1.011 |    -1.034 |    -1.018 |   -0.1551 |
+#> |.....................|    -1.097 |   -0.9684 |   -0.8747 |   -0.7833 |
+#> |    U|     306.58875 |     94.13 |   -0.1549 |     2.164 |     1.544 |
+#> |.....................|   0.02384 |    0.7369 |     1.150 |     1.277 |
+#> |    X|     306.58875 |     94.13 |    0.8565 |     8.705 |     1.544 |
+#> |.....................|   0.02384 |    0.7369 |     1.150 |     1.277 |
+#> |    F| Forward Diff. |     31.47 |    0.6484 |     1.332 |    -31.08 |
+#> |.....................|    -4.101 |   -0.4354 |    -3.617 |    -5.999 |
+#> |   47|     306.17343 |     1.006 |    -1.035 |    -1.020 |   -0.1399 |
+#> |.....................|    -1.114 |   -0.9699 |   -0.8732 |   -0.7802 |
+#> |    U|     306.17343 |     93.70 |   -0.1561 |     2.162 |     1.552 |
+#> |.....................|   0.02362 |    0.7358 |     1.152 |     1.280 |
+#> |    X|     306.17343 |     93.70 |    0.8554 |     8.686 |     1.552 |
+#> |.....................|   0.02362 |    0.7358 |     1.152 |     1.280 |
+#> |    F| Forward Diff. |    -31.81 |    0.5683 |    0.9956 |    -30.69 |
+#> |.....................|    -4.225 |   -0.4059 |    -3.540 |    -5.903 |
+#> |   48|     305.76609 |     1.011 |    -1.036 |    -1.022 |   -0.1248 |
+#> |.....................|    -1.132 |   -0.9702 |   -0.8722 |   -0.7778 |
+#> |    U|     305.76609 |     94.14 |   -0.1573 |     2.160 |     1.560 |
+#> |.....................|   0.02340 |    0.7356 |     1.153 |     1.283 |
+#> |    X|     305.76609 |     94.14 |    0.8545 |     8.668 |     1.560 |
+#> |.....................|   0.02340 |    0.7356 |     1.153 |     1.283 |
+#> |    F| Forward Diff. |     30.78 |    0.6301 |     1.297 |    -30.24 |
+#> |.....................|    -3.891 |   -0.4278 |    -3.502 |    -5.825 |
+#> |   49|     305.37620 |     1.007 |    -1.037 |    -1.024 |   -0.1098 |
+#> |.....................|    -1.149 |   -0.9705 |   -0.8714 |   -0.7755 |
+#> |    U|      305.3762 |     93.72 |   -0.1584 |     2.158 |     1.569 |
+#> |.....................|   0.02318 |    0.7354 |     1.154 |     1.285 |
+#> |    X|      305.3762 |     93.72 |    0.8535 |     8.651 |     1.569 |
+#> |.....................|   0.02318 |    0.7354 |     1.154 |     1.285 |
+#> |    F| Forward Diff. |    -32.45 |    0.5512 |    0.9611 |    -29.28 |
+#> |.....................|    -3.904 |  -0.09870 |    -3.459 |    -5.767 |
+#> |   50|     304.99974 |     1.011 |    -1.039 |    -1.026 |  -0.09561 |
+#> |.....................|    -1.167 |   -0.9731 |   -0.8699 |   -0.7723 |
+#> |    U|     304.99974 |     94.15 |   -0.1595 |     2.156 |     1.576 |
+#> |.....................|   0.02295 |    0.7335 |     1.155 |     1.288 |
+#> |    X|     304.99974 |     94.15 |    0.8526 |     8.633 |     1.576 |
+#> |.....................|   0.02295 |    0.7335 |     1.155 |     1.288 |
+#> |    F| Forward Diff. |     30.20 |    0.6130 |     1.265 |    -28.57 |
+#> |.....................|    -3.511 |  -0.04200 |    -3.403 |    -5.652 |
+#> |   51|     304.64794 |     1.007 |    -1.040 |    -1.028 |  -0.08217 |
+#> |.....................|    -1.185 |   -0.9783 |   -0.8678 |   -0.7682 |
+#> |    U|     304.64794 |     93.75 |   -0.1607 |     2.153 |     1.584 |
+#> |.....................|   0.02273 |    0.7297 |     1.157 |     1.293 |
+#> |    X|     304.64794 |     93.75 |    0.8516 |     8.614 |     1.584 |
+#> |.....................|   0.02273 |    0.7297 |     1.157 |     1.293 |
+#> |    F| Forward Diff. |    -30.08 |    0.5385 |    0.9408 |    -28.96 |
+#> |.....................|    -3.779 |   -0.3908 |    -3.281 |    -5.515 |
+#> |   52|     304.28931 |     1.011 |    -1.041 |    -1.030 |  -0.06828 |
+#> |.....................|    -1.203 |   -0.9811 |   -0.8668 |   -0.7655 |
+#> |    U|     304.28931 |     94.14 |   -0.1618 |     2.151 |     1.591 |
+#> |.....................|   0.02250 |    0.7277 |     1.158 |     1.296 |
+#> |    X|     304.28931 |     94.14 |    0.8506 |     8.597 |     1.591 |
+#> |.....................|   0.02250 |    0.7277 |     1.158 |     1.296 |
+#> |   53|     304.03244 |     1.011 |    -1.042 |    -1.033 |  -0.05709 |
+#> |.....................|    -1.225 |   -0.9843 |   -0.8662 |   -0.7633 |
+#> |    U|     304.03244 |     94.13 |   -0.1630 |     2.149 |     1.597 |
+#> |.....................|   0.02223 |    0.7253 |     1.159 |     1.298 |
+#> |    X|     304.03244 |     94.13 |    0.8496 |     8.578 |     1.597 |
+#> |.....................|   0.02223 |    0.7253 |     1.159 |     1.298 |
+#> |   54|     302.98899 |     1.011 |    -1.047 |    -1.041 |  -0.01055 |
+#> |.....................|    -1.314 |   -0.9977 |   -0.8638 |   -0.7544 |
+#> |    U|     302.98899 |     94.10 |   -0.1678 |     2.140 |     1.623 |
+#> |.....................|   0.02111 |    0.7156 |     1.161 |     1.308 |
+#> |    X|     302.98899 |     94.10 |    0.8455 |     8.503 |     1.623 |
+#> |.....................|   0.02111 |    0.7156 |     1.161 |     1.308 |
+#> |   55|     298.89653 |     1.010 |    -1.068 |    -1.080 |    0.1944 |
+#> |.....................|    -1.708 |    -1.057 |   -0.8531 |   -0.7150 |
+#> |    U|     298.89653 |     93.99 |   -0.1892 |     2.101 |     1.735 |
+#> |.....................|   0.01618 |    0.6726 |     1.173 |     1.350 |
+#> |    X|     298.89653 |     93.99 |    0.8276 |     8.177 |     1.735 |
+#> |.....................|   0.01618 |    0.6726 |     1.173 |     1.350 |
+#> |   56|     292.24425 |     1.012 |    -1.205 |    -1.331 |     1.218 |
+#> |.....................|    -2.997 |    -1.313 |   -0.8095 |   -0.4981 |
+#> |    U|     292.24425 |     94.21 |   -0.3257 |     1.851 |     2.296 |
+#> |.....................| 5.960e-07 |    0.4863 |     1.218 |     1.582 |
+#> |    X|     292.24425 |     94.21 |    0.7221 |     6.365 |     2.296 |
+#> |.....................| 5.960e-07 |    0.4863 |     1.218 |     1.582 |
+#> |    F| Forward Diff. |    -17.20 |    -1.896 |    -10.23 |    0.3663 |
+#> |.....................|  0.002021 |    -17.85 |    0.1528 |     5.292 |
+#> |   57|     309.71599 |    0.9897 |    -1.187 |   -0.4357 |     2.442 |
+#> |.....................|    -2.997 |    0.5394 |   -0.6812 |   -0.7129 |
+#> |    U|     309.71599 |     92.14 |   -0.3076 |     2.746 |     2.966 |
+#> |.....................| 5.960e-07 |     1.833 |     1.352 |     1.352 |
+#> |    X|     309.71599 |     92.14 |    0.7352 |     15.58 |     2.966 |
+#> |.....................| 5.960e-07 |     1.833 |     1.352 |     1.352 |
+#> |   58|     292.01474 |     1.005 |    -1.198 |    -1.013 |     1.651 |
+#> |.....................|    -2.997 |   -0.6561 |   -0.7641 |   -0.5745 |
+#> |    U|     292.01474 |     93.60 |   -0.3191 |     2.168 |     2.533 |
+#> |.....................| 5.960e-07 |    0.9640 |     1.266 |     1.501 |
+#> |    X|     292.01474 |     93.60 |    0.7268 |     8.745 |     2.533 |
+#> |.....................| 5.960e-07 |    0.9640 |     1.266 |     1.501 |
+#> |    F| Forward Diff. |    -172.4 |    -2.986 |     3.411 |     4.977 |
+#> |.....................|   0.05585 |     3.841 |     3.028 |    0.3322 |
+#> |   59|     292.30890 |     1.013 |   -0.8632 |    -1.158 |     1.672 |
+#> |.....................|    -2.997 |   -0.5770 |   -0.9665 |   -0.6082 |
+#> |    U|      292.3089 |     94.28 |   0.01586 |     2.024 |     2.544 |
+#> |.....................| 5.960e-07 |     1.022 |     1.054 |     1.464 |
+#> |    X|      292.3089 |     94.28 |     1.016 |     7.565 |     2.544 |
+#> |.....................| 5.960e-07 |     1.022 |     1.054 |     1.464 |
+#> |   60|     291.20170 |     1.015 |    -1.046 |    -1.079 |     1.660 |
+#> |.....................|    -2.997 |   -0.6203 |   -0.8561 |   -0.5898 |
+#> |    U|      291.2017 |     94.51 |   -0.1669 |     2.103 |     2.538 |
+#> |.....................| 5.960e-07 |    0.9900 |     1.170 |     1.484 |
+#> |    X|      291.2017 |     94.51 |    0.8462 |     8.187 |     2.538 |
+#> |.....................| 5.960e-07 |    0.9900 |     1.170 |     1.484 |
+#> |    F| Forward Diff. |     39.51 |    0.9033 |     2.112 |     5.106 |
+#> |.....................|   0.03418 |     2.863 |    -2.696 |   -0.7695 |
+#> |   61|     291.43833 |     1.017 |    -1.033 |    -1.136 |     1.600 |
+#> |.....................|    -2.997 |   -0.6066 |   -0.6851 |   -0.5537 |
+#> |    U|     291.43833 |     94.73 |   -0.1542 |     2.046 |     2.505 |
+#> |.....................| 5.960e-07 |     1.000 |     1.348 |     1.523 |
+#> |    X|     291.43833 |     94.73 |    0.8571 |     7.739 |     2.505 |
+#> |.....................| 5.960e-07 |     1.000 |     1.348 |     1.523 |
+#> |   62|     290.99248 |     1.014 |    -1.041 |    -1.101 |     1.637 |
+#> |.....................|    -2.997 |   -0.6152 |   -0.7907 |   -0.5760 |
+#> |    U|     290.99248 |     94.43 |   -0.1621 |     2.081 |     2.525 |
+#> |.....................| 5.960e-07 |    0.9938 |     1.238 |     1.499 |
+#> |    X|     290.99248 |     94.43 |    0.8503 |     8.012 |     2.525 |
+#> |.....................| 5.960e-07 |    0.9938 |     1.238 |     1.499 |
+#> |    F| Forward Diff. |     14.98 |     1.278 |     1.101 |     4.858 |
+#> |.....................|   0.03639 |     3.021 |    0.9673 |   -0.2780 |
+#> |   63|     291.02454 |     1.009 |    -1.102 |    -1.088 |     1.608 |
+#> |.....................|    -2.997 |   -0.6330 |   -0.7900 |   -0.5542 |
+#> |    U|     291.02454 |     93.95 |   -0.2228 |     2.094 |     2.510 |
+#> |.....................| 5.960e-07 |    0.9808 |     1.239 |     1.522 |
+#> |    X|     291.02454 |     93.95 |    0.8003 |     8.118 |     2.510 |
+#> |.....................| 5.960e-07 |    0.9808 |     1.239 |     1.522 |
+#> |   64|     291.12722 |     1.009 |    -1.068 |    -1.095 |     1.623 |
+#> |.....................|    -2.997 |   -0.6237 |   -0.7906 |   -0.5663 |
+#> |    U|     291.12722 |     93.94 |   -0.1892 |     2.087 |     2.518 |
+#> |.....................| 5.960e-07 |    0.9876 |     1.238 |     1.509 |
+#> |    X|     291.12722 |     93.94 |    0.8276 |     8.057 |     2.518 |
+#> |.....................| 5.960e-07 |    0.9876 |     1.238 |     1.509 |
+#> |   65|     291.20836 |     1.009 |    -1.048 |    -1.100 |     1.633 |
+#> |.....................|    -2.997 |   -0.6180 |   -0.7910 |   -0.5738 |
+#> |    U|     291.20836 |     93.93 |   -0.1686 |     2.082 |     2.523 |
+#> |.....................| 5.960e-07 |    0.9918 |     1.238 |     1.501 |
+#> |    X|     291.20836 |     93.93 |    0.8449 |     8.020 |     2.523 |
+#> |.....................| 5.960e-07 |    0.9918 |     1.238 |     1.501 |
+#> |   66|     290.99661 |     1.013 |    -1.041 |    -1.101 |     1.637 |
+#> |.....................|    -2.997 |   -0.6156 |   -0.7909 |   -0.5760 |
+#> |    U|     290.99661 |     94.27 |   -0.1623 |     2.081 |     2.525 |
+#> |.....................| 5.960e-07 |    0.9935 |     1.238 |     1.499 |
+#> |    X|     290.99661 |     94.27 |    0.8502 |     8.011 |     2.525 |
+#> |.....................| 5.960e-07 |    0.9935 |     1.238 |     1.499 |
+#> |   67|     290.98636 |     1.014 |    -1.041 |    -1.101 |     1.637 |
+#> |.....................|    -2.997 |   -0.6154 |   -0.7908 |   -0.5760 |
+#> |    U|     290.98636 |     94.36 |   -0.1622 |     2.081 |     2.525 |
+#> |.....................| 5.960e-07 |    0.9936 |     1.238 |     1.499 |
+#> |    X|     290.98636 |     94.36 |    0.8503 |     8.012 |     2.525 |
+#> |.....................| 5.960e-07 |    0.9936 |     1.238 |     1.499 |
+#> |    F| Forward Diff. |    -1.956 |     1.256 |    0.9523 |     4.835 |
+#> |.....................|   0.03649 |     3.031 |    0.9657 |   -0.2695 |
+#> |   68|     290.98211 |     1.014 |    -1.041 |    -1.101 |     1.636 |
+#> |.....................|    -2.997 |   -0.6157 |   -0.7909 |   -0.5760 |
+#> |    U|     290.98211 |     94.38 |   -0.1623 |     2.081 |     2.525 |
+#> |.....................| 5.960e-07 |    0.9934 |     1.238 |     1.499 |
+#> |    X|     290.98211 |     94.38 |    0.8502 |     8.011 |     2.525 |
+#> |.....................| 5.960e-07 |    0.9934 |     1.238 |     1.499 |
+#> |   69|     290.97746 |     1.014 |    -1.042 |    -1.101 |     1.635 |
+#> |.....................|    -2.997 |   -0.6167 |   -0.7912 |   -0.5759 |
+#> |    U|     290.97746 |     94.44 |   -0.1627 |     2.081 |     2.524 |
+#> |.....................| 5.960e-07 |    0.9927 |     1.237 |     1.499 |
+#> |    X|     290.97746 |     94.44 |    0.8498 |     8.009 |     2.524 |
+#> |.....................| 5.960e-07 |    0.9927 |     1.237 |     1.499 |
+#> |    F| Forward Diff. |     17.70 |     1.268 |     1.108 |     4.855 |
+#> |.....................|   0.04257 |     3.066 |    0.9427 |   -0.2771 |
+#> |   70|     290.96180 |     1.014 |    -1.044 |    -1.101 |     1.634 |
+#> |.....................|    -2.997 |   -0.6175 |   -0.7910 |   -0.5752 |
+#> |    U|      290.9618 |     94.36 |   -0.1647 |     2.081 |     2.523 |
+#> |.....................| 5.960e-07 |    0.9921 |     1.238 |     1.500 |
+#> |    X|      290.9618 |     94.36 |    0.8481 |     8.013 |     2.523 |
+#> |.....................| 5.960e-07 |    0.9921 |     1.238 |     1.500 |
+#> |    F| Forward Diff. |    -1.598 |     1.197 |    0.9704 |     4.824 |
+#> |.....................|   0.03731 |     2.941 |    0.9551 |   -0.2334 |
+#> |   71|     290.95083 |     1.014 |    -1.044 |    -1.101 |     1.632 |
+#> |.....................|    -2.997 |   -0.6188 |   -0.7915 |   -0.5751 |
+#> |    U|     290.95083 |     94.43 |   -0.1653 |     2.081 |     2.522 |
+#> |.....................| 5.960e-07 |    0.9912 |     1.237 |     1.500 |
+#> |    X|     290.95083 |     94.43 |    0.8477 |     8.010 |     2.522 |
+#> |.....................| 5.960e-07 |    0.9912 |     1.237 |     1.500 |
+#> |    F| Forward Diff. |     14.81 |     1.204 |     1.097 |     4.820 |
+#> |.....................|   0.03908 |     3.014 |    0.9116 |   -0.2462 |
+#> |   72|     290.93714 |     1.014 |    -1.046 |    -1.101 |     1.630 |
+#> |.....................|    -2.997 |   -0.6196 |   -0.7913 |   -0.5744 |
+#> |    U|     290.93714 |     94.36 |   -0.1673 |     2.081 |     2.522 |
+#> |.....................| 5.960e-07 |    0.9906 |     1.237 |     1.501 |
+#> |    X|     290.93714 |     94.36 |    0.8459 |     8.014 |     2.522 |
+#> |.....................| 5.960e-07 |    0.9906 |     1.237 |     1.501 |
+#> |    F| Forward Diff. |    -1.943 |     1.135 |    0.9791 |     4.793 |
+#> |.....................|   0.03360 |     3.051 |    0.9080 |   -0.2200 |
+#> |   73|     290.92845 |     1.014 |    -1.047 |    -1.101 |     1.628 |
+#> |.....................|    -2.997 |   -0.6209 |   -0.7917 |   -0.5743 |
+#> |    U|     290.92845 |     94.44 |   -0.1678 |     2.081 |     2.521 |
+#> |.....................| 5.960e-07 |    0.9896 |     1.237 |     1.501 |
+#> |    X|     290.92845 |     94.44 |    0.8455 |     8.011 |     2.521 |
+#> |.....................| 5.960e-07 |    0.9896 |     1.237 |     1.501 |
+#> |    F| Forward Diff. |     17.70 |     1.147 |     1.134 |     4.752 |
+#> |.....................|   0.02729 |     3.018 |    0.8867 |   -0.2229 |
+#> |   74|     290.91300 |     1.014 |    -1.049 |    -1.100 |     1.627 |
+#> |.....................|    -2.997 |   -0.6219 |   -0.7915 |   -0.5737 |
+#> |    U|       290.913 |     94.36 |   -0.1698 |     2.081 |     2.520 |
+#> |.....................| 5.960e-07 |    0.9889 |     1.237 |     1.501 |
+#> |    X|       290.913 |     94.36 |    0.8439 |     8.016 |     2.520 |
+#> |.....................| 5.960e-07 |    0.9889 |     1.237 |     1.501 |
+#> |    F| Forward Diff. |    -1.940 |     1.078 |    0.9981 |     4.722 |
+#> |.....................|   0.04064 |     3.105 |    0.9143 |   -0.1849 |
+#> |   75|     290.90444 |     1.014 |    -1.049 |    -1.101 |     1.625 |
+#> |.....................|    -2.997 |   -0.6232 |   -0.7919 |   -0.5736 |
+#> |    U|     290.90444 |     94.44 |   -0.1702 |     2.081 |     2.519 |
+#> |.....................| 5.960e-07 |    0.9879 |     1.237 |     1.501 |
+#> |    X|     290.90444 |     94.44 |    0.8435 |     8.013 |     2.519 |
+#> |.....................| 5.960e-07 |    0.9879 |     1.237 |     1.501 |
+#> |    F| Forward Diff. |     17.76 |     1.091 |     1.153 |     4.713 |
+#> |.....................|   0.03198 |     2.950 |    0.8627 |   -0.2001 |
+#> |   76|     290.88905 |     1.014 |    -1.051 |    -1.100 |     1.624 |
+#> |.....................|    -2.997 |   -0.6243 |   -0.7916 |   -0.5732 |
+#> |    U|     290.88905 |     94.36 |   -0.1722 |     2.082 |     2.518 |
+#> |.....................| 5.960e-07 |    0.9872 |     1.237 |     1.502 |
+#> |    X|     290.88905 |     94.36 |    0.8418 |     8.019 |     2.518 |
+#> |.....................| 5.960e-07 |    0.9872 |     1.237 |     1.502 |
+#> |    F| Forward Diff. |    -2.112 |     1.022 |     1.016 |     4.749 |
+#> |.....................|   0.03990 |     3.117 |    0.8810 |   -0.1779 |
+#> |   77|     290.87937 |     1.014 |    -1.052 |    -1.100 |     1.622 |
+#> |.....................|    -2.997 |   -0.6257 |   -0.7918 |   -0.5730 |
+#> |    U|     290.87937 |     94.43 |   -0.1731 |     2.082 |     2.517 |
+#> |.....................| 5.960e-07 |    0.9861 |     1.237 |     1.502 |
+#> |    X|     290.87937 |     94.43 |    0.8411 |     8.018 |     2.517 |
+#> |.....................| 5.960e-07 |    0.9861 |     1.237 |     1.502 |
+#> |    F| Forward Diff. |     15.72 |     1.022 |     1.168 |     4.728 |
+#> |.....................|   0.04036 |     3.118 |    0.8621 |   -0.1806 |
+#> |   78|     290.86528 |     1.014 |    -1.054 |    -1.099 |     1.621 |
+#> |.....................|    -2.997 |   -0.6269 |   -0.7915 |   -0.5727 |
+#> |    U|     290.86528 |     94.36 |   -0.1749 |     2.083 |     2.516 |
+#> |.....................| 5.960e-07 |    0.9853 |     1.237 |     1.502 |
+#> |    X|     290.86528 |     94.36 |    0.8396 |     8.025 |     2.516 |
+#> |.....................| 5.960e-07 |    0.9853 |     1.237 |     1.502 |
+#> |    F| Forward Diff. |    -2.089 |    0.9583 |     1.055 |     4.711 |
+#> |.....................|   0.04161 |     3.089 |    0.8790 |   -0.1555 |
+#> |   79|     290.85625 |     1.014 |    -1.055 |    -1.099 |     1.619 |
+#> |.....................|    -2.997 |   -0.6283 |   -0.7918 |   -0.5726 |
+#> |    U|     290.85625 |     94.44 |   -0.1756 |     2.082 |     2.515 |
+#> |.....................| 5.960e-07 |    0.9842 |     1.237 |     1.503 |
+#> |    X|     290.85625 |     94.44 |    0.8389 |     8.023 |     2.515 |
+#> |.....................| 5.960e-07 |    0.9842 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |     16.77 |    0.9641 |     1.212 |     4.706 |
+#> |.....................|   0.04215 |     3.138 |    0.8554 |   -0.1643 |
+#> |   80|     290.84140 |     1.014 |    -1.056 |    -1.099 |     1.618 |
+#> |.....................|    -2.997 |   -0.6296 |   -0.7915 |   -0.5724 |
+#> |    U|      290.8414 |     94.36 |   -0.1774 |     2.083 |     2.515 |
+#> |.....................| 5.960e-07 |    0.9833 |     1.237 |     1.503 |
+#> |    X|      290.8414 |     94.36 |    0.8375 |     8.030 |     2.515 |
+#> |.....................| 5.960e-07 |    0.9833 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |    -1.641 |    0.9006 |     1.093 |     4.694 |
+#> |.....................|   0.04205 |     3.147 |    0.8775 |   -0.1452 |
+#> |   81|     290.83107 |     1.014 |    -1.057 |    -1.099 |     1.616 |
+#> |.....................|    -2.997 |   -0.6310 |   -0.7919 |   -0.5723 |
+#> |    U|     290.83107 |     94.43 |   -0.1778 |     2.083 |     2.514 |
+#> |.....................| 5.960e-07 |    0.9823 |     1.237 |     1.503 |
+#> |    X|     290.83107 |     94.43 |    0.8371 |     8.026 |     2.514 |
+#> |.....................| 5.960e-07 |    0.9823 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |     15.22 |    0.9116 |     1.221 |     4.655 |
+#> |.....................|   0.04015 |     3.140 |    0.8393 |   -0.1501 |
+#> |   82|     290.81725 |     1.014 |    -1.059 |    -1.098 |     1.615 |
+#> |.....................|    -2.997 |   -0.6323 |   -0.7916 |   -0.5722 |
+#> |    U|     290.81725 |     94.36 |   -0.1795 |     2.084 |     2.513 |
+#> |.....................| 5.960e-07 |    0.9813 |     1.237 |     1.503 |
+#> |    X|     290.81725 |     94.36 |    0.8357 |     8.034 |     2.513 |
+#> |.....................| 5.960e-07 |    0.9813 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |    -2.105 |    0.8517 |     1.114 |     4.660 |
+#> |.....................|   0.03878 |     3.162 |    0.8666 |   -0.1313 |
+#> |   83|     290.80795 |     1.014 |    -1.059 |    -1.098 |     1.613 |
+#> |.....................|    -2.997 |   -0.6339 |   -0.7918 |   -0.5722 |
+#> |    U|     290.80795 |     94.43 |   -0.1802 |     2.084 |     2.512 |
+#> |.....................| 5.960e-07 |    0.9802 |     1.237 |     1.503 |
+#> |    X|     290.80795 |     94.43 |    0.8351 |     8.033 |     2.512 |
+#> |.....................| 5.960e-07 |    0.9802 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |     16.11 |    0.8564 |     1.267 |     4.653 |
+#> |.....................|   0.04303 |     3.178 |    0.8469 |   -0.1413 |
+#> |   84|     290.79348 |     1.014 |    -1.061 |    -1.097 |     1.611 |
+#> |.....................|    -2.997 |   -0.6353 |   -0.7914 |   -0.5722 |
+#> |    U|     290.79348 |     94.36 |   -0.1817 |     2.084 |     2.511 |
+#> |.....................| 5.960e-07 |    0.9792 |     1.237 |     1.503 |
+#> |    X|     290.79348 |     94.36 |    0.8338 |     8.041 |     2.511 |
+#> |.....................| 5.960e-07 |    0.9792 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |    -1.840 |    0.7976 |     1.155 |     4.587 |
+#> |.....................|   0.02723 |     3.115 |    0.8603 |   -0.1275 |
+#> |   85|     290.78474 |     1.014 |    -1.061 |    -1.098 |     1.609 |
+#> |.....................|    -2.997 |   -0.6367 |   -0.7918 |   -0.5721 |
+#> |    U|     290.78474 |     94.44 |   -0.1821 |     2.084 |     2.510 |
+#> |.....................| 5.960e-07 |    0.9781 |     1.237 |     1.503 |
+#> |    X|     290.78474 |     94.44 |    0.8335 |     8.036 |     2.510 |
+#> |.....................| 5.960e-07 |    0.9781 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |     17.19 |    0.8130 |     1.300 |     4.618 |
+#> |.....................|   0.03919 |     3.190 |    0.8345 |   -0.1328 |
+#> |   86|     290.76934 |     1.014 |    -1.063 |    -1.097 |     1.608 |
+#> |.....................|    -2.997 |   -0.6382 |   -0.7915 |   -0.5722 |
+#> |    U|     290.76934 |     94.36 |   -0.1836 |     2.085 |     2.510 |
+#> |.....................| 5.960e-07 |    0.9771 |     1.237 |     1.503 |
+#> |    X|     290.76934 |     94.36 |    0.8322 |     8.044 |     2.510 |
+#> |.....................| 5.960e-07 |    0.9771 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |    -1.203 |    0.7543 |     1.182 |     4.565 |
+#> |.....................|   0.03490 |     3.166 |    0.8589 |   -0.1256 |
+#> |   87|     290.75687 |     1.014 |    -1.063 |    -1.097 |     1.606 |
+#> |.....................|    -2.997 |   -0.6397 |   -0.7919 |   -0.5722 |
+#> |    U|     290.75687 |     94.41 |   -0.1840 |     2.084 |     2.508 |
+#> |.....................| 5.960e-07 |    0.9760 |     1.237 |     1.503 |
+#> |    X|     290.75687 |     94.41 |    0.8319 |     8.039 |     2.508 |
+#> |.....................| 5.960e-07 |    0.9760 |     1.237 |     1.503 |
+#> |   88|     290.75123 |     1.015 |    -1.063 |    -1.098 |     1.604 |
+#> |.....................|    -2.997 |   -0.6414 |   -0.7924 |   -0.5721 |
+#> |    U|     290.75123 |     94.47 |   -0.1844 |     2.084 |     2.507 |
+#> |.....................| 5.960e-07 |    0.9747 |     1.236 |     1.503 |
+#> |    X|     290.75123 |     94.47 |    0.8316 |     8.034 |     2.507 |
+#> |.....................| 5.960e-07 |    0.9747 |     1.236 |     1.503 |
+#> |    F| Forward Diff. |     26.23 |    0.7709 |     1.374 |     4.560 |
+#> |.....................|   0.04194 |     3.213 |    0.7966 |   -0.1353 |
+#> |   89|     290.71744 |     1.014 |    -1.067 |    -1.096 |     1.601 |
+#> |.....................|    -2.997 |   -0.6448 |   -0.7915 |   -0.5726 |
+#> |    U|     290.71744 |     94.37 |   -0.1875 |     2.086 |     2.506 |
+#> |.....................| 5.960e-07 |    0.9722 |     1.237 |     1.503 |
+#> |    X|     290.71744 |     94.37 |    0.8291 |     8.054 |     2.506 |
+#> |.....................| 5.960e-07 |    0.9722 |     1.237 |     1.503 |
+#> |    F| Forward Diff. |    0.1928 |    0.6670 |     1.256 |     4.555 |
+#> |.....................|   0.04212 |     3.227 |    0.8436 |   -0.1302 |
+#> |   90|     290.68496 |     1.013 |    -1.067 |    -1.097 |     1.597 |
+#> |.....................|    -2.997 |   -0.6481 |   -0.7924 |   -0.5725 |
+#> |    U|     290.68496 |     94.35 |   -0.1881 |     2.085 |     2.503 |
+#> |.....................| 5.960e-07 |    0.9698 |     1.236 |     1.503 |
+#> |    X|     290.68496 |     94.35 |    0.8285 |     8.044 |     2.503 |
+#> |.....................| 5.960e-07 |    0.9698 |     1.236 |     1.503 |
+#> |   91|     290.59496 |     1.013 |    -1.069 |    -1.101 |     1.583 |
+#> |.....................|    -2.997 |   -0.6580 |   -0.7950 |   -0.5721 |
+#> |    U|     290.59496 |     94.29 |   -0.1902 |     2.081 |     2.496 |
+#> |.....................| 5.960e-07 |    0.9627 |     1.233 |     1.503 |
+#> |    X|     290.59496 |     94.29 |    0.8268 |     8.013 |     2.496 |
+#> |.....................| 5.960e-07 |    0.9627 |     1.233 |     1.503 |
+#> |   92|     290.34408 |     1.010 |    -1.077 |    -1.116 |     1.527 |
+#> |.....................|    -2.997 |   -0.6974 |   -0.8053 |   -0.5705 |
+#> |    U|     290.34408 |     94.08 |   -0.1983 |     2.066 |     2.465 |
+#> |.....................| 5.960e-07 |    0.9340 |     1.223 |     1.505 |
+#> |    X|     290.34408 |     94.08 |    0.8201 |     7.891 |     2.465 |
+#> |.....................| 5.960e-07 |    0.9340 |     1.223 |     1.505 |
+#> |    F| Forward Diff. |    -74.08 |    0.3588 |   -0.1794 |     3.803 |
+#> |.....................|   0.04205 |     3.779 |   0.06785 | -0.005437 |
+#> |   93|     289.95778 |     1.012 |    -1.081 |    -1.068 |     1.490 |
+#> |.....................|    -2.997 |   -0.7670 |   -0.7909 |   -0.5845 |
+#> |    U|     289.95778 |     94.18 |   -0.2020 |     2.114 |     2.445 |
+#> |.....................| 5.960e-07 |    0.8834 |     1.238 |     1.490 |
+#> |    X|     289.95778 |     94.18 |    0.8171 |     8.282 |     2.445 |
+#> |.....................| 5.960e-07 |    0.8834 |     1.238 |     1.490 |
+#> |   94|     289.83089 |     1.009 |    -1.086 |    -1.006 |     1.442 |
+#> |.....................|    -2.997 |   -0.8563 |   -0.7725 |   -0.6025 |
+#> |    U|     289.83089 |     93.98 |   -0.2067 |     2.176 |     2.418 |
+#> |.....................| 5.960e-07 |    0.8185 |     1.257 |     1.470 |
+#> |    X|     289.83089 |     93.98 |    0.8132 |     8.812 |     2.418 |
+#> |.....................| 5.960e-07 |    0.8185 |     1.257 |     1.470 |
+#> |    F| Forward Diff. |    -65.01 |  -0.01626 |     4.198 |     3.297 |
+#> |.....................|   0.05097 |     3.562 |     1.909 |   -0.3175 |
+#> |   95|     290.63229 |     1.014 |    -1.226 |    -1.068 |     1.287 |
+#> |.....................|    -2.997 |    -1.101 |   -0.7595 |   -0.8853 |
+#> |    U|     290.63229 |     94.43 |   -0.3467 |     2.113 |     2.333 |
+#> |.....................| 5.960e-07 |    0.6407 |     1.271 |     1.167 |
+#> |    X|     290.63229 |     94.43 |    0.7070 |     8.277 |     2.333 |
+#> |.....................| 5.960e-07 |    0.6407 |     1.271 |     1.167 |
+#> |   96|     289.56584 |     1.017 |    -1.134 |    -1.028 |     1.388 |
+#> |.....................|    -2.997 |   -0.9416 |   -0.7681 |   -0.7007 |
+#> |    U|     289.56584 |     94.70 |   -0.2554 |     2.154 |     2.389 |
+#> |.....................| 5.960e-07 |    0.7564 |     1.261 |     1.365 |
+#> |    X|     289.56584 |     94.70 |    0.7746 |     8.619 |     2.389 |
+#> |.....................| 5.960e-07 |    0.7564 |     1.261 |     1.365 |
+#> |    F| Forward Diff. |     59.80 |   -0.9076 |     3.450 |     2.884 |
+#> |.....................|   0.04168 |     2.247 |     1.868 |    -3.338 |
+#> |   97|     289.16078 |     1.017 |    -1.094 |    -1.010 |     1.317 |
+#> |.....................|    -2.997 |   -0.9798 |   -0.7948 |   -0.5837 |
+#> |    U|     289.16078 |     94.64 |   -0.2152 |     2.172 |     2.350 |
+#> |.....................| 5.960e-07 |    0.7287 |     1.234 |     1.491 |
+#> |    X|     289.16078 |     94.64 |    0.8063 |     8.773 |     2.350 |
+#> |.....................| 5.960e-07 |    0.7287 |     1.234 |     1.491 |
+#> |    F| Forward Diff. |     50.77 |  -0.08196 |     5.132 |     1.948 |
+#> |.....................|   0.04608 |     1.474 |    0.6389 |    0.4459 |
+#> |   98|     290.19527 |     1.002 |    -1.018 |    -1.037 |     1.157 |
+#> |.....................|    -2.997 |    -1.195 |   -0.7989 |   -0.6967 |
+#> |    U|     290.19527 |     93.32 |   -0.1385 |     2.145 |     2.263 |
+#> |.....................| 5.960e-07 |    0.5724 |     1.229 |     1.370 |
+#> |    X|     290.19527 |     93.32 |    0.8707 |     8.542 |     2.263 |
+#> |.....................| 5.960e-07 |    0.5724 |     1.229 |     1.370 |
+#> |   99|     289.65582 |     1.003 |    -1.072 |    -1.019 |     1.270 |
+#> |.....................|    -2.997 |    -1.043 |   -0.7961 |   -0.6170 |
+#> |    U|     289.65582 |     93.34 |   -0.1926 |     2.163 |     2.324 |
+#> |.....................| 5.960e-07 |    0.6825 |     1.232 |     1.455 |
+#> |    X|     289.65582 |     93.34 |    0.8248 |     8.696 |     2.324 |
+#> |.....................| 5.960e-07 |    0.6825 |     1.232 |     1.455 |
+#> |  100|     289.77865 |     1.003 |    -1.088 |    -1.014 |     1.303 |
+#> |.....................|    -2.997 |   -0.9984 |   -0.7953 |   -0.5934 |
+#> |    U|     289.77865 |     93.35 |   -0.2087 |     2.168 |     2.342 |
+#> |.....................| 5.960e-07 |    0.7151 |     1.233 |     1.480 |
+#> |    X|     289.77865 |     93.35 |    0.8116 |     8.742 |     2.342 |
+#> |.....................| 5.960e-07 |    0.7151 |     1.233 |     1.480 |
+#> |  101|     289.23886 |     1.008 |    -1.094 |    -1.011 |     1.317 |
+#> |.....................|    -2.997 |   -0.9800 |   -0.7949 |   -0.5837 |
+#> |    U|     289.23886 |     93.87 |   -0.2152 |     2.171 |     2.350 |
+#> |.....................| 5.960e-07 |    0.7285 |     1.234 |     1.491 |
+#> |    X|     289.23886 |     93.87 |    0.8064 |     8.765 |     2.350 |
+#> |.....................| 5.960e-07 |    0.7285 |     1.234 |     1.491 |
+#> |  102|     289.07165 |     1.013 |    -1.094 |    -1.010 |     1.317 |
+#> |.....................|    -2.997 |   -0.9799 |   -0.7948 |   -0.5837 |
+#> |    U|     289.07165 |     94.31 |   -0.2152 |     2.171 |     2.350 |
+#> |.....................| 5.960e-07 |    0.7286 |     1.234 |     1.491 |
+#> |    X|     289.07165 |     94.31 |    0.8063 |     8.770 |     2.350 |
+#> |.....................| 5.960e-07 |    0.7286 |     1.234 |     1.491 |
+#> |    F| Forward Diff. |   -0.3607 |   -0.1394 |     4.728 |     1.937 |
+#> |.....................|   0.04518 |     1.333 |    0.6601 |    0.3686 |
+#> |  103|     289.05383 |     1.013 |    -1.094 |    -1.014 |     1.315 |
+#> |.....................|    -2.997 |   -0.9807 |   -0.7952 |   -0.5839 |
+#> |    U|     289.05383 |     94.33 |   -0.2152 |     2.168 |     2.349 |
+#> |.....................| 5.960e-07 |    0.7280 |     1.233 |     1.490 |
+#> |    X|     289.05383 |     94.33 |    0.8064 |     8.742 |     2.349 |
+#> |.....................| 5.960e-07 |    0.7280 |     1.233 |     1.490 |
+#> |  104|     289.00706 |     1.014 |    -1.094 |    -1.023 |     1.312 |
+#> |.....................|    -2.997 |   -0.9834 |   -0.7965 |   -0.5847 |
+#> |    U|     289.00706 |     94.40 |   -0.2149 |     2.159 |     2.347 |
+#> |.....................| 5.960e-07 |    0.7260 |     1.232 |     1.490 |
+#> |    X|     289.00706 |     94.40 |    0.8066 |     8.661 |     2.347 |
+#> |.....................| 5.960e-07 |    0.7260 |     1.232 |     1.490 |
+#> |  105|     288.92149 |     1.016 |    -1.093 |    -1.055 |     1.299 |
+#> |.....................|    -2.997 |   -0.9924 |   -0.8010 |   -0.5872 |
+#> |    U|     288.92149 |     94.63 |   -0.2139 |     2.127 |     2.340 |
+#> |.....................| 5.960e-07 |    0.7195 |     1.227 |     1.487 |
+#> |    X|     288.92149 |     94.63 |    0.8074 |     8.388 |     2.340 |
+#> |.....................| 5.960e-07 |    0.7195 |     1.227 |     1.487 |
+#> |    F| Forward Diff. |     43.21 |   0.03028 |     3.221 |     1.557 |
+#> |.....................|  0.008151 |     1.175 |    0.2057 |   -0.1154 |
+#> |  106|     288.79118 |     1.014 |    -1.096 |    -1.061 |     1.264 |
+#> |.....................|    -2.997 |    -1.027 |   -0.7973 |   -0.5956 |
+#> |    U|     288.79118 |     94.43 |   -0.2174 |     2.120 |     2.321 |
+#> |.....................| 5.960e-07 |    0.6943 |     1.231 |     1.478 |
+#> |    X|     288.79118 |     94.43 |    0.8046 |     8.334 |     2.321 |
+#> |.....................| 5.960e-07 |    0.6943 |     1.231 |     1.478 |
+#> |    F| Forward Diff. |     10.81 |  -0.06252 |     2.679 |     1.204 |
+#> |.....................|   0.03262 |   -0.1240 |    0.4322 |   -0.2470 |
+#> |  107|     288.75294 |     1.013 |    -1.132 |    -1.081 |     1.252 |
+#> |.....................|    -2.997 |    -1.011 |   -0.7930 |   -0.5741 |
+#> |    U|     288.75294 |     94.35 |   -0.2531 |     2.101 |     2.314 |
+#> |.....................| 5.960e-07 |    0.7060 |     1.235 |     1.501 |
+#> |    X|     288.75294 |     94.35 |    0.7764 |     8.173 |     2.314 |
+#> |.....................| 5.960e-07 |    0.7060 |     1.235 |     1.501 |
+#> |    F| Forward Diff. |    -3.091 |   -0.8602 |     1.971 |     1.009 |
+#> |.....................|   0.04475 |    0.5130 |    0.7746 |    0.2303 |
+#> |  108|     288.69834 |     1.013 |    -1.093 |    -1.104 |     1.232 |
+#> |.....................|    -2.997 |    -1.011 |   -0.7973 |   -0.5721 |
+#> |    U|     288.69834 |     94.27 |   -0.2136 |     2.078 |     2.303 |
+#> |.....................| 5.960e-07 |    0.7061 |     1.231 |     1.503 |
+#> |    X|     288.69834 |     94.27 |    0.8077 |     7.987 |     2.303 |
+#> |.....................| 5.960e-07 |    0.7061 |     1.231 |     1.503 |
+#> |    F| Forward Diff. |    -16.61 |   0.06814 |    0.8311 |    0.6184 |
+#> |.....................|   0.03151 |    0.5612 |    0.4558 |    0.3067 |
+#> |  109|     288.67099 |     1.014 |    -1.108 |    -1.122 |     1.197 |
+#> |.....................|    -2.997 |    -1.038 |   -0.8030 |   -0.5758 |
+#> |    U|     288.67099 |     94.36 |   -0.2285 |     2.060 |     2.284 |
+#> |.....................| 5.960e-07 |    0.6866 |     1.225 |     1.499 |
+#> |    X|     288.67099 |     94.36 |    0.7957 |     7.847 |     2.284 |
+#> |.....................| 5.960e-07 |    0.6866 |     1.225 |     1.499 |
+#> |    F| Forward Diff. |    -4.975 |   -0.2154 |    0.1983 |    0.1047 |
+#> |.....................|   0.03564 |   -0.4652 |    0.1266 |    0.2269 |
+#> |  110|     288.66432 |     1.014 |    -1.097 |    -1.128 |     1.196 |
+#> |.....................|    -2.997 |    -1.027 |   -0.8055 |   -0.5813 |
+#> |    U|     288.66432 |     94.40 |   -0.2184 |     2.053 |     2.283 |
+#> |.....................| 5.960e-07 |    0.6941 |     1.222 |     1.493 |
+#> |    X|     288.66432 |     94.40 |    0.8038 |     7.793 |     2.283 |
+#> |.....................| 5.960e-07 |    0.6941 |     1.222 |     1.493 |
+#> |    F| Forward Diff. |    0.3927 |   0.02780 |  -0.05986 |   0.04997 |
+#> |.....................|   0.03453 |  -0.01180 |  -0.03408 |   0.03556 |
+#> |  111|     288.66432 |     1.014 |    -1.097 |    -1.128 |     1.196 |
+#> |.....................|    -2.997 |    -1.027 |   -0.8055 |   -0.5813 |
+#> |    U|     288.66432 |     94.40 |   -0.2184 |     2.053 |     2.283 |
+#> |.....................| 5.960e-07 |    0.6941 |     1.222 |     1.493 |
+#> |    X|     288.66432 |     94.40 |    0.8038 |     7.793 |     2.283 |
+#> |.....................| 5.960e-07 |    0.6941 |     1.222 |     1.493 |
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: parameter estimate near boundary; covariance not calculated
+#>  use 'getVarCov' to calculate anyway
                                                                #> Warning: gradient problems with initial estimate; see $scaleInfo
                                                                
+AIC(
+  f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm,
+  f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm,
+  f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm,
+  f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm,
+  f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm)
+
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #>                           df      AIC
+#> f_nlmixr_sfo_saem$nm       5 627.9197
+#> f_nlmixr_sfo_focei$nm      5 625.0512
+#> f_nlmixr_fomc_saem$nm      7 463.7245
+#> f_nlmixr_fomc_focei$nm     7 468.0822
+#> f_nlmixr_dfop_saem$nm      9 518.5794
+#> f_nlmixr_dfop_focei$nm     9 537.6309
+#> f_nlmixr_hs_saem$nm        9 535.9011
+#> f_nlmixr_hs_focei$nm       9 544.7590
+#> f_nlmixr_fomc_saem_tc$nm   8 463.5871
+#> f_nlmixr_fomc_focei_tc$nm  8 470.0733
                                                                
+AIC(nlme(f_mmkin_parent["FOMC", ]))
+
                                                                #> [1] 468.0781
                                                                AIC(nlme(f_mmkin_parent["HS", ]))
+
                                                                #> [1] 535.609
                                                                
+# nlme is comparable to nlmixr with focei, saem finds a better
+# solution, the two-component error model does not improve it
+plot(f_nlmixr_fomc_saem)
+
                                                                
+sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+  A1 = mkinsub("SFO"))
+
                                                                #> Temporary DLL for differentials generated and loaded
                                                                fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
+  A1 = mkinsub("SFO"))
+
                                                                #> Temporary DLL for differentials generated and loaded
                                                                dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+  A1 = mkinsub("SFO"))
+
                                                                #> Temporary DLL for differentials generated and loaded
                                                                
+f_mmkin_const <- mmkin(list(
+    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
+  ds, quiet = TRUE, error_model = "const")
+f_mmkin_obs <- mmkin(list(
+    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
+  ds, quiet = TRUE, error_model = "obs")
+f_mmkin_tc <- mmkin(list(
+    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
+  ds, quiet = TRUE, error_model = "tc")
+
+# A single constant variance is currently only possible with est = 'focei' in nlmixr
+f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |log_k_parent |  log_k_A1 |f_parent_qlogis |
+#> |.....................|     sigma |        o1 |        o2 |        o3 |
+#> |.....................|        o4 |...........|...........|...........|
+#> |    1|     756.06625 |     1.000 |   -0.9701 |    -1.000 |   -0.9071 |
+#> |.....................|   -0.8050 |   -0.8844 |   -0.8800 |   -0.8744 |
+#> |.....................|   -0.8785 |...........|...........|...........|
+#> |    U|     756.06625 |     86.53 |    -3.207 |    -4.567 |   -0.3341 |
+#> |.....................|     4.315 |    0.7003 |    0.9008 |     1.156 |
+#> |.....................|    0.9657 |...........|...........|...........|
+#> |    X|     756.06625 |     86.53 |   0.04048 |   0.01039 |    0.4172 |
+#> |.....................|     4.315 |    0.7003 |    0.9008 |     1.156 |
+#> |.....................|    0.9657 |...........|...........|...........|
+#> |    G|    Gill Diff. |     59.54 |   0.01874 |    0.7243 |    0.3705 |
+#> |.....................|    -28.18 |     5.148 |     2.958 |    -8.197 |
+#> |.....................|    -5.917 |...........|...........|...........|
+#> |    2|     3309.1113 |    0.1102 |   -0.9704 |    -1.011 |   -0.9126 |
+#> |.....................|   -0.3838 |   -0.9613 |   -0.9242 |   -0.7519 |
+#> |.....................|   -0.7901 |...........|...........|...........|
+#> |    U|     3309.1113 |     9.535 |    -3.207 |    -4.578 |   -0.3359 |
+#> |.....................|     5.223 |    0.6464 |    0.8610 |     1.297 |
+#> |.....................|     1.051 |...........|...........|...........|
+#> |    X|     3309.1113 |     9.535 |   0.04047 |   0.01027 |    0.4168 |
+#> |.....................|     5.223 |    0.6464 |    0.8610 |     1.297 |
+#> |.....................|     1.051 |...........|...........|...........|
+#> |    3|     782.04188 |    0.9110 |   -0.9702 |    -1.001 |   -0.9076 |
+#> |.....................|   -0.7629 |   -0.8921 |   -0.8844 |   -0.8621 |
+#> |.....................|   -0.8697 |...........|...........|...........|
+#> |    U|     782.04188 |     78.83 |    -3.207 |    -4.568 |   -0.3343 |
+#> |.....................|     4.406 |    0.6949 |    0.8968 |     1.170 |
+#> |.....................|    0.9742 |...........|...........|...........|
+#> |    X|     782.04188 |     78.83 |   0.04048 |   0.01037 |    0.4172 |
+#> |.....................|     4.406 |    0.6949 |    0.8968 |     1.170 |
+#> |.....................|    0.9742 |...........|...........|...........|
+#> |    4|     755.73406 |    0.9909 |   -0.9701 |    -1.000 |   -0.9071 |
+#> |.....................|   -0.8007 |   -0.8851 |   -0.8804 |   -0.8731 |
+#> |.....................|   -0.8776 |...........|...........|...........|
+#> |    U|     755.73406 |     85.75 |    -3.207 |    -4.567 |   -0.3341 |
+#> |.....................|     4.324 |    0.6997 |    0.9004 |     1.157 |
+#> |.....................|    0.9666 |...........|...........|...........|
+#> |    X|     755.73406 |     85.75 |   0.04048 |   0.01038 |    0.4172 |
+#> |.....................|     4.324 |    0.6997 |    0.9004 |     1.157 |
+#> |.....................|    0.9666 |...........|...........|...........|
+#> |    F| Forward Diff. |    -16.83 |   0.07808 |    0.6495 |    0.3224 |
+#> |.....................|    -27.54 |     3.811 |     2.903 |    -8.359 |
+#> |.....................|    -5.718 |...........|...........|...........|
+#> |    5|     755.49648 |    0.9959 |   -0.9702 |    -1.000 |   -0.9072 |
+#> |.....................|   -0.7924 |   -0.8863 |   -0.8813 |   -0.8706 |
+#> |.....................|   -0.8759 |...........|...........|...........|
+#> |    U|     755.49648 |     86.18 |    -3.207 |    -4.568 |   -0.3341 |
+#> |.....................|     4.342 |    0.6989 |    0.8996 |     1.160 |
+#> |.....................|    0.9682 |...........|...........|...........|
+#> |    X|     755.49648 |     86.18 |   0.04048 |   0.01038 |    0.4172 |
+#> |.....................|     4.342 |    0.6989 |    0.8996 |     1.160 |
+#> |.....................|    0.9682 |...........|...........|...........|
+#> |    F| Forward Diff. |     25.35 |   0.04484 |    0.6934 |    0.3535 |
+#> |.....................|    -25.80 |     4.244 |     2.831 |    -8.249 |
+#> |.....................|    -5.719 |...........|...........|...........|
+#> |    6|     755.31010 |    0.9891 |   -0.9702 |    -1.000 |   -0.9073 |
+#> |.....................|   -0.7855 |   -0.8874 |   -0.8820 |   -0.8684 |
+#> |.....................|   -0.8744 |...........|...........|...........|
+#> |    U|      755.3101 |     85.59 |    -3.207 |    -4.568 |   -0.3342 |
+#> |.....................|     4.357 |    0.6981 |    0.8989 |     1.163 |
+#> |.....................|    0.9697 |...........|...........|...........|
+#> |    X|      755.3101 |     85.59 |   0.04048 |   0.01038 |    0.4172 |
+#> |.....................|     4.357 |    0.6981 |    0.8989 |     1.163 |
+#> |.....................|    0.9697 |...........|...........|...........|
+#> |    F| Forward Diff. |    -31.39 |   0.08909 |    0.6380 |    0.3185 |
+#> |.....................|    -24.71 |     3.519 |     2.751 |    -7.972 |
+#> |.....................|    -5.525 |...........|...........|...........|
+#> |    7|     755.09582 |    0.9961 |   -0.9702 |    -1.001 |   -0.9074 |
+#> |.....................|   -0.7787 |   -0.8884 |   -0.8828 |   -0.8661 |
+#> |.....................|   -0.8728 |...........|...........|...........|
+#> |    U|     755.09582 |     86.20 |    -3.207 |    -4.568 |   -0.3342 |
+#> |.....................|     4.372 |    0.6974 |    0.8982 |     1.165 |
+#> |.....................|    0.9712 |...........|...........|...........|
+#> |    X|     755.09582 |     86.20 |   0.04047 |   0.01038 |    0.4172 |
+#> |.....................|     4.372 |    0.6974 |    0.8982 |     1.165 |
+#> |.....................|    0.9712 |...........|...........|...........|
+#> |    F| Forward Diff. |     26.63 |   0.04269 |    0.6973 |    0.3604 |
+#> |.....................|    -23.22 |     4.086 |     2.689 |    -8.043 |
+#> |.....................|    -5.569 |...........|...........|...........|
+#> |    8|     754.90743 |    0.9894 |   -0.9702 |    -1.001 |   -0.9075 |
+#> |.....................|   -0.7716 |   -0.8897 |   -0.8836 |   -0.8636 |
+#> |.....................|   -0.8711 |...........|...........|...........|
+#> |    U|     754.90743 |     85.62 |    -3.207 |    -4.568 |   -0.3342 |
+#> |.....................|     4.387 |    0.6966 |    0.8975 |     1.168 |
+#> |.....................|    0.9729 |...........|...........|...........|
+#> |    X|     754.90743 |     85.62 |   0.04047 |   0.01038 |    0.4172 |
+#> |.....................|     4.387 |    0.6966 |    0.8975 |     1.168 |
+#> |.....................|    0.9729 |...........|...........|...........|
+#> |    F| Forward Diff. |    -27.88 |   0.08581 |    0.6437 |    0.3265 |
+#> |.....................|    -22.15 |     3.354 |     2.606 |    -7.748 |
+#> |.....................|    -5.369 |...........|...........|...........|
+#> |    9|     754.70769 |    0.9959 |   -0.9702 |    -1.001 |   -0.9076 |
+#> |.....................|   -0.7645 |   -0.8908 |   -0.8845 |   -0.8610 |
+#> |.....................|   -0.8693 |...........|...........|...........|
+#> |    U|     754.70769 |     86.18 |    -3.207 |    -4.568 |   -0.3343 |
+#> |.....................|     4.402 |    0.6958 |    0.8967 |     1.171 |
+#> |.....................|    0.9747 |...........|...........|...........|
+#> |    X|     754.70769 |     86.18 |   0.04047 |   0.01037 |    0.4172 |
+#> |.....................|     4.402 |    0.6958 |    0.8967 |     1.171 |
+#> |.....................|    0.9747 |...........|...........|...........|
+#> |    F| Forward Diff. |     25.01 |   0.04305 |    0.6984 |    0.3661 |
+#> |.....................|    -20.67 |     3.871 |     2.535 |    -7.809 |
+#> |.....................|    -5.388 |...........|...........|...........|
+#> |   10|     754.52507 |    0.9898 |   -0.9703 |    -1.001 |   -0.9078 |
+#> |.....................|   -0.7574 |   -0.8922 |   -0.8854 |   -0.8580 |
+#> |.....................|   -0.8672 |...........|...........|...........|
+#> |    U|     754.52507 |     85.65 |    -3.207 |    -4.569 |   -0.3343 |
+#> |.....................|     4.417 |    0.6948 |    0.8958 |     1.175 |
+#> |.....................|    0.9766 |...........|...........|...........|
+#> |    X|     754.52507 |     85.65 |   0.04047 |   0.01037 |    0.4172 |
+#> |.....................|     4.417 |    0.6948 |    0.8958 |     1.175 |
+#> |.....................|    0.9766 |...........|...........|...........|
+#> |    F| Forward Diff. |    -24.90 |   0.08308 |    0.6490 |    0.3352 |
+#> |.....................|    -19.59 |     3.181 |     2.445 |    -7.663 |
+#> |.....................|    -5.179 |...........|...........|...........|
+#> |   11|     754.34076 |    0.9957 |   -0.9703 |    -1.002 |   -0.9079 |
+#> |.....................|   -0.7502 |   -0.8935 |   -0.8864 |   -0.8548 |
+#> |.....................|   -0.8650 |...........|...........|...........|
+#> |    U|     754.34076 |     86.16 |    -3.207 |    -4.569 |   -0.3344 |
+#> |.....................|     4.433 |    0.6939 |    0.8950 |     1.178 |
+#> |.....................|    0.9787 |...........|...........|...........|
+#> |    X|     754.34076 |     86.16 |   0.04047 |   0.01037 |    0.4172 |
+#> |.....................|     4.433 |    0.6939 |    0.8950 |     1.178 |
+#> |.....................|    0.9787 |...........|...........|...........|
+#> |    F| Forward Diff. |     23.15 |   0.04366 |    0.6990 |    0.3728 |
+#> |.....................|    -18.16 |     3.647 |     2.362 |    -7.534 |
+#> |.....................|    -5.170 |...........|...........|...........|
+#> |   12|     754.16941 |    0.9900 |   -0.9703 |    -1.002 |   -0.9081 |
+#> |.....................|   -0.7432 |   -0.8951 |   -0.8875 |   -0.8512 |
+#> |.....................|   -0.8626 |...........|...........|...........|
+#> |    U|     754.16941 |     85.67 |    -3.207 |    -4.569 |   -0.3344 |
+#> |.....................|     4.448 |    0.6928 |    0.8940 |     1.182 |
+#> |.....................|    0.9811 |...........|...........|...........|
+#> |    X|     754.16941 |     85.67 |   0.04047 |   0.01036 |    0.4172 |
+#> |.....................|     4.448 |    0.6928 |    0.8940 |     1.182 |
+#> |.....................|    0.9811 |...........|...........|...........|
+#> |    F| Forward Diff. |    -22.36 |   0.07996 |    0.6524 |    0.3446 |
+#> |.....................|    -17.12 |     3.002 |     2.262 |    -7.362 |
+#> |.....................|    -4.949 |...........|...........|...........|
+#> |   13|     754.00081 |    0.9955 |   -0.9704 |    -1.002 |   -0.9083 |
+#> |.....................|   -0.7363 |   -0.8967 |   -0.8886 |   -0.8472 |
+#> |.....................|   -0.8599 |...........|...........|...........|
+#> |    U|     754.00081 |     86.14 |    -3.207 |    -4.570 |   -0.3345 |
+#> |.....................|     4.463 |    0.6916 |    0.8930 |     1.187 |
+#> |.....................|    0.9836 |...........|...........|...........|
+#> |    X|     754.00081 |     86.14 |   0.04047 |   0.01036 |    0.4171 |
+#> |.....................|     4.463 |    0.6916 |    0.8930 |     1.187 |
+#> |.....................|    0.9836 |...........|...........|...........|
+#> |    F| Forward Diff. |     21.00 |   0.04440 |    0.6979 |    0.3804 |
+#> |.....................|    -15.79 |     3.414 |     2.168 |    -7.205 |
+#> |.....................|    -4.903 |...........|...........|...........|
+#> |   14|     753.84435 |    0.9903 |   -0.9704 |    -1.003 |   -0.9086 |
+#> |.....................|   -0.7296 |   -0.8985 |   -0.8898 |   -0.8427 |
+#> |.....................|   -0.8570 |...........|...........|...........|
+#> |    U|     753.84435 |     85.70 |    -3.207 |    -4.570 |   -0.3346 |
+#> |.....................|     4.477 |    0.6903 |    0.8919 |     1.192 |
+#> |.....................|    0.9865 |...........|...........|...........|
+#> |    X|     753.84435 |     85.70 |   0.04047 |   0.01036 |    0.4171 |
+#> |.....................|     4.477 |    0.6903 |    0.8919 |     1.192 |
+#> |.....................|    0.9865 |...........|...........|...........|
+#> |    F| Forward Diff. |    -19.93 |   0.07681 |    0.6538 |    0.3555 |
+#> |.....................|    -14.84 |     2.820 |     2.056 |    -6.999 |
+#> |.....................|    -4.662 |...........|...........|...........|
+#> |   15|     753.69372 |    0.9952 |   -0.9704 |    -1.003 |   -0.9089 |
+#> |.....................|   -0.7234 |   -0.9005 |   -0.8911 |   -0.8377 |
+#> |.....................|   -0.8537 |...........|...........|...........|
+#> |    U|     753.69372 |     86.12 |    -3.207 |    -4.571 |   -0.3347 |
+#> |.....................|     4.491 |    0.6890 |    0.8908 |     1.198 |
+#> |.....................|    0.9897 |...........|...........|...........|
+#> |    X|     753.69372 |     86.12 |   0.04046 |   0.01035 |    0.4171 |
+#> |.....................|     4.491 |    0.6890 |    0.8908 |     1.198 |
+#> |.....................|    0.9897 |...........|...........|...........|
+#> |    F| Forward Diff. |     18.81 |   0.04462 |    0.6942 |    0.3896 |
+#> |.....................|    -13.66 |     3.180 |     1.953 |    -6.807 |
+#> |.....................|    -4.573 |...........|...........|...........|
+#> |   16|     753.55534 |    0.9906 |   -0.9705 |    -1.004 |   -0.9093 |
+#> |.....................|   -0.7176 |   -0.9027 |   -0.8924 |   -0.8322 |
+#> |.....................|   -0.8502 |...........|...........|...........|
+#> |    U|     753.55534 |     85.72 |    -3.207 |    -4.571 |   -0.3348 |
+#> |.....................|     4.503 |    0.6875 |    0.8896 |     1.204 |
+#> |.....................|    0.9931 |...........|...........|...........|
+#> |    X|     753.55534 |     85.72 |   0.04046 |   0.01034 |    0.4171 |
+#> |.....................|     4.503 |    0.6875 |    0.8896 |     1.204 |
+#> |.....................|    0.9931 |...........|...........|...........|
+#> |    F| Forward Diff. |    -17.61 |   0.07313 |    0.6517 |    0.3679 |
+#> |.....................|    -12.86 |     2.639 |     1.835 |    -6.564 |
+#> |.....................|    -4.309 |...........|...........|...........|
+#> |   17|     753.42478 |    0.9950 |   -0.9706 |    -1.005 |   -0.9097 |
+#> |.....................|   -0.7124 |   -0.9049 |   -0.8937 |   -0.8262 |
+#> |.....................|   -0.8464 |...........|...........|...........|
+#> |    U|     753.42478 |     86.11 |    -3.207 |    -4.572 |   -0.3350 |
+#> |.....................|     4.515 |    0.6859 |    0.8884 |     1.211 |
+#> |.....................|    0.9967 |...........|...........|...........|
+#> |    X|     753.42478 |     86.11 |   0.04046 |   0.01034 |    0.4170 |
+#> |.....................|     4.515 |    0.6859 |    0.8884 |     1.211 |
+#> |.....................|    0.9967 |...........|...........|...........|
+#> |    F| Forward Diff. |     16.74 |   0.04433 |    0.6853 |    0.4002 |
+#> |.....................|    -11.89 |     2.952 |     1.729 |    -6.336 |
+#> |.....................|    -4.181 |...........|...........|...........|
+#> |   18|     753.30602 |    0.9909 |   -0.9706 |    -1.006 |   -0.9103 |
+#> |.....................|   -0.7078 |   -0.9075 |   -0.8949 |   -0.8197 |
+#> |.....................|   -0.8425 |...........|...........|...........|
+#> |    U|     753.30602 |     85.74 |    -3.207 |    -4.573 |   -0.3352 |
+#> |.....................|     4.525 |    0.6841 |    0.8873 |     1.219 |
+#> |.....................|     1.001 |...........|...........|...........|
+#> |    X|     753.30602 |     85.74 |   0.04046 |   0.01033 |    0.4170 |
+#> |.....................|     4.525 |    0.6841 |    0.8873 |     1.219 |
+#> |.....................|     1.001 |...........|...........|...........|
+#> |    F| Forward Diff. |    -15.54 |   0.06924 |    0.6430 |    0.3812 |
+#> |.....................|    -11.26 |     2.462 |     1.618 |    -6.066 |
+#> |.....................|    -3.903 |...........|...........|...........|
+#> |   19|     753.19508 |    0.9949 |   -0.9707 |    -1.007 |   -0.9109 |
+#> |.....................|   -0.7036 |   -0.9102 |   -0.8961 |   -0.8129 |
+#> |.....................|   -0.8385 |...........|...........|...........|
+#> |    U|     753.19508 |     86.09 |    -3.208 |    -4.574 |   -0.3354 |
+#> |.....................|     4.533 |    0.6822 |    0.8862 |     1.227 |
+#> |.....................|     1.004 |...........|...........|...........|
+#> |    X|     753.19508 |     86.09 |   0.04045 |   0.01032 |    0.4169 |
+#> |.....................|     4.533 |    0.6822 |    0.8862 |     1.227 |
+#> |.....................|     1.004 |...........|...........|...........|
+#> |    F| Forward Diff. |     14.90 |   0.04352 |    0.6689 |    0.4113 |
+#> |.....................|    -10.49 |     2.732 |     1.522 |    -5.813 |
+#> |.....................|    -3.751 |...........|...........|...........|
+#> |   20|     753.09443 |    0.9911 |   -0.9708 |    -1.008 |   -0.9117 |
+#> |.....................|   -0.7001 |   -0.9132 |   -0.8972 |   -0.8058 |
+#> |.....................|   -0.8346 |...........|...........|...........|
+#> |    U|     753.09443 |     85.77 |    -3.208 |    -4.575 |   -0.3356 |
+#> |.....................|     4.541 |    0.6801 |    0.8852 |     1.235 |
+#> |.....................|     1.008 |...........|...........|...........|
+#> |    X|     753.09443 |     85.77 |   0.04045 |   0.01031 |    0.4169 |
+#> |.....................|     4.541 |    0.6801 |    0.8852 |     1.235 |
+#> |.....................|     1.008 |...........|...........|...........|
+#> |    F| Forward Diff. |    -13.80 |   0.06521 |    0.6240 |    0.3942 |
+#> |.....................|    -10.02 |     2.285 |     1.423 |    -5.526 |
+#> |.....................|    -3.476 |...........|...........|...........|
+#> |   21|     753.00021 |    0.9948 |   -0.9709 |    -1.009 |   -0.9127 |
+#> |.....................|   -0.6968 |   -0.9163 |   -0.8982 |   -0.7985 |
+#> |.....................|   -0.8307 |...........|...........|...........|
+#> |    U|     753.00021 |     86.08 |    -3.208 |    -4.576 |   -0.3360 |
+#> |.....................|     4.548 |    0.6779 |    0.8843 |     1.243 |
+#> |.....................|     1.012 |...........|...........|...........|
+#> |    X|     753.00021 |     86.08 |   0.04045 |   0.01029 |    0.4168 |
+#> |.....................|     4.548 |    0.6779 |    0.8843 |     1.243 |
+#> |.....................|     1.012 |...........|...........|...........|
+#> |    F| Forward Diff. |     13.31 |   0.04216 |    0.6406 |    0.4217 |
+#> |.....................|    -9.402 |     2.517 |     1.347 |    -5.262 |
+#> |.....................|    -3.321 |...........|...........|...........|
+#> |   22|     752.91432 |    0.9914 |   -0.9710 |    -1.010 |   -0.9139 |
+#> |.....................|   -0.6939 |   -0.9197 |   -0.8991 |   -0.7911 |
+#> |.....................|   -0.8272 |...........|...........|...........|
+#> |    U|     752.91432 |     85.79 |    -3.208 |    -4.578 |   -0.3364 |
+#> |.....................|     4.555 |    0.6755 |    0.8835 |     1.252 |
+#> |.....................|     1.015 |...........|...........|...........|
+#> |    X|     752.91432 |     85.79 |   0.04044 |   0.01028 |    0.4167 |
+#> |.....................|     4.555 |    0.6755 |    0.8835 |     1.252 |
+#> |.....................|     1.015 |...........|...........|...........|
+#> |    F| Forward Diff. |    -12.35 |   0.06128 |    0.5909 |    0.4053 |
+#> |.....................|    -9.027 |     2.101 |     1.271 |    -4.717 |
+#> |.....................|    -3.067 |...........|...........|...........|
+#> |   23|     752.83200 |    0.9948 |   -0.9711 |    -1.012 |   -0.9155 |
+#> |.....................|   -0.6906 |   -0.9238 |   -0.9000 |   -0.7843 |
+#> |.....................|   -0.8235 |...........|...........|...........|
+#> |    U|       752.832 |     86.09 |    -3.208 |    -4.580 |   -0.3369 |
+#> |.....................|     4.561 |    0.6727 |    0.8827 |     1.260 |
+#> |.....................|     1.019 |...........|...........|...........|
+#> |    X|       752.832 |     86.09 |   0.04044 |   0.01026 |    0.4166 |
+#> |.....................|     4.561 |    0.6727 |    0.8827 |     1.260 |
+#> |.....................|     1.019 |...........|...........|...........|
+#> |    F| Forward Diff. |     12.74 |   0.03978 |    0.5956 |    0.4312 |
+#> |.....................|    -8.422 |     2.296 |     1.202 |    -4.471 |
+#> |.....................|    -2.914 |...........|...........|...........|
+#> |   24|     752.75140 |    0.9918 |   -0.9713 |    -1.014 |   -0.9179 |
+#> |.....................|   -0.6872 |   -0.9288 |   -0.9011 |   -0.7785 |
+#> |.....................|   -0.8198 |...........|...........|...........|
+#> |    U|      752.7514 |     85.82 |    -3.208 |    -4.582 |   -0.3377 |
+#> |.....................|     4.569 |    0.6692 |    0.8818 |     1.266 |
+#> |.....................|     1.022 |...........|...........|...........|
+#> |    X|      752.7514 |     85.82 |   0.04043 |   0.01024 |    0.4164 |
+#> |.....................|     4.569 |    0.6692 |    0.8818 |     1.266 |
+#> |.....................|     1.022 |...........|...........|...........|
+#> |    F| Forward Diff. |    -10.02 |   0.05546 |    0.5361 |    0.4172 |
+#> |.....................|    -7.958 |     1.872 |     1.117 |    -4.424 |
+#> |.....................|    -2.664 |...........|...........|...........|
+#> |   25|     752.68235 |    0.9947 |   -0.9715 |    -1.016 |   -0.9205 |
+#> |.....................|   -0.6845 |   -0.9329 |   -0.9018 |   -0.7712 |
+#> |.....................|   -0.8173 |...........|...........|...........|
+#> |    U|     752.68235 |     86.07 |    -3.208 |    -4.584 |   -0.3386 |
+#> |.....................|     4.575 |    0.6663 |    0.8811 |     1.275 |
+#> |.....................|     1.025 |...........|...........|...........|
+#> |    X|     752.68235 |     86.07 |   0.04042 |   0.01022 |    0.4162 |
+#> |.....................|     4.575 |    0.6663 |    0.8811 |     1.275 |
+#> |.....................|     1.025 |...........|...........|...........|
+#> |    F| Forward Diff. |     10.53 |   0.03715 |    0.5273 |    0.4360 |
+#> |.....................|    -7.447 |     2.014 |     1.063 |    -3.990 |
+#> |.....................|    -2.556 |...........|...........|...........|
+#> |   26|     752.62160 |    0.9918 |   -0.9717 |    -1.019 |   -0.9237 |
+#> |.....................|   -0.6821 |   -0.9370 |   -0.9025 |   -0.7637 |
+#> |.....................|   -0.8151 |...........|...........|...........|
+#> |    U|      752.6216 |     85.83 |    -3.209 |    -4.586 |   -0.3397 |
+#> |.....................|     4.580 |    0.6635 |    0.8804 |     1.284 |
+#> |.....................|     1.027 |...........|...........|...........|
+#> |    X|      752.6216 |     85.83 |   0.04042 |   0.01020 |    0.4159 |
+#> |.....................|     4.580 |    0.6635 |    0.8804 |     1.284 |
+#> |.....................|     1.027 |...........|...........|...........|
+#> |    F| Forward Diff. |    -10.27 |   0.05173 |    0.4657 |    0.4178 |
+#> |.....................|    -7.153 |     1.648 |     1.004 |    -3.701 |
+#> |.....................|    -2.385 |...........|...........|...........|
+#> |   27|     752.55758 |    0.9944 |   -0.9719 |    -1.021 |   -0.9287 |
+#> |.....................|   -0.6786 |   -0.9418 |   -0.9036 |   -0.7591 |
+#> |.....................|   -0.8121 |...........|...........|...........|
+#> |    U|     752.55758 |     86.05 |    -3.209 |    -4.588 |   -0.3413 |
+#> |.....................|     4.587 |    0.6600 |    0.8795 |     1.289 |
+#> |.....................|     1.030 |...........|...........|...........|
+#> |    X|     752.55758 |     86.05 |   0.04040 |   0.01017 |    0.4155 |
+#> |.....................|     4.587 |    0.6600 |    0.8795 |     1.289 |
+#> |.....................|     1.030 |...........|...........|...........|
+#> |    F| Forward Diff. |     7.976 |   0.03464 |    0.4539 |    0.4351 |
+#> |.....................|    -6.545 |     1.728 |    0.9236 |    -3.536 |
+#> |.....................|    -2.257 |...........|...........|...........|
+#> |   28|     752.50465 |    0.9921 |   -0.9722 |    -1.023 |   -0.9345 |
+#> |.....................|   -0.6755 |   -0.9456 |   -0.9043 |   -0.7539 |
+#> |.....................|   -0.8090 |...........|...........|...........|
+#> |    U|     752.50465 |     85.85 |    -3.209 |    -4.590 |   -0.3432 |
+#> |.....................|     4.594 |    0.6574 |    0.8788 |     1.295 |
+#> |.....................|     1.033 |...........|...........|...........|
+#> |    X|     752.50465 |     85.85 |   0.04039 |   0.01015 |    0.4150 |
+#> |.....................|     4.594 |    0.6574 |    0.8788 |     1.295 |
+#> |.....................|     1.033 |...........|...........|...........|
+#> |    F| Forward Diff. |    -8.947 |   0.04577 |    0.4043 |    0.4205 |
+#> |.....................|    -6.122 |     1.399 |    0.8644 |    -3.339 |
+#> |.....................|    -2.062 |...........|...........|...........|
+#> |   29|     752.46010 |    0.9944 |   -0.9724 |    -1.024 |   -0.9405 |
+#> |.....................|   -0.6742 |   -0.9477 |   -0.9048 |   -0.7467 |
+#> |.....................|   -0.8068 |...........|...........|...........|
+#> |    U|      752.4601 |     86.05 |    -3.209 |    -4.591 |   -0.3452 |
+#> |.....................|     4.597 |    0.6559 |    0.8784 |     1.303 |
+#> |.....................|     1.035 |...........|...........|...........|
+#> |    X|      752.4601 |     86.05 |   0.04039 |   0.01014 |    0.4145 |
+#> |.....................|     4.597 |    0.6559 |    0.8784 |     1.303 |
+#> |.....................|     1.035 |...........|...........|...........|
+#> |    F| Forward Diff. |     6.603 |   0.03134 |    0.3976 |    0.4307 |
+#> |.....................|    -5.878 |     1.523 |    0.8347 |    -3.098 |
+#> |.....................|    -1.971 |...........|...........|...........|
+#> |   30|     752.42045 |    0.9923 |   -0.9726 |    -1.025 |   -0.9478 |
+#> |.....................|   -0.6717 |   -0.9497 |   -0.9056 |   -0.7410 |
+#> |.....................|   -0.8056 |...........|...........|...........|
+#> |    U|     752.42045 |     85.87 |    -3.210 |    -4.593 |   -0.3477 |
+#> |.....................|     4.602 |    0.6545 |    0.8777 |     1.310 |
+#> |.....................|     1.036 |...........|...........|...........|
+#> |    X|     752.42045 |     85.87 |   0.04038 |   0.01013 |    0.4139 |
+#> |.....................|     4.602 |    0.6545 |    0.8777 |     1.310 |
+#> |.....................|     1.036 |...........|...........|...........|
+#> |    F| Forward Diff. |    -7.567 |   0.04074 |    0.3551 |    0.4112 |
+#> |.....................|    -5.553 |     1.278 |    0.7625 |    -2.890 |
+#> |.....................|    -1.881 |...........|...........|...........|
+#> |   31|     752.38271 |    0.9943 |   -0.9729 |    -1.026 |   -0.9563 |
+#> |.....................|   -0.6682 |   -0.9523 |   -0.9058 |   -0.7392 |
+#> |.....................|   -0.8032 |...........|...........|...........|
+#> |    U|     752.38271 |     86.04 |    -3.210 |    -4.594 |   -0.3505 |
+#> |.....................|     4.610 |    0.6527 |    0.8775 |     1.312 |
+#> |.....................|     1.038 |...........|...........|...........|
+#> |    X|     752.38271 |     86.04 |   0.04037 |   0.01012 |    0.4133 |
+#> |.....................|     4.610 |    0.6527 |    0.8775 |     1.312 |
+#> |.....................|     1.038 |...........|...........|...........|
+#> |    F| Forward Diff. |     5.602 |   0.02847 |    0.3641 |    0.4189 |
+#> |.....................|    -5.001 |     1.344 |    0.7516 |    -2.828 |
+#> |.....................|    -1.805 |...........|...........|...........|
+#> |   32|     752.35435 |    0.9925 |   -0.9730 |    -1.028 |   -0.9633 |
+#> |.....................|   -0.6679 |   -0.9545 |   -0.9069 |   -0.7341 |
+#> |.....................|   -0.7988 |...........|...........|...........|
+#> |    U|     752.35435 |     85.89 |    -3.210 |    -4.595 |   -0.3529 |
+#> |.....................|     4.611 |    0.6511 |    0.8766 |     1.318 |
+#> |.....................|     1.043 |...........|...........|...........|
+#> |    X|     752.35435 |     85.89 |   0.04036 |   0.01010 |    0.4127 |
+#> |.....................|     4.611 |    0.6511 |    0.8766 |     1.318 |
+#> |.....................|     1.043 |...........|...........|...........|
+#> |    F| Forward Diff. |    -6.571 |   0.03612 |    0.3357 |    0.4086 |
+#> |.....................|    -4.992 |     1.118 |    0.6605 |    -2.632 |
+#> |.....................|    -1.560 |...........|...........|...........|
+#> |   33|     752.32772 |    0.9943 |   -0.9732 |    -1.029 |   -0.9711 |
+#> |.....................|   -0.6669 |   -0.9557 |   -0.9071 |   -0.7282 |
+#> |.....................|   -0.7989 |...........|...........|...........|
+#> |    U|     752.32772 |     86.04 |    -3.210 |    -4.596 |   -0.3555 |
+#> |.....................|     4.613 |    0.6503 |    0.8764 |     1.325 |
+#> |.....................|     1.043 |...........|...........|...........|
+#> |    X|     752.32772 |     86.04 |   0.04035 |   0.01009 |    0.4121 |
+#> |.....................|     4.613 |    0.6503 |    0.8764 |     1.325 |
+#> |.....................|     1.043 |...........|...........|...........|
+#> |    F| Forward Diff. |     5.212 |   0.02538 |    0.3153 |    0.4089 |
+#> |.....................|    -4.808 |     1.231 |    0.6502 |    -2.445 |
+#> |.....................|    -1.583 |...........|...........|...........|
+#> |   34|     752.30453 |    0.9927 |   -0.9733 |    -1.030 |   -0.9795 |
+#> |.....................|   -0.6622 |   -0.9567 |   -0.9058 |   -0.7271 |
+#> |.....................|   -0.8012 |...........|...........|...........|
+#> |    U|     752.30453 |     85.90 |    -3.210 |    -4.598 |   -0.3583 |
+#> |.....................|     4.623 |    0.6496 |    0.8775 |     1.326 |
+#> |.....................|     1.040 |...........|...........|...........|
+#> |    X|     752.30453 |     85.90 |   0.04035 |   0.01008 |    0.4114 |
+#> |.....................|     4.623 |    0.6496 |    0.8775 |     1.326 |
+#> |.....................|     1.040 |...........|...........|...........|
+#> |    F| Forward Diff. |    -5.777 |   0.03360 |    0.2795 |    0.3849 |
+#> |.....................|    -4.177 |     1.041 |    0.7583 |    -2.411 |
+#> |.....................|    -1.694 |...........|...........|...........|
+#> |   35|     752.28211 |    0.9943 |   -0.9735 |    -1.030 |   -0.9865 |
+#> |.....................|   -0.6621 |   -0.9586 |   -0.9093 |   -0.7251 |
+#> |.....................|   -0.7954 |...........|...........|...........|
+#> |    U|     752.28211 |     86.04 |    -3.210 |    -4.598 |   -0.3606 |
+#> |.....................|     4.623 |    0.6483 |    0.8743 |     1.328 |
+#> |.....................|     1.046 |...........|...........|...........|
+#> |    X|     752.28211 |     86.04 |   0.04034 |   0.01008 |    0.4108 |
+#> |.....................|     4.623 |    0.6483 |    0.8743 |     1.328 |
+#> |.....................|     1.046 |...........|...........|...........|
+#> |    F| Forward Diff. |     4.685 |   0.02318 |    0.3105 |    0.3984 |
+#> |.....................|    -4.118 |     1.106 |    0.4577 |    -2.335 |
+#> |.....................|    -1.438 |...........|...........|...........|
+#> |   36|     752.26507 |    0.9926 |   -0.9736 |    -1.031 |   -0.9930 |
+#> |.....................|   -0.6630 |   -0.9604 |   -0.9091 |   -0.7199 |
+#> |.....................|   -0.7902 |...........|...........|...........|
+#> |    U|     752.26507 |     85.89 |    -3.210 |    -4.598 |   -0.3628 |
+#> |.....................|     4.621 |    0.6470 |    0.8745 |     1.334 |
+#> |.....................|     1.051 |...........|...........|...........|
+#> |    X|     752.26507 |     85.89 |   0.04034 |   0.01007 |    0.4103 |
+#> |.....................|     4.621 |    0.6470 |    0.8745 |     1.334 |
+#> |.....................|     1.051 |...........|...........|...........|
+#> |    F| Forward Diff. |    -6.810 |   0.03096 |    0.2910 |    0.3899 |
+#> |.....................|    -4.283 |    0.8991 |    0.4756 |    -2.130 |
+#> |.....................|    -1.153 |...........|...........|...........|
+#> |   37|     752.24597 |    0.9942 |   -0.9737 |    -1.033 |    -1.000 |
+#> |.....................|   -0.6608 |   -0.9614 |   -0.9045 |   -0.7160 |
+#> |.....................|   -0.7919 |...........|...........|...........|
+#> |    U|     752.24597 |     86.03 |    -3.211 |    -4.600 |   -0.3653 |
+#> |.....................|     4.626 |    0.6463 |    0.8787 |     1.339 |
+#> |.....................|     1.049 |...........|...........|...........|
+#> |    X|     752.24597 |     86.03 |   0.04033 |   0.01005 |    0.4097 |
+#> |.....................|     4.626 |    0.6463 |    0.8787 |     1.339 |
+#> |.....................|     1.049 |...........|...........|...........|
+#> |    F| Forward Diff. |     3.512 |   0.02244 |    0.2659 |    0.3868 |
+#> |.....................|    -3.943 |    0.9821 |    0.8784 |    -2.032 |
+#> |.....................|    -1.263 |...........|...........|...........|
+#> |   38|     752.22949 |    0.9926 |   -0.9738 |    -1.034 |    -1.007 |
+#> |.....................|   -0.6572 |   -0.9618 |   -0.9098 |   -0.7144 |
+#> |.....................|   -0.7948 |...........|...........|...........|
+#> |    U|     752.22949 |     85.90 |    -3.211 |    -4.601 |   -0.3676 |
+#> |.....................|     4.634 |    0.6461 |    0.8739 |     1.341 |
+#> |.....................|     1.047 |...........|...........|...........|
+#> |    X|     752.22949 |     85.90 |   0.04033 |   0.01004 |    0.4091 |
+#> |.....................|     4.634 |    0.6461 |    0.8739 |     1.341 |
+#> |.....................|     1.047 |...........|...........|...........|
+#> |    F| Forward Diff. |    -6.652 |   0.02915 |    0.2261 |    0.3631 |
+#> |.....................|    -3.474 |    0.8493 |    0.4224 |    -1.980 |
+#> |.....................|    -1.394 |...........|...........|...........|
+#> |   39|     752.21433 |    0.9945 |   -0.9739 |    -1.034 |    -1.016 |
+#> |.....................|   -0.6569 |   -0.9629 |   -0.9144 |   -0.7124 |
+#> |.....................|   -0.7922 |...........|...........|...........|
+#> |    U|     752.21433 |     86.05 |    -3.211 |    -4.601 |   -0.3704 |
+#> |.....................|     4.634 |    0.6453 |    0.8697 |     1.343 |
+#> |.....................|     1.049 |...........|...........|...........|
+#> |    X|     752.21433 |     86.05 |   0.04032 |   0.01004 |    0.4085 |
+#> |.....................|     4.634 |    0.6453 |    0.8697 |     1.343 |
+#> |.....................|     1.049 |...........|...........|...........|
+#> |    F| Forward Diff. |     5.271 |   0.01812 |    0.2470 |    0.3694 |
+#> |.....................|    -3.388 |    0.9655 |   0.02976 |    -1.920 |
+#> |.....................|    -1.299 |...........|...........|...........|
+#> |   40|     752.19821 |    0.9933 |   -0.9740 |    -1.034 |    -1.022 |
+#> |.....................|   -0.6566 |   -0.9648 |   -0.9096 |   -0.7099 |
+#> |.....................|   -0.7872 |...........|...........|...........|
+#> |    U|     752.19821 |     85.95 |    -3.211 |    -4.602 |   -0.3726 |
+#> |.....................|     4.635 |    0.6440 |    0.8741 |     1.346 |
+#> |.....................|     1.054 |...........|...........|...........|
+#> |    X|     752.19821 |     85.95 |   0.04032 |   0.01004 |    0.4079 |
+#> |.....................|     4.635 |    0.6440 |    0.8741 |     1.346 |
+#> |.....................|     1.054 |...........|...........|...........|
+#> |    F| Forward Diff. |    -2.667 |   0.02369 |    0.2481 |    0.3640 |
+#> |.....................|    -3.371 |    0.7751 |    0.4401 |    -1.801 |
+#> |.....................|    -1.045 |...........|...........|...........|
+#> |   41|     752.18532 |    0.9951 |   -0.9741 |    -1.036 |    -1.031 |
+#> |.....................|   -0.6545 |   -0.9659 |   -0.9070 |   -0.7062 |
+#> |.....................|   -0.7858 |...........|...........|...........|
+#> |    U|     752.18532 |     86.11 |    -3.211 |    -4.603 |   -0.3754 |
+#> |.....................|     4.639 |    0.6432 |    0.8764 |     1.350 |
+#> |.....................|     1.055 |...........|...........|...........|
+#> |    X|     752.18532 |     86.11 |   0.04032 |   0.01002 |    0.4072 |
+#> |.....................|     4.639 |    0.6432 |    0.8764 |     1.350 |
+#> |.....................|     1.055 |...........|...........|...........|
+#> |    F| Forward Diff. |     8.833 |   0.01368 |    0.2421 |    0.3674 |
+#> |.....................|    -3.039 |    0.8770 |    0.6679 |    -1.687 |
+#> |.....................|    -1.010 |...........|...........|...........|
+#> |   42|     752.16831 |    0.9936 |   -0.9742 |    -1.037 |    -1.039 |
+#> |.....................|   -0.6539 |   -0.9664 |   -0.9110 |   -0.7027 |
+#> |.....................|   -0.7873 |...........|...........|...........|
+#> |    U|     752.16831 |     85.98 |    -3.211 |    -4.605 |   -0.3782 |
+#> |.....................|     4.641 |    0.6428 |    0.8728 |     1.354 |
+#> |.....................|     1.054 |...........|...........|...........|
+#> |    X|     752.16831 |     85.98 |   0.04031 |   0.01001 |    0.4066 |
+#> |.....................|     4.641 |    0.6428 |    0.8728 |     1.354 |
+#> |.....................|     1.054 |...........|...........|...........|
+#> |    F| Forward Diff. |   -0.7512 |   0.02003 |    0.1902 |    0.3449 |
+#> |.....................|    -2.985 |    0.7407 |    0.3269 |    -1.581 |
+#> |.....................|    -1.064 |...........|...........|...........|
+#> |   43|     752.14828 |    0.9957 |   -0.9743 |    -1.038 |    -1.040 |
+#> |.....................|   -0.6457 |   -0.9684 |   -0.9119 |   -0.6984 |
+#> |.....................|   -0.7843 |...........|...........|...........|
+#> |    U|     752.14828 |     86.16 |    -3.211 |    -4.605 |   -0.3785 |
+#> |.....................|     4.658 |    0.6414 |    0.8720 |     1.359 |
+#> |.....................|     1.057 |...........|...........|...........|
+#> |    X|     752.14828 |     86.16 |   0.04031 |   0.01000 |    0.4065 |
+#> |.....................|     4.658 |    0.6414 |    0.8720 |     1.359 |
+#> |.....................|     1.057 |...........|...........|...........|
+#> |    F| Forward Diff. |     12.68 |  0.008742 |    0.2033 |    0.3626 |
+#> |.....................|    -1.835 |    0.8163 |    0.2532 |    -1.452 |
+#> |.....................|   -0.9466 |...........|...........|...........|
+#> |   44|     752.12689 |    0.9938 |   -0.9744 |    -1.038 |    -1.049 |
+#> |.....................|   -0.6468 |   -0.9706 |   -0.9116 |   -0.6946 |
+#> |.....................|   -0.7819 |...........|...........|...........|
+#> |    U|     752.12689 |     86.00 |    -3.211 |    -4.606 |   -0.3814 |
+#> |.....................|     4.656 |    0.6399 |    0.8723 |     1.363 |
+#> |.....................|     1.059 |...........|...........|...........|
+#> |    X|     752.12689 |     86.00 |   0.04030 |  0.009996 |    0.4058 |
+#> |.....................|     4.656 |    0.6399 |    0.8723 |     1.363 |
+#> |.....................|     1.059 |...........|...........|...........|
+#> |    F| Forward Diff. |  -0.08747 |   0.01751 |    0.1808 |    0.3434 |
+#> |.....................|    -2.013 |    0.5634 |    0.2760 |    -1.320 |
+#> |.....................|   -0.7971 |...........|...........|...........|
+#> |   45|     752.10460 |    0.9941 |   -0.9745 |    -1.039 |    -1.050 |
+#> |.....................|   -0.6390 |   -0.9728 |   -0.9127 |   -0.6895 |
+#> |.....................|   -0.7788 |...........|...........|...........|
+#> |    U|      752.1046 |     86.03 |    -3.211 |    -4.606 |   -0.3818 |
+#> |.....................|     4.673 |    0.6383 |    0.8713 |     1.369 |
+#> |.....................|     1.062 |...........|...........|...........|
+#> |    X|      752.1046 |     86.03 |   0.04030 |  0.009989 |    0.4057 |
+#> |.....................|     4.673 |    0.6383 |    0.8713 |     1.369 |
+#> |.....................|     1.062 |...........|...........|...........|
+#> |   46|     752.09051 |    0.9947 |   -0.9746 |    -1.040 |    -1.052 |
+#> |.....................|   -0.6247 |   -0.9768 |   -0.9147 |   -0.6801 |
+#> |.....................|   -0.7732 |...........|...........|...........|
+#> |    U|     752.09051 |     86.08 |    -3.211 |    -4.608 |   -0.3827 |
+#> |.....................|     4.704 |    0.6355 |    0.8695 |     1.380 |
+#> |.....................|     1.067 |...........|...........|...........|
+#> |    X|     752.09051 |     86.08 |   0.04030 |  0.009976 |    0.4055 |
+#> |.....................|     4.704 |    0.6355 |    0.8695 |     1.380 |
+#> |.....................|     1.067 |...........|...........|...........|
+#> |    F| Forward Diff. |     5.771 |   0.01029 |    0.1542 |    0.3620 |
+#> |.....................|    0.8997 |    0.2873 |   0.01810 |   -0.9019 |
+#> |.....................|   -0.3639 |...........|...........|...........|
+#> |   47|     752.06630 |    0.9944 |   -0.9751 |    -1.045 |    -1.068 |
+#> |.....................|   -0.6300 |   -0.9815 |   -0.9184 |   -0.6573 |
+#> |.....................|   -0.7726 |...........|...........|...........|
+#> |    U|      752.0663 |     86.05 |    -3.212 |    -4.613 |   -0.3878 |
+#> |.....................|     4.692 |    0.6323 |    0.8661 |     1.407 |
+#> |.....................|     1.068 |...........|...........|...........|
+#> |    X|      752.0663 |     86.05 |   0.04028 |  0.009926 |    0.4043 |
+#> |.....................|     4.692 |    0.6323 |    0.8661 |     1.407 |
+#> |.....................|     1.068 |...........|...........|...........|
+#> |    F| Forward Diff. |     3.128 |  0.007908 |  0.004436 |    0.3353 |
+#> |.....................|    0.2209 |    0.1645 |   -0.3029 |   -0.2852 |
+#> |.....................|   -0.2419 |...........|...........|...........|
+#> |   48|     752.06241 |    0.9926 |   -0.9758 |    -1.042 |    -1.095 |
+#> |.....................|   -0.6306 |   -0.9841 |   -0.9113 |   -0.6557 |
+#> |.....................|   -0.7685 |...........|...........|...........|
+#> |    U|     752.06241 |     85.89 |    -3.213 |    -4.609 |   -0.3969 |
+#> |.....................|     4.691 |    0.6304 |    0.8725 |     1.408 |
+#> |.....................|     1.072 |...........|...........|...........|
+#> |    X|     752.06241 |     85.89 |   0.04025 |  0.009958 |    0.4021 |
+#> |.....................|     4.691 |    0.6304 |    0.8725 |     1.408 |
+#> |.....................|     1.072 |...........|...........|...........|
+#> |    F| Forward Diff. |    -8.924 |   0.01284 |    0.1020 |    0.2919 |
+#> |.....................|    0.1011 |  -0.08995 |    0.3194 |   -0.2130 |
+#> |.....................|  -0.05120 |...........|...........|...........|
+#> |   49|     752.04768 |    0.9941 |   -0.9763 |    -1.043 |    -1.124 |
+#> |.....................|   -0.6313 |   -0.9862 |   -0.9116 |   -0.6566 |
+#> |.....................|   -0.7644 |...........|...........|...........|
+#> |    U|     752.04768 |     86.02 |    -3.213 |    -4.611 |   -0.4065 |
+#> |.....................|     4.690 |    0.6289 |    0.8723 |     1.407 |
+#> |.....................|     1.076 |...........|...........|...........|
+#> |    X|     752.04768 |     86.02 |   0.04023 |  0.009946 |    0.3998 |
+#> |.....................|     4.690 |    0.6289 |    0.8723 |     1.407 |
+#> |.....................|     1.076 |...........|...........|...........|
+#> |    F| Forward Diff. |   0.04447 |  0.001311 |    0.1345 |    0.2729 |
+#> |.....................|   0.05334 |  -0.06694 |    0.2984 |   -0.1966 |
+#> |.....................|   0.06514 |...........|...........|...........|
+#> |   50|     752.04768 |    0.9941 |   -0.9763 |    -1.043 |    -1.124 |
+#> |.....................|   -0.6313 |   -0.9862 |   -0.9116 |   -0.6566 |
+#> |.....................|   -0.7644 |...........|...........|...........|
+#> |    U|     752.04768 |     86.02 |    -3.213 |    -4.611 |   -0.4065 |
+#> |.....................|     4.690 |    0.6289 |    0.8723 |     1.407 |
+#> |.....................|     1.076 |...........|...........|...........|
+#> |    X|     752.04768 |     86.02 |   0.04023 |  0.009946 |    0.3998 |
+#> |.....................|     4.690 |    0.6289 |    0.8723 |     1.407 |
+#> |.....................|     1.076 |...........|...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |
+#> |.....................|  log_beta |     sigma |        o1 |        o2 |
+#> |.....................|        o3 |        o4 |        o5 |...........|
+#> |    1|     491.68697 |     1.000 |    -1.000 |   -0.9113 |   -0.8954 |
+#> |.....................|   -0.8491 |   -0.8582 |   -0.8760 |   -0.8739 |
+#> |.....................|   -0.8673 |   -0.8694 |   -0.8683 |...........|
+#> |    U|     491.68697 |     94.21 |    -5.416 |   -0.9966 |   -0.2046 |
+#> |.....................|     2.098 |     1.647 |    0.7612 |    0.8665 |
+#> |.....................|     1.192 |     1.089 |     1.144 |...........|
+#> |    X|     491.68697 |     94.21 |  0.004447 |    0.2696 |    0.8150 |
+#> |.....................|     8.153 |     1.647 |    0.7612 |    0.8665 |
+#> |.....................|     1.192 |     1.089 |     1.144 |...........|
+#> |    G|    Gill Diff. |     19.86 |     1.831 |   -0.1132 |  -0.03447 |
+#> |.....................|   -0.1365 |    -48.08 |     10.28 |     8.952 |
+#> |.....................|    -12.04 |    -8.764 |    -10.61 |...........|
+#> |    2|     1105.9428 |    0.6506 |    -1.032 |   -0.9093 |   -0.8948 |
+#> |.....................|   -0.8467 |  -0.01215 |    -1.057 |    -1.031 |
+#> |.....................|   -0.6554 |   -0.7152 |   -0.6817 |...........|
+#> |    U|     1105.9428 |     61.29 |    -5.448 |   -0.9946 |   -0.2040 |
+#> |.....................|     2.101 |     2.344 |    0.6235 |    0.7300 |
+#> |.....................|     1.445 |     1.256 |     1.357 |...........|
+#> |    X|     1105.9428 |     61.29 |  0.004306 |    0.2700 |    0.8155 |
+#> |.....................|     8.173 |     2.344 |    0.6235 |    0.7300 |
+#> |.....................|     1.445 |     1.256 |     1.357 |...........|
+#> |    3|     499.02505 |    0.9651 |    -1.003 |   -0.9111 |   -0.8953 |
+#> |.....................|   -0.8489 |   -0.7736 |   -0.8941 |   -0.8896 |
+#> |.....................|   -0.8462 |   -0.8540 |   -0.8497 |...........|
+#> |    U|     499.02505 |     90.91 |    -5.419 |   -0.9964 |   -0.2045 |
+#> |.....................|     2.099 |     1.717 |    0.7475 |    0.8529 |
+#> |.....................|     1.217 |     1.105 |     1.165 |...........|
+#> |    X|     499.02505 |     90.91 |  0.004433 |    0.2696 |    0.8150 |
+#> |.....................|     8.155 |     1.717 |    0.7475 |    0.8529 |
+#> |.....................|     1.217 |     1.105 |     1.165 |...........|
+#> |    4|     491.11153 |    0.9924 |    -1.001 |   -0.9112 |   -0.8954 |
+#> |.....................|   -0.8491 |   -0.8397 |   -0.8799 |   -0.8773 |
+#> |.....................|   -0.8627 |   -0.8661 |   -0.8642 |...........|
+#> |    U|     491.11153 |     93.49 |    -5.416 |   -0.9966 |   -0.2046 |
+#> |.....................|     2.098 |     1.663 |    0.7582 |    0.8635 |
+#> |.....................|     1.198 |     1.092 |     1.148 |...........|
+#> |    X|     491.11153 |     93.49 |  0.004444 |    0.2696 |    0.8150 |
+#> |.....................|     8.154 |     1.663 |    0.7582 |    0.8635 |
+#> |.....................|     1.198 |     1.092 |     1.148 |...........|
+#> |    F| Forward Diff. |    -141.0 |     1.761 |   -0.2309 |   -0.1084 |
+#> |.....................|   -0.3671 |    -44.06 |     11.23 |     7.698 |
+#> |.....................|    -11.77 |    -8.480 |    -10.17 |...........|
+#> |    5|     489.72110 |     1.001 |    -1.001 |   -0.9112 |   -0.8954 |
+#> |.....................|   -0.8490 |   -0.8217 |   -0.8840 |   -0.8806 |
+#> |.....................|   -0.8581 |   -0.8627 |   -0.8602 |...........|
+#> |    U|      489.7211 |     94.29 |    -5.417 |   -0.9965 |   -0.2046 |
+#> |.....................|     2.099 |     1.678 |    0.7552 |    0.8607 |
+#> |.....................|     1.203 |     1.096 |     1.153 |...........|
+#> |    X|      489.7211 |     94.29 |  0.004441 |    0.2696 |    0.8150 |
+#> |.....................|     8.154 |     1.678 |    0.7552 |    0.8607 |
+#> |.....................|     1.203 |     1.096 |     1.153 |...........|
+#> |    F| Forward Diff. |     37.99 |     1.786 |  -0.09663 |  -0.03934 |
+#> |.....................|   -0.1210 |    -40.49 |     9.520 |     7.642 |
+#> |.....................|    -11.65 |    -8.313 |    -10.04 |...........|
+#> |    6|     488.87741 |    0.9957 |    -1.002 |   -0.9111 |   -0.8953 |
+#> |.....................|   -0.8490 |   -0.8027 |   -0.8883 |   -0.8842 |
+#> |.....................|   -0.8530 |   -0.8591 |   -0.8558 |...........|
+#> |    U|     488.87741 |     93.80 |    -5.418 |   -0.9965 |   -0.2045 |
+#> |.....................|     2.099 |     1.693 |    0.7519 |    0.8576 |
+#> |.....................|     1.209 |     1.100 |     1.158 |...........|
+#> |    X|     488.87741 |     93.80 |  0.004437 |    0.2696 |    0.8150 |
+#> |.....................|     8.155 |     1.693 |    0.7519 |    0.8576 |
+#> |.....................|     1.209 |     1.100 |     1.158 |...........|
+#> |    F| Forward Diff. |    -68.52 |     1.732 |   -0.1791 |  -0.08434 |
+#> |.....................|   -0.2775 |    -36.72 |     9.505 |     7.234 |
+#> |.....................|    -11.37 |    -8.098 |    -9.790 |...........|
+#> |    7|     487.98842 |     1.002 |    -1.003 |   -0.9111 |   -0.8953 |
+#> |.....................|   -0.8489 |   -0.7841 |   -0.8926 |   -0.8878 |
+#> |.....................|   -0.8478 |   -0.8553 |   -0.8512 |...........|
+#> |    U|     487.98842 |     94.37 |    -5.418 |   -0.9964 |   -0.2045 |
+#> |.....................|     2.099 |     1.708 |    0.7486 |    0.8545 |
+#> |.....................|     1.215 |     1.104 |     1.163 |...........|
+#> |    X|     487.98842 |     94.37 |  0.004434 |    0.2697 |    0.8150 |
+#> |.....................|     8.156 |     1.708 |    0.7486 |    0.8545 |
+#> |.....................|     1.215 |     1.104 |     1.163 |...........|
+#> |    F| Forward Diff. |     53.83 |     1.743 |  -0.07921 |  -0.03701 |
+#> |.....................|  -0.09401 |    -33.22 |     8.823 |     7.101 |
+#> |.....................|    -11.24 |    -7.914 |    -9.621 |...........|
+#> |    8|     487.18834 |    0.9967 |    -1.004 |   -0.9110 |   -0.8953 |
+#> |.....................|   -0.8488 |   -0.7657 |   -0.8973 |   -0.8916 |
+#> |.....................|   -0.8421 |   -0.8512 |   -0.8463 |...........|
+#> |    U|     487.18834 |     93.89 |    -5.419 |   -0.9963 |   -0.2045 |
+#> |.....................|     2.099 |     1.724 |    0.7451 |    0.8512 |
+#> |.....................|     1.222 |     1.108 |     1.169 |...........|
+#> |    X|     487.18834 |     93.89 |  0.004430 |    0.2697 |    0.8151 |
+#> |.....................|     8.156 |     1.724 |    0.7451 |    0.8512 |
+#> |.....................|     1.222 |     1.108 |     1.169 |...........|
+#> |    F| Forward Diff. |    -47.29 |     1.692 |   -0.1608 |  -0.08286 |
+#> |.....................|   -0.2512 |    -29.89 |     8.493 |     6.629 |
+#> |.....................|    -10.92 |    -7.677 |    -9.350 |...........|
+#> |    9|     486.46922 |     1.002 |    -1.005 |   -0.9109 |   -0.8952 |
+#> |.....................|   -0.8487 |   -0.7480 |   -0.9022 |   -0.8958 |
+#> |.....................|   -0.8355 |   -0.8466 |   -0.8406 |...........|
+#> |    U|     486.46922 |     94.36 |    -5.420 |   -0.9963 |   -0.2045 |
+#> |.....................|     2.099 |     1.738 |    0.7413 |    0.8476 |
+#> |.....................|     1.230 |     1.113 |     1.175 |...........|
+#> |    X|     486.46922 |     94.36 |  0.004425 |    0.2697 |    0.8151 |
+#> |.....................|     8.157 |     1.738 |    0.7413 |    0.8476 |
+#> |.....................|     1.230 |     1.113 |     1.175 |...........|
+#> |    F| Forward Diff. |     49.83 |     1.694 |  -0.07480 |  -0.03429 |
+#> |.....................|  -0.09436 |    -26.68 |     8.123 |     6.503 |
+#> |.....................|    -10.68 |    -7.439 |    -9.119 |...........|
+#> |   10|     485.78721 |    0.9968 |    -1.006 |   -0.9109 |   -0.8952 |
+#> |.....................|   -0.8486 |   -0.7319 |   -0.9078 |   -0.9005 |
+#> |.....................|   -0.8277 |   -0.8412 |   -0.8339 |...........|
+#> |    U|     485.78721 |     93.91 |    -5.422 |   -0.9962 |   -0.2044 |
+#> |.....................|     2.099 |     1.752 |    0.7370 |    0.8435 |
+#> |.....................|     1.239 |     1.119 |     1.183 |...........|
+#> |    X|     485.78721 |     93.91 |  0.004420 |    0.2697 |    0.8151 |
+#> |.....................|     8.158 |     1.752 |    0.7370 |    0.8435 |
+#> |.....................|     1.239 |     1.119 |     1.183 |...........|
+#> |    F| Forward Diff. |    -42.45 |     1.646 |   -0.1526 |  -0.07491 |
+#> |.....................|   -0.2510 |    -24.12 |     7.576 |     5.974 |
+#> |.....................|    -10.35 |    -7.128 |    -8.768 |...........|
+#> |   11|     485.17009 |     1.001 |    -1.008 |   -0.9107 |   -0.8952 |
+#> |.....................|   -0.8484 |   -0.7183 |   -0.9141 |   -0.9058 |
+#> |.....................|   -0.8180 |   -0.8347 |   -0.8257 |...........|
+#> |    U|     485.17009 |     94.32 |    -5.423 |   -0.9961 |   -0.2044 |
+#> |.....................|     2.099 |     1.763 |    0.7322 |    0.8389 |
+#> |.....................|     1.251 |     1.126 |     1.192 |...........|
+#> |    X|     485.17009 |     94.32 |  0.004413 |    0.2697 |    0.8152 |
+#> |.....................|     8.160 |     1.763 |    0.7322 |    0.8389 |
+#> |.....................|     1.251 |     1.126 |     1.192 |...........|
+#> |   12|     484.56759 |     1.002 |    -1.010 |   -0.9106 |   -0.8951 |
+#> |.....................|   -0.8481 |   -0.7038 |   -0.9212 |   -0.9119 |
+#> |.....................|   -0.8067 |   -0.8272 |   -0.8163 |...........|
+#> |    U|     484.56759 |     94.37 |    -5.425 |   -0.9959 |   -0.2043 |
+#> |.....................|     2.099 |     1.775 |    0.7268 |    0.8336 |
+#> |.....................|     1.264 |     1.134 |     1.203 |...........|
+#> |    X|     484.56759 |     94.37 |  0.004404 |    0.2697 |    0.8152 |
+#> |.....................|     8.162 |     1.775 |    0.7268 |    0.8336 |
+#> |.....................|     1.264 |     1.134 |     1.203 |...........|
+#> |   13|     483.17982 |     1.003 |    -1.015 |   -0.9102 |   -0.8949 |
+#> |.....................|   -0.8475 |   -0.6634 |   -0.9410 |   -0.9287 |
+#> |.....................|   -0.7754 |   -0.8064 |   -0.7900 |...........|
+#> |    U|     483.17982 |     94.51 |    -5.431 |   -0.9956 |   -0.2042 |
+#> |.....................|     2.100 |     1.808 |    0.7117 |    0.8190 |
+#> |.....................|     1.302 |     1.157 |     1.233 |...........|
+#> |    X|     483.17982 |     94.51 |  0.004381 |    0.2698 |    0.8153 |
+#> |.....................|     8.167 |     1.808 |    0.7117 |    0.8190 |
+#> |.....................|     1.302 |     1.157 |     1.233 |...........|
+#> |    F| Forward Diff. |     68.60 |     1.559 |  0.008498 |  -0.01857 |
+#> |.....................|  -0.01950 |    -13.38 |     5.413 |     4.461 |
+#> |.....................|    -8.084 |    -5.202 |    -6.751 |...........|
+#> |   14|     482.50435 |    0.9937 |    -1.034 |   -0.9105 |   -0.8944 |
+#> |.....................|   -0.8462 |   -0.6947 |   -0.9713 |   -0.9553 |
+#> |.....................|   -0.7043 |   -0.7694 |   -0.7343 |...........|
+#> |    U|     482.50435 |     93.61 |    -5.449 |   -0.9958 |   -0.2036 |
+#> |.....................|     2.101 |     1.782 |    0.6887 |    0.7959 |
+#> |.....................|     1.386 |     1.197 |     1.297 |...........|
+#> |    X|     482.50435 |     93.61 |  0.004300 |    0.2698 |    0.8158 |
+#> |.....................|     8.177 |     1.782 |    0.6887 |    0.7959 |
+#> |.....................|     1.386 |     1.197 |     1.297 |...........|
+#> |    F| Forward Diff. |    -85.62 |     1.442 |   -0.1650 |  -0.08233 |
+#> |.....................|   -0.3434 |    -17.31 |     3.930 |     3.048 |
+#> |.....................|    -4.934 |    -3.045 |    -4.080 |...........|
+#> |   15|     481.97261 |     1.003 |    -1.090 |   -0.9106 |   -0.8929 |
+#> |.....................|   -0.8403 |   -0.7109 |   -0.9936 |   -0.9798 |
+#> |.....................|   -0.6305 |   -0.7595 |   -0.6850 |...........|
+#> |    U|     481.97261 |     94.53 |    -5.505 |   -0.9959 |   -0.2021 |
+#> |.....................|     2.107 |     1.769 |    0.6717 |    0.7747 |
+#> |.....................|     1.474 |     1.208 |     1.353 |...........|
+#> |    X|     481.97261 |     94.53 |  0.004066 |    0.2697 |    0.8170 |
+#> |.....................|     8.226 |     1.769 |    0.6717 |    0.7747 |
+#> |.....................|     1.474 |     1.208 |     1.353 |...........|
+#> |    F| Forward Diff. |     56.89 |     1.274 |    0.1237 |   0.02279 |
+#> |.....................|    0.2367 |    -19.64 |     1.923 |     2.281 |
+#> |.....................|    -1.663 |    -2.419 |    -1.870 |...........|
+#> |   16|     481.06506 |     1.001 |    -1.169 |   -0.9152 |   -0.8919 |
+#> |.....................|   -0.8407 |   -0.6475 |   -0.9528 |   -0.9773 |
+#> |.....................|   -0.6368 |   -0.7786 |   -0.6952 |...........|
+#> |    U|     481.06506 |     94.29 |    -5.585 |    -1.000 |   -0.2011 |
+#> |.....................|     2.107 |     1.821 |    0.7028 |    0.7769 |
+#> |.....................|     1.467 |     1.187 |     1.341 |...........|
+#> |    X|     481.06506 |     94.29 |  0.003755 |    0.2688 |    0.8179 |
+#> |.....................|     8.223 |     1.821 |    0.7028 |    0.7769 |
+#> |.....................|     1.467 |     1.187 |     1.341 |...........|
+#> |    F| Forward Diff. |     24.24 |    0.9898 |   -0.1087 |   0.01886 |
+#> |.....................|    0.1247 |    -10.78 |     3.743 |     2.188 |
+#> |.....................|    -2.085 |    -3.507 |    -2.452 |...........|
+#> |   17|     481.22982 |    0.9921 |    -1.212 |   -0.9099 |   -0.8928 |
+#> |.....................|   -0.8459 |   -0.6315 |    -1.015 |   -0.9814 |
+#> |.....................|   -0.6906 |   -0.7213 |   -0.7106 |...........|
+#> |    U|     481.22982 |     93.46 |    -5.628 |   -0.9952 |   -0.2020 |
+#> |.....................|     2.102 |     1.834 |    0.6553 |    0.7733 |
+#> |.....................|     1.403 |     1.250 |     1.324 |...........|
+#> |    X|     481.22982 |     93.46 |  0.003596 |    0.2699 |    0.8171 |
+#> |.....................|     8.180 |     1.834 |    0.6553 |    0.7733 |
+#> |.....................|     1.403 |     1.250 |     1.324 |...........|
+#> |   18|     481.29798 |    0.9919 |    -1.186 |   -0.9131 |   -0.8922 |
+#> |.....................|   -0.8428 |   -0.6388 |   -0.9780 |   -0.9794 |
+#> |.....................|   -0.6574 |   -0.7554 |   -0.7007 |...........|
+#> |    U|     481.29798 |     93.44 |    -5.602 |   -0.9984 |   -0.2014 |
+#> |.....................|     2.105 |     1.828 |    0.6836 |    0.7751 |
+#> |.....................|     1.442 |     1.213 |     1.335 |...........|
+#> |    X|     481.29798 |     93.44 |  0.003691 |    0.2693 |    0.8176 |
+#> |.....................|     8.206 |     1.828 |    0.6836 |    0.7751 |
+#> |.....................|     1.442 |     1.213 |     1.335 |...........|
+#> |   19|     481.41397 |    0.9918 |    -1.173 |   -0.9147 |   -0.8919 |
+#> |.....................|   -0.8412 |   -0.6424 |   -0.9596 |   -0.9784 |
+#> |.....................|   -0.6408 |   -0.7724 |   -0.6957 |...........|
+#> |    U|     481.41397 |     93.43 |    -5.589 |    -1.000 |   -0.2012 |
+#> |.....................|     2.106 |     1.825 |    0.6976 |    0.7759 |
+#> |.....................|     1.462 |     1.194 |     1.341 |...........|
+#> |    X|     481.41397 |     93.43 |  0.003739 |    0.2689 |    0.8178 |
+#> |.....................|     8.219 |     1.825 |    0.6976 |    0.7759 |
+#> |.....................|     1.462 |     1.194 |     1.341 |...........|
+#> |   20|     481.05031 |    0.9977 |    -1.169 |   -0.9152 |   -0.8919 |
+#> |.....................|   -0.8407 |   -0.6461 |   -0.9533 |   -0.9776 |
+#> |.....................|   -0.6366 |   -0.7782 |   -0.6949 |...........|
+#> |    U|     481.05031 |     93.99 |    -5.585 |    -1.000 |   -0.2011 |
+#> |.....................|     2.107 |     1.822 |    0.7024 |    0.7766 |
+#> |.....................|     1.467 |     1.188 |     1.342 |...........|
+#> |    X|     481.05031 |     93.99 |  0.003754 |    0.2688 |    0.8179 |
+#> |.....................|     8.223 |     1.822 |    0.7024 |    0.7766 |
+#> |.....................|     1.467 |     1.188 |     1.342 |...........|
+#> |    F| Forward Diff. |    -27.42 |    0.9768 |   -0.2107 |  -0.01109 |
+#> |.....................|  -0.02839 |    -10.63 |     3.585 |     2.076 |
+#> |.....................|    -2.082 |    -3.487 |    -2.432 |...........|
+#> |   21|     481.00693 |    0.9997 |    -1.170 |   -0.9150 |   -0.8919 |
+#> |.....................|   -0.8408 |   -0.6450 |   -0.9548 |   -0.9778 |
+#> |.....................|   -0.6377 |   -0.7765 |   -0.6951 |...........|
+#> |    U|     481.00693 |     94.18 |    -5.586 |    -1.000 |   -0.2011 |
+#> |.....................|     2.107 |     1.823 |    0.7012 |    0.7764 |
+#> |.....................|     1.466 |     1.190 |     1.342 |...........|
+#> |    X|     481.00693 |     94.18 |  0.003750 |    0.2689 |    0.8178 |
+#> |.....................|     8.222 |     1.823 |    0.7012 |    0.7764 |
+#> |.....................|     1.466 |     1.190 |     1.342 |...........|
+#> |    F| Forward Diff. |     5.549 |    0.9801 |   -0.1366 |  0.007724 |
+#> |.....................|   0.06864 |    -10.47 |     3.736 |     2.095 |
+#> |.....................|    -2.145 |    -3.386 |    -2.439 |...........|
+#> |   22|     480.97727 |    0.9982 |    -1.171 |   -0.9150 |   -0.8919 |
+#> |.....................|   -0.8408 |   -0.6422 |   -0.9558 |   -0.9784 |
+#> |.....................|   -0.6371 |   -0.7756 |   -0.6944 |...........|
+#> |    U|     480.97727 |     94.04 |    -5.586 |    -1.000 |   -0.2011 |
+#> |.....................|     2.107 |     1.825 |    0.7005 |    0.7760 |
+#> |.....................|     1.466 |     1.191 |     1.342 |...........|
+#> |    X|     480.97727 |     94.04 |  0.003749 |    0.2689 |    0.8178 |
+#> |.....................|     8.222 |     1.825 |    0.7005 |    0.7760 |
+#> |.....................|     1.466 |     1.191 |     1.342 |...........|
+#> |    F| Forward Diff. |    -18.22 |    0.9728 |   -0.1820 | -0.005388 |
+#> |.....................| -0.004679 |    -10.15 |     3.348 |     1.956 |
+#> |.....................|    -2.141 |    -3.348 |    -2.415 |...........|
+#> |   23|     480.94781 |    0.9999 |    -1.172 |   -0.9148 |   -0.8919 |
+#> |.....................|   -0.8410 |   -0.6410 |   -0.9575 |   -0.9785 |
+#> |.....................|   -0.6383 |   -0.7738 |   -0.6946 |...........|
+#> |    U|     480.94781 |     94.20 |    -5.587 |    -1.000 |   -0.2011 |
+#> |.....................|     2.107 |     1.826 |    0.6992 |    0.7758 |
+#> |.....................|     1.465 |     1.193 |     1.342 |...........|
+#> |    X|     480.94781 |     94.20 |  0.003745 |    0.2689 |    0.8178 |
+#> |.....................|     8.220 |     1.826 |    0.6992 |    0.7758 |
+#> |.....................|     1.465 |     1.193 |     1.342 |...........|
+#> |    F| Forward Diff. |     8.568 |    0.9740 |   -0.1199 |  0.009837 |
+#> |.....................|   0.07469 |    -9.926 |     3.371 |    0.7973 |
+#> |.....................|    -2.181 |    -3.230 |    -2.408 |...........|
+#> |   24|     480.92664 |    0.9984 |    -1.173 |   -0.9147 |   -0.8919 |
+#> |.....................|   -0.8411 |   -0.6390 |   -0.9589 |   -0.9778 |
+#> |.....................|   -0.6386 |   -0.7721 |   -0.6942 |...........|
+#> |    U|     480.92664 |     94.06 |    -5.588 |    -1.000 |   -0.2011 |
+#> |.....................|     2.107 |     1.828 |    0.6981 |    0.7765 |
+#> |.....................|     1.465 |     1.195 |     1.343 |...........|
+#> |    X|     480.92664 |     94.06 |  0.003741 |    0.2689 |    0.8178 |
+#> |.....................|     8.219 |     1.828 |    0.6981 |    0.7765 |
+#> |.....................|     1.465 |     1.195 |     1.343 |...........|
+#> |    F| Forward Diff. |    -15.24 |    0.9644 |   -0.1632 | -0.002739 |
+#> |.....................| -0.008738 |    -9.656 |     3.177 |    0.7945 |
+#> |.....................|    -2.140 |    -3.159 |    -2.407 |...........|
+#> |   25|     480.90633 |    0.9999 |    -1.174 |   -0.9146 |   -0.8920 |
+#> |.....................|   -0.8412 |   -0.6376 |   -0.9602 |   -0.9760 |
+#> |.....................|   -0.6390 |   -0.7705 |   -0.6939 |...........|
+#> |    U|     480.90633 |     94.20 |    -5.589 |   -0.9999 |   -0.2012 |
+#> |.....................|     2.106 |     1.829 |    0.6971 |    0.7780 |
+#> |.....................|     1.464 |     1.196 |     1.343 |...........|
+#> |    X|     480.90633 |     94.20 |  0.003737 |    0.2690 |    0.8178 |
+#> |.....................|     8.219 |     1.829 |    0.6971 |    0.7780 |
+#> |.....................|     1.464 |     1.196 |     1.343 |...........|
+#> |    F| Forward Diff. |     8.878 |    0.9654 |   -0.1149 |  0.008298 |
+#> |.....................|   0.06381 |    -9.456 |     3.199 |     2.165 |
+#> |.....................|    -2.158 |    -3.035 |    -2.359 |...........|
+#> |   26|     480.88677 |    0.9984 |    -1.175 |   -0.9145 |   -0.8920 |
+#> |.....................|   -0.8413 |   -0.6358 |   -0.9617 |   -0.9757 |
+#> |.....................|   -0.6395 |   -0.7687 |   -0.6936 |...........|
+#> |    U|     480.88677 |     94.05 |    -5.591 |   -0.9998 |   -0.2012 |
+#> |.....................|     2.106 |     1.831 |    0.6960 |    0.7783 |
+#> |.....................|     1.464 |     1.198 |     1.343 |...........|
+#> |    X|     480.88677 |     94.05 |  0.003733 |    0.2690 |    0.8178 |
+#> |.....................|     8.218 |     1.831 |    0.6960 |    0.7783 |
+#> |.....................|     1.464 |     1.198 |     1.343 |...........|
+#> |    F| Forward Diff. |    -15.55 |    0.9550 |   -0.1566 | -0.004027 |
+#> |.....................|  -0.01529 |    -9.334 |     3.082 |    0.8457 |
+#> |.....................|    -2.216 |    -2.967 |    -2.371 |...........|
+#> |   27|     480.86430 |    0.9998 |    -1.177 |   -0.9143 |   -0.8920 |
+#> |.....................|   -0.8414 |   -0.6346 |   -0.9633 |   -0.9749 |
+#> |.....................|   -0.6404 |   -0.7668 |   -0.6935 |...........|
+#> |    U|      480.8643 |     94.19 |    -5.592 |   -0.9996 |   -0.2012 |
+#> |.....................|     2.106 |     1.832 |    0.6948 |    0.7790 |
+#> |.....................|     1.463 |     1.200 |     1.343 |...........|
+#> |    X|      480.8643 |     94.19 |  0.003727 |    0.2690 |    0.8177 |
+#> |.....................|     8.217 |     1.832 |    0.6948 |    0.7790 |
+#> |.....................|     1.463 |     1.200 |     1.343 |...........|
+#> |    F| Forward Diff. |     6.756 |    0.9537 |   -0.1079 |  0.006011 |
+#> |.....................|   0.04748 |    -9.023 |     3.021 |     2.222 |
+#> |.....................|    -2.227 |    -2.836 |    -2.339 |...........|
+#> |   28|     480.84403 |    0.9982 |    -1.178 |   -0.9142 |   -0.8920 |
+#> |.....................|   -0.8415 |   -0.6324 |   -0.9646 |   -0.9751 |
+#> |.....................|   -0.6405 |   -0.7653 |   -0.6931 |...........|
+#> |    U|     480.84403 |     94.04 |    -5.593 |   -0.9995 |   -0.2012 |
+#> |.....................|     2.106 |     1.833 |    0.6938 |    0.7788 |
+#> |.....................|     1.462 |     1.202 |     1.344 |...........|
+#> |    X|     480.84403 |     94.04 |  0.003723 |    0.2690 |    0.8177 |
+#> |.....................|     8.216 |     1.833 |    0.6938 |    0.7788 |
+#> |.....................|     1.462 |     1.202 |     1.344 |...........|
+#> |    F| Forward Diff. |    -17.74 |    0.9443 |   -0.1486 | -0.005686 |
+#> |.....................|  -0.02964 |    -8.905 |     2.905 |     2.091 |
+#> |.....................|    -2.264 |    -2.753 |    -2.319 |...........|
+#> |   29|     480.81486 |    0.9998 |    -1.179 |   -0.9140 |   -0.8921 |
+#> |.....................|   -0.8417 |   -0.6315 |   -0.9657 |   -0.9770 |
+#> |.....................|   -0.6415 |   -0.7640 |   -0.6932 |...........|
+#> |    U|     480.81486 |     94.18 |    -5.595 |   -0.9993 |   -0.2013 |
+#> |.....................|     2.106 |     1.834 |    0.6930 |    0.7772 |
+#> |.....................|     1.461 |     1.203 |     1.344 |...........|
+#> |    X|     480.81486 |     94.18 |  0.003718 |    0.2691 |    0.8177 |
+#> |.....................|     8.215 |     1.834 |    0.6930 |    0.7772 |
+#> |.....................|     1.461 |     1.203 |     1.344 |...........|
+#> |    F| Forward Diff. |     6.172 |    0.9439 |  -0.09077 |  0.005496 |
+#> |.....................|   0.04002 |    -8.557 |     3.060 |    0.8688 |
+#> |.....................|    -2.237 |    -2.681 |    -2.329 |...........|
+#> |   30|     480.79675 |    0.9982 |    -1.180 |   -0.9139 |   -0.8921 |
+#> |.....................|   -0.8418 |   -0.6292 |   -0.9672 |   -0.9770 |
+#> |.....................|   -0.6415 |   -0.7628 |   -0.6927 |...........|
+#> |    U|     480.79675 |     94.04 |    -5.596 |   -0.9992 |   -0.2013 |
+#> |.....................|     2.106 |     1.836 |    0.6918 |    0.7772 |
+#> |.....................|     1.461 |     1.205 |     1.344 |...........|
+#> |    X|     480.79675 |     94.04 |  0.003714 |    0.2691 |    0.8177 |
+#> |.....................|     8.214 |     1.836 |    0.6918 |    0.7772 |
+#> |.....................|     1.461 |     1.205 |     1.344 |...........|
+#> |    F| Forward Diff. |    -18.05 |    0.9344 |   -0.1333 | -0.006636 |
+#> |.....................|  -0.03697 |    -8.406 |     2.763 |    0.7695 |
+#> |.....................|    -2.291 |    -2.623 |    -2.307 |...........|
+#> |   31|     480.77804 |    0.9997 |    -1.182 |   -0.9138 |   -0.8921 |
+#> |.....................|   -0.8419 |   -0.6281 |   -0.9686 |   -0.9750 |
+#> |.....................|   -0.6417 |   -0.7615 |   -0.6923 |...........|
+#> |    U|     480.77804 |     94.18 |    -5.597 |   -0.9991 |   -0.2013 |
+#> |.....................|     2.106 |     1.837 |    0.6907 |    0.7789 |
+#> |.....................|     1.461 |     1.206 |     1.345 |...........|
+#> |    X|     480.77804 |     94.18 |  0.003708 |    0.2691 |    0.8176 |
+#> |.....................|     8.213 |     1.837 |    0.6907 |    0.7789 |
+#> |.....................|     1.461 |     1.206 |     1.345 |...........|
+#> |    F| Forward Diff. |     5.466 |    0.9331 |  -0.08875 |  0.003744 |
+#> |.....................|   0.02543 |    -8.171 |     2.670 |     2.155 |
+#> |.....................|    -2.279 |    -2.534 |    -2.278 |...........|
+#> |   32|     480.75892 |    0.9982 |    -1.183 |   -0.9137 |   -0.8921 |
+#> |.....................|   -0.8419 |   -0.6258 |   -0.9698 |   -0.9756 |
+#> |.....................|   -0.6414 |   -0.7603 |   -0.6917 |...........|
+#> |    U|     480.75892 |     94.03 |    -5.598 |   -0.9991 |   -0.2014 |
+#> |.....................|     2.106 |     1.839 |    0.6899 |    0.7784 |
+#> |.....................|     1.461 |     1.207 |     1.346 |...........|
+#> |    X|     480.75892 |     94.03 |  0.003704 |    0.2691 |    0.8176 |
+#> |.....................|     8.212 |     1.839 |    0.6899 |    0.7784 |
+#> |.....................|     1.461 |     1.207 |     1.346 |...........|
+#> |    F| Forward Diff. |    -18.29 |    0.9240 |   -0.1279 | -0.008301 |
+#> |.....................|  -0.04619 |    -7.961 |     2.584 |    0.8229 |
+#> |.....................|    -2.311 |    -2.476 |    -2.253 |...........|
+#> |   33|     480.73432 |    0.9997 |    -1.185 |   -0.9136 |   -0.8922 |
+#> |.....................|   -0.8421 |   -0.6250 |   -0.9708 |   -0.9758 |
+#> |.....................|   -0.6420 |   -0.7587 |   -0.6914 |...........|
+#> |    U|     480.73432 |     94.18 |    -5.601 |   -0.9989 |   -0.2014 |
+#> |.....................|     2.105 |     1.840 |    0.6891 |    0.7782 |
+#> |.....................|     1.461 |     1.209 |     1.346 |...........|
+#> |    X|     480.73432 |     94.18 |  0.003695 |    0.2692 |    0.8176 |
+#> |.....................|     8.211 |     1.840 |    0.6891 |    0.7782 |
+#> |.....................|     1.461 |     1.209 |     1.346 |...........|
+#> |    F| Forward Diff. |     5.056 |    0.9202 |  -0.07575 |  0.002374 |
+#> |.....................|   0.02179 |    -7.789 |     2.502 |     2.101 |
+#> |.....................|    -2.273 |    -2.370 |    -2.217 |...........|
+#> |   34|     480.71449 |    0.9983 |    -1.187 |   -0.9135 |   -0.8922 |
+#> |.....................|   -0.8422 |   -0.6227 |   -0.9719 |   -0.9765 |
+#> |.....................|   -0.6416 |   -0.7575 |   -0.6908 |...........|
+#> |    U|     480.71449 |     94.05 |    -5.602 |   -0.9988 |   -0.2014 |
+#> |.....................|     2.105 |     1.841 |    0.6883 |    0.7776 |
+#> |.....................|     1.461 |     1.210 |     1.347 |...........|
+#> |    X|     480.71449 |     94.05 |  0.003690 |    0.2692 |    0.8175 |
+#> |.....................|     8.210 |     1.841 |    0.6883 |    0.7776 |
+#> |.....................|     1.461 |     1.210 |     1.347 |...........|
+#> |    F| Forward Diff. |    -16.10 |    0.9104 |   -0.1099 | -0.008208 |
+#> |.....................|  -0.04557 |    -7.571 |     2.606 |     1.992 |
+#> |.....................|    -2.295 |    -2.312 |    -2.196 |...........|
+#> |   35|     480.68777 |    0.9997 |    -1.189 |   -0.9134 |   -0.8923 |
+#> |.....................|   -0.8423 |   -0.6220 |   -0.9726 |   -0.9789 |
+#> |.....................|   -0.6421 |   -0.7569 |   -0.6908 |...........|
+#> |    U|     480.68777 |     94.18 |    -5.604 |   -0.9987 |   -0.2015 |
+#> |.....................|     2.105 |     1.842 |    0.6877 |    0.7755 |
+#> |.....................|     1.461 |     1.211 |     1.347 |...........|
+#> |    X|     480.68777 |     94.18 |  0.003683 |    0.2692 |    0.8175 |
+#> |.....................|     8.209 |     1.842 |    0.6877 |    0.7755 |
+#> |.....................|     1.461 |     1.211 |     1.347 |...........|
+#> |    F| Forward Diff. |     4.858 |    0.9091 |  -0.06076 |  0.001972 |
+#> |.....................|   0.01464 |    -7.318 |     2.391 |    0.7174 |
+#> |.....................|    -2.245 |    -2.255 |    -2.188 |...........|
+#> |   36|     480.67297 |    0.9982 |    -1.190 |   -0.9134 |   -0.8923 |
+#> |.....................|   -0.8424 |   -0.6196 |   -0.9738 |   -0.9789 |
+#> |.....................|   -0.6415 |   -0.7559 |   -0.6900 |...........|
+#> |    U|     480.67297 |     94.03 |    -5.605 |   -0.9987 |   -0.2015 |
+#> |.....................|     2.105 |     1.844 |    0.6868 |    0.7755 |
+#> |.....................|     1.461 |     1.212 |     1.348 |...........|
+#> |    X|     480.67297 |     94.03 |  0.003678 |    0.2692 |    0.8175 |
+#> |.....................|     8.209 |     1.844 |    0.6868 |    0.7755 |
+#> |.....................|     1.461 |     1.212 |     1.348 |...........|
+#> |    F| Forward Diff. |    -18.29 |    0.8994 |   -0.1037 |  -0.01039 |
+#> |.....................|  -0.05604 |    -7.086 |     2.324 |    0.6431 |
+#> |.....................|    -2.272 |    -2.229 |    -2.170 |...........|
+#> |   37|     480.65610 |    0.9996 |    -1.192 |   -0.9134 |   -0.8923 |
+#> |.....................|   -0.8424 |   -0.6187 |   -0.9745 |   -0.9768 |
+#> |.....................|   -0.6410 |   -0.7549 |   -0.6892 |...........|
+#> |    U|      480.6561 |     94.17 |    -5.607 |   -0.9987 |   -0.2015 |
+#> |.....................|     2.105 |     1.845 |    0.6862 |    0.7773 |
+#> |.....................|     1.462 |     1.213 |     1.348 |...........|
+#> |    X|      480.6561 |     94.17 |  0.003671 |    0.2692 |    0.8175 |
+#> |.....................|     8.208 |     1.845 |    0.6862 |    0.7773 |
+#> |.....................|     1.462 |     1.213 |     1.348 |...........|
+#> |    F| Forward Diff. |     3.523 |    0.8967 |  -0.06519 |-0.0005238 |
+#> |.....................|  0.007306 |    -6.938 |     2.250 |    0.8205 |
+#> |.....................|    -2.209 |    -2.143 |    -2.109 |...........|
+#> |   38|     480.63930 |    0.9982 |    -1.192 |   -0.9133 |   -0.8923 |
+#> |.....................|   -0.8425 |   -0.6159 |   -0.9754 |   -0.9772 |
+#> |.....................|   -0.6401 |   -0.7540 |   -0.6884 |...........|
+#> |    U|      480.6393 |     94.04 |    -5.608 |   -0.9987 |   -0.2015 |
+#> |.....................|     2.105 |     1.847 |    0.6856 |    0.7770 |
+#> |.....................|     1.463 |     1.214 |     1.349 |...........|
+#> |    X|      480.6393 |     94.04 |  0.003670 |    0.2692 |    0.8175 |
+#> |.....................|     8.208 |     1.847 |    0.6856 |    0.7770 |
+#> |.....................|     1.463 |     1.214 |     1.349 |...........|
+#> |    F| Forward Diff. |    -17.45 |    0.8903 |   -0.1044 |  -0.01155 |
+#> |.....................|  -0.05881 |    -6.641 |     2.195 |     1.966 |
+#> |.....................|    -2.207 |    -2.119 |    -2.090 |...........|
+#> |   39|     480.61554 |    0.9996 |    -1.195 |   -0.9133 |   -0.8924 |
+#> |.....................|   -0.8426 |   -0.6153 |   -0.9757 |   -0.9778 |
+#> |.....................|   -0.6400 |   -0.7531 |   -0.6877 |...........|
+#> |    U|     480.61554 |     94.16 |    -5.611 |   -0.9986 |   -0.2016 |
+#> |.....................|     2.105 |     1.848 |    0.6853 |    0.7765 |
+#> |.....................|     1.463 |     1.215 |     1.350 |...........|
+#> |    X|     480.61554 |     94.16 |  0.003659 |    0.2692 |    0.8174 |
+#> |.....................|     8.207 |     1.848 |    0.6853 |    0.7765 |
+#> |.....................|     1.463 |     1.215 |     1.350 |...........|
+#> |    F| Forward Diff. |     2.395 |    0.8850 |  -0.05988 | -0.001937 |
+#> |.....................| 0.0008548 |    -6.531 |     2.145 |    0.7341 |
+#> |.....................|    -2.178 |    -2.045 |    -2.040 |...........|
+#> |   40|     480.59501 |    0.9985 |    -1.195 |   -0.9132 |   -0.8924 |
+#> |.....................|   -0.8426 |   -0.6124 |   -0.9766 |   -0.9781 |
+#> |.....................|   -0.6390 |   -0.7522 |   -0.6868 |...........|
+#> |    U|     480.59501 |     94.06 |    -5.611 |   -0.9986 |   -0.2016 |
+#> |.....................|     2.105 |     1.850 |    0.6846 |    0.7762 |
+#> |.....................|     1.464 |     1.216 |     1.351 |...........|
+#> |    X|     480.59501 |     94.06 |  0.003658 |    0.2692 |    0.8174 |
+#> |.....................|     8.207 |     1.850 |    0.6846 |    0.7762 |
+#> |.....................|     1.464 |     1.216 |     1.351 |...........|
+#> |    F| Forward Diff. |    -13.20 |    0.8797 |  -0.08878 |  -0.01245 |
+#> |.....................|  -0.05202 |    -6.149 |     2.097 |     1.936 |
+#> |.....................|    -2.128 |    -2.007 |    -2.021 |...........|
+#> |   41|     480.57374 |    0.9995 |    -1.198 |   -0.9132 |   -0.8924 |
+#> |.....................|   -0.8426 |   -0.6117 |   -0.9768 |   -0.9794 |
+#> |.....................|   -0.6387 |   -0.7515 |   -0.6862 |...........|
+#> |    U|     480.57374 |     94.16 |    -5.614 |   -0.9986 |   -0.2016 |
+#> |.....................|     2.105 |     1.851 |    0.6845 |    0.7751 |
+#> |.....................|     1.464 |     1.217 |     1.352 |...........|
+#> |    X|     480.57374 |     94.16 |  0.003647 |    0.2692 |    0.8174 |
+#> |.....................|     8.207 |     1.851 |    0.6845 |    0.7751 |
+#> |.....................|     1.464 |     1.217 |     1.352 |...........|
+#> |   42|     480.55656 |    0.9993 |    -1.203 |   -0.9133 |   -0.8924 |
+#> |.....................|   -0.8427 |   -0.6115 |   -0.9767 |   -0.9815 |
+#> |.....................|   -0.6386 |   -0.7506 |   -0.6853 |...........|
+#> |    U|     480.55656 |     94.14 |    -5.619 |   -0.9986 |   -0.2016 |
+#> |.....................|     2.105 |     1.851 |    0.6846 |    0.7733 |
+#> |.....................|     1.465 |     1.218 |     1.353 |...........|
+#> |    X|     480.55656 |     94.14 |  0.003629 |    0.2692 |    0.8174 |
+#> |.....................|     8.206 |     1.851 |    0.6846 |    0.7733 |
+#> |.....................|     1.465 |     1.218 |     1.353 |...........|
+#> |   43|     480.48642 |    0.9984 |    -1.228 |   -0.9134 |   -0.8925 |
+#> |.....................|   -0.8432 |   -0.6102 |   -0.9761 |   -0.9914 |
+#> |.....................|   -0.6380 |   -0.7463 |   -0.6812 |...........|
+#> |    U|     480.48642 |     94.05 |    -5.643 |   -0.9987 |   -0.2017 |
+#> |.....................|     2.104 |     1.852 |    0.6850 |    0.7647 |
+#> |.....................|     1.465 |     1.223 |     1.357 |...........|
+#> |    X|     480.48642 |     94.05 |  0.003541 |    0.2692 |    0.8174 |
+#> |.....................|     8.202 |     1.852 |    0.6850 |    0.7647 |
+#> |.....................|     1.465 |     1.223 |     1.357 |...........|
+#> |   44|     480.43193 |    0.9946 |    -1.325 |   -0.9138 |   -0.8928 |
+#> |.....................|   -0.8452 |   -0.6054 |   -0.9741 |    -1.031 |
+#> |.....................|   -0.6354 |   -0.7292 |   -0.6649 |...........|
+#> |    U|     480.43193 |     93.70 |    -5.741 |   -0.9991 |   -0.2020 |
+#> |.....................|     2.102 |     1.856 |    0.6866 |    0.7303 |
+#> |.....................|     1.469 |     1.241 |     1.376 |...........|
+#> |    X|     480.43193 |     93.70 |  0.003212 |    0.2691 |    0.8171 |
+#> |.....................|     8.185 |     1.856 |    0.6866 |    0.7303 |
+#> |.....................|     1.469 |     1.241 |     1.376 |...........|
+#> |    F| Forward Diff. |    -73.68 |    0.5532 |  -0.05170 |  -0.03792 |
+#> |.....................|   -0.2632 |    -4.949 |     2.751 |    -2.063 |
+#> |.....................|    -2.027 |   -0.5538 |    -1.006 |...........|
+#> |   45|     480.12037 |    0.9986 |    -1.465 |   -0.9157 |   -0.8935 |
+#> |.....................|   -0.8478 |   -0.6011 |   -0.9922 |    -1.022 |
+#> |.....................|   -0.6184 |   -0.7143 |   -0.6451 |...........|
+#> |    U|     480.12037 |     94.07 |    -5.880 |    -1.001 |   -0.2027 |
+#> |.....................|     2.100 |     1.859 |    0.6728 |    0.7378 |
+#> |.....................|     1.489 |     1.257 |     1.399 |...........|
+#> |    X|     480.12037 |     94.07 |  0.002795 |    0.2687 |    0.8166 |
+#> |.....................|     8.164 |     1.859 |    0.6728 |    0.7378 |
+#> |.....................|     1.489 |     1.257 |     1.399 |...........|
+#> |    F| Forward Diff. |    -14.31 |    0.1919 | -0.006458 | -0.005637 |
+#> |.....................|   -0.1500 |    -5.088 |    0.6605 |   -0.1467 |
+#> |.....................|    -1.672 |   0.02074 |   -0.4009 |...........|
+#> |   46|     480.21684 |    0.9998 |    -1.532 |   -0.9143 |   -0.8951 |
+#> |.....................|   -0.8360 |   -0.5884 |   -0.9862 |    -1.032 |
+#> |.....................|   -0.5071 |   -0.7680 |   -0.6684 |...........|
+#> |    U|     480.21684 |     94.19 |    -5.947 |   -0.9996 |   -0.2043 |
+#> |.....................|     2.112 |     1.870 |    0.6773 |    0.7298 |
+#> |.....................|     1.621 |     1.199 |     1.372 |...........|
+#> |    X|     480.21684 |     94.19 |  0.002613 |    0.2690 |    0.8152 |
+#> |.....................|     8.261 |     1.870 |    0.6773 |    0.7298 |
+#> |.....................|     1.621 |     1.199 |     1.372 |...........|
+#> |   47|     480.06028 |     1.000 |    -1.489 |   -0.9152 |   -0.8941 |
+#> |.....................|   -0.8435 |   -0.5961 |   -0.9901 |    -1.026 |
+#> |.....................|   -0.5774 |   -0.7340 |   -0.6536 |...........|
+#> |    U|     480.06028 |     94.21 |    -5.905 |    -1.000 |   -0.2033 |
+#> |.....................|     2.104 |     1.863 |    0.6744 |    0.7349 |
+#> |.....................|     1.538 |     1.236 |     1.389 |...........|
+#> |    X|     480.06028 |     94.21 |  0.002726 |    0.2688 |    0.8161 |
+#> |.....................|     8.200 |     1.863 |    0.6744 |    0.7349 |
+#> |.....................|     1.538 |     1.236 |     1.389 |...........|
+#> |    F| Forward Diff. |     6.437 |    0.1507 |   0.07551 | -0.008836 |
+#> |.....................|   0.08632 |    -3.858 |    0.8547 |    0.1963 |
+#> |.....................|    0.4591 |   -0.8475 |   -0.5830 |...........|
+#> |   48|     480.03665 |    0.9987 |    -1.532 |   -0.9229 |   -0.8934 |
+#> |.....................|   -0.8415 |   -0.5884 |    -1.015 |    -1.029 |
+#> |.....................|   -0.5816 |   -0.7442 |   -0.6445 |...........|
+#> |    U|     480.03665 |     94.09 |    -5.948 |    -1.008 |   -0.2026 |
+#> |.....................|     2.106 |     1.870 |    0.6552 |    0.7323 |
+#> |.....................|     1.533 |     1.225 |     1.399 |...........|
+#> |    X|     480.03665 |     94.09 |  0.002612 |    0.2673 |    0.8166 |
+#> |.....................|     8.216 |     1.870 |    0.6552 |    0.7323 |
+#> |.....................|     1.533 |     1.225 |     1.399 |...........|
+#> |    F| Forward Diff. |    -11.33 |   0.04720 |   -0.3576 | -0.009993 |
+#> |.....................|   0.09366 |    -3.049 |   -0.8552 |     2.379 |
+#> |.....................|   0.07272 |    -1.673 |   -0.4189 |...........|
+#> |   49|     480.00388 |    0.9997 |    -1.574 |   -0.9191 |   -0.8927 |
+#> |.....................|   -0.8426 |   -0.5789 |    -1.009 |    -1.024 |
+#> |.....................|   -0.5828 |   -0.7165 |   -0.6339 |...........|
+#> |    U|     480.00388 |     94.18 |    -5.990 |    -1.004 |   -0.2019 |
+#> |.....................|     2.105 |     1.878 |    0.6600 |    0.7361 |
+#> |.....................|     1.531 |     1.255 |     1.412 |...........|
+#> |    X|     480.00388 |     94.18 |  0.002504 |    0.2681 |    0.8172 |
+#> |.....................|     8.207 |     1.878 |    0.6600 |    0.7361 |
+#> |.....................|     1.531 |     1.255 |     1.412 |...........|
+#> |    F| Forward Diff. |     1.604 |  -0.07853 |   -0.1199 |   0.02191 |
+#> |.....................|    0.1056 |    -1.650 |   -0.4080 |    0.6580 |
+#> |.....................|    0.2834 |    0.2201 |    0.3460 |...........|
+#> |   50|     480.03472 |     1.000 |    -1.551 |   -0.8873 |   -0.8972 |
+#> |.....................|   -0.8660 |   -0.5703 |    -1.019 |    -1.030 |
+#> |.....................|   -0.5914 |   -0.7201 |   -0.6545 |...........|
+#> |    U|     480.03472 |     94.21 |    -5.967 |   -0.9727 |   -0.2064 |
+#> |.....................|     2.082 |     1.885 |    0.6528 |    0.7314 |
+#> |.....................|     1.521 |     1.251 |     1.388 |...........|
+#> |    X|     480.03472 |     94.21 |  0.002563 |    0.2743 |    0.8135 |
+#> |.....................|     8.017 |     1.885 |    0.6528 |    0.7314 |
+#> |.....................|     1.521 |     1.251 |     1.388 |...........|
+#> |   51|     480.00362 |    0.9987 |    -1.569 |   -0.9113 |   -0.8938 |
+#> |.....................|   -0.8484 |   -0.5757 |    -1.011 |    -1.026 |
+#> |.....................|   -0.5851 |   -0.7175 |   -0.6392 |...........|
+#> |    U|     480.00362 |     94.09 |    -5.984 |   -0.9966 |   -0.2030 |
+#> |.....................|     2.099 |     1.880 |    0.6585 |    0.7346 |
+#> |.....................|     1.528 |     1.254 |     1.406 |...........|
+#> |    X|     480.00362 |     94.09 |  0.002519 |    0.2696 |    0.8163 |
+#> |.....................|     8.160 |     1.880 |    0.6585 |    0.7346 |
+#> |.....................|     1.528 |     1.254 |     1.406 |...........|
+#> |    F| Forward Diff. |    -11.27 |  -0.06004 |    0.2734 | -0.003181 |
+#> |.....................|   -0.1459 |    -1.804 |   -0.6958 |    0.2356 |
+#> |.....................|  -0.08489 |   -0.1057 |   -0.1437 |...........|
+#> |   52|     479.99564 |     1.000 |    -1.563 |   -0.9133 |   -0.8943 |
+#> |.....................|   -0.8490 |   -0.5744 |    -1.010 |    -1.027 |
+#> |.....................|   -0.5870 |   -0.7192 |   -0.6381 |...........|
+#> |    U|     479.99564 |     94.21 |    -5.979 |   -0.9986 |   -0.2035 |
+#> |.....................|     2.099 |     1.881 |    0.6592 |    0.7342 |
+#> |.....................|     1.526 |     1.252 |     1.407 |...........|
+#> |    X|     479.99564 |     94.21 |  0.002532 |    0.2692 |    0.8159 |
+#> |.....................|     8.155 |     1.881 |    0.6592 |    0.7342 |
+#> |.....................|     1.526 |     1.252 |     1.407 |...........|
+#> |    F| Forward Diff. |     5.442 |  -0.04353 |    0.2015 | -0.005586 |
+#> |.....................|   -0.1078 |    -1.130 |   -0.4765 |   -0.6210 |
+#> |.....................|   0.09560 |   0.04932 |    0.1423 |...........|
+#> |   53|     479.99256 |    0.9995 |    -1.560 |   -0.9178 |   -0.8945 |
+#> |.....................|   -0.8473 |   -0.5732 |    -1.008 |    -1.026 |
+#> |.....................|   -0.5881 |   -0.7196 |   -0.6366 |...........|
+#> |    U|     479.99256 |     94.16 |    -5.975 |    -1.003 |   -0.2037 |
+#> |.....................|     2.100 |     1.882 |    0.6609 |    0.7344 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99256 |     94.16 |  0.002541 |    0.2683 |    0.8157 |
+#> |.....................|     8.169 |     1.882 |    0.6609 |    0.7344 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    F| Forward Diff. |    -1.663 |  -0.03616 |  -0.04918 |  -0.01811 |
+#> |.....................|  -0.07323 |    -1.616 |   -0.5475 |   -0.9126 |
+#> |.....................|   -0.2713 |   -0.2260 |  -0.04317 |...........|
+#> |   54|     479.99337 |    0.9995 |    -1.558 |   -0.9178 |   -0.8940 |
+#> |.....................|   -0.8453 |   -0.5718 |    -1.004 |    -1.025 |
+#> |.....................|   -0.5887 |   -0.7198 |   -0.6325 |...........|
+#> |    U|     479.99337 |     94.16 |    -5.974 |    -1.003 |   -0.2032 |
+#> |.....................|     2.102 |     1.883 |    0.6641 |    0.7358 |
+#> |.....................|     1.524 |     1.251 |     1.413 |...........|
+#> |    X|     479.99337 |     94.16 |  0.002545 |    0.2683 |    0.8161 |
+#> |.....................|     8.185 |     1.883 |    0.6641 |    0.7358 |
+#> |.....................|     1.524 |     1.251 |     1.413 |...........|
+#> |   55|     479.99257 |    0.9996 |    -1.559 |   -0.9178 |   -0.8942 |
+#> |.....................|   -0.8464 |   -0.5725 |    -1.006 |    -1.026 |
+#> |.....................|   -0.5884 |   -0.7197 |   -0.6348 |...........|
+#> |    U|     479.99257 |     94.17 |    -5.975 |    -1.003 |   -0.2035 |
+#> |.....................|     2.101 |     1.883 |    0.6623 |    0.7351 |
+#> |.....................|     1.525 |     1.252 |     1.411 |...........|
+#> |    X|     479.99257 |     94.17 |  0.002543 |    0.2683 |    0.8159 |
+#> |.....................|     8.175 |     1.883 |    0.6623 |    0.7351 |
+#> |.....................|     1.525 |     1.252 |     1.411 |...........|
+#> |   56|     479.99255 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99255 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99255 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    C| Central Diff. |     1.014 |  -0.03924 |  -0.07311 |  -0.03520 |
+#> |.....................|  -0.07193 |    -1.047 |   -0.3482 |   -0.6653 |
+#> |.....................| -0.001386 |  0.002313 |  -0.01832 |...........|
+#> |   57|     479.99382 |    0.9993 |    -1.559 |   -0.9177 |   -0.8943 |
+#> |.....................|   -0.8469 |   -0.5723 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99382 |     94.14 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6617 |    0.7350 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99382 |     94.14 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6617 |    0.7350 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   58|     479.99260 |    0.9996 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5726 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|      479.9926 |     94.17 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|      479.9926 |     94.17 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   59|     479.99255 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99255 |     94.17 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99255 |     94.17 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   60|     479.99254 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99254 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99254 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    C| Central Diff. |    0.7083 |  -0.03937 |  -0.07377 |  -0.03537 |
+#> |.....................|  -0.07427 |    -1.038 |   -0.3482 |   -0.6698 |
+#> |.....................| -0.009774 |   0.01032 |  -0.01719 |...........|
+#> |   61|     479.99255 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99255 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99255 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   62|     479.99264 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99264 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99264 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   63|     479.99259 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99259 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99259 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   64|     479.99259 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99259 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99259 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   65|     479.99259 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99259 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99259 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   66|     479.99259 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99259 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99259 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   67|     479.99259 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99259 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99259 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   68|     479.99259 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99259 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99259 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   69|     479.99258 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99258 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99258 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   70|     479.99258 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99258 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99258 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |   71|     479.99258 |    0.9997 |    -1.559 |   -0.9178 |   -0.8944 |
+#> |.....................|   -0.8469 |   -0.5727 |    -1.007 |    -1.026 |
+#> |.....................|   -0.5882 |   -0.7196 |   -0.6358 |...........|
+#> |    U|     479.99258 |     94.18 |    -5.975 |    -1.003 |   -0.2036 |
+#> |.....................|     2.101 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> |    X|     479.99258 |     94.18 |  0.002542 |    0.2683 |    0.8158 |
+#> |.....................|     8.172 |     1.883 |    0.6616 |    0.7348 |
+#> |.....................|     1.525 |     1.252 |     1.409 |...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |
+#> |.....................|    log_k2 |  g_qlogis |     sigma |        o1 |
+#> |.....................|        o2 |        o3 |        o4 |        o5 |
+#> |.....................|        o6 |...........|...........|...........|
+#> |    1|     514.27068 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     514.27068 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     514.27068 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    G|    Gill Diff. |     26.19 |     1.724 |   -0.1273 |   0.01210 |
+#> |.....................|   -0.2599 |   0.04964 |    -46.10 |     17.02 |
+#> |.....................|     9.682 |    -11.00 |    -4.182 |     3.869 |
+#> |.....................|    -10.57 |...........|...........|...........|
+#> |    2|     1072.3430 |    0.5548 |    -1.029 |   -0.9091 |   -0.9298 |
+#> |.....................|   -0.9733 |   -0.8898 |  -0.07504 |    -1.166 |
+#> |.....................|    -1.039 |   -0.6809 |   -0.8005 |   -0.9394 |
+#> |.....................|   -0.6887 |...........|...........|...........|
+#> |    U|      1072.343 |     52.05 |    -5.403 |   -0.9690 |    -1.880 |
+#> |.....................|    -4.266 |    0.1355 |     2.292 |    0.5199 |
+#> |.....................|    0.7209 |     1.403 |     1.065 |    0.8339 |
+#> |.....................|     1.368 |...........|...........|...........|
+#> |    X|      1072.343 |     52.05 |  0.004504 |    0.2751 |    0.1526 |
+#> |.....................|   0.01403 |    0.5338 |     2.292 |    0.5199 |
+#> |.....................|    0.7209 |     1.403 |     1.065 |    0.8339 |
+#> |.....................|     1.368 |...........|...........|...........|
+#> |    3|     539.25377 |    0.9555 |    -1.003 |   -0.9110 |   -0.9296 |
+#> |.....................|   -0.9773 |   -0.8890 |   -0.7801 |   -0.9058 |
+#> |.....................|   -0.8907 |   -0.8491 |   -0.8645 |   -0.8802 |
+#> |.....................|   -0.8503 |...........|...........|...........|
+#> |    U|     539.25377 |     89.63 |    -5.376 |   -0.9709 |    -1.880 |
+#> |.....................|    -4.270 |    0.1356 |     1.712 |    0.7103 |
+#> |.....................|    0.8487 |     1.204 |     1.001 |    0.8867 |
+#> |.....................|     1.181 |...........|...........|...........|
+#> |    X|     539.25377 |     89.63 |  0.004625 |    0.2747 |    0.1526 |
+#> |.....................|   0.01398 |    0.5339 |     1.712 |    0.7103 |
+#> |.....................|    0.8487 |     1.204 |     1.001 |    0.8867 |
+#> |.....................|     1.181 |...........|...........|...........|
+#> |    4|     527.20532 |    0.9955 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9777 |   -0.8889 |   -0.8506 |   -0.8798 |
+#> |.....................|   -0.8759 |   -0.8659 |   -0.8709 |   -0.8743 |
+#> |.....................|   -0.8665 |...........|...........|...........|
+#> |    U|     527.20532 |     93.39 |    -5.374 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.654 |    0.7293 |
+#> |.....................|    0.8615 |     1.184 |    0.9947 |    0.8920 |
+#> |.....................|     1.162 |...........|...........|...........|
+#> |    X|     527.20532 |     93.39 |  0.004637 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.654 |    0.7293 |
+#> |.....................|    0.8615 |     1.184 |    0.9947 |    0.8920 |
+#> |.....................|     1.162 |...........|...........|...........|
+#> |    5|     527.55150 |    0.9996 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8576 |   -0.8772 |
+#> |.....................|   -0.8744 |   -0.8676 |   -0.8715 |   -0.8737 |
+#> |.....................|   -0.8681 |...........|...........|...........|
+#> |    U|      527.5515 |     93.77 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.648 |    0.7312 |
+#> |.....................|    0.8628 |     1.182 |    0.9941 |    0.8925 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|      527.5515 |     93.77 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.648 |    0.7312 |
+#> |.....................|    0.8628 |     1.182 |    0.9941 |    0.8925 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    6|     527.60332 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8743 |   -0.8678 |   -0.8716 |   -0.8737 |
+#> |.....................|   -0.8682 |...........|...........|...........|
+#> |    U|     527.60332 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60332 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    7|     527.60868 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     527.60868 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60868 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    8|     527.60932 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     527.60932 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60932 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    9|     527.60939 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     527.60939 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60939 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |   10|     527.60940 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|      527.6094 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|      527.6094 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |   11|     527.60940 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|      527.6094 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|      527.6094 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |   12|     527.60940 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|      527.6094 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|      527.6094 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |   13|     527.60941 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     527.60941 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60941 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |   14|     527.60941 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     527.60941 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60941 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |   15|     527.60941 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     527.60941 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60941 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |   16|     527.60941 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     527.60941 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60941 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |   17|     527.60941 |     1.000 |    -1.000 |   -0.9112 |   -0.9296 |
+#> |.....................|   -0.9778 |   -0.8889 |   -0.8584 |   -0.8769 |
+#> |.....................|   -0.8742 |   -0.8678 |   -0.8716 |   -0.8736 |
+#> |.....................|   -0.8683 |...........|...........|...........|
+#> |    U|     527.60941 |     93.81 |    -5.373 |   -0.9711 |    -1.880 |
+#> |.....................|    -4.271 |    0.1356 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> |    X|     527.60941 |     93.81 |  0.004638 |    0.2747 |    0.1526 |
+#> |.....................|   0.01397 |    0.5339 |     1.647 |    0.7314 |
+#> |.....................|    0.8629 |     1.182 |    0.9940 |    0.8926 |
+#> |.....................|     1.160 |...........|...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                
+# Variance by variable is supported by 'saem' and 'focei'
+f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1:    93.6104   -5.6552   -0.1308    2.1755   -1.1174    2.9315    1.6064    0.6616    0.5897    0.4753    9.7765   10.2253
+#> 2:    93.8838   -5.6936   -0.1062    2.2361   -1.0529    2.7849    1.5260    0.6285    0.5602    0.4515    7.9206    5.2721
+#> 3:    93.9304   -5.7260   -0.0940    2.2480   -1.0317    2.6457    1.4889    0.5971    0.5322    0.4290    7.5051    3.6573
+#> 4:    93.6107   -5.7914   -0.0929    2.2382   -1.0171    2.5134    2.0027    0.5676    0.5056    0.4075    7.3763    3.1438
+#> 5:    93.7262   -5.7517   -0.0926    2.2365   -1.0306    2.3877    1.9026    0.5679    0.4803    0.3871    7.2914    3.0275
+#> 6:    93.7261   -5.7719   -0.0823    2.2625   -1.0391    2.2683    2.1168    0.5638    0.4563    0.3678    7.0857    2.8196
+#> 7:    93.5991   -5.8553   -0.0917    2.2659   -1.0146    2.1549    2.3708    0.5618    0.4335    0.3494    6.9413    2.7447
+#> 8:    93.4288   -5.8969   -0.0885    2.2757   -1.0253    2.1183    2.4324    0.5615    0.4118    0.3319    7.2269    2.6781
+#> 9:    93.4049   -6.1188   -0.0863    2.2841   -1.0154    2.0124    3.0090    0.5633    0.3912    0.3153    7.2084    2.7464
+#> 10:    93.4773   -6.1940   -0.0816    2.2893   -1.0174    1.9958    3.6308    0.5540    0.3716    0.2996    7.2414    2.8980
+#> 11:    93.5334   -6.1739   -0.0772    2.2901   -1.0479    2.2841    3.4492    0.5567    0.3531    0.2846    7.0567    2.8159
+#> 12:    93.5824   -6.3716   -0.0875    2.2706   -1.0452    2.1699    4.3087    0.5505    0.3354    0.2704    7.2970    2.3790
+#> 13:    93.8528   -6.3302   -0.0846    2.2564   -1.0302    2.0614    4.6014    0.5475    0.3186    0.2568    7.3901    2.1942
+#> 14:    94.0343   -6.1408   -0.0887    2.2666   -1.0280    1.9995    4.3714    0.5202    0.3027    0.2440    7.1696    2.0730
+#> 15:    94.1712   -6.3900   -0.0759    2.2825   -1.0112    1.8995    5.0913    0.5358    0.2876    0.2318    7.2155    2.0259
+#> 16:    93.9481   -6.1284   -0.0798    2.2707   -1.0264    1.8046    4.8368    0.5501    0.2732    0.2202    7.2731    2.0912
+#> 17:    93.7828   -6.2736   -0.0852    2.2870   -1.0249    1.7143    4.5949    0.5408    0.2595    0.2092    7.0213    2.0417
+#> 18:    93.8758   -6.3616   -0.0851    2.2713   -1.0157    1.8699    4.9132    0.5349    0.2465    0.1987    7.0613    1.8601
+#> 19:    93.7565   -6.5413   -0.0866    2.2695   -1.0166    2.5251    5.9754    0.5312    0.2547    0.1888    7.2555    1.7947
+#> 20:    93.7233   -6.3942   -0.0970    2.2620   -1.0195    2.3989    5.6766    0.5484    0.2576    0.1794    7.0292    1.8687
+#> 21:    93.8298   -6.2619   -0.0974    2.2570   -1.0118    2.2789    5.3928    0.5497    0.2545    0.1704    6.7138    1.8157
+#> 22:    93.9520   -6.1633   -0.0874    2.2777   -1.0274    2.1650    5.1232    0.5437    0.2641    0.1622    6.8254    1.8443
+#> 23:    93.8442   -6.3255   -0.0855    2.2568   -1.0151    2.1243    4.9615    0.5334    0.2885    0.1556    6.8049    1.8073
+#> 24:    93.9659   -6.5470   -0.0855    2.2572   -1.0178    2.0788    6.2156    0.5425    0.2834    0.1583    6.9598    1.8686
+#> 25:    94.3004   -6.4881   -0.0920    2.2371   -1.0187    3.2507    5.9048    0.5367    0.2872    0.1609    6.8709    1.8839
+#> 26:    94.1750   -6.4437   -0.0964    2.2337   -1.0301    3.1136    5.6096    0.5307    0.2820    0.1611    6.5948    1.8742
+#> 27:    94.6007   -6.3072   -0.0750    2.2936   -1.0343    3.9844    5.3291    0.5042    0.2679    0.1695    6.7524    1.8335
+#> 28:    94.4915   -6.1389   -0.0826    2.2730   -1.0223    3.7852    5.0626    0.4998    0.2590    0.1812    6.4646    1.8937
+#> 29:    94.1900   -6.1516   -0.0836    2.2680   -1.0287    3.7861    4.8095    0.4976    0.2612    0.1875    6.4674    1.8998
+#> 30:    94.6632   -6.0574   -0.0773    2.2637   -1.0280    3.5968    4.5690    0.4948    0.2525    0.2040    6.5945    1.9022
+#> 31:    94.3460   -6.1684   -0.0761    2.2677   -1.0276    3.4170    4.3406    0.4901    0.2690    0.2038    6.9918    1.8446
+#> 32:    94.4385   -5.9347   -0.0751    2.2893   -1.0146    3.3283    4.1235    0.4882    0.2576    0.2002    6.7622    1.7754
+#> 33:    94.7021   -5.9329   -0.0787    2.2987   -1.0108    3.3485    3.9174    0.4859    0.2640    0.1941    6.9648    1.8014
+#> 34:    94.4058   -6.0311   -0.0692    2.2980   -1.0125    3.1811    3.7215    0.4994    0.2676    0.1936    6.9791    1.7561
+#> 35:    94.4503   -6.0470   -0.0692    2.2950   -1.0100    3.5600    3.7611    0.4994    0.2637    0.1928    6.8010    1.7890
+#> 36:    94.3400   -6.0339   -0.0792    2.2960   -1.0204    3.3820    3.5731    0.4822    0.2638    0.1887    6.6462    1.6763
+#> 37:    94.1497   -6.0221   -0.0879    2.2653   -1.0073    3.2129    3.3944    0.4979    0.2506    0.1793    6.4853    1.7911
+#> 38:    94.1574   -5.8638   -0.0884    2.2752   -1.0156    3.0523    3.2247    0.4992    0.2435    0.1772    6.4329    1.7707
+#> 39:    94.1680   -5.9558   -0.0948    2.2535   -1.0205    2.8997    3.0635    0.5065    0.2448    0.1819    6.4462    1.8100
+#> 40:    94.0516   -6.0814   -0.0881    2.2531   -1.0356    2.7547    3.4976    0.4949    0.2515    0.1827    6.4734    1.8133
+#> 41:    94.1522   -6.1880   -0.0849    2.2618   -1.0230    2.6170    4.1610    0.5129    0.2389    0.1797    6.4165    1.7782
+#> 42:    94.2178   -6.1829   -0.0854    2.2791   -1.0325    2.8092    4.1174    0.5052    0.2288    0.1853    6.4332    1.7883
+#> 43:    93.9083   -6.1600   -0.0831    2.2860   -1.0350    2.9631    3.9116    0.4914    0.2310    0.1826    6.4865    1.8449
+#> 44:    93.9636   -6.1494   -0.0824    2.2903   -1.0150    2.8149    3.7221    0.4921    0.2214    0.1805    6.4818    1.9385
+#> 45:    93.9937   -6.2329   -0.0895    2.2832   -1.0157    4.2815    4.5622    0.5075    0.2250    0.1796    6.4098    1.8355
+#> 46:    93.8001   -6.1784   -0.0944    2.2664   -1.0212    4.0674    4.3341    0.5023    0.2274    0.1795    6.5539    1.7875
+#> 47:    93.8997   -6.3400   -0.0945    2.2627   -1.0183    3.8641    4.9860    0.5017    0.2312    0.1834    6.5497    1.7838
+#> 48:    93.7861   -6.3496   -0.0944    2.2713   -1.0255    3.6709    5.3403    0.5025    0.2197    0.1839    6.1766    1.9080
+#> 49:    93.7128   -6.3914   -0.0944    2.2752   -1.0137    3.4873    5.6007    0.5051    0.2198    0.1788    6.3050    1.8320
+#> 50:    94.1645   -6.3056   -0.0945    2.2755   -1.0062    3.3130    5.3207    0.4998    0.2176    0.1781    6.4998    1.8516
+#> 51:    93.9897   -6.1556   -0.1026    2.2633   -1.0097    3.1473    5.0547    0.4853    0.2439    0.1796    6.3184    1.7981
+#> 52:    93.7604   -6.2264   -0.1068    2.2485   -0.9936    2.9899    4.8209    0.4887    0.2542    0.1793    6.5076    1.7916
+#> 53:    93.8821   -6.5447   -0.1049    2.2546   -1.0020    2.8404    6.5603    0.4701    0.2556    0.1789    6.5735    1.7763
+#> 54:    93.8865   -6.4028   -0.1081    2.2507   -1.0162    2.6984    6.2323    0.4724    0.2576    0.1846    6.3607    1.8295
+#> 55:    94.0120   -6.5455   -0.0986    2.2728   -1.0119    2.5635    6.3983    0.4550    0.2686    0.1773    6.6815    1.7869
+#> 56:    94.1921   -6.6581   -0.0953    2.2713   -1.0151    2.4353    8.2169    0.4478    0.2675    0.1763    6.6257    1.7873
+#> 57:    93.8812   -6.4499   -0.1081    2.2447   -1.0182    2.3136    7.8060    0.4683    0.2562    0.1804    6.2421    1.8455
+#> 58:    93.9830   -6.5112   -0.1092    2.2436   -1.0136    2.1979    7.4157    0.4695    0.2569    0.1762    6.3196    1.8224
+#> 59:    93.8537   -6.6528   -0.1105    2.2390   -1.0089    2.0880    9.0039    0.4689    0.2534    0.1692    6.3735    1.8049
+#> 60:    93.7399   -6.4780   -0.1212    2.2263   -0.9979    1.9836    8.5537    0.4565    0.2445    0.1696    6.4748    1.8439
+#> 61:    93.8180   -6.4608   -0.1243    2.2275   -1.0039    1.8844    8.1260    0.4630    0.2414    0.1693    6.3936    1.7653
+#> 62:    93.5774   -6.3127   -0.1298    2.2250   -1.0022    1.7902    7.7197    0.4711    0.2452    0.1708    6.5708    1.8014
+#> 63:    93.5731   -6.2060   -0.1327    2.2213   -1.0031    1.7007    7.3337    0.4685    0.2426    0.1712    6.4933    1.8318
+#> 64:    93.3587   -6.2299   -0.1316    2.2290   -1.0004    1.6302    6.9671    0.4694    0.2460    0.1710    6.2584    1.8361
+#> 65:    93.2982   -6.1900   -0.1354    2.2341   -0.9963    1.5487    6.6187    0.4685    0.2482    0.1750    6.0950    1.8341
+#> 66:    93.4532   -6.2107   -0.1251    2.2254   -0.9786    1.4713    6.2878    0.4822    0.2489    0.1701    6.3732    1.7951
+#> 67:    93.5878   -6.1823   -0.1208    2.2455   -0.9766    1.3977    5.9734    0.4860    0.2407    0.1668    6.4456    1.8371
+#> 68:    93.5819   -5.9209   -0.1200    2.2599   -0.9792    1.3278    5.6747    0.4793    0.2412    0.1686    6.5728    1.8144
+#> 69:    93.4002   -6.1142   -0.1242    2.2542   -0.9878    1.4433    5.3910    0.4730    0.2511    0.1830    6.3888    1.7900
+#> 70:    93.2631   -6.1875   -0.1271    2.2639   -0.9844    1.5244    5.1214    0.4711    0.2444    0.1770    6.5093    1.7117
+#> 71:    93.2629   -6.2944   -0.1275    2.2418   -0.9805    1.4481    4.8654    0.4612    0.2522    0.1748    6.4659    1.8500
+#> 72:    93.0324   -6.2727   -0.1332    2.2421   -0.9766    1.3757    5.1467    0.4519    0.2524    0.1673    6.3452    1.8054
+#> 73:    93.0174   -6.4402   -0.1391    2.2320   -0.9795    1.3069    6.1963    0.4480    0.2563    0.1637    6.3915    1.8506
+#> 74:    93.0073   -6.4286   -0.1450    2.2241   -0.9962    1.2416    6.0011    0.4510    0.2461    0.1682    6.6924    1.8302
+#> 75:    93.2607   -6.5056   -0.1379    2.2233   -0.9926    1.1795    6.0508    0.4573    0.2540    0.1669    6.4813    1.7896
+#> 76:    93.2937   -6.1637   -0.1404    2.2228   -0.9970    1.1205    5.7483    0.4588    0.2529    0.1656    6.3781    1.7976
+#> 77:    93.2223   -6.1702   -0.1381    2.2200   -0.9858    1.4369    5.4609    0.4633    0.2585    0.1697    6.3510    1.8749
+#> 78:    93.3189   -6.1924   -0.1355    2.2238   -0.9944    1.3651    5.1878    0.4608    0.2631    0.1612    6.1888    1.7669
+#> 79:    93.2417   -6.6345   -0.1335    2.2340   -0.9865    1.2968    7.3486    0.4570    0.2564    0.1532    6.0902    1.7505
+#> 80:    93.3476   -6.3069   -0.1305    2.2319   -0.9880    1.6281    6.9812    0.4649    0.2525    0.1514    6.0659    1.7582
+#> 81:    93.4798   -6.3145   -0.1253    2.2468   -0.9989    1.9108    6.6321    0.4447    0.2583    0.1579    6.0843    1.7959
+#> 82:    93.2745   -6.2461   -0.1184    2.2529   -0.9937    1.8153    6.3005    0.4439    0.2602    0.1691    6.2826    1.7896
+#> 83:    93.4628   -6.3953   -0.1189    2.2640   -0.9880    1.7245    6.1094    0.4430    0.2612    0.1709    6.4474    1.6820
+#> 84:    93.3664   -6.2885   -0.1105    2.2675   -0.9875    1.6383    6.1170    0.4498    0.2689    0.1719    6.4847    1.6731
+#> 85:    93.5090   -6.3029   -0.1095    2.2709   -0.9898    1.6666    6.1406    0.4365    0.2693    0.1710    6.2452    1.6594
+#> 86:    93.5097   -6.2256   -0.1106    2.2701   -0.9928    1.5833    6.2468    0.4365    0.2749    0.1632    6.2007    1.7178
+#> 87:    93.5165   -6.3038   -0.1046    2.2731   -0.9877    1.5041    5.9345    0.4398    0.2667    0.1603    6.3928    1.7003
+#> 88:    93.3766   -6.2723   -0.1071    2.2771   -0.9881    1.4289    5.6378    0.4241    0.2538    0.1598    6.1043    1.6772
+#> 89:    93.4448   -6.0430   -0.1102    2.2781   -0.9725    1.3575    5.3559    0.4187    0.2915    0.1518    6.0153    1.7593
+#> 90:    93.2843   -6.1065   -0.1089    2.2866   -0.9705    1.5362    5.0881    0.4203    0.2844    0.1656    5.9235    1.6631
+#> 91:    93.4159   -6.0210   -0.1095    2.2879   -0.9798    2.1371    4.8337    0.4245    0.2857    0.1573    5.9182    1.7482
+#> 92:    93.3198   -6.2526   -0.1075    2.2919   -0.9791    2.0303    4.7352    0.4159    0.2918    0.1590    6.0853    1.6755
+#> 93:    93.3269   -6.1838   -0.1173    2.2809   -0.9999    1.9287    4.4985    0.4211    0.2893    0.1684    6.1189    1.6734
+#> 94:    93.2077   -6.1086   -0.1148    2.2890   -0.9918    2.1061    4.2736    0.4230    0.2802    0.1662    5.9328    1.7116
+#> 95:    93.0207   -6.1510   -0.1170    2.2665   -0.9791    2.1360    4.0630    0.4199    0.2937    0.1734    6.1415    1.6737
+#> 96:    93.2134   -6.1614   -0.1152    2.2861   -0.9711    2.5372    4.1579    0.4211    0.2790    0.1647    6.1575    1.6338
+#> 97:    93.1425   -6.2333   -0.1140    2.2912   -0.9665    2.4103    4.4551    0.4136    0.2835    0.1645    6.0790    1.6652
+#> 98:    92.9412   -6.2651   -0.1167    2.2847   -0.9738    2.2898    4.7233    0.4095    0.2882    0.1836    5.9305    1.6158
+#> 99:    92.9087   -6.1870   -0.1177    2.2833   -0.9744    2.1753    4.4872    0.4142    0.2913    0.1876    5.9838    1.7003
+#> 100:    92.7788   -6.2113   -0.1146    2.2928   -0.9939    2.0665    4.4195    0.4109    0.2945    0.1866    6.0195    1.7275
+#> 101:    92.8783   -6.0718   -0.1080    2.2959   -0.9968    1.9632    4.1985    0.4142    0.2966    0.1778    6.2542    1.6844
+#> 102:    93.0451   -6.3706   -0.1086    2.2894   -0.9974    1.8650    5.2121    0.4135    0.3030    0.1769    6.2204    1.6281
+#> 103:    93.2901   -6.4069   -0.1066    2.2943   -0.9896    1.7718    5.7453    0.4152    0.2879    0.1818    6.0239    1.7299
+#> 104:    93.3437   -6.3694   -0.1063    2.2769   -0.9914    1.6832    5.8903    0.4210    0.2884    0.1855    6.1116    1.7415
+#> 105:    93.4609   -6.2767   -0.1060    2.2751   -1.0157    1.5990    5.5958    0.4214    0.2865    0.1841    6.1287    1.7322
+#> 106:    93.5833   -6.2340   -0.1006    2.2879   -1.0084    1.8669    5.3160    0.4272    0.2982    0.1829    6.0211    1.6726
+#> 107:    93.7800   -6.1505   -0.0948    2.2685   -1.0219    1.7735    5.0502    0.4325    0.2841    0.1753    5.8556    1.7636
+#> 108:    93.8532   -6.3744   -0.0938    2.2650   -1.0210    2.0297    5.7080    0.4307    0.2836    0.1701    6.0669    1.6804
+#> 109:    93.8994   -6.3544   -0.0829    2.2862   -1.0287    1.9282    5.4226    0.4184    0.3113    0.1789    6.2343    1.6667
+#> 110:    94.0150   -6.5609   -0.0905    2.2821   -1.0088    2.1118    6.8121    0.4276    0.3275    0.1845    6.1640    1.6706
+#> 111:    93.7887   -6.0185   -0.0925    2.2831   -1.0097    2.0062    6.4715    0.4209    0.3255    0.1852    6.2823    1.6301
+#> 112:    93.9709   -6.0918   -0.0934    2.2857   -1.0067    2.2032    6.1479    0.4207    0.3285    0.1817    6.1718    1.6494
+#> 113:    93.8761   -6.3434   -0.0955    2.2919   -1.0223    2.5209    5.8405    0.4259    0.3293    0.1842    6.0377    1.6431
+#> 114:    93.6959   -6.2312   -0.0934    2.2782   -1.0154    2.3949    5.5485    0.4237    0.3460    0.1814    6.2225    1.6229
+#> 115:    93.5487   -6.0915   -0.0971    2.2836   -1.0083    2.2751    5.2711    0.4199    0.3557    0.1783    6.5929    1.6479
+#> 116:    93.5953   -6.1479   -0.1013    2.2760   -1.0018    2.1614    5.0075    0.4163    0.3399    0.1794    6.1822    1.6222
+#> 117:    93.3508   -6.1730   -0.1076    2.2632   -0.9953    2.0533    4.7571    0.4057    0.3303    0.1803    6.3444    1.7106
+#> 118:    93.4462   -5.9724   -0.1177    2.2557   -0.9963    2.0318    4.5193    0.3956    0.3349    0.1920    6.0439    1.7146
+#> 119:    93.5841   -6.0400   -0.1151    2.2480   -1.0035    1.9956    4.2933    0.3968    0.3448    0.1929    6.0754    1.6750
+#> 120:    93.4891   -6.0937   -0.1175    2.2499   -1.0006    1.8958    4.0786    0.3927    0.3392    0.1927    6.1654    1.6495
+#> 121:    93.4611   -6.1371   -0.1217    2.2538   -1.0067    1.8011    3.8747    0.3864    0.3549    0.1851    5.9558    1.6940
+#> 122:    93.4636   -6.1015   -0.1243    2.2564   -1.0002    1.7414    3.6810    0.3840    0.3557    0.1860    6.0583    1.6629
+#> 123:    93.2988   -5.9318   -0.1243    2.2601   -0.9989    2.2063    3.4969    0.3840    0.3543    0.1833    5.9686    1.5966
+#> 124:    93.4200   -5.9847   -0.1231    2.2594   -0.9991    2.0959    3.3221    0.3846    0.3544    0.1787    6.1292    1.5957
+#> 125:    93.3727   -6.1217   -0.1239    2.2584   -1.0082    1.9911    3.6395    0.3838    0.3577    0.1782    6.2794    1.6262
+#> 126:    93.4956   -6.0529   -0.1244    2.2482   -1.0096    1.8916    3.4576    0.3847    0.3505    0.1753    6.1181    1.6347
+#> 127:    93.6265   -5.9360   -0.1298    2.2342   -1.0075    1.7970    3.2847    0.3887    0.3367    0.1691    6.2315    1.7051
+#> 128:    93.4446   -6.0523   -0.1337    2.2453   -1.0079    1.7072    3.1205    0.3840    0.3302    0.1759    6.2082    1.6705
+#> 129:    93.4470   -6.0065   -0.1321    2.2321   -1.0015    1.6636    2.9644    0.3853    0.3303    0.1671    6.1479    1.6733
+#> 130:    93.3205   -5.9628   -0.1290    2.2252   -0.9954    2.0336    2.9210    0.3879    0.3284    0.1634    6.0582    1.6372
+#> 131:    93.3836   -5.8919   -0.1358    2.2375   -0.9930    2.1392    2.7749    0.3801    0.3202    0.1644    5.9972    1.6837
+#> 132:    93.1041   -5.9265   -0.1203    2.2552   -0.9929    2.0323    2.8741    0.3831    0.3353    0.1755    6.0648    1.5934
+#> 133:    93.1617   -6.0668   -0.1175    2.2538   -0.9963    1.9306    3.6825    0.3846    0.3187    0.1790    6.0732    1.5684
+#> 134:    93.1503   -6.1208   -0.1232    2.2644   -0.9851    2.3429    3.8026    0.3788    0.3296    0.1737    5.8807    1.5722
+#> 135:    92.8629   -5.9726   -0.1197    2.2650   -0.9761    2.2257    3.6124    0.3802    0.3407    0.1765    5.8408    1.5446
+#> 136:    93.1460   -6.0654   -0.1227    2.2661   -0.9736    2.1144    3.4583    0.3770    0.3434    0.1700    5.7690    1.5561
+#> 137:    93.1243   -6.2350   -0.1274    2.2472   -0.9811    2.0087    4.3526    0.3733    0.3670    0.1615    5.9377    1.5224
+#> 138:    93.1203   -6.1704   -0.1283    2.2472   -0.9891    1.9083    4.1557    0.3788    0.3671    0.1641    5.8765    1.5525
+#> 139:    93.2841   -6.0586   -0.1366    2.2404   -0.9894    1.8129    4.3184    0.3718    0.3693    0.1630    6.1854    1.6388
+#> 140:    93.4239   -6.2398   -0.1382    2.2459   -0.9713    1.7241    4.5903    0.3713    0.3627    0.1548    6.0737    1.5826
+#> 141:    93.4149   -6.1972   -0.1388    2.2605   -0.9686    2.2179    4.5557    0.3701    0.3675    0.1486    6.0793    1.5603
+#> 142:    93.4404   -5.8955   -0.1203    2.2682   -0.9706    2.1070    4.3279    0.3830    0.3719    0.1581    5.9534    1.6189
+#> 143:    93.3108   -5.8069   -0.1142    2.2835   -0.9672    2.0194    4.1115    0.3787    0.3924    0.1592    5.9410    1.5521
+#> 144:    93.3953   -5.7456   -0.1154    2.2891   -0.9553    2.2741    3.9059    0.3787    0.3849    0.1633    6.0163    1.5640
+#> 145:    93.3322   -5.8301   -0.1100    2.2926   -0.9595    2.1604    3.7106    0.3687    0.3754    0.1657    5.8968    1.5844
+#> 146:    93.0844   -5.8926   -0.1084    2.2870   -0.9605    2.0524    3.5251    0.3649    0.3713    0.1646    6.1960    1.5691
+#> 147:    93.2106   -6.0084   -0.1074    2.2931   -0.9654    1.9498    3.5341    0.3646    0.3669    0.1641    6.0548    1.5230
+#> 148:    93.2005   -6.1989   -0.1065    2.2924   -0.9740    1.8523    4.4855    0.3631    0.3660    0.1759    5.9600    1.5194
+#> 149:    93.0788   -6.2470   -0.1108    2.2861   -0.9836    2.1348    4.7630    0.3597    0.3815    0.1815    5.9584    1.5227
+#> 150:    93.2241   -6.2660   -0.1126    2.2847   -0.9912    2.1149    5.0574    0.3656    0.3788    0.1781    5.7213    1.5379
+#> 151:    93.0046   -6.5379   -0.1164    2.2757   -0.9845    2.0092    6.8660    0.3719    0.3827    0.1807    5.7612    1.5697
+#> 152:    93.2222   -6.4637   -0.1154    2.2737   -0.9950    1.6744    6.2289    0.3670    0.3881    0.1638    5.8514    1.5920
+#> 153:    93.1619   -6.3230   -0.1224    2.2638   -0.9924    1.7907    5.5429    0.3842    0.3946    0.1720    5.7562    1.5493
+#> 154:    93.0402   -6.4004   -0.1205    2.2633   -0.9868    1.7620    6.2494    0.3860    0.3891    0.1737    5.7577    1.5109
+#> 155:    93.1692   -6.4353   -0.1203    2.2696   -0.9761    1.8710    6.4519    0.3949    0.3962    0.1721    5.8348    1.4949
+#> 156:    93.2709   -6.2672   -0.1203    2.2663   -0.9708    2.1172    5.1692    0.3949    0.4187    0.1637    6.1251    1.5012
+#> 157:    93.1264   -6.1931   -0.1208    2.2728   -0.9669    1.9985    4.7739    0.3938    0.4031    0.1696    6.1014    1.5627
+#> 158:    93.1263   -6.1951   -0.1237    2.2826   -0.9729    1.7675    4.6131    0.3928    0.3904    0.1659    6.1582    1.5647
+#> 159:    92.9780   -6.2831   -0.1242    2.2726   -0.9770    1.8348    5.4674    0.3938    0.3887    0.1631    6.0622    1.5787
+#> 160:    93.1289   -6.4397   -0.1263    2.2651   -0.9675    2.4637    6.0560    0.3919    0.4017    0.1626    5.9486    1.5859
+#> 161:    93.2629   -6.3336   -0.1294    2.2670   -0.9666    2.9602    5.4966    0.3872    0.3988    0.1667    5.9034    1.5421
+#> 162:    93.1652   -6.3800   -0.1342    2.2518   -0.9754    2.8800    5.6206    0.3908    0.4158    0.1627    5.9332    1.5306
+#> 163:    93.2886   -6.4115   -0.1437    2.2330   -0.9685    1.9997    6.2760    0.4015    0.4076    0.1623    5.7905    1.5398
+#> 164:    93.4631   -6.7246   -0.1396    2.2358   -0.9854    1.8885    7.8014    0.3952    0.4028    0.1573    5.7052    1.5695
+#> 165:    93.4757   -6.8408   -0.1404    2.2346   -0.9825    2.4877    9.3632    0.3948    0.4019    0.1615    5.8406    1.5902
+#> 166:    93.9075   -6.7707   -0.1428    2.2331   -0.9848    1.9761    8.9292    0.3939    0.3909    0.1610    5.7600    1.5966
+#> 167:    93.8895   -7.1938   -0.1363    2.2449   -0.9870    2.0894   11.4058    0.3850    0.3899    0.1627    5.8501    1.5748
+#> 168:    93.5849   -6.8478   -0.1294    2.2466   -0.9888    2.3573    9.4037    0.3935    0.3808    0.1645    6.0206    1.6591
+#> 169:    93.4931   -6.4550   -0.1173    2.2727   -0.9990    2.1948    6.5738    0.3844    0.4029    0.1699    6.0990    1.6123
+#> 170:    93.7188   -6.4015   -0.1173    2.2715   -0.9981    1.8800    6.1745    0.3844    0.4001    0.1635    6.1990    1.5745
+#> 171:    93.5938   -6.4389   -0.1119    2.2663   -0.9893    2.5731    6.5397    0.3858    0.4044    0.1554    6.1636    1.5631
+#> 172:    93.4515   -6.2049   -0.1050    2.2937   -0.9701    2.6134    4.6813    0.3687    0.4017    0.1715    6.3875    1.5006
+#> 173:    93.2254   -6.2074   -0.1041    2.3111   -0.9661    2.5799    4.6939    0.3669    0.4016    0.1738    6.5633    1.5229
+#> 174:    93.4116   -6.1198   -0.1050    2.3075   -0.9711    3.0196    4.3080    0.3720    0.3988    0.1778    6.4856    1.5214
+#> 175:    93.4952   -6.0439   -0.1050    2.3008   -0.9714    3.1172    3.7728    0.3720    0.3979    0.1749    6.1918    1.4985
+#> 176:    93.6186   -6.0891   -0.1061    2.3033   -0.9794    2.1081    3.8909    0.3705    0.4029    0.1796    6.1064    1.4657
+#> 177:    93.6432   -5.9977   -0.1031    2.2953   -0.9950    1.9411    3.4156    0.3694    0.3970    0.1843    6.0473    1.4918
+#> 178:    93.5736   -6.0079   -0.0996    2.2986   -0.9809    1.7778    3.5107    0.3696    0.3909    0.1840    6.1243    1.4937
+#> 179:    93.6407   -6.0246   -0.0977    2.3042   -0.9770    2.0631    3.8144    0.3718    0.3885    0.1798    6.1851    1.5212
+#> 180:    93.6336   -5.8865   -0.0969    2.3217   -0.9871    2.2566    3.1377    0.3721    0.3715    0.1784    6.0747    1.5546
+#> 181:    93.5075   -5.8632   -0.0965    2.3140   -0.9764    2.5812    2.9771    0.3715    0.3728    0.1876    5.9833    1.5356
+#> 182:    93.4464   -5.8627   -0.0930    2.3211   -0.9713    2.5956    2.8054    0.3836    0.3759    0.1861    6.1293    1.6259
+#> 183:    93.2737   -5.8238   -0.0977    2.3127   -0.9642    2.8739    2.6277    0.3846    0.3743    0.1868    6.0451    1.6493
+#> 184:    93.2191   -5.9175   -0.0993    2.3107   -0.9592    2.3088    3.0689    0.3829    0.3515    0.1711    6.1487    1.6666
+#> 185:    93.3626   -5.8872   -0.1070    2.3112   -0.9413    2.2812    3.2719    0.3712    0.3555    0.1783    6.1295    1.6288
+#> 186:    93.1585   -5.8532   -0.1053    2.3140   -0.9665    2.7906    2.8415    0.3734    0.3531    0.1680    6.0294    1.6104
+#> 187:    93.3041   -5.6798   -0.0957    2.3158   -0.9608    3.1056    2.0850    0.3813    0.3484    0.1728    6.1191    1.5813
+#> 188:    93.2466   -5.6791   -0.0954    2.3172   -0.9446    3.8296    2.1956    0.3816    0.3439    0.1757    5.9670    1.5445
+#> 189:    93.3532   -5.6883   -0.0859    2.3335   -0.9594    2.8968    2.3125    0.3691    0.3512    0.1812    5.9467    1.6101
+#> 190:    93.5064   -5.6288   -0.0726    2.3548   -0.9562    2.8233    2.1930    0.3334    0.3700    0.1759    6.4036    1.5877
+#> 191:    93.4145   -5.6906   -0.0726    2.3467   -0.9624    2.8818    2.3581    0.3334    0.3771    0.1712    6.2046    1.4952
+#> 192:    93.2060   -5.7479   -0.0716    2.3433   -0.9618    2.5221    2.6613    0.3324    0.3909    0.1552    6.1651    1.4971
+#> 193:    93.2904   -5.7634   -0.0811    2.3327   -0.9585    2.6968    2.6324    0.3339    0.3856    0.1632    6.5621    1.5258
+#> 194:    93.5271   -5.7859   -0.0874    2.3419   -0.9580    2.8361    2.8424    0.3286    0.3784    0.1636    6.3714    1.5386
+#> 195:    93.3944   -5.9358   -0.0838    2.3407   -0.9718    3.4161    3.2427    0.3315    0.3787    0.1678    6.3722    1.5181
+#> 196:    93.2341   -5.9078   -0.0701    2.3492   -0.9816    3.1580    3.0586    0.3285    0.3666    0.1681    6.4633    1.5382
+#> 197:    93.2967   -6.0131   -0.0745    2.3426   -0.9991    3.7978    3.6459    0.3353    0.3491    0.1796    6.2264    1.5310
+#> 198:    93.2628   -5.7991   -0.0730    2.3434   -0.9819    2.3896    2.6695    0.3371    0.3431    0.1762    6.3141    1.5254
+#> 199:    93.2765   -5.9078   -0.0782    2.3553   -0.9864    2.2760    3.3883    0.3420    0.3459    0.1866    6.0192    1.4982
+#> 200:    93.0447   -5.9148   -0.0769    2.3543   -0.9759    2.1516    2.9675    0.3455    0.3476    0.1870    5.9079    1.4688
+#> 201:    93.1655   -5.8951   -0.0763    2.3493   -0.9707    1.8254    2.9481    0.3448    0.3526    0.1831    6.0676    1.5097
+#> 202:    93.1082   -5.8916   -0.0768    2.3499   -0.9673    1.8503    2.9562    0.3447    0.3574    0.1821    6.1282    1.5026
+#> 203:    93.0728   -5.9316   -0.0774    2.3506   -0.9650    2.0210    3.2306    0.3441    0.3563    0.1827    6.1253    1.4974
+#> 204:    93.0846   -5.9347   -0.0773    2.3494   -0.9648    2.1463    3.2567    0.3453    0.3563    0.1824    6.1301    1.4911
+#> 205:    93.0929   -5.9439   -0.0781    2.3491   -0.9659    2.2204    3.3165    0.3453    0.3572    0.1823    6.1098    1.4941
+#> 206:    93.1795   -5.9401   -0.0795    2.3481   -0.9681    2.2588    3.2940    0.3470    0.3568    0.1829    6.1132    1.4996
+#> 207:    93.2303   -5.9158   -0.0805    2.3467   -0.9703    2.3439    3.1823    0.3484    0.3571    0.1845    6.1021    1.5059
+#> 208:    93.2161   -5.8969   -0.0825    2.3440   -0.9700    2.3306    3.0999    0.3496    0.3563    0.1848    6.0998    1.5177
+#> 209:    93.2077   -5.8842   -0.0848    2.3413   -0.9681    2.3580    3.0406    0.3499    0.3553    0.1841    6.0829    1.5199
+#> 210:    93.1951   -5.8661   -0.0867    2.3383   -0.9656    2.4170    2.9578    0.3501    0.3543    0.1833    6.0562    1.5261
+#> 211:    93.1870   -5.8543   -0.0892    2.3347   -0.9645    2.4650    2.9307    0.3502    0.3548    0.1831    6.0286    1.5289
+#> 212:    93.2077   -5.8506   -0.0915    2.3316   -0.9626    2.4909    2.9544    0.3504    0.3555    0.1835    6.0079    1.5300
+#> 213:    93.2104   -5.8492   -0.0938    2.3283   -0.9612    2.4695    2.9635    0.3503    0.3548    0.1841    5.9859    1.5341
+#> 214:    93.2059   -5.8537   -0.0959    2.3255   -0.9615    2.4264    3.0084    0.3499    0.3540    0.1835    5.9698    1.5370
+#> 215:    93.2051   -5.8569   -0.0977    2.3227   -0.9608    2.4277    3.0541    0.3495    0.3534    0.1830    5.9586    1.5374
+#> 216:    93.1879   -5.8596   -0.0993    2.3199   -0.9600    2.4347    3.0802    0.3493    0.3534    0.1828    5.9465    1.5380
+#> 217:    93.1834   -5.8621   -0.1008    2.3173   -0.9594    2.4479    3.0998    0.3491    0.3535    0.1827    5.9369    1.5402
+#> 218:    93.1796   -5.8657   -0.1021    2.3152   -0.9593    2.4234    3.1238    0.3492    0.3534    0.1835    5.9184    1.5441
+#> 219:    93.1680   -5.8721   -0.1032    2.3132   -0.9588    2.4640    3.1464    0.3494    0.3531    0.1839    5.8929    1.5493
+#> 220:    93.1579   -5.8839   -0.1044    2.3118   -0.9586    2.5707    3.1909    0.3495    0.3531    0.1847    5.8754    1.5496
+#> 221:    93.1557   -5.8882   -0.1058    2.3100   -0.9583    2.6662    3.2052    0.3492    0.3533    0.1854    5.8662    1.5518
+#> 222:    93.1624   -5.8832   -0.1074    2.3075   -0.9578    2.7993    3.1736    0.3490    0.3542    0.1861    5.8489    1.5546
+#> 223:    93.1699   -5.8771   -0.1086    2.3052   -0.9583    2.9085    3.1456    0.3488    0.3558    0.1871    5.8436    1.5610
+#> 224:    93.1870   -5.8751   -0.1097    2.3037   -0.9583    2.9988    3.1279    0.3487    0.3570    0.1878    5.8390    1.5628
+#> 225:    93.2094   -5.8719   -0.1110    2.3012   -0.9583    3.0581    3.1018    0.3485    0.3574    0.1885    5.8214    1.5656
+#> 226:    93.2352   -5.8683   -0.1122    2.2988   -0.9587    3.1297    3.0761    0.3482    0.3584    0.1895    5.8105    1.5680
+#> 227:    93.2611   -5.8653   -0.1132    2.2964   -0.9589    3.1563    3.0610    0.3476    0.3594    0.1904    5.8038    1.5701
+#> 228:    93.2741   -5.8593   -0.1140    2.2943   -0.9591    3.1641    3.0356    0.3470    0.3603    0.1911    5.7984    1.5730
+#> 229:    93.2899   -5.8593   -0.1151    2.2919   -0.9595    3.1626    3.0313    0.3466    0.3613    0.1918    5.7999    1.5745
+#> 230:    93.3048   -5.8650   -0.1164    2.2899   -0.9593    3.1743    3.0542    0.3460    0.3624    0.1921    5.7990    1.5753
+#> 231:    93.3159   -5.8638   -0.1177    2.2875   -0.9592    3.1930    3.0524    0.3454    0.3631    0.1924    5.7956    1.5748
+#> 232:    93.3209   -5.8611   -0.1189    2.2852   -0.9590    3.1872    3.0420    0.3450    0.3639    0.1926    5.7921    1.5755
+#> 233:    93.3196   -5.8556   -0.1200    2.2833   -0.9589    3.1861    3.0209    0.3445    0.3644    0.1926    5.7852    1.5779
+#> 234:    93.3245   -5.8530   -0.1210    2.2813   -0.9591    3.1890    3.0115    0.3441    0.3651    0.1922    5.7781    1.5786
+#> 235:    93.3219   -5.8522   -0.1218    2.2800   -0.9593    3.1573    3.0042    0.3437    0.3659    0.1917    5.7813    1.5797
+#> 236:    93.3155   -5.8524   -0.1227    2.2789   -0.9595    3.1542    3.0035    0.3433    0.3669    0.1913    5.7834    1.5800
+#> 237:    93.3060   -5.8556   -0.1235    2.2779   -0.9599    3.1308    3.0158    0.3430    0.3678    0.1910    5.7833    1.5809
+#> 238:    93.3111   -5.8563   -0.1242    2.2772   -0.9602    3.1194    3.0099    0.3427    0.3683    0.1907    5.7842    1.5809
+#> 239:    93.3177   -5.8580   -0.1248    2.2764   -0.9605    3.0944    3.0130    0.3423    0.3686    0.1904    5.7840    1.5815
+#> 240:    93.3222   -5.8606   -0.1255    2.2754   -0.9608    3.0739    3.0140    0.3420    0.3686    0.1902    5.7843    1.5825
+#> 241:    93.3289   -5.8627   -0.1262    2.2740   -0.9611    3.0848    3.0167    0.3417    0.3688    0.1900    5.7836    1.5840
+#> 242:    93.3366   -5.8627   -0.1270    2.2727   -0.9612    3.1273    3.0103    0.3415    0.3691    0.1898    5.7855    1.5850
+#> 243:    93.3441   -5.8646   -0.1277    2.2714   -0.9614    3.1530    3.0218    0.3414    0.3692    0.1896    5.7829    1.5856
+#> 244:    93.3499   -5.8645   -0.1285    2.2700   -0.9618    3.1705    3.0265    0.3412    0.3694    0.1894    5.7778    1.5874
+#> 245:    93.3619   -5.8673   -0.1294    2.2686   -0.9622    3.1863    3.0397    0.3412    0.3694    0.1892    5.7752    1.5889
+#> 246:    93.3745   -5.8698   -0.1301    2.2671   -0.9627    3.2105    3.0484    0.3412    0.3693    0.1890    5.7716    1.5905
+#> 247:    93.3838   -5.8757   -0.1307    2.2659   -0.9632    3.2158    3.0715    0.3412    0.3693    0.1889    5.7688    1.5922
+#> 248:    93.3914   -5.8799   -0.1314    2.2650   -0.9640    3.2268    3.0851    0.3413    0.3690    0.1889    5.7648    1.5934
+#> 249:    93.3983   -5.8844   -0.1319    2.2640   -0.9648    3.2471    3.0990    0.3415    0.3691    0.1889    5.7641    1.5944
+#> 250:    93.4032   -5.8898   -0.1324    2.2629   -0.9655    3.2828    3.1197    0.3414    0.3694    0.1887    5.7623    1.5965
+#> 251:    93.4053   -5.8939   -0.1329    2.2621   -0.9657    3.3074    3.1303    0.3414    0.3698    0.1887    5.7611    1.5978
+#> 252:    93.4095   -5.8950   -0.1334    2.2613   -0.9658    3.3479    3.1281    0.3414    0.3701    0.1887    5.7578    1.5986
+#> 253:    93.4132   -5.8956   -0.1340    2.2606   -0.9660    3.3486    3.1283    0.3413    0.3703    0.1887    5.7559    1.5999
+#> 254:    93.4201   -5.8966   -0.1345    2.2597   -0.9660    3.3502    3.1298    0.3413    0.3706    0.1888    5.7593    1.5997
+#> 255:    93.4235   -5.8953   -0.1349    2.2590   -0.9656    3.3332    3.1220    0.3412    0.3706    0.1887    5.7571    1.6012
+#> 256:    93.4231   -5.8926   -0.1353    2.2585   -0.9651    3.3255    3.1104    0.3411    0.3706    0.1886    5.7569    1.6018
+#> 257:    93.4247   -5.8874   -0.1356    2.2582   -0.9646    3.3164    3.0917    0.3410    0.3705    0.1885    5.7585    1.6030
+#> 258:    93.4198   -5.8857   -0.1359    2.2580   -0.9641    3.3086    3.0828    0.3409    0.3702    0.1885    5.7608    1.6026
+#> 259:    93.4125   -5.8833   -0.1362    2.2576   -0.9638    3.2926    3.0726    0.3408    0.3701    0.1885    5.7651    1.6023
+#> 260:    93.4073   -5.8847   -0.1365    2.2572   -0.9640    3.2737    3.0759    0.3406    0.3703    0.1885    5.7687    1.6030
+#> 261:    93.4049   -5.8885   -0.1368    2.2571   -0.9642    3.2510    3.0904    0.3402    0.3702    0.1882    5.7742    1.6028
+#> 262:    93.4036   -5.8931   -0.1371    2.2566   -0.9645    3.2279    3.1104    0.3397    0.3699    0.1880    5.7766    1.6033
+#> 263:    93.4026   -5.8964   -0.1375    2.2562   -0.9647    3.2024    3.1313    0.3395    0.3696    0.1877    5.7786    1.6029
+#> 264:    93.3990   -5.9003   -0.1377    2.2559   -0.9649    3.1808    3.1545    0.3393    0.3694    0.1874    5.7778    1.6022
+#> 265:    93.4005   -5.9013   -0.1380    2.2555   -0.9650    3.1664    3.1680    0.3390    0.3693    0.1871    5.7765    1.6021
+#> 266:    93.4005   -5.9011   -0.1382    2.2552   -0.9653    3.1530    3.1708    0.3387    0.3692    0.1869    5.7763    1.6020
+#> 267:    93.4006   -5.9035   -0.1384    2.2549   -0.9654    3.1384    3.1902    0.3384    0.3690    0.1866    5.7768    1.6014
+#> 268:    93.3972   -5.9086   -0.1385    2.2547   -0.9653    3.1224    3.2331    0.3380    0.3688    0.1863    5.7778    1.6008
+#> 269:    93.3936   -5.9113   -0.1386    2.2547   -0.9654    3.0959    3.2552    0.3377    0.3688    0.1861    5.7782    1.6001
+#> 270:    93.3867   -5.9139   -0.1387    2.2547   -0.9653    3.0853    3.2756    0.3372    0.3687    0.1859    5.7787    1.5989
+#> 271:    93.3836   -5.9154   -0.1389    2.2545   -0.9654    3.0824    3.2889    0.3367    0.3686    0.1858    5.7761    1.5980
+#> 272:    93.3812   -5.9160   -0.1390    2.2543   -0.9653    3.0741    3.2919    0.3362    0.3686    0.1857    5.7729    1.5977
+#> 273:    93.3767   -5.9174   -0.1390    2.2542   -0.9652    3.0663    3.2992    0.3358    0.3687    0.1856    5.7699    1.5970
+#> 274:    93.3696   -5.9171   -0.1391    2.2543   -0.9652    3.0604    3.2940    0.3355    0.3687    0.1855    5.7688    1.5958
+#> 275:    93.3658   -5.9177   -0.1393    2.2544   -0.9651    3.0605    3.2961    0.3353    0.3687    0.1853    5.7675    1.5952
+#> 276:    93.3621   -5.9185   -0.1395    2.2543   -0.9649    3.0508    3.2992    0.3351    0.3686    0.1852    5.7672    1.5940
+#> 277:    93.3602   -5.9206   -0.1397    2.2542   -0.9649    3.0453    3.3087    0.3349    0.3685    0.1851    5.7679    1.5935
+#> 278:    93.3565   -5.9213   -0.1400    2.2539   -0.9648    3.0366    3.3117    0.3347    0.3683    0.1852    5.7695    1.5931
+#> 279:    93.3548   -5.9222   -0.1403    2.2535   -0.9647    3.0284    3.3179    0.3345    0.3682    0.1854    5.7703    1.5928
+#> 280:    93.3544   -5.9215   -0.1407    2.2528   -0.9647    3.0193    3.3141    0.3344    0.3683    0.1854    5.7714    1.5927
+#> 281:    93.3533   -5.9205   -0.1410    2.2522   -0.9647    3.0130    3.3090    0.3341    0.3685    0.1855    5.7706    1.5927
+#> 282:    93.3564   -5.9189   -0.1414    2.2514   -0.9648    3.0025    3.3019    0.3339    0.3686    0.1856    5.7682    1.5930
+#> 283:    93.3571   -5.9164   -0.1417    2.2508   -0.9646    2.9990    3.2926    0.3337    0.3686    0.1858    5.7642    1.5943
+#> 284:    93.3576   -5.9154   -0.1421    2.2501   -0.9644    2.9976    3.2895    0.3336    0.3686    0.1860    5.7625    1.5942
+#> 285:    93.3584   -5.9142   -0.1425    2.2496   -0.9644    2.9906    3.2835    0.3334    0.3684    0.1861    5.7591    1.5939
+#> 286:    93.3609   -5.9137   -0.1429    2.2491   -0.9642    2.9852    3.2817    0.3332    0.3682    0.1863    5.7572    1.5939
+#> 287:    93.3641   -5.9131   -0.1433    2.2485   -0.9641    2.9732    3.2785    0.3331    0.3680    0.1863    5.7547    1.5944
+#> 288:    93.3671   -5.9128   -0.1436    2.2480   -0.9641    2.9673    3.2767    0.3330    0.3679    0.1864    5.7540    1.5939
+#> 289:    93.3676   -5.9125   -0.1440    2.2474   -0.9639    2.9663    3.2765    0.3329    0.3678    0.1865    5.7536    1.5939
+#> 290:    93.3659   -5.9126   -0.1443    2.2469   -0.9637    2.9570    3.2776    0.3328    0.3678    0.1866    5.7523    1.5941
+#> 291:    93.3620   -5.9109   -0.1447    2.2466   -0.9634    2.9472    3.2713    0.3327    0.3676    0.1866    5.7527    1.5943
+#> 292:    93.3601   -5.9096   -0.1450    2.2462   -0.9632    2.9359    3.2664    0.3326    0.3675    0.1866    5.7517    1.5944
+#> 293:    93.3582   -5.9077   -0.1453    2.2457   -0.9629    2.9295    3.2586    0.3326    0.3675    0.1866    5.7514    1.5945
+#> 294:    93.3583   -5.9054   -0.1456    2.2454   -0.9626    2.9203    3.2478    0.3326    0.3676    0.1867    5.7508    1.5942
+#> 295:    93.3577   -5.9037   -0.1459    2.2449   -0.9624    2.9216    3.2406    0.3325    0.3678    0.1867    5.7493    1.5934
+#> 296:    93.3570   -5.9016   -0.1462    2.2445   -0.9623    2.9304    3.2334    0.3323    0.3680    0.1868    5.7502    1.5933
+#> 297:    93.3538   -5.8988   -0.1462    2.2441   -0.9621    2.9429    3.2217    0.3321    0.3681    0.1870    5.7539    1.5939
+#> 298:    93.3525   -5.8966   -0.1463    2.2438   -0.9620    2.9662    3.2118    0.3319    0.3683    0.1870    5.7555    1.5942
+#> 299:    93.3526   -5.8957   -0.1465    2.2437   -0.9619    2.9812    3.2056    0.3318    0.3685    0.1870    5.7582    1.5938
+#> 300:    93.3504   -5.8953   -0.1467    2.2436   -0.9616    2.9982    3.2029    0.3316    0.3688    0.1873    5.7609    1.5937
+#> 301:    93.3469   -5.8941   -0.1469    2.2434   -0.9612    3.0124    3.1993    0.3315    0.3690    0.1875    5.7641    1.5933
+#> 302:    93.3442   -5.8944   -0.1472    2.2434   -0.9609    3.0353    3.2015    0.3313    0.3692    0.1876    5.7660    1.5937
+#> 303:    93.3428   -5.8970   -0.1474    2.2432   -0.9607    3.0454    3.2160    0.3312    0.3692    0.1876    5.7654    1.5938
+#> 304:    93.3407   -5.9012   -0.1475    2.2430   -0.9607    3.0626    3.2409    0.3310    0.3693    0.1877    5.7649    1.5932
+#> 305:    93.3395   -5.9051   -0.1476    2.2429   -0.9607    3.0756    3.2632    0.3308    0.3693    0.1879    5.7650    1.5924
+#> 306:    93.3398   -5.9099   -0.1478    2.2429   -0.9607    3.0881    3.2952    0.3306    0.3694    0.1880    5.7655    1.5920
+#> 307:    93.3406   -5.9128   -0.1479    2.2427   -0.9608    3.0995    3.3163    0.3305    0.3695    0.1880    5.7666    1.5921
+#> 308:    93.3418   -5.9165   -0.1480    2.2426   -0.9610    3.1060    3.3420    0.3303    0.3696    0.1881    5.7674    1.5914
+#> 309:    93.3437   -5.9205   -0.1481    2.2424   -0.9610    3.1185    3.3703    0.3301    0.3697    0.1882    5.7665    1.5908
+#> 310:    93.3442   -5.9236   -0.1482    2.2422   -0.9612    3.1270    3.3902    0.3299    0.3698    0.1882    5.7650    1.5904
+#> 311:    93.3482   -5.9268   -0.1482    2.2421   -0.9614    3.1333    3.4086    0.3296    0.3698    0.1882    5.7636    1.5900
+#> 312:    93.3529   -5.9286   -0.1482    2.2420   -0.9615    3.1348    3.4186    0.3294    0.3699    0.1882    5.7622    1.5895
+#> 313:    93.3573   -5.9290   -0.1481    2.2419   -0.9617    3.1332    3.4199    0.3291    0.3699    0.1882    5.7621    1.5891
+#> 314:    93.3630   -5.9293   -0.1482    2.2418   -0.9619    3.1398    3.4211    0.3289    0.3700    0.1883    5.7594    1.5888
+#> 315:    93.3669   -5.9284   -0.1483    2.2416   -0.9622    3.1464    3.4155    0.3286    0.3702    0.1885    5.7586    1.5889
+#> 316:    93.3724   -5.9279   -0.1485    2.2412   -0.9624    3.1426    3.4124    0.3283    0.3704    0.1887    5.7581    1.5887
+#> 317:    93.3763   -5.9281   -0.1487    2.2409   -0.9626    3.1335    3.4108    0.3281    0.3706    0.1888    5.7573    1.5880
+#> 318:    93.3786   -5.9275   -0.1488    2.2405   -0.9627    3.1262    3.4057    0.3279    0.3709    0.1888    5.7579    1.5876
+#> 319:    93.3821   -5.9275   -0.1490    2.2402   -0.9628    3.1273    3.4032    0.3276    0.3711    0.1889    5.7570    1.5870
+#> 320:    93.3856   -5.9272   -0.1491    2.2401   -0.9629    3.1337    3.3989    0.3273    0.3715    0.1888    5.7563    1.5861
+#> 321:    93.3902   -5.9263   -0.1492    2.2399   -0.9631    3.1388    3.3931    0.3269    0.3718    0.1887    5.7555    1.5852
+#> 322:    93.3951   -5.9251   -0.1493    2.2397   -0.9631    3.1415    3.3856    0.3266    0.3721    0.1886    5.7552    1.5846
+#> 323:    93.3988   -5.9251   -0.1493    2.2395   -0.9632    3.1377    3.3824    0.3262    0.3724    0.1885    5.7556    1.5841
+#> 324:    93.4030   -5.9236   -0.1494    2.2394   -0.9633    3.1355    3.3738    0.3259    0.3727    0.1885    5.7562    1.5837
+#> 325:    93.4047   -5.9219   -0.1495    2.2393   -0.9633    3.1415    3.3647    0.3256    0.3731    0.1884    5.7553    1.5831
+#> 326:    93.4077   -5.9204   -0.1495    2.2391   -0.9634    3.1489    3.3564    0.3254    0.3735    0.1884    5.7562    1.5829
+#> 327:    93.4121   -5.9185   -0.1496    2.2390   -0.9635    3.1503    3.3472    0.3250    0.3739    0.1884    5.7562    1.5825
+#> 328:    93.4157   -5.9182   -0.1496    2.2389   -0.9636    3.1564    3.3432    0.3246    0.3743    0.1884    5.7559    1.5823
+#> 329:    93.4181   -5.9169   -0.1496    2.2388   -0.9638    3.1666    3.3361    0.3243    0.3746    0.1884    5.7544    1.5822
+#> 330:    93.4206   -5.9171   -0.1497    2.2386   -0.9640    3.1726    3.3349    0.3239    0.3748    0.1885    5.7538    1.5824
+#> 331:    93.4214   -5.9172   -0.1497    2.2385   -0.9642    3.1764    3.3332    0.3236    0.3750    0.1886    5.7540    1.5824
+#> 332:    93.4226   -5.9171   -0.1497    2.2385   -0.9645    3.1787    3.3303    0.3232    0.3752    0.1887    5.7539    1.5826
+#> 333:    93.4242   -5.9168   -0.1497    2.2384   -0.9645    3.1757    3.3287    0.3229    0.3755    0.1886    5.7545    1.5823
+#> 334:    93.4273   -5.9167   -0.1497    2.2383   -0.9645    3.1832    3.3290    0.3226    0.3758    0.1887    5.7540    1.5818
+#> 335:    93.4306   -5.9170   -0.1498    2.2384   -0.9644    3.1910    3.3318    0.3223    0.3760    0.1887    5.7548    1.5814
+#> 336:    93.4315   -5.9177   -0.1498    2.2384   -0.9644    3.1999    3.3355    0.3219    0.3762    0.1887    5.7558    1.5811
+#> 337:    93.4332   -5.9181   -0.1499    2.2384   -0.9643    3.2145    3.3360    0.3216    0.3764    0.1887    5.7581    1.5805
+#> 338:    93.4352   -5.9169   -0.1498    2.2384   -0.9643    3.2221    3.3307    0.3213    0.3767    0.1887    5.7592    1.5802
+#> 339:    93.4385   -5.9152   -0.1498    2.2384   -0.9643    3.2356    3.3242    0.3210    0.3770    0.1887    5.7605    1.5797
+#> 340:    93.4417   -5.9130   -0.1498    2.2384   -0.9643    3.2506    3.3167    0.3207    0.3773    0.1888    5.7599    1.5794
+#> 341:    93.4452   -5.9102   -0.1497    2.2382   -0.9641    3.2568    3.3064    0.3205    0.3772    0.1888    5.7590    1.5799
+#> 342:    93.4487   -5.9077   -0.1497    2.2381   -0.9641    3.2628    3.2970    0.3203    0.3772    0.1889    5.7587    1.5802
+#> 343:    93.4519   -5.9055   -0.1497    2.2380   -0.9642    3.2685    3.2892    0.3201    0.3772    0.1889    5.7585    1.5810
+#> 344:    93.4556   -5.9048   -0.1497    2.2379   -0.9643    3.2690    3.2847    0.3200    0.3771    0.1891    5.7573    1.5812
+#> 345:    93.4588   -5.9041   -0.1498    2.2377   -0.9645    3.2704    3.2807    0.3199    0.3771    0.1893    5.7567    1.5811
+#> 346:    93.4605   -5.9033   -0.1498    2.2376   -0.9647    3.2655    3.2747    0.3198    0.3770    0.1893    5.7557    1.5808
+#> 347:    93.4638   -5.9027   -0.1498    2.2375   -0.9648    3.2725    3.2701    0.3198    0.3768    0.1894    5.7532    1.5808
+#> 348:    93.4643   -5.9028   -0.1498    2.2373   -0.9649    3.2764    3.2676    0.3197    0.3768    0.1893    5.7523    1.5807
+#> 349:    93.4664   -5.9023   -0.1497    2.2372   -0.9650    3.2806    3.2638    0.3197    0.3767    0.1893    5.7527    1.5815
+#> 350:    93.4700   -5.9014   -0.1497    2.2370   -0.9651    3.2817    3.2585    0.3196    0.3767    0.1892    5.7534    1.5817
+#> 351:    93.4724   -5.9001   -0.1497    2.2369   -0.9652    3.2825    3.2522    0.3196    0.3768    0.1892    5.7541    1.5818
+#> 352:    93.4744   -5.8986   -0.1497    2.2369   -0.9653    3.2875    3.2460    0.3195    0.3768    0.1891    5.7546    1.5819
+#> 353:    93.4738   -5.8975   -0.1496    2.2369   -0.9653    3.2891    3.2407    0.3195    0.3769    0.1889    5.7560    1.5822
+#> 354:    93.4733   -5.8960   -0.1496    2.2369   -0.9652    3.2856    3.2333    0.3194    0.3768    0.1889    5.7579    1.5824
+#> 355:    93.4731   -5.8944   -0.1496    2.2370   -0.9652    3.2893    3.2259    0.3194    0.3767    0.1888    5.7599    1.5826
+#> 356:    93.4724   -5.8933   -0.1495    2.2373   -0.9652    3.2924    3.2197    0.3194    0.3767    0.1888    5.7608    1.5832
+#> 357:    93.4723   -5.8929   -0.1493    2.2376   -0.9654    3.2907    3.2164    0.3194    0.3767    0.1887    5.7605    1.5833
+#> 358:    93.4723   -5.8923   -0.1491    2.2378   -0.9654    3.2875    3.2120    0.3194    0.3766    0.1886    5.7608    1.5837
+#> 359:    93.4705   -5.8931   -0.1490    2.2379   -0.9656    3.2875    3.2121    0.3194    0.3764    0.1886    5.7606    1.5843
+#> 360:    93.4699   -5.8938   -0.1488    2.2382   -0.9658    3.2837    3.2133    0.3195    0.3763    0.1886    5.7606    1.5848
+#> 361:    93.4693   -5.8951   -0.1487    2.2383   -0.9659    3.2822    3.2164    0.3195    0.3763    0.1886    5.7600    1.5852
+#> 362:    93.4691   -5.8963   -0.1486    2.2385   -0.9660    3.2770    3.2196    0.3195    0.3763    0.1884    5.7618    1.5856
+#> 363:    93.4681   -5.8970   -0.1485    2.2387   -0.9660    3.2706    3.2208    0.3195    0.3762    0.1883    5.7639    1.5857
+#> 364:    93.4674   -5.8970   -0.1484    2.2389   -0.9660    3.2593    3.2189    0.3195    0.3760    0.1881    5.7659    1.5855
+#> 365:    93.4680   -5.8968   -0.1482    2.2391   -0.9659    3.2513    3.2174    0.3196    0.3758    0.1881    5.7686    1.5857
+#> 366:    93.4672   -5.8962   -0.1480    2.2393   -0.9658    3.2493    3.2161    0.3196    0.3755    0.1880    5.7714    1.5861
+#> 367:    93.4656   -5.8953   -0.1479    2.2396   -0.9657    3.2462    3.2121    0.3195    0.3753    0.1881    5.7721    1.5862
+#> 368:    93.4645   -5.8946   -0.1478    2.2398   -0.9657    3.2469    3.2083    0.3194    0.3750    0.1882    5.7724    1.5860
+#> 369:    93.4638   -5.8946   -0.1476    2.2401   -0.9657    3.2544    3.2068    0.3194    0.3749    0.1882    5.7713    1.5856
+#> 370:    93.4639   -5.8946   -0.1475    2.2404   -0.9657    3.2547    3.2066    0.3194    0.3748    0.1882    5.7719    1.5853
+#> 371:    93.4646   -5.8959   -0.1474    2.2407   -0.9657    3.2584    3.2129    0.3194    0.3746    0.1883    5.7725    1.5847
+#> 372:    93.4648   -5.8964   -0.1473    2.2409   -0.9658    3.2649    3.2172    0.3193    0.3745    0.1883    5.7730    1.5843
+#> 373:    93.4658   -5.8958   -0.1471    2.2411   -0.9659    3.2744    3.2135    0.3193    0.3743    0.1884    5.7730    1.5843
+#> 374:    93.4678   -5.8953   -0.1470    2.2412   -0.9662    3.2855    3.2100    0.3192    0.3742    0.1885    5.7727    1.5847
+#> 375:    93.4697   -5.8955   -0.1470    2.2413   -0.9663    3.2917    3.2087    0.3190    0.3742    0.1885    5.7733    1.5845
+#> 376:    93.4707   -5.8960   -0.1469    2.2414   -0.9664    3.2997    3.2095    0.3189    0.3741    0.1885    5.7726    1.5841
+#> 377:    93.4712   -5.8965   -0.1468    2.2415   -0.9665    3.3016    3.2100    0.3188    0.3741    0.1885    5.7724    1.5836
+#> 378:    93.4706   -5.8971   -0.1468    2.2416   -0.9665    3.2958    3.2113    0.3187    0.3741    0.1884    5.7733    1.5829
+#> 379:    93.4699   -5.8983   -0.1467    2.2418   -0.9666    3.2940    3.2174    0.3186    0.3741    0.1883    5.7732    1.5827
+#> 380:    93.4709   -5.8993   -0.1467    2.2418   -0.9667    3.2907    3.2225    0.3185    0.3739    0.1882    5.7726    1.5826
+#> 381:    93.4730   -5.9009   -0.1467    2.2418   -0.9667    3.2861    3.2325    0.3185    0.3737    0.1881    5.7709    1.5825
+#> 382:    93.4746   -5.9018   -0.1467    2.2418   -0.9667    3.2841    3.2407    0.3184    0.3734    0.1880    5.7692    1.5822
+#> 383:    93.4744   -5.9033   -0.1468    2.2418   -0.9667    3.2847    3.2537    0.3184    0.3732    0.1878    5.7672    1.5819
+#> 384:    93.4747   -5.9049   -0.1468    2.2418   -0.9667    3.2854    3.2640    0.3184    0.3729    0.1878    5.7657    1.5816
+#> 385:    93.4751   -5.9062   -0.1468    2.2418   -0.9666    3.2917    3.2702    0.3184    0.3727    0.1877    5.7642    1.5813
+#> 386:    93.4756   -5.9074   -0.1468    2.2418   -0.9666    3.2971    3.2753    0.3185    0.3725    0.1876    5.7625    1.5810
+#> 387:    93.4761   -5.9084   -0.1469    2.2417   -0.9666    3.2988    3.2789    0.3185    0.3723    0.1875    5.7613    1.5804
+#> 388:    93.4777   -5.9092   -0.1469    2.2417   -0.9666    3.3055    3.2811    0.3185    0.3721    0.1875    5.7599    1.5803
+#> 389:    93.4805   -5.9092   -0.1468    2.2417   -0.9667    3.3138    3.2802    0.3185    0.3719    0.1874    5.7588    1.5803
+#> 390:    93.4828   -5.9089   -0.1468    2.2417   -0.9667    3.3164    3.2782    0.3186    0.3718    0.1873    5.7576    1.5806
+#> 391:    93.4854   -5.9094   -0.1467    2.2416   -0.9668    3.3265    3.2800    0.3186    0.3716    0.1873    5.7556    1.5804
+#> 392:    93.4877   -5.9103   -0.1467    2.2416   -0.9669    3.3327    3.2836    0.3187    0.3715    0.1873    5.7535    1.5803
+#> 393:    93.4899   -5.9110   -0.1467    2.2416   -0.9669    3.3419    3.2876    0.3187    0.3715    0.1873    5.7517    1.5803
+#> 394:    93.4925   -5.9117   -0.1467    2.2416   -0.9669    3.3494    3.2903    0.3187    0.3714    0.1873    5.7508    1.5801
+#> 395:    93.4945   -5.9121   -0.1467    2.2416   -0.9670    3.3536    3.2912    0.3187    0.3714    0.1873    5.7497    1.5796
+#> 396:    93.4951   -5.9124   -0.1467    2.2416   -0.9670    3.3590    3.2918    0.3187    0.3715    0.1873    5.7476    1.5793
+#> 397:    93.4955   -5.9123   -0.1467    2.2416   -0.9669    3.3626    3.2904    0.3186    0.3715    0.1873    5.7456    1.5788
+#> 398:    93.4971   -5.9120   -0.1467    2.2416   -0.9669    3.3735    3.2887    0.3186    0.3716    0.1873    5.7433    1.5786
+#> 399:    93.4995   -5.9116   -0.1467    2.2415   -0.9669    3.3854    3.2866    0.3186    0.3716    0.1873    5.7422    1.5785
+#> 400:    93.5007   -5.9116   -0.1466    2.2415   -0.9669    3.3923    3.2856    0.3186    0.3717    0.1873    5.7416    1.5786
+#> 401:    93.5028   -5.9109   -0.1467    2.2415   -0.9669    3.4020    3.2820    0.3186    0.3718    0.1873    5.7412    1.5787
+#> 402:    93.5042   -5.9099   -0.1467    2.2414   -0.9669    3.4114    3.2781    0.3186    0.3719    0.1874    5.7406    1.5788
+#> 403:    93.5054   -5.9090   -0.1467    2.2413   -0.9670    3.4179    3.2735    0.3186    0.3720    0.1874    5.7401    1.5785
+#> 404:    93.5071   -5.9093   -0.1468    2.2412   -0.9670    3.4190    3.2726    0.3186    0.3720    0.1875    5.7392    1.5779
+#> 405:    93.5087   -5.9087   -0.1468    2.2411   -0.9671    3.4186    3.2689    0.3186    0.3721    0.1876    5.7386    1.5776
+#> 406:    93.5091   -5.9087   -0.1469    2.2411   -0.9671    3.4228    3.2688    0.3186    0.3721    0.1876    5.7377    1.5774
+#> 407:    93.5094   -5.9091   -0.1470    2.2411   -0.9672    3.4285    3.2698    0.3186    0.3720    0.1877    5.7368    1.5770
+#> 408:    93.5108   -5.9081   -0.1470    2.2410   -0.9672    3.4378    3.2648    0.3187    0.3719    0.1877    5.7358    1.5766
+#> 409:    93.5113   -5.9082   -0.1470    2.2410   -0.9672    3.4444    3.2643    0.3187    0.3719    0.1878    5.7357    1.5763
+#> 410:    93.5102   -5.9099   -0.1470    2.2410   -0.9672    3.4502    3.2731    0.3188    0.3719    0.1878    5.7359    1.5756
+#> 411:    93.5097   -5.9109   -0.1469    2.2410   -0.9673    3.4534    3.2793    0.3188    0.3718    0.1878    5.7348    1.5753
+#> 412:    93.5102   -5.9114   -0.1469    2.2410   -0.9673    3.4522    3.2836    0.3189    0.3717    0.1878    5.7330    1.5753
+#> 413:    93.5110   -5.9120   -0.1469    2.2410   -0.9675    3.4534    3.2885    0.3189    0.3716    0.1878    5.7320    1.5756
+#> 414:    93.5126   -5.9130   -0.1469    2.2410   -0.9675    3.4550    3.2943    0.3190    0.3716    0.1878    5.7314    1.5753
+#> 415:    93.5144   -5.9140   -0.1469    2.2409   -0.9676    3.4574    3.3003    0.3190    0.3715    0.1878    5.7304    1.5751
+#> 416:    93.5147   -5.9149   -0.1469    2.2409   -0.9676    3.4632    3.3059    0.3191    0.3714    0.1878    5.7292    1.5750
+#> 417:    93.5132   -5.9156   -0.1468    2.2410   -0.9677    3.4675    3.3090    0.3192    0.3713    0.1878    5.7292    1.5747
+#> 418:    93.5131   -5.9165   -0.1468    2.2410   -0.9678    3.4680    3.3130    0.3192    0.3712    0.1878    5.7296    1.5747
+#> 419:    93.5142   -5.9166   -0.1467    2.2411   -0.9678    3.4663    3.3143    0.3193    0.3712    0.1879    5.7302    1.5744
+#> 420:    93.5150   -5.9164   -0.1466    2.2412   -0.9679    3.4626    3.3130    0.3193    0.3712    0.1879    5.7303    1.5744
+#> 421:    93.5162   -5.9169   -0.1465    2.2413   -0.9681    3.4596    3.3158    0.3194    0.3713    0.1880    5.7315    1.5743
+#> 422:    93.5173   -5.9172   -0.1465    2.2414   -0.9682    3.4567    3.3165    0.3194    0.3714    0.1881    5.7332    1.5740
+#> 423:    93.5174   -5.9178   -0.1464    2.2415   -0.9684    3.4550    3.3185    0.3194    0.3715    0.1882    5.7348    1.5741
+#> 424:    93.5174   -5.9189   -0.1464    2.2417   -0.9685    3.4531    3.3225    0.3193    0.3716    0.1882    5.7360    1.5737
+#> 425:    93.5171   -5.9184   -0.1463    2.2418   -0.9685    3.4508    3.3186    0.3192    0.3718    0.1882    5.7372    1.5738
+#> 426:    93.5167   -5.9177   -0.1462    2.2419   -0.9686    3.4566    3.3143    0.3192    0.3720    0.1882    5.7385    1.5735
+#> 427:    93.5185   -5.9174   -0.1462    2.2420   -0.9687    3.4561    3.3114    0.3191    0.3721    0.1881    5.7389    1.5734
+#> 428:    93.5192   -5.9177   -0.1461    2.2421   -0.9688    3.4574    3.3112    0.3191    0.3722    0.1880    5.7398    1.5731
+#> 429:    93.5184   -5.9179   -0.1460    2.2421   -0.9689    3.4558    3.3102    0.3190    0.3723    0.1879    5.7405    1.5729
+#> 430:    93.5170   -5.9187   -0.1460    2.2421   -0.9690    3.4575    3.3132    0.3190    0.3724    0.1879    5.7404    1.5727
+#> 431:    93.5156   -5.9192   -0.1460    2.2422   -0.9691    3.4556    3.3150    0.3190    0.3724    0.1879    5.7405    1.5726
+#> 432:    93.5148   -5.9203   -0.1459    2.2422   -0.9692    3.4557    3.3201    0.3190    0.3725    0.1878    5.7409    1.5727
+#> 433:    93.5134   -5.9215   -0.1459    2.2422   -0.9692    3.4569    3.3263    0.3190    0.3726    0.1878    5.7415    1.5731
+#> 434:    93.5128   -5.9222   -0.1459    2.2423   -0.9691    3.4623    3.3304    0.3190    0.3726    0.1877    5.7422    1.5728
+#> 435:    93.5116   -5.9231   -0.1459    2.2424   -0.9691    3.4672    3.3376    0.3191    0.3727    0.1877    5.7424    1.5726
+#> 436:    93.5111   -5.9228   -0.1459    2.2425   -0.9692    3.4658    3.3352    0.3190    0.3727    0.1876    5.7429    1.5725
+#> 437:    93.5100   -5.9227   -0.1459    2.2425   -0.9692    3.4651    3.3328    0.3190    0.3727    0.1876    5.7430    1.5725
+#> 438:    93.5071   -5.9230   -0.1459    2.2425   -0.9692    3.4614    3.3329    0.3190    0.3728    0.1876    5.7437    1.5725
+#> 439:    93.5035   -5.9225   -0.1459    2.2426   -0.9691    3.4555    3.3298    0.3190    0.3728    0.1875    5.7449    1.5725
+#> 440:    93.5006   -5.9222   -0.1459    2.2426   -0.9690    3.4503    3.3286    0.3190    0.3728    0.1874    5.7461    1.5723
+#> 441:    93.4988   -5.9220   -0.1459    2.2427   -0.9689    3.4445    3.3272    0.3190    0.3728    0.1874    5.7466    1.5721
+#> 442:    93.4971   -5.9216   -0.1459    2.2428   -0.9688    3.4392    3.3265    0.3190    0.3728    0.1874    5.7475    1.5721
+#> 443:    93.4957   -5.9214   -0.1458    2.2429   -0.9688    3.4338    3.3256    0.3190    0.3729    0.1874    5.7487    1.5723
+#> 444:    93.4949   -5.9210   -0.1458    2.2430   -0.9688    3.4288    3.3236    0.3189    0.3729    0.1874    5.7502    1.5721
+#> 445:    93.4932   -5.9210   -0.1458    2.2430   -0.9687    3.4283    3.3237    0.3189    0.3731    0.1874    5.7516    1.5719
+#> 446:    93.4922   -5.9205   -0.1458    2.2430   -0.9687    3.4253    3.3215    0.3188    0.3733    0.1873    5.7524    1.5717
+#> 447:    93.4917   -5.9205   -0.1458    2.2430   -0.9686    3.4257    3.3213    0.3187    0.3736    0.1873    5.7528    1.5715
+#> 448:    93.4924   -5.9205   -0.1458    2.2430   -0.9685    3.4296    3.3209    0.3186    0.3737    0.1872    5.7532    1.5717
+#> 449:    93.4920   -5.9203   -0.1459    2.2430   -0.9684    3.4302    3.3194    0.3185    0.3739    0.1872    5.7542    1.5717
+#> 450:    93.4915   -5.9207   -0.1459    2.2430   -0.9684    3.4314    3.3217    0.3184    0.3741    0.1871    5.7551    1.5715
+#> 451:    93.4915   -5.9214   -0.1459    2.2430   -0.9684    3.4371    3.3253    0.3183    0.3743    0.1871    5.7562    1.5717
+#> 452:    93.4926   -5.9212   -0.1458    2.2430   -0.9683    3.4417    3.3242    0.3182    0.3745    0.1870    5.7567    1.5717
+#> 453:    93.4935   -5.9211   -0.1459    2.2430   -0.9683    3.4413    3.3232    0.3182    0.3746    0.1870    5.7574    1.5714
+#> 454:    93.4941   -5.9209   -0.1459    2.2429   -0.9683    3.4406    3.3222    0.3182    0.3748    0.1870    5.7580    1.5713
+#> 455:    93.4947   -5.9212   -0.1459    2.2429   -0.9684    3.4450    3.3232    0.3181    0.3750    0.1870    5.7580    1.5710
+#> 456:    93.4950   -5.9214   -0.1459    2.2429   -0.9684    3.4481    3.3236    0.3181    0.3751    0.1870    5.7585    1.5708
+#> 457:    93.4961   -5.9220   -0.1459    2.2429   -0.9685    3.4516    3.3266    0.3180    0.3752    0.1869    5.7590    1.5707
+#> 458:    93.4965   -5.9218   -0.1459    2.2428   -0.9685    3.4553    3.3257    0.3179    0.3753    0.1869    5.7589    1.5707
+#> 459:    93.4959   -5.9212   -0.1459    2.2428   -0.9685    3.4572    3.3229    0.3178    0.3754    0.1868    5.7596    1.5705
+#> 460:    93.4960   -5.9209   -0.1459    2.2428   -0.9685    3.4573    3.3209    0.3178    0.3755    0.1868    5.7598    1.5704
+#> 461:    93.4944   -5.9211   -0.1459    2.2428   -0.9685    3.4592    3.3202    0.3177    0.3757    0.1868    5.7609    1.5701
+#> 462:    93.4941   -5.9214   -0.1459    2.2428   -0.9686    3.4630    3.3206    0.3176    0.3759    0.1868    5.7617    1.5700
+#> 463:    93.4932   -5.9215   -0.1459    2.2429   -0.9686    3.4708    3.3197    0.3175    0.3761    0.1868    5.7622    1.5699
+#> 464:    93.4933   -5.9209   -0.1459    2.2429   -0.9685    3.4759    3.3162    0.3175    0.3762    0.1869    5.7628    1.5696
+#> 465:    93.4928   -5.9204   -0.1459    2.2428   -0.9685    3.4794    3.3133    0.3174    0.3764    0.1870    5.7642    1.5693
+#> 466:    93.4934   -5.9197   -0.1460    2.2428   -0.9685    3.4838    3.3105    0.3173    0.3766    0.1870    5.7659    1.5693
+#> 467:    93.4931   -5.9197   -0.1460    2.2428   -0.9685    3.4866    3.3094    0.3172    0.3768    0.1871    5.7667    1.5691
+#> 468:    93.4933   -5.9198   -0.1460    2.2428   -0.9685    3.4916    3.3099    0.3172    0.3769    0.1871    5.7672    1.5690
+#> 469:    93.4936   -5.9200   -0.1461    2.2427   -0.9685    3.4929    3.3119    0.3171    0.3771    0.1871    5.7681    1.5689
+#> 470:    93.4938   -5.9200   -0.1461    2.2427   -0.9685    3.4931    3.3111    0.3171    0.3773    0.1871    5.7685    1.5687
+#> 471:    93.4943   -5.9198   -0.1461    2.2427   -0.9685    3.4932    3.3097    0.3170    0.3776    0.1871    5.7681    1.5686
+#> 472:    93.4931   -5.9197   -0.1461    2.2427   -0.9684    3.4923    3.3092    0.3170    0.3778    0.1870    5.7683    1.5686
+#> 473:    93.4928   -5.9193   -0.1461    2.2426   -0.9684    3.4918    3.3068    0.3169    0.3781    0.1870    5.7690    1.5685
+#> 474:    93.4920   -5.9193   -0.1462    2.2426   -0.9683    3.4878    3.3075    0.3169    0.3781    0.1870    5.7687    1.5688
+#> 475:    93.4909   -5.9191   -0.1463    2.2425   -0.9683    3.4868    3.3069    0.3169    0.3782    0.1869    5.7681    1.5692
+#> 476:    93.4887   -5.9190   -0.1464    2.2424   -0.9682    3.4881    3.3072    0.3169    0.3783    0.1869    5.7673    1.5694
+#> 477:    93.4875   -5.9185   -0.1465    2.2423   -0.9681    3.4847    3.3059    0.3169    0.3784    0.1868    5.7667    1.5696
+#> 478:    93.4867   -5.9182   -0.1466    2.2421   -0.9681    3.4804    3.3056    0.3170    0.3784    0.1867    5.7661    1.5700
+#> 479:    93.4865   -5.9178   -0.1468    2.2419   -0.9681    3.4768    3.3043    0.3171    0.3784    0.1867    5.7657    1.5702
+#> 480:    93.4863   -5.9181   -0.1469    2.2417   -0.9680    3.4733    3.3057    0.3172    0.3784    0.1866    5.7656    1.5702
+#> 481:    93.4865   -5.9182   -0.1470    2.2415   -0.9680    3.4694    3.3069    0.3173    0.3784    0.1866    5.7648    1.5705
+#> 482:    93.4871   -5.9187   -0.1472    2.2412   -0.9681    3.4667    3.3089    0.3173    0.3784    0.1865    5.7631    1.5709
+#> 483:    93.4860   -5.9192   -0.1473    2.2410   -0.9681    3.4668    3.3107    0.3174    0.3785    0.1865    5.7624    1.5709
+#> 484:    93.4858   -5.9193   -0.1474    2.2408   -0.9681    3.4681    3.3111    0.3174    0.3786    0.1864    5.7615    1.5713
+#> 485:    93.4858   -5.9195   -0.1476    2.2406   -0.9681    3.4643    3.3110    0.3174    0.3787    0.1864    5.7612    1.5717
+#> 486:    93.4853   -5.9198   -0.1477    2.2404   -0.9682    3.4665    3.3115    0.3174    0.3788    0.1864    5.7612    1.5717
+#> 487:    93.4856   -5.9201   -0.1478    2.2402   -0.9682    3.4687    3.3143    0.3173    0.3790    0.1864    5.7612    1.5719
+#> 488:    93.4858   -5.9209   -0.1479    2.2401   -0.9683    3.4688    3.3186    0.3173    0.3792    0.1864    5.7626    1.5722
+#> 489:    93.4870   -5.9211   -0.1480    2.2399   -0.9684    3.4681    3.3198    0.3174    0.3794    0.1863    5.7640    1.5725
+#> 490:    93.4881   -5.9213   -0.1481    2.2398   -0.9684    3.4694    3.3211    0.3174    0.3797    0.1864    5.7650    1.5728
+#> 491:    93.4892   -5.9210   -0.1482    2.2395   -0.9685    3.4716    3.3193    0.3173    0.3799    0.1864    5.7650    1.5732
+#> 492:    93.4907   -5.9211   -0.1483    2.2393   -0.9686    3.4754    3.3179    0.3173    0.3801    0.1865    5.7648    1.5736
+#> 493:    93.4928   -5.9215   -0.1484    2.2390   -0.9686    3.4858    3.3185    0.3173    0.3803    0.1865    5.7640    1.5738
+#> 494:    93.4937   -5.9217   -0.1485    2.2388   -0.9687    3.4940    3.3182    0.3172    0.3805    0.1865    5.7639    1.5740
+#> 495:    93.4945   -5.9213   -0.1485    2.2386   -0.9688    3.4998    3.3151    0.3172    0.3808    0.1866    5.7638    1.5742
+#> 496:    93.4953   -5.9208   -0.1486    2.2384   -0.9688    3.5036    3.3123    0.3172    0.3810    0.1867    5.7635    1.5745
+#> 497:    93.4969   -5.9205   -0.1487    2.2382   -0.9689    3.5064    3.3109    0.3172    0.3813    0.1868    5.7637    1.5747
+#> 498:    93.4980   -5.9205   -0.1488    2.2379   -0.9690    3.5057    3.3104    0.3171    0.3815    0.1868    5.7639    1.5752
+#> 499:    93.4999   -5.9205   -0.1488    2.2377   -0.9691    3.5095    3.3102    0.3171    0.3817    0.1869    5.7639    1.5756
+#> 500:    93.5013   -5.9210   -0.1489    2.2376   -0.9691    3.5093    3.3135    0.3171    0.3818    0.1869    5.7644    1.5758
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |
+#> |.....................|  log_beta |sigma_parent |  sigma_A1 |        o1 |
+#> |.....................|        o2 |        o3 |        o4 |        o5 |
+#> |    1|     470.09130 |     1.000 |    -1.000 |   -0.9119 |   -0.8960 |
+#> |.....................|   -0.8494 |   -0.8528 |   -0.8683 |   -0.8768 |
+#> |.....................|   -0.8744 |   -0.8681 |   -0.8700 |   -0.8694 |
+#> |    U|      470.0913 |     94.11 |    -5.371 |   -0.9909 |   -0.1965 |
+#> |.....................|     2.121 |     1.952 |     1.178 |    0.7545 |
+#> |.....................|    0.8769 |     1.189 |     1.095 |     1.127 |
+#> |    X|      470.0913 |     94.11 |  0.004648 |    0.2707 |    0.8216 |
+#> |.....................|     8.339 |     1.952 |     1.178 |    0.7545 |
+#> |.....................|    0.8769 |     1.189 |     1.095 |     1.127 |
+#> |    G|    Gill Diff. |     72.01 |     2.213 |   -0.2476 |   -0.3163 |
+#> |.....................|   -0.8532 |    -32.82 |    -13.44 |     9.552 |
+#> |.....................|     11.72 |    -12.16 |    -9.599 |    -9.049 |
+#> |    2|     5180.4321 |    0.1393 |    -1.026 |   -0.9090 |   -0.8922 |
+#> |.....................|   -0.8392 |   -0.4605 |   -0.7077 |   -0.9910 |
+#> |.....................|    -1.014 |   -0.7228 |   -0.7553 |   -0.7612 |
+#> |    U|     5180.4321 |     13.11 |    -5.398 |   -0.9880 |   -0.1927 |
+#> |.....................|     2.131 |     2.334 |     1.272 |    0.6684 |
+#> |.....................|    0.7541 |     1.362 |     1.220 |     1.248 |
+#> |    X|     5180.4321 |     13.11 |  0.004526 |    0.2713 |    0.8247 |
+#> |.....................|     8.424 |     2.334 |     1.272 |    0.6684 |
+#> |.....................|    0.7541 |     1.362 |     1.220 |     1.248 |
+#> |    3|     529.93288 |    0.9139 |    -1.003 |   -0.9116 |   -0.8956 |
+#> |.....................|   -0.8484 |   -0.8135 |   -0.8523 |   -0.8883 |
+#> |.....................|   -0.8884 |   -0.8536 |   -0.8585 |   -0.8585 |
+#> |    U|     529.93288 |     86.01 |    -5.374 |   -0.9906 |   -0.1961 |
+#> |.....................|     2.122 |     1.990 |     1.187 |    0.7459 |
+#> |.....................|    0.8647 |     1.206 |     1.107 |     1.139 |
+#> |    X|     529.93288 |     86.01 |  0.004635 |    0.2708 |    0.8219 |
+#> |.....................|     8.347 |     1.990 |     1.187 |    0.7459 |
+#> |.....................|    0.8647 |     1.206 |     1.107 |     1.139 |
+#> |    4|     469.96296 |    0.9914 |    -1.000 |   -0.9119 |   -0.8959 |
+#> |.....................|   -0.8493 |   -0.8489 |   -0.8667 |   -0.8780 |
+#> |.....................|   -0.8758 |   -0.8667 |   -0.8689 |   -0.8683 |
+#> |    U|     469.96296 |     93.30 |    -5.372 |   -0.9909 |   -0.1965 |
+#> |.....................|     2.121 |     1.955 |     1.179 |    0.7536 |
+#> |.....................|    0.8757 |     1.191 |     1.096 |     1.128 |
+#> |    X|     469.96296 |     93.30 |  0.004646 |    0.2707 |    0.8216 |
+#> |.....................|     8.339 |     1.955 |     1.179 |    0.7536 |
+#> |.....................|    0.8757 |     1.191 |     1.096 |     1.128 |
+#> |    F| Forward Diff. |    -91.63 |     2.121 |   -0.4143 |   -0.3985 |
+#> |.....................|    -1.124 |    -34.23 |    -12.87 |     9.567 |
+#> |.....................|     8.592 |    -11.79 |    -9.469 |    -8.518 |
+#> |    5|     469.41305 |    0.9973 |    -1.001 |   -0.9118 |   -0.8959 |
+#> |.....................|   -0.8491 |   -0.8424 |   -0.8642 |   -0.8798 |
+#> |.....................|   -0.8776 |   -0.8644 |   -0.8670 |   -0.8666 |
+#> |    U|     469.41305 |     93.85 |    -5.372 |   -0.9908 |   -0.1964 |
+#> |.....................|     2.121 |     1.962 |     1.180 |    0.7523 |
+#> |.....................|    0.8741 |     1.193 |     1.098 |     1.130 |
+#> |    X|     469.41305 |     93.85 |  0.004644 |    0.2707 |    0.8217 |
+#> |.....................|     8.341 |     1.962 |     1.180 |    0.7523 |
+#> |.....................|    0.8741 |     1.193 |     1.098 |     1.130 |
+#> |    F| Forward Diff. |     19.88 |     2.163 |   -0.2989 |   -0.3449 |
+#> |.....................|   -0.9473 |    -32.84 |    -13.22 |     8.952 |
+#> |.....................|     11.37 |    -11.75 |    -9.421 |    -8.530 |
+#> |    6|     469.13124 |    0.9930 |    -1.001 |   -0.9118 |   -0.8958 |
+#> |.....................|   -0.8489 |   -0.8354 |   -0.8614 |   -0.8817 |
+#> |.....................|   -0.8801 |   -0.8619 |   -0.8650 |   -0.8648 |
+#> |    U|     469.13124 |     93.45 |    -5.373 |   -0.9908 |   -0.1963 |
+#> |.....................|     2.121 |     1.969 |     1.182 |    0.7508 |
+#> |.....................|    0.8719 |     1.196 |     1.100 |     1.132 |
+#> |    X|     469.13124 |     93.45 |  0.004642 |    0.2708 |    0.8218 |
+#> |.....................|     8.343 |     1.969 |     1.182 |    0.7508 |
+#> |.....................|    0.8719 |     1.196 |     1.100 |     1.132 |
+#> |    F| Forward Diff. |    -60.06 |     2.108 |   -0.3845 |   -0.3876 |
+#> |.....................|    -1.088 |    -32.82 |    -12.89 |     8.720 |
+#> |.....................|     9.663 |    -11.60 |    -9.301 |    -8.348 |
+#> |    7|     468.71336 |    0.9979 |    -1.002 |   -0.9117 |   -0.8957 |
+#> |.....................|   -0.8487 |   -0.8285 |   -0.8586 |   -0.8835 |
+#> |.....................|   -0.8823 |   -0.8594 |   -0.8631 |   -0.8630 |
+#> |    U|     468.71336 |     93.91 |    -5.373 |   -0.9907 |   -0.1962 |
+#> |.....................|     2.122 |     1.975 |     1.183 |    0.7495 |
+#> |.....................|    0.8700 |     1.199 |     1.102 |     1.134 |
+#> |    X|     468.71336 |     93.91 |  0.004640 |    0.2708 |    0.8218 |
+#> |.....................|     8.345 |     1.975 |     1.183 |    0.7495 |
+#> |.....................|    0.8700 |     1.199 |     1.102 |     1.134 |
+#> |    F| Forward Diff. |     31.80 |     2.131 |   -0.3007 |   -0.3556 |
+#> |.....................|   -0.9543 |    -30.66 |    -12.35 |     8.979 |
+#> |.....................|     9.681 |    -11.54 |    -9.231 |    -8.330 |
+#> |    8|     468.42878 |    0.9931 |    -1.002 |   -0.9116 |   -0.8956 |
+#> |.....................|   -0.8484 |   -0.8217 |   -0.8559 |   -0.8855 |
+#> |.....................|   -0.8845 |   -0.8568 |   -0.8610 |   -0.8612 |
+#> |    U|     468.42878 |     93.46 |    -5.373 |   -0.9906 |   -0.1962 |
+#> |.....................|     2.122 |     1.982 |     1.185 |    0.7480 |
+#> |.....................|    0.8681 |     1.202 |     1.105 |     1.136 |
+#> |    X|     468.42878 |     93.46 |  0.004638 |    0.2708 |    0.8219 |
+#> |.....................|     8.346 |     1.982 |     1.185 |    0.7480 |
+#> |.....................|    0.8681 |     1.202 |     1.105 |     1.136 |
+#> |    F| Forward Diff. |    -55.97 |     2.081 |   -0.3855 |   -0.3928 |
+#> |.....................|    -1.100 |    -30.89 |    -12.11 |     8.596 |
+#> |.....................|     9.353 |    -11.36 |    -9.087 |    -8.137 |
+#> |    9|     468.02528 |    0.9977 |    -1.003 |   -0.9115 |   -0.8955 |
+#> |.....................|   -0.8482 |   -0.8148 |   -0.8531 |   -0.8875 |
+#> |.....................|   -0.8866 |   -0.8542 |   -0.8589 |   -0.8593 |
+#> |    U|     468.02528 |     93.90 |    -5.374 |   -0.9905 |   -0.1961 |
+#> |.....................|     2.122 |     1.989 |     1.187 |    0.7465 |
+#> |.....................|    0.8662 |     1.206 |     1.107 |     1.138 |
+#> |    X|     468.02528 |     93.90 |  0.004636 |    0.2708 |    0.8220 |
+#> |.....................|     8.348 |     1.989 |     1.187 |    0.7465 |
+#> |.....................|    0.8662 |     1.206 |     1.107 |     1.138 |
+#> |    F| Forward Diff. |     28.40 |     2.101 |   -0.3066 |   -0.3612 |
+#> |.....................|   -0.9721 |    -29.21 |    -11.91 |     8.561 |
+#> |.....................|     9.360 |    -11.31 |    -9.026 |    -8.108 |
+#> |   10|     467.76129 |    0.9930 |    -1.003 |   -0.9115 |   -0.8954 |
+#> |.....................|   -0.8479 |   -0.8081 |   -0.8503 |   -0.8895 |
+#> |.....................|   -0.8888 |   -0.8515 |   -0.8567 |   -0.8574 |
+#> |    U|     467.76129 |     93.46 |    -5.374 |   -0.9905 |   -0.1960 |
+#> |.....................|     2.122 |     1.995 |     1.188 |    0.7450 |
+#> |.....................|    0.8643 |     1.209 |     1.109 |     1.140 |
+#> |    X|     467.76129 |     93.46 |  0.004633 |    0.2708 |    0.8220 |
+#> |.....................|     8.351 |     1.995 |     1.188 |    0.7450 |
+#> |.....................|    0.8643 |     1.209 |     1.109 |     1.140 |
+#> |    F| Forward Diff. |    -56.33 |     2.052 |   -0.3905 |   -0.3944 |
+#> |.....................|    -1.108 |    -29.62 |    -11.80 |     8.124 |
+#> |.....................|     9.000 |    -11.14 |    -8.878 |    -7.912 |
+#> |   11|     467.36507 |    0.9976 |    -1.004 |   -0.9114 |   -0.8953 |
+#> |.....................|   -0.8477 |   -0.8013 |   -0.8475 |   -0.8914 |
+#> |.....................|   -0.8910 |   -0.8487 |   -0.8545 |   -0.8554 |
+#> |    U|     467.36507 |     93.88 |    -5.375 |   -0.9904 |   -0.1959 |
+#> |.....................|     2.123 |     2.002 |     1.190 |    0.7435 |
+#> |.....................|    0.8624 |     1.212 |     1.112 |     1.142 |
+#> |    X|     467.36507 |     93.88 |  0.004631 |    0.2708 |    0.8221 |
+#> |.....................|     8.353 |     2.002 |     1.190 |    0.7435 |
+#> |.....................|    0.8624 |     1.212 |     1.112 |     1.142 |
+#> |    F| Forward Diff. |     25.62 |     2.072 |   -0.2964 |   -0.3658 |
+#> |.....................|   -0.9890 |    -26.78 |    -10.91 |     8.547 |
+#> |.....................|     9.002 |    -11.08 |    -8.799 |    -7.879 |
+#> |   12|     467.13453 |    0.9928 |    -1.004 |   -0.9113 |   -0.8952 |
+#> |.....................|   -0.8474 |   -0.7947 |   -0.8448 |   -0.8935 |
+#> |.....................|   -0.8932 |   -0.8459 |   -0.8523 |   -0.8534 |
+#> |    U|     467.13453 |     93.43 |    -5.376 |   -0.9903 |   -0.1958 |
+#> |.....................|     2.123 |     2.008 |     1.191 |    0.7419 |
+#> |.....................|    0.8604 |     1.215 |     1.114 |     1.145 |
+#> |    X|     467.13453 |     93.43 |  0.004628 |    0.2709 |    0.8222 |
+#> |.....................|     8.355 |     2.008 |     1.191 |    0.7419 |
+#> |.....................|    0.8604 |     1.215 |     1.114 |     1.145 |
+#> |    F| Forward Diff. |    -59.86 |     2.021 |   -0.3893 |   -0.4093 |
+#> |.....................|    -1.140 |    -28.00 |    -11.13 |     7.926 |
+#> |.....................|     9.918 |    -10.90 |    -8.684 |    -7.680 |
+#> |   13|     466.72836 |    0.9971 |    -1.005 |   -0.9112 |   -0.8951 |
+#> |.....................|   -0.8471 |   -0.7882 |   -0.8421 |   -0.8957 |
+#> |.....................|   -0.8959 |   -0.8428 |   -0.8499 |   -0.8513 |
+#> |    U|     466.72836 |     93.84 |    -5.376 |   -0.9902 |   -0.1956 |
+#> |.....................|     2.123 |     2.015 |     1.193 |    0.7403 |
+#> |.....................|    0.8581 |     1.219 |     1.117 |     1.147 |
+#> |    X|     466.72836 |     93.84 |  0.004626 |    0.2709 |    0.8223 |
+#> |.....................|     8.358 |     2.015 |     1.193 |    0.7403 |
+#> |.....................|    0.8581 |     1.219 |     1.117 |     1.147 |
+#> |    F| Forward Diff. |     18.13 |     2.039 |   -0.3145 |   -0.3694 |
+#> |.....................|    -1.015 |    -26.10 |    -10.63 |     8.044 |
+#> |.....................|     8.616 |    -10.80 |    -8.580 |    -7.637 |
+#> |   14|     466.53378 |    0.9925 |    -1.005 |   -0.9111 |   -0.8950 |
+#> |.....................|   -0.8468 |   -0.7815 |   -0.8394 |   -0.8978 |
+#> |.....................|   -0.8981 |   -0.8400 |   -0.8477 |   -0.8494 |
+#> |    U|     466.53378 |     93.40 |    -5.377 |   -0.9901 |   -0.1956 |
+#> |.....................|     2.123 |     2.021 |     1.195 |    0.7387 |
+#> |.....................|    0.8562 |     1.222 |     1.119 |     1.149 |
+#> |    X|     466.53378 |     93.40 |  0.004623 |    0.2709 |    0.8224 |
+#> |.....................|     8.360 |     2.021 |     1.195 |    0.7387 |
+#> |.....................|    0.8562 |     1.222 |     1.119 |     1.149 |
+#> |    F| Forward Diff. |    -63.81 |     1.989 |   -0.4067 |   -0.4178 |
+#> |.....................|    -1.167 |    -26.39 |    -10.45 |     7.924 |
+#> |.....................|     8.221 |    -10.62 |    -8.445 |    -7.432 |
+#> |   15|     466.13347 |    0.9972 |    -1.006 |   -0.9110 |   -0.8949 |
+#> |.....................|   -0.8464 |   -0.7752 |   -0.8368 |   -0.9000 |
+#> |.....................|   -0.9002 |   -0.8369 |   -0.8452 |   -0.8472 |
+#> |    U|     466.13347 |     93.85 |    -5.377 |   -0.9900 |   -0.1954 |
+#> |.....................|     2.124 |     2.027 |     1.196 |    0.7370 |
+#> |.....................|    0.8543 |     1.226 |     1.122 |     1.152 |
+#> |    X|     466.13347 |     93.85 |  0.004620 |    0.2709 |    0.8225 |
+#> |.....................|     8.363 |     2.027 |     1.196 |    0.7370 |
+#> |.....................|    0.8543 |     1.226 |     1.122 |     1.152 |
+#> |    F| Forward Diff. |     18.92 |     2.012 |   -0.3108 |   -0.3757 |
+#> |.....................|    -1.021 |    -25.52 |    -10.81 |     7.279 |
+#> |.....................|     9.661 |    -10.54 |    -8.331 |    -7.395 |
+#> |   16|     465.94504 |    0.9925 |    -1.006 |   -0.9109 |   -0.8948 |
+#> |.....................|   -0.8461 |   -0.7686 |   -0.8339 |   -0.9019 |
+#> |.....................|   -0.9028 |   -0.8341 |   -0.8430 |   -0.8453 |
+#> |    U|     465.94504 |     93.41 |    -5.378 |   -0.9899 |   -0.1953 |
+#> |.....................|     2.124 |     2.034 |     1.198 |    0.7356 |
+#> |.....................|    0.8521 |     1.229 |     1.124 |     1.154 |
+#> |    X|     465.94504 |     93.41 |  0.004618 |    0.2709 |    0.8226 |
+#> |.....................|     8.366 |     2.034 |     1.198 |    0.7356 |
+#> |.....................|    0.8521 |     1.229 |     1.124 |     1.154 |
+#> |    F| Forward Diff. |    -61.65 |     1.961 |   -0.4097 |   -0.4254 |
+#> |.....................|    -1.181 |    -25.22 |    -10.13 |     7.338 |
+#> |.....................|     9.206 |    -10.38 |    -8.223 |    -7.205 |
+#> |   17|     465.56754 |    0.9973 |    -1.007 |   -0.9108 |   -0.8946 |
+#> |.....................|   -0.8457 |   -0.7626 |   -0.8312 |   -0.9037 |
+#> |.....................|   -0.9058 |   -0.8309 |   -0.8405 |   -0.8432 |
+#> |    U|     465.56754 |     93.86 |    -5.378 |   -0.9898 |   -0.1952 |
+#> |.....................|     2.125 |     2.040 |     1.199 |    0.7342 |
+#> |.....................|    0.8494 |     1.233 |     1.127 |     1.156 |
+#> |    X|     465.56754 |     93.86 |  0.004615 |    0.2710 |    0.8227 |
+#> |.....................|     8.369 |     2.040 |     1.199 |    0.7342 |
+#> |.....................|    0.8494 |     1.233 |     1.127 |     1.156 |
+#> |    F| Forward Diff. |     20.78 |     1.982 |   -0.3060 |   -0.3796 |
+#> |.....................|    -1.026 |    -23.61 |    -9.859 |     7.282 |
+#> |.....................|     6.603 |    -10.29 |    -8.096 |    -7.167 |
+#> |   18|     465.36858 |    0.9928 |    -1.008 |   -0.9107 |   -0.8945 |
+#> |.....................|   -0.8454 |   -0.7560 |   -0.8284 |   -0.9059 |
+#> |.....................|   -0.9077 |   -0.8278 |   -0.8381 |   -0.8410 |
+#> |    U|     465.36858 |     93.44 |    -5.379 |   -0.9897 |   -0.1950 |
+#> |.....................|     2.125 |     2.046 |     1.201 |    0.7326 |
+#> |.....................|    0.8477 |     1.237 |     1.130 |     1.159 |
+#> |    X|     465.36858 |     93.44 |  0.004612 |    0.2710 |    0.8228 |
+#> |.....................|     8.372 |     2.046 |     1.201 |    0.7326 |
+#> |.....................|    0.8477 |     1.237 |     1.130 |     1.159 |
+#> |    F| Forward Diff. |    -55.43 |     1.935 |   -0.4028 |   -0.4254 |
+#> |.....................|    -1.182 |    -23.34 |    -9.189 |     7.305 |
+#> |.....................|     7.555 |    -10.07 |    -7.946 |    -6.960 |
+#> |   19|     465.01863 |    0.9972 |    -1.008 |   -0.9105 |   -0.8943 |
+#> |.....................|   -0.8449 |   -0.7499 |   -0.8257 |   -0.9082 |
+#> |.....................|   -0.9092 |   -0.8240 |   -0.8352 |   -0.8386 |
+#> |    U|     465.01863 |     93.84 |    -5.380 |   -0.9895 |   -0.1948 |
+#> |.....................|     2.125 |     2.052 |     1.203 |    0.7308 |
+#> |.....................|    0.8464 |     1.241 |     1.133 |     1.161 |
+#> |    X|     465.01863 |     93.84 |  0.004609 |    0.2710 |    0.8230 |
+#> |.....................|     8.376 |     2.052 |     1.203 |    0.7308 |
+#> |.....................|    0.8464 |     1.241 |     1.133 |     1.161 |
+#> |    F| Forward Diff. |     18.74 |     1.956 |   -0.3105 |   -0.3857 |
+#> |.....................|    -1.041 |    -22.36 |    -9.386 |     7.151 |
+#> |.....................|     7.639 |    -9.969 |    -7.832 |    -6.900 |
+#> |   20|     464.81883 |    0.9930 |    -1.009 |   -0.9104 |   -0.8942 |
+#> |.....................|   -0.8445 |   -0.7435 |   -0.8230 |   -0.9105 |
+#> |.....................|   -0.9115 |   -0.8207 |   -0.8326 |   -0.8363 |
+#> |    U|     464.81883 |     93.45 |    -5.381 |   -0.9894 |   -0.1947 |
+#> |.....................|     2.126 |     2.058 |     1.204 |    0.7291 |
+#> |.....................|    0.8444 |     1.245 |     1.136 |     1.164 |
+#> |    X|     464.81883 |     93.45 |  0.004605 |    0.2710 |    0.8231 |
+#> |.....................|     8.380 |     2.058 |     1.204 |    0.7291 |
+#> |.....................|    0.8444 |     1.245 |     1.136 |     1.164 |
+#> |    F| Forward Diff. |    -51.40 |     1.910 |   -0.3971 |   -0.4173 |
+#> |.....................|    -1.192 |    -21.85 |    -8.569 |     7.088 |
+#> |.....................|     7.257 |    -9.784 |    -7.694 |    -6.698 |
+#> |   21|     464.49434 |    0.9973 |    -1.010 |   -0.9102 |   -0.8940 |
+#> |.....................|   -0.8439 |   -0.7380 |   -0.8206 |   -0.9131 |
+#> |.....................|   -0.9139 |   -0.8168 |   -0.8296 |   -0.8338 |
+#> |    U|     464.49434 |     93.85 |    -5.381 |   -0.9892 |   -0.1945 |
+#> |.....................|     2.126 |     2.064 |     1.206 |    0.7271 |
+#> |.....................|    0.8423 |     1.250 |     1.139 |     1.167 |
+#> |    X|     464.49434 |     93.85 |  0.004602 |    0.2711 |    0.8233 |
+#> |.....................|     8.385 |     2.064 |     1.206 |    0.7271 |
+#> |.....................|    0.8423 |     1.250 |     1.139 |     1.167 |
+#> |    F| Forward Diff. |     20.43 |     1.927 |   -0.3065 |   -0.3887 |
+#> |.....................|    -1.043 |    -20.85 |    -8.676 |     6.819 |
+#> |.....................|     7.291 |    -9.652 |    -7.555 |    -6.636 |
+#> |   22|     464.27900 |    0.9935 |    -1.011 |   -0.9101 |   -0.8938 |
+#> |.....................|   -0.8433 |   -0.7319 |   -0.8180 |   -0.9156 |
+#> |.....................|   -0.9164 |   -0.8129 |   -0.8266 |   -0.8314 |
+#> |    U|       464.279 |     93.50 |    -5.382 |   -0.9891 |   -0.1943 |
+#> |.....................|     2.127 |     2.070 |     1.207 |    0.7252 |
+#> |.....................|    0.8401 |     1.255 |     1.142 |     1.169 |
+#> |    X|       464.279 |     93.50 |  0.004598 |    0.2711 |    0.8234 |
+#> |.....................|     8.389 |     2.070 |     1.207 |    0.7252 |
+#> |.....................|    0.8401 |     1.255 |     1.142 |     1.169 |
+#> |    F| Forward Diff. |    -42.65 |     1.884 |   -0.3905 |   -0.4168 |
+#> |.....................|    -1.174 |    -21.12 |    -8.566 |     6.431 |
+#> |.....................|     8.301 |    -9.439 |    -7.399 |    -6.436 |
+#> |   23|     463.98221 |    0.9971 |    -1.012 |   -0.9099 |   -0.8935 |
+#> |.....................|   -0.8426 |   -0.7266 |   -0.8156 |   -0.9179 |
+#> |.....................|   -0.9200 |   -0.8088 |   -0.8235 |   -0.8288 |
+#> |    U|     463.98221 |     93.84 |    -5.383 |   -0.9889 |   -0.1940 |
+#> |.....................|     2.128 |     2.075 |     1.209 |    0.7235 |
+#> |.....................|    0.8370 |     1.260 |     1.146 |     1.172 |
+#> |    X|     463.98221 |     93.84 |  0.004593 |    0.2711 |    0.8236 |
+#> |.....................|     8.395 |     2.075 |     1.209 |    0.7235 |
+#> |.....................|    0.8370 |     1.260 |     1.146 |     1.172 |
+#> |    F| Forward Diff. |     17.69 |     1.891 |   -0.3039 |   -0.3774 |
+#> |.....................|    -1.038 |    -20.36 |    -8.704 |     6.334 |
+#> |.....................|     6.886 |    -9.291 |    -7.246 |    -6.355 |
+#> |   24|     463.80345 |    0.9930 |    -1.013 |   -0.9097 |   -0.8933 |
+#> |.....................|   -0.8421 |   -0.7205 |   -0.8127 |   -0.9199 |
+#> |.....................|   -0.9227 |   -0.8053 |   -0.8209 |   -0.8265 |
+#> |    U|     463.80345 |     93.45 |    -5.384 |   -0.9887 |   -0.1939 |
+#> |.....................|     2.128 |     2.081 |     1.210 |    0.7220 |
+#> |.....................|    0.8346 |     1.264 |     1.148 |     1.175 |
+#> |    X|     463.80345 |     93.45 |  0.004590 |    0.2712 |    0.8238 |
+#> |.....................|     8.399 |     2.081 |     1.210 |    0.7220 |
+#> |.....................|    0.8346 |     1.264 |     1.148 |     1.175 |
+#> |    F| Forward Diff. |    -49.16 |     1.846 |   -0.3979 |   -0.4233 |
+#> |.....................|    -1.191 |    -20.11 |    -8.128 |     6.150 |
+#> |.....................|     7.842 |    -9.114 |    -7.113 |    -6.163 |
+#> |   25|     463.50095 |    0.9970 |    -1.014 |   -0.9095 |   -0.8930 |
+#> |.....................|   -0.8413 |   -0.7152 |   -0.8100 |   -0.9219 |
+#> |.....................|   -0.9258 |   -0.8011 |   -0.8178 |   -0.8240 |
+#> |    U|     463.50095 |     93.83 |    -5.385 |   -0.9885 |   -0.1936 |
+#> |.....................|     2.129 |     2.086 |     1.212 |    0.7205 |
+#> |.....................|    0.8318 |     1.269 |     1.152 |     1.178 |
+#> |    X|     463.50095 |     93.83 |  0.004585 |    0.2712 |    0.8240 |
+#> |.....................|     8.406 |     2.086 |     1.212 |    0.7205 |
+#> |.....................|    0.8318 |     1.269 |     1.152 |     1.178 |
+#> |    F| Forward Diff. |     15.76 |     1.857 |   -0.2989 |   -0.3817 |
+#> |.....................|    -1.050 |    -19.47 |    -8.354 |     5.597 |
+#> |.....................|     5.177 |    -8.956 |    -6.950 |    -6.091 |
+#> |   26|     463.33971 |    0.9930 |    -1.014 |   -0.9093 |   -0.8928 |
+#> |.....................|   -0.8408 |   -0.7088 |   -0.8070 |   -0.9237 |
+#> |.....................|   -0.9274 |   -0.7974 |   -0.8150 |   -0.8217 |
+#> |    U|     463.33971 |     93.45 |    -5.386 |   -0.9883 |   -0.1934 |
+#> |.....................|     2.129 |     2.092 |     1.214 |    0.7192 |
+#> |.....................|    0.8304 |     1.273 |     1.155 |     1.180 |
+#> |    X|     463.33971 |     93.45 |  0.004581 |    0.2712 |    0.8242 |
+#> |.....................|     8.411 |     2.092 |     1.214 |    0.7192 |
+#> |.....................|    0.8304 |     1.273 |     1.155 |     1.180 |
+#> |    F| Forward Diff. |    -49.38 |     1.817 |   -0.3945 |   -0.4254 |
+#> |.....................|    -1.192 |    -18.49 |    -7.219 |     6.140 |
+#> |.....................|     6.147 |    -8.752 |    -6.775 |    -5.892 |
+#> |   27|     463.06378 |    0.9971 |    -1.016 |   -0.9091 |   -0.8925 |
+#> |.....................|   -0.8398 |   -0.7035 |   -0.8044 |   -0.9255 |
+#> |.....................|   -0.9274 |   -0.7927 |   -0.8116 |   -0.8189 |
+#> |    U|     463.06378 |     93.84 |    -5.387 |   -0.9881 |   -0.1930 |
+#> |.....................|     2.130 |     2.097 |     1.215 |    0.7178 |
+#> |.....................|    0.8305 |     1.279 |     1.159 |     1.184 |
+#> |    X|     463.06378 |     93.84 |  0.004575 |    0.2713 |    0.8245 |
+#> |.....................|     8.419 |     2.097 |     1.215 |    0.7178 |
+#> |.....................|    0.8305 |     1.279 |     1.159 |     1.184 |
+#> |    F| Forward Diff. |     17.15 |     1.839 |   -0.2941 |   -0.3829 |
+#> |.....................|    -1.046 |    -18.21 |    -7.786 |     5.595 |
+#> |.....................|     7.714 |    -8.592 |    -6.652 |    -5.814 |
+#> |   28|     462.87224 |    0.9938 |    -1.017 |   -0.9088 |   -0.8922 |
+#> |.....................|   -0.8390 |   -0.6982 |   -0.8019 |   -0.9277 |
+#> |.....................|   -0.9311 |   -0.7885 |   -0.8085 |   -0.8163 |
+#> |    U|     462.87224 |     93.52 |    -5.388 |   -0.9879 |   -0.1927 |
+#> |.....................|     2.131 |     2.102 |     1.217 |    0.7161 |
+#> |.....................|    0.8272 |     1.284 |     1.162 |     1.186 |
+#> |    X|     462.87224 |     93.52 |  0.004570 |    0.2713 |    0.8247 |
+#> |.....................|     8.425 |     2.102 |     1.217 |    0.7161 |
+#> |.....................|    0.8272 |     1.284 |     1.162 |     1.186 |
+#> |    F| Forward Diff. |    -35.81 |     1.797 |   -0.3699 |   -0.4180 |
+#> |.....................|    -1.164 |    -17.54 |    -6.949 |     5.683 |
+#> |.....................|     5.938 |    -8.368 |    -6.484 |    -5.617 |
+#> |   29|     462.64279 |    0.9976 |    -1.018 |   -0.9085 |   -0.8918 |
+#> |.....................|   -0.8379 |   -0.6938 |   -0.7998 |   -0.9297 |
+#> |.....................|   -0.9347 |   -0.7837 |   -0.8051 |   -0.8136 |
+#> |    U|     462.64279 |     93.88 |    -5.390 |   -0.9876 |   -0.1923 |
+#> |.....................|     2.132 |     2.107 |     1.218 |    0.7146 |
+#> |.....................|    0.8240 |     1.289 |     1.166 |     1.189 |
+#> |    X|     462.64279 |     93.88 |  0.004563 |    0.2714 |    0.8250 |
+#> |.....................|     8.435 |     2.107 |     1.218 |    0.7146 |
+#> |.....................|    0.8240 |     1.289 |     1.166 |     1.189 |
+#> |    F| Forward Diff. |     23.89 |     1.802 |   -0.2695 |   -0.3764 |
+#> |.....................|    -1.014 |    -17.48 |    -7.590 |     5.234 |
+#> |.....................|     7.275 |    -8.199 |    -6.306 |    -5.540 |
+#> |   30|     462.43086 |    0.9946 |    -1.020 |   -0.9083 |   -0.8914 |
+#> |.....................|   -0.8367 |   -0.6890 |   -0.7974 |   -0.9317 |
+#> |.....................|   -0.9381 |   -0.7789 |   -0.8017 |   -0.8108 |
+#> |    U|     462.43086 |     93.61 |    -5.391 |   -0.9873 |   -0.1919 |
+#> |.....................|     2.134 |     2.111 |     1.219 |    0.7131 |
+#> |.....................|    0.8211 |     1.295 |     1.169 |     1.193 |
+#> |    X|     462.43086 |     93.61 |  0.004556 |    0.2715 |    0.8254 |
+#> |.....................|     8.445 |     2.111 |     1.219 |    0.7131 |
+#> |.....................|    0.8211 |     1.295 |     1.169 |     1.193 |
+#> |    F| Forward Diff. |    -22.12 |     1.763 |   -0.3409 |   -0.4033 |
+#> |.....................|    -1.105 |    -16.76 |    -6.743 |     5.132 |
+#> |.....................|     5.573 |    -7.935 |    -6.123 |    -5.337 |
+#> |   31|     462.24769 |    0.9981 |    -1.021 |   -0.9079 |   -0.8909 |
+#> |.....................|   -0.8355 |   -0.6838 |   -0.7950 |   -0.9332 |
+#> |.....................|   -0.9404 |   -0.7741 |   -0.7984 |   -0.8080 |
+#> |    U|     462.24769 |     93.94 |    -5.393 |   -0.9870 |   -0.1915 |
+#> |.....................|     2.135 |     2.117 |     1.221 |    0.7120 |
+#> |.....................|    0.8190 |     1.301 |     1.173 |     1.196 |
+#> |    X|     462.24769 |     93.94 |  0.004549 |    0.2715 |    0.8258 |
+#> |.....................|     8.455 |     2.117 |     1.221 |    0.7120 |
+#> |.....................|    0.8190 |     1.301 |     1.173 |     1.196 |
+#> |    F| Forward Diff. |     32.76 |     1.771 |   -0.2440 |   -0.3645 |
+#> |.....................|   -0.9678 |    -16.08 |    -6.874 |     5.077 |
+#> |.....................|     5.606 |    -7.758 |    -5.959 |    -5.256 |
+#> |   32|     462.04894 |    0.9949 |    -1.023 |   -0.9076 |   -0.8904 |
+#> |.....................|   -0.8341 |   -0.6790 |   -0.7932 |   -0.9353 |
+#> |.....................|   -0.9395 |   -0.7687 |   -0.7947 |   -0.8049 |
+#> |    U|     462.04894 |     93.63 |    -5.395 |   -0.9866 |   -0.1909 |
+#> |.....................|     2.136 |     2.121 |     1.222 |    0.7104 |
+#> |.....................|    0.8198 |     1.307 |     1.177 |     1.199 |
+#> |    X|     462.04894 |     93.63 |  0.004540 |    0.2716 |    0.8262 |
+#> |.....................|     8.467 |     2.121 |     1.222 |    0.7104 |
+#> |.....................|    0.8198 |     1.307 |     1.177 |     1.199 |
+#> |    F| Forward Diff. |    -16.92 |     1.743 |   -0.3189 |   -0.3951 |
+#> |.....................|    -1.072 |    -15.84 |    -6.430 |     4.847 |
+#> |.....................|     5.467 |    -7.483 |    -5.756 |    -5.023 |
+#> |   33|     461.88553 |    0.9980 |    -1.025 |   -0.9073 |   -0.8898 |
+#> |.....................|   -0.8327 |   -0.6736 |   -0.7912 |   -0.9375 |
+#> |.....................|   -0.9397 |   -0.7637 |   -0.7912 |   -0.8019 |
+#> |    U|     461.88553 |     93.92 |    -5.397 |   -0.9863 |   -0.1904 |
+#> |.....................|     2.138 |     2.126 |     1.223 |    0.7088 |
+#> |.....................|    0.8197 |     1.313 |     1.181 |     1.203 |
+#> |    X|     461.88553 |     93.92 |  0.004531 |    0.2716 |    0.8266 |
+#> |.....................|     8.479 |     2.126 |     1.223 |    0.7088 |
+#> |.....................|    0.8197 |     1.313 |     1.181 |     1.203 |
+#> |    F| Forward Diff. |     30.55 |     1.755 |   -0.2327 |   -0.3563 |
+#> |.....................|   -0.9551 |    -15.13 |    -6.434 |     4.973 |
+#> |.....................|     5.515 |    -7.304 |    -5.584 |    -4.904 |
+#> |   34|     461.69674 |    0.9949 |    -1.028 |   -0.9069 |   -0.8892 |
+#> |.....................|   -0.8309 |   -0.6692 |   -0.7896 |   -0.9402 |
+#> |.....................|   -0.9399 |   -0.7583 |   -0.7876 |   -0.7990 |
+#> |    U|     461.69674 |     93.63 |    -5.400 |   -0.9859 |   -0.1897 |
+#> |.....................|     2.139 |     2.131 |     1.224 |    0.7067 |
+#> |.....................|    0.8195 |     1.320 |     1.185 |     1.206 |
+#> |    X|     461.69674 |     93.63 |  0.004519 |    0.2717 |    0.8272 |
+#> |.....................|     8.494 |     2.131 |     1.224 |    0.7067 |
+#> |.....................|    0.8195 |     1.320 |     1.185 |     1.206 |
+#> |    F| Forward Diff. |    -16.57 |     1.720 |   -0.3086 |   -0.3856 |
+#> |.....................|    -1.039 |    -14.73 |    -5.908 |     4.823 |
+#> |.....................|     5.359 |    -7.008 |    -5.393 |    -4.695 |
+#> |   35|     461.54208 |    0.9978 |    -1.031 |   -0.9065 |   -0.8885 |
+#> |.....................|   -0.8293 |   -0.6648 |   -0.7883 |   -0.9440 |
+#> |.....................|   -0.9414 |   -0.7533 |   -0.7842 |   -0.7963 |
+#> |    U|     461.54208 |     93.91 |    -5.402 |   -0.9855 |   -0.1891 |
+#> |.....................|     2.141 |     2.135 |     1.225 |    0.7038 |
+#> |.....................|    0.8182 |     1.325 |     1.189 |     1.209 |
+#> |    X|     461.54208 |     93.91 |  0.004507 |    0.2718 |    0.8277 |
+#> |.....................|     8.508 |     2.135 |     1.225 |    0.7038 |
+#> |.....................|    0.8182 |     1.325 |     1.189 |     1.209 |
+#> |    F| Forward Diff. |     27.49 |     1.722 |   -0.2172 |   -0.3438 |
+#> |.....................|   -0.9069 |    -13.76 |    -5.979 |     4.702 |
+#> |.....................|     5.353 |    -6.828 |    -5.231 |    -4.587 |
+#> |   36|     461.38014 |    0.9949 |    -1.034 |   -0.9061 |   -0.8878 |
+#> |.....................|   -0.8274 |   -0.6624 |   -0.7872 |   -0.9482 |
+#> |.....................|   -0.9437 |   -0.7482 |   -0.7807 |   -0.7935 |
+#> |    U|     461.38014 |     93.63 |    -5.405 |   -0.9851 |   -0.1883 |
+#> |.....................|     2.143 |     2.137 |     1.225 |    0.7007 |
+#> |.....................|    0.8162 |     1.332 |     1.192 |     1.212 |
+#> |    X|     461.38014 |     93.63 |  0.004492 |    0.2719 |    0.8283 |
+#> |.....................|     8.524 |     2.137 |     1.225 |    0.7007 |
+#> |.....................|    0.8162 |     1.332 |     1.192 |     1.212 |
+#> |    F| Forward Diff. |    -16.54 |     1.681 |   -0.2967 |   -0.3702 |
+#> |.....................|    -1.003 |    -14.15 |    -5.693 |     4.358 |
+#> |.....................|     5.078 |    -6.560 |    -5.051 |    -4.397 |
+#> |   37|     461.22820 |    0.9976 |    -1.038 |   -0.9057 |   -0.8870 |
+#> |.....................|   -0.8255 |   -0.6585 |   -0.7854 |   -0.9513 |
+#> |.....................|   -0.9460 |   -0.7433 |   -0.7774 |   -0.7908 |
+#> |    U|      461.2282 |     93.88 |    -5.409 |   -0.9847 |   -0.1876 |
+#> |.....................|     2.145 |     2.141 |     1.226 |    0.6983 |
+#> |.....................|    0.8141 |     1.337 |     1.196 |     1.215 |
+#> |    X|      461.2282 |     93.88 |  0.004476 |    0.2720 |    0.8290 |
+#> |.....................|     8.540 |     2.141 |     1.226 |    0.6983 |
+#> |.....................|    0.8141 |     1.337 |     1.196 |     1.215 |
+#> |    F| Forward Diff. |     22.68 |     1.675 |   -0.2117 |   -0.3293 |
+#> |.....................|   -0.8651 |    -13.27 |    -5.458 |     4.237 |
+#> |.....................|     3.708 |    -6.326 |    -4.874 |    -4.289 |
+#> |   38|     461.10880 |    0.9948 |    -1.041 |   -0.9053 |   -0.8864 |
+#> |.....................|   -0.8238 |   -0.6532 |   -0.7845 |   -0.9533 |
+#> |.....................|   -0.9419 |   -0.7394 |   -0.7747 |   -0.7885 |
+#> |    U|      461.1088 |     93.62 |    -5.412 |   -0.9844 |   -0.1869 |
+#> |.....................|     2.146 |     2.146 |     1.227 |    0.6968 |
+#> |.....................|    0.8177 |     1.342 |     1.199 |     1.218 |
+#> |    X|      461.1088 |     93.62 |  0.004461 |    0.2720 |    0.8295 |
+#> |.....................|     8.555 |     2.146 |     1.227 |    0.6968 |
+#> |.....................|    0.8177 |     1.342 |     1.199 |     1.218 |
+#> |    F| Forward Diff. |    -17.23 |     1.655 |   -0.2888 |   -0.3567 |
+#> |.....................|   -0.9524 |    -13.71 |    -5.652 |     3.877 |
+#> |.....................|     5.125 |    -6.149 |    -4.743 |    -4.110 |
+#> |   39|     460.99174 |    0.9974 |    -1.045 |   -0.9049 |   -0.8856 |
+#> |.....................|   -0.8221 |   -0.6468 |   -0.7824 |   -0.9536 |
+#> |.....................|   -0.9388 |   -0.7360 |   -0.7723 |   -0.7867 |
+#> |    U|     460.99174 |     93.87 |    -5.416 |   -0.9840 |   -0.1862 |
+#> |.....................|     2.148 |     2.153 |     1.228 |    0.6966 |
+#> |.....................|    0.8204 |     1.346 |     1.202 |     1.220 |
+#> |    X|     460.99174 |     93.87 |  0.004444 |    0.2721 |    0.8301 |
+#> |.....................|     8.569 |     2.153 |     1.228 |    0.6966 |
+#> |.....................|    0.8204 |     1.346 |     1.202 |     1.220 |
+#> |    F| Forward Diff. |     21.44 |     1.663 |   -0.2166 |   -0.3206 |
+#> |.....................|   -0.8444 |    -13.00 |    -5.647 |     3.881 |
+#> |.....................|     5.370 |    -6.036 |    -4.631 |    -4.039 |
+#> |   40|     460.85317 |    0.9948 |    -1.049 |   -0.9044 |   -0.8849 |
+#> |.....................|   -0.8203 |   -0.6417 |   -0.7791 |   -0.9516 |
+#> |.....................|   -0.9438 |   -0.7341 |   -0.7712 |   -0.7862 |
+#> |    U|     460.85317 |     93.62 |    -5.420 |   -0.9835 |   -0.1854 |
+#> |.....................|     2.150 |     2.158 |     1.230 |    0.6981 |
+#> |.....................|    0.8161 |     1.348 |     1.203 |     1.220 |
+#> |    X|     460.85317 |     93.62 |  0.004425 |    0.2722 |    0.8308 |
+#> |.....................|     8.585 |     2.158 |     1.230 |    0.6981 |
+#> |.....................|    0.8161 |     1.348 |     1.203 |     1.220 |
+#> |    F| Forward Diff. |    -17.08 |     1.613 |   -0.2650 |   -0.3380 |
+#> |.....................|   -0.8994 |    -12.83 |    -5.261 |     3.879 |
+#> |.....................|     3.650 |    -5.911 |    -4.518 |    -3.985 |
+#> |   41|     460.73362 |    0.9974 |    -1.054 |   -0.9040 |   -0.8841 |
+#> |.....................|   -0.8184 |   -0.6359 |   -0.7754 |   -0.9517 |
+#> |.....................|   -0.9423 |   -0.7308 |   -0.7693 |   -0.7845 |
+#> |    U|     460.73362 |     93.86 |    -5.425 |   -0.9831 |   -0.1846 |
+#> |.....................|     2.152 |     2.163 |     1.232 |    0.6980 |
+#> |.....................|    0.8173 |     1.352 |     1.205 |     1.222 |
+#> |    X|     460.73362 |     93.86 |  0.004404 |    0.2723 |    0.8314 |
+#> |.....................|     8.601 |     2.163 |     1.232 |    0.6980 |
+#> |.....................|    0.8173 |     1.352 |     1.205 |     1.222 |
+#> |    F| Forward Diff. |     20.68 |     1.612 |   -0.1811 |   -0.2966 |
+#> |.....................|   -0.7710 |    -11.91 |    -4.976 |     4.011 |
+#> |.....................|     3.788 |    -5.788 |    -4.468 |    -3.936 |
+#> |   42|     460.64877 |    0.9948 |    -1.058 |   -0.9038 |   -0.8835 |
+#> |.....................|   -0.8171 |   -0.6318 |   -0.7737 |   -0.9543 |
+#> |.....................|   -0.9372 |   -0.7272 |   -0.7669 |   -0.7822 |
+#> |    U|     460.64877 |     93.62 |    -5.429 |   -0.9829 |   -0.1841 |
+#> |.....................|     2.153 |     2.167 |     1.233 |    0.6961 |
+#> |.....................|    0.8219 |     1.357 |     1.208 |     1.225 |
+#> |    X|     460.64877 |     93.62 |  0.004387 |    0.2723 |    0.8319 |
+#> |.....................|     8.612 |     2.167 |     1.233 |    0.6961 |
+#> |.....................|    0.8219 |     1.357 |     1.208 |     1.225 |
+#> |    F| Forward Diff. |    -16.17 |     1.594 |   -0.2646 |   -0.3254 |
+#> |.....................|   -0.8335 |    -11.77 |    -4.666 |     3.810 |
+#> |.....................|     5.289 |    -5.625 |    -4.348 |    -3.754 |
+#> |   43|     460.54180 |    0.9972 |    -1.063 |   -0.9035 |   -0.8829 |
+#> |.....................|   -0.8158 |   -0.6297 |   -0.7745 |   -0.9584 |
+#> |.....................|   -0.9393 |   -0.7227 |   -0.7634 |   -0.7794 |
+#> |    U|      460.5418 |     93.85 |    -5.434 |   -0.9826 |   -0.1834 |
+#> |.....................|     2.154 |     2.169 |     1.233 |    0.6929 |
+#> |.....................|    0.8200 |     1.362 |     1.211 |     1.228 |
+#> |    X|      460.5418 |     93.85 |  0.004366 |    0.2724 |    0.8324 |
+#> |.....................|     8.623 |     2.169 |     1.233 |    0.6929 |
+#> |.....................|    0.8200 |     1.362 |     1.211 |     1.228 |
+#> |    F| Forward Diff. |     18.48 |     1.582 |   -0.1851 |   -0.2851 |
+#> |.....................|   -0.7462 |    -11.38 |    -4.808 |     3.651 |
+#> |.....................|     5.261 |    -5.402 |    -4.159 |    -3.623 |
+#> |   44|     460.43711 |    0.9948 |    -1.067 |   -0.9032 |   -0.8823 |
+#> |.....................|   -0.8147 |   -0.6284 |   -0.7753 |   -0.9609 |
+#> |.....................|   -0.9464 |   -0.7199 |   -0.7613 |   -0.7778 |
+#> |    U|     460.43711 |     93.63 |    -5.438 |   -0.9823 |   -0.1829 |
+#> |.....................|     2.156 |     2.171 |     1.232 |    0.6911 |
+#> |.....................|    0.8138 |     1.365 |     1.214 |     1.230 |
+#> |    X|     460.43711 |     93.63 |  0.004347 |    0.2724 |    0.8329 |
+#> |.....................|     8.632 |     2.171 |     1.232 |    0.6911 |
+#> |.....................|    0.8138 |     1.365 |     1.214 |     1.230 |
+#> |   45|     460.35910 |    0.9948 |    -1.072 |   -0.9029 |   -0.8817 |
+#> |.....................|   -0.8135 |   -0.6285 |   -0.7770 |   -0.9633 |
+#> |.....................|   -0.9542 |   -0.7172 |   -0.7594 |   -0.7765 |
+#> |    U|      460.3591 |     93.63 |    -5.443 |   -0.9820 |   -0.1822 |
+#> |.....................|     2.157 |     2.170 |     1.231 |    0.6893 |
+#> |.....................|    0.8069 |     1.368 |     1.216 |     1.231 |
+#> |    X|      460.3591 |     93.63 |  0.004325 |    0.2725 |    0.8334 |
+#> |.....................|     8.643 |     2.170 |     1.231 |    0.6893 |
+#> |.....................|    0.8069 |     1.368 |     1.216 |     1.231 |
+#> |   46|     460.06586 |    0.9948 |    -1.095 |   -0.9016 |   -0.8789 |
+#> |.....................|   -0.8080 |   -0.6294 |   -0.7850 |   -0.9744 |
+#> |.....................|   -0.9902 |   -0.7052 |   -0.7507 |   -0.7704 |
+#> |    U|     460.06586 |     93.63 |    -5.466 |   -0.9807 |   -0.1794 |
+#> |.....................|     2.162 |     2.170 |     1.227 |    0.6809 |
+#> |.....................|    0.7753 |     1.383 |     1.225 |     1.238 |
+#> |    X|     460.06586 |     93.63 |  0.004227 |    0.2728 |    0.8358 |
+#> |.....................|     8.691 |     2.170 |     1.227 |    0.6809 |
+#> |.....................|    0.7753 |     1.383 |     1.225 |     1.238 |
+#> |   47|     459.86897 |    0.9949 |    -1.169 |   -0.8972 |   -0.8697 |
+#> |.....................|   -0.7899 |   -0.6321 |   -0.8109 |    -1.010 |
+#> |.....................|    -1.107 |   -0.6662 |   -0.7224 |   -0.7508 |
+#> |    U|     459.86897 |     93.63 |    -5.541 |   -0.9763 |   -0.1702 |
+#> |.....................|     2.180 |     2.167 |     1.211 |    0.6537 |
+#> |.....................|    0.6731 |     1.429 |     1.256 |     1.260 |
+#> |    X|     459.86897 |     93.63 |  0.003924 |    0.2736 |    0.8435 |
+#> |.....................|     8.849 |     2.167 |     1.211 |    0.6537 |
+#> |.....................|    0.6731 |     1.429 |     1.256 |     1.260 |
+#> |    F| Forward Diff. |    -18.09 |    0.8663 |    0.2544 |  0.003114 |
+#> |.....................|   -0.1212 |    -11.64 |    -7.047 |    0.1395 |
+#> |.....................|    -6.727 |    -2.881 |    -1.866 |    -2.263 |
+#> |   48|     458.58262 |    0.9946 |    -1.323 |   -0.9067 |   -0.8597 |
+#> |.....................|   -0.7710 |   -0.5295 |   -0.7001 |   -0.9650 |
+#> |.....................|    -1.113 |   -0.6398 |   -0.7228 |   -0.7390 |
+#> |    U|     458.58262 |     93.60 |    -5.695 |   -0.9858 |   -0.1602 |
+#> |.....................|     2.199 |     2.267 |     1.277 |    0.6880 |
+#> |.....................|    0.6674 |     1.460 |     1.256 |     1.273 |
+#> |    X|     458.58262 |     93.60 |  0.003363 |    0.2717 |    0.8520 |
+#> |.....................|     9.019 |     2.267 |     1.277 |    0.6880 |
+#> |.....................|    0.6674 |     1.460 |     1.256 |     1.273 |
+#> |    F| Forward Diff. |    -24.91 |    0.5848 |  -0.03458 |    0.2475 |
+#> |.....................|    0.3762 |    -4.573 |  -0.04388 |     1.648 |
+#> |.....................|    -5.878 |    -2.073 |    -1.935 |    -2.146 |
+#> |   49|     460.44377 |    0.9922 |    -1.558 |   -0.9059 |   -0.8818 |
+#> |.....................|   -0.8081 |   -0.3861 |   -0.8607 |    -1.070 |
+#> |.....................|   -0.9432 |   -0.5131 |   -0.5915 |   -0.5851 |
+#> |    U|     460.44377 |     93.38 |    -5.929 |   -0.9849 |   -0.1824 |
+#> |.....................|     2.162 |     2.407 |     1.182 |    0.6086 |
+#> |.....................|    0.8166 |     1.611 |     1.400 |     1.447 |
+#> |    X|     460.44377 |     93.38 |  0.002660 |    0.2719 |    0.8333 |
+#> |.....................|     8.690 |     2.407 |     1.182 |    0.6086 |
+#> |.....................|    0.8166 |     1.611 |     1.400 |     1.447 |
+#> |   50|     458.18867 |    0.9958 |    -1.393 |   -0.9065 |   -0.8663 |
+#> |.....................|   -0.7821 |   -0.4865 |   -0.7479 |   -0.9965 |
+#> |.....................|    -1.062 |   -0.6019 |   -0.6835 |   -0.6930 |
+#> |    U|     458.18867 |     93.71 |    -5.765 |   -0.9855 |   -0.1668 |
+#> |.....................|     2.188 |     2.309 |     1.248 |    0.6642 |
+#> |.....................|    0.7122 |     1.506 |     1.299 |     1.325 |
+#> |    X|     458.18867 |     93.71 |  0.003136 |    0.2718 |    0.8463 |
+#> |.....................|     8.919 |     2.309 |     1.248 |    0.6642 |
+#> |.....................|    0.7122 |     1.506 |     1.299 |     1.325 |
+#> |    F| Forward Diff. |    -3.049 |    0.4396 |   -0.1330 |   0.02964 |
+#> |.....................|  -0.08039 |    -2.599 |    -3.012 |   -0.1957 |
+#> |.....................|    -2.463 |   -0.6721 |    0.3494 |    0.7476 |
+#> |   51|     458.45407 |    0.9980 |    -1.449 |   -0.8787 |   -0.8738 |
+#> |.....................|   -0.7836 |   -0.4935 |   -0.7244 |    -1.061 |
+#> |.....................|    -1.034 |   -0.5419 |   -0.6952 |   -0.7610 |
+#> |    U|     458.45407 |     93.92 |    -5.821 |   -0.9579 |   -0.1743 |
+#> |.....................|     2.187 |     2.302 |     1.262 |    0.6155 |
+#> |.....................|    0.7366 |     1.577 |     1.286 |     1.249 |
+#> |    X|     458.45407 |     93.92 |  0.002965 |    0.2773 |    0.8400 |
+#> |.....................|     8.906 |     2.302 |     1.262 |    0.6155 |
+#> |.....................|    0.7366 |     1.577 |     1.286 |     1.249 |
+#> |   52|     458.19883 |    0.9985 |    -1.406 |   -0.9001 |   -0.8680 |
+#> |.....................|   -0.7823 |   -0.4861 |   -0.7404 |    -1.011 |
+#> |.....................|    -1.054 |   -0.5879 |   -0.6864 |   -0.7089 |
+#> |    U|     458.19883 |     93.97 |    -5.778 |   -0.9792 |   -0.1685 |
+#> |.....................|     2.188 |     2.309 |     1.253 |    0.6534 |
+#> |.....................|    0.7193 |     1.522 |     1.296 |     1.307 |
+#> |    X|     458.19883 |     93.97 |  0.003096 |    0.2731 |    0.8449 |
+#> |.....................|     8.917 |     2.309 |     1.253 |    0.6534 |
+#> |.....................|    0.7193 |     1.522 |     1.296 |     1.307 |
+#> |   53|     458.20478 |    0.9986 |    -1.399 |   -0.9039 |   -0.8670 |
+#> |.....................|   -0.7821 |   -0.4848 |   -0.7433 |    -1.002 |
+#> |.....................|    -1.058 |   -0.5961 |   -0.6848 |   -0.6996 |
+#> |    U|     458.20478 |     93.98 |    -5.770 |   -0.9830 |   -0.1675 |
+#> |.....................|     2.188 |     2.311 |     1.251 |    0.6601 |
+#> |.....................|    0.7162 |     1.512 |     1.297 |     1.318 |
+#> |    X|     458.20478 |     93.98 |  0.003120 |    0.2723 |    0.8458 |
+#> |.....................|     8.919 |     2.311 |     1.251 |    0.6601 |
+#> |.....................|    0.7162 |     1.512 |     1.297 |     1.318 |
+#> |   54|     458.21371 |    0.9986 |    -1.394 |   -0.9063 |   -0.8663 |
+#> |.....................|   -0.7820 |   -0.4840 |   -0.7451 |   -0.9963 |
+#> |.....................|    -1.060 |   -0.6013 |   -0.6838 |   -0.6937 |
+#> |    U|     458.21371 |     93.98 |    -5.765 |   -0.9854 |   -0.1669 |
+#> |.....................|     2.188 |     2.311 |     1.250 |    0.6644 |
+#> |.....................|    0.7142 |     1.506 |     1.298 |     1.325 |
+#> |    X|     458.21371 |     93.98 |  0.003135 |    0.2718 |    0.8463 |
+#> |.....................|     8.920 |     2.311 |     1.250 |    0.6644 |
+#> |.....................|    0.7142 |     1.506 |     1.298 |     1.325 |
+#> |   55|     458.18572 |    0.9965 |    -1.393 |   -0.9064 |   -0.8663 |
+#> |.....................|   -0.7820 |   -0.4858 |   -0.7472 |   -0.9964 |
+#> |.....................|    -1.062 |   -0.6017 |   -0.6836 |   -0.6932 |
+#> |    U|     458.18572 |     93.79 |    -5.765 |   -0.9855 |   -0.1668 |
+#> |.....................|     2.188 |     2.310 |     1.249 |    0.6643 |
+#> |.....................|    0.7128 |     1.506 |     1.299 |     1.325 |
+#> |    X|     458.18572 |     93.79 |  0.003136 |    0.2718 |    0.8463 |
+#> |.....................|     8.919 |     2.310 |     1.249 |    0.6643 |
+#> |.....................|    0.7128 |     1.506 |     1.299 |     1.325 |
+#> |    F| Forward Diff. |     5.905 |    0.4355 |   -0.1157 |   0.02634 |
+#> |.....................|  -0.05151 |    -1.735 |    -2.785 |  -0.07657 |
+#> |.....................|    -2.587 |   -0.1320 |   0.06282 |    0.8041 |
+#> |   56|     458.18221 |    0.9957 |    -1.394 |   -0.9063 |   -0.8663 |
+#> |.....................|   -0.7820 |   -0.4856 |   -0.7465 |   -0.9968 |
+#> |.....................|    -1.061 |   -0.6016 |   -0.6835 |   -0.6937 |
+#> |    U|     458.18221 |     93.70 |    -5.765 |   -0.9853 |   -0.1669 |
+#> |.....................|     2.188 |     2.310 |     1.249 |    0.6640 |
+#> |.....................|    0.7132 |     1.506 |     1.299 |     1.325 |
+#> |    X|     458.18221 |     93.70 |  0.003135 |    0.2718 |    0.8463 |
+#> |.....................|     8.920 |     2.310 |     1.249 |    0.6640 |
+#> |.....................|    0.7132 |     1.506 |     1.299 |     1.325 |
+#> |    F| Forward Diff. |    -4.339 |    0.4378 |   -0.1282 |   0.03581 |
+#> |.....................|  -0.09329 |    -1.978 |    -2.551 |  -0.01933 |
+#> |.....................|    -3.951 |   -0.1424 |   0.01723 |    0.8408 |
+#> |   57|     458.17882 |    0.9963 |    -1.394 |   -0.9061 |   -0.8663 |
+#> |.....................|   -0.7819 |   -0.4855 |   -0.7459 |   -0.9972 |
+#> |.....................|    -1.060 |   -0.6016 |   -0.6832 |   -0.6941 |
+#> |    U|     458.17882 |     93.76 |    -5.766 |   -0.9852 |   -0.1669 |
+#> |.....................|     2.188 |     2.310 |     1.250 |    0.6637 |
+#> |.....................|    0.7139 |     1.506 |     1.299 |     1.324 |
+#> |    X|     458.17882 |     93.76 |  0.003134 |    0.2719 |    0.8463 |
+#> |.....................|     8.920 |     2.310 |     1.250 |    0.6637 |
+#> |.....................|    0.7139 |     1.506 |     1.299 |     1.324 |
+#> |    F| Forward Diff. |     2.737 |    0.4289 |   -0.1193 |   0.04099 |
+#> |.....................|  -0.07175 |    -2.104 |    -2.655 |   -0.1084 |
+#> |.....................|    -2.489 |  -0.08715 |    0.1037 |    0.7775 |
+#> |   58|     458.17628 |    0.9955 |    -1.394 |   -0.9061 |   -0.8663 |
+#> |.....................|   -0.7819 |   -0.4849 |   -0.7451 |   -0.9972 |
+#> |.....................|    -1.060 |   -0.6016 |   -0.6832 |   -0.6943 |
+#> |    U|     458.17628 |     93.69 |    -5.766 |   -0.9851 |   -0.1669 |
+#> |.....................|     2.188 |     2.311 |     1.250 |    0.6637 |
+#> |.....................|    0.7145 |     1.506 |     1.299 |     1.324 |
+#> |    X|     458.17628 |     93.69 |  0.003133 |    0.2719 |    0.8463 |
+#> |.....................|     8.920 |     2.311 |     1.250 |    0.6637 |
+#> |.....................|    0.7145 |     1.506 |     1.299 |     1.324 |
+#> |    F| Forward Diff. |    -5.829 |    0.4364 |   -0.1238 |   0.03009 |
+#> |.....................|  -0.09450 |    -1.871 |    -2.366 |   0.01771 |
+#> |.....................|    -2.486 |  -0.08743 |   0.03350 |    0.7982 |
+#> |   59|     458.17323 |    0.9963 |    -1.395 |   -0.9059 |   -0.8664 |
+#> |.....................|   -0.7819 |   -0.4846 |   -0.7446 |   -0.9977 |
+#> |.....................|    -1.059 |   -0.6018 |   -0.6829 |   -0.6949 |
+#> |    U|     458.17323 |     93.77 |    -5.766 |   -0.9850 |   -0.1669 |
+#> |.....................|     2.188 |     2.311 |     1.250 |    0.6633 |
+#> |.....................|    0.7149 |     1.506 |     1.299 |     1.323 |
+#> |    X|     458.17323 |     93.77 |  0.003132 |    0.2719 |    0.8463 |
+#> |.....................|     8.921 |     2.311 |     1.250 |    0.6633 |
+#> |.....................|    0.7149 |     1.506 |     1.299 |     1.323 |
+#> |    F| Forward Diff. |     3.135 |    0.4259 |   -0.1111 |   0.03860 |
+#> |.....................|  -0.07150 |    -1.713 |    -2.294 |   -0.1635 |
+#> |.....................|    -3.755 |   -0.1071 |    0.1242 |    0.7274 |
+#> |   60|     458.17055 |    0.9957 |    -1.395 |   -0.9058 |   -0.8664 |
+#> |.....................|   -0.7818 |   -0.4843 |   -0.7440 |   -0.9980 |
+#> |.....................|    -1.058 |   -0.6018 |   -0.6828 |   -0.6953 |
+#> |    U|     458.17055 |     93.70 |    -5.766 |   -0.9848 |   -0.1669 |
+#> |.....................|     2.188 |     2.311 |     1.251 |    0.6631 |
+#> |.....................|    0.7157 |     1.506 |     1.300 |     1.323 |
+#> |    X|     458.17055 |     93.70 |  0.003131 |    0.2719 |    0.8463 |
+#> |.....................|     8.921 |     2.311 |     1.251 |    0.6631 |
+#> |.....................|    0.7157 |     1.506 |     1.300 |     1.323 |
+#> |    F| Forward Diff. |    -3.767 |    0.4346 |   -0.1027 |   0.03296 |
+#> |.....................|  -0.07232 |    -2.503 |    -3.089 |   -0.1630 |
+#> |.....................|    -2.382 |  -0.08570 |    0.1151 |    0.7161 |
+#> |   61|     458.16819 |    0.9965 |    -1.395 |   -0.9058 |   -0.8664 |
+#> |.....................|   -0.7818 |   -0.4837 |   -0.7432 |   -0.9981 |
+#> |.....................|    -1.058 |   -0.6018 |   -0.6828 |   -0.6955 |
+#> |    U|     458.16819 |     93.79 |    -5.767 |   -0.9848 |   -0.1669 |
+#> |.....................|     2.188 |     2.312 |     1.251 |    0.6630 |
+#> |.....................|    0.7162 |     1.506 |     1.300 |     1.322 |
+#> |    X|     458.16819 |     93.79 |  0.003130 |    0.2719 |    0.8462 |
+#> |.....................|     8.921 |     2.312 |     1.251 |    0.6630 |
+#> |.....................|    0.7162 |     1.506 |     1.300 |     1.322 |
+#> |    F| Forward Diff. |     6.568 |    0.4333 |  -0.07429 |   0.03599 |
+#> |.....................|  -0.03802 |    -2.553 |    -3.191 |   -0.5393 |
+#> |.....................|   -0.9714 |   -0.8035 |    0.1031 |    0.6902 |
+#> |   62|     458.16513 |    0.9957 |    -1.396 |   -0.9056 |   -0.8666 |
+#> |.....................|   -0.7821 |   -0.4835 |   -0.7425 |   -0.9983 |
+#> |.....................|    -1.057 |   -0.6019 |   -0.6824 |   -0.6959 |
+#> |    U|     458.16513 |     93.70 |    -5.767 |   -0.9847 |   -0.1672 |
+#> |.....................|     2.188 |     2.312 |     1.252 |    0.6629 |
+#> |.....................|    0.7164 |     1.506 |     1.300 |     1.322 |
+#> |    X|     458.16513 |     93.70 |  0.003129 |    0.2720 |    0.8461 |
+#> |.....................|     8.919 |     2.312 |     1.252 |    0.6629 |
+#> |.....................|    0.7164 |     1.506 |     1.300 |     1.322 |
+#> |    F| Forward Diff. |    -3.933 |    0.4306 |  -0.09800 |   0.02413 |
+#> |.....................|  -0.09225 |    -1.469 |    -2.000 |  -0.05194 |
+#> |.....................|    -3.675 |  -0.07209 |   0.09082 |    0.7196 |
+#> |   63|     458.16261 |    0.9962 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4834 |   -0.7420 |   -0.9986 |
+#> |.....................|    -1.057 |   -0.6017 |   -0.6820 |   -0.6964 |
+#> |    U|     458.16261 |     93.76 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.312 |     1.252 |    0.6626 |
+#> |.....................|    0.7170 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16261 |     93.76 |  0.003127 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.312 |     1.252 |    0.6626 |
+#> |.....................|    0.7170 |     1.506 |     1.300 |     1.321 |
+#> |    F| Forward Diff. |     2.233 |    0.4197 |  -0.09277 |   0.03004 |
+#> |.....................|  -0.08165 |    -1.772 |    -2.245 |  -0.08206 |
+#> |.....................|    -2.339 |   -0.1510 |   0.07888 |    0.6887 |
+#> |   64|     458.16062 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16062 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16062 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    M|   Mixed Diff. |    -6.515 |    0.4169 |   -0.1028 |-1.670e+05 |
+#> |.....................|   -0.1097 |    -2.956 |    -2.997 |   -0.5657 |
+#> |.....................|    -4.153 |   -0.6659 |   -0.7853 |    0.1256 |
+#> |   65|     458.16519 |    0.9948 |    -1.397 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4822 |   -0.7405 |   -0.9986 |
+#> |.....................|    -1.055 |   -0.6016 |   -0.6821 |   -0.6969 |
+#> |    U|     458.16519 |     93.62 |    -5.768 |   -0.9844 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.253 |    0.6627 |
+#> |.....................|    0.7183 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16519 |     93.62 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.253 |    0.6627 |
+#> |.....................|    0.7183 |     1.506 |     1.300 |     1.321 |
+#> |   66|     458.16209 |    0.9951 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4825 |   -0.7409 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6968 |
+#> |    U|     458.16209 |     93.65 |    -5.768 |   -0.9844 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.253 |    0.6626 |
+#> |.....................|    0.7180 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16209 |     93.65 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.253 |    0.6626 |
+#> |.....................|    0.7180 |     1.506 |     1.300 |     1.321 |
+#> |   67|     458.16115 |    0.9953 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4827 |   -0.7410 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16115 |     93.67 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.253 |    0.6626 |
+#> |.....................|    0.7178 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16115 |     93.67 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.253 |    0.6626 |
+#> |.....................|    0.7178 |     1.506 |     1.300 |     1.321 |
+#> |   68|     458.16084 |    0.9954 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4827 |   -0.7411 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16084 |     93.68 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7177 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16084 |     93.68 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7177 |     1.506 |     1.300 |     1.321 |
+#> |   69|     458.16072 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16072 |     93.68 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7177 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16072 |     93.68 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7177 |     1.506 |     1.300 |     1.321 |
+#> |   70|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7177 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7177 |     1.506 |     1.300 |     1.321 |
+#> |   71|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   72|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   73|     458.16072 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16072 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16072 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   74|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   75|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   76|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   77|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   78|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   79|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   80|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   81|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   82|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   83|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   84|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   85|     458.16068 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16068 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16068 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   86|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   87|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   88|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   89|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   90|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   91|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |   92|     458.16067 |    0.9955 |    -1.396 |   -0.9054 |   -0.8667 |
+#> |.....................|   -0.7822 |   -0.4828 |   -0.7412 |   -0.9986 |
+#> |.....................|    -1.056 |   -0.6017 |   -0.6821 |   -0.6967 |
+#> |    U|     458.16067 |     93.69 |    -5.768 |   -0.9845 |   -0.1673 |
+#> |.....................|     2.188 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> |    X|     458.16067 |     93.69 |  0.003126 |    0.2720 |    0.8460 |
+#> |.....................|     8.918 |     2.313 |     1.252 |    0.6626 |
+#> |.....................|    0.7176 |     1.506 |     1.300 |     1.321 |
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1:    93.5791   -5.6199   -2.0817   -3.9984   -1.2037    0.1481    4.5359    1.6042    1.1515    2.4545    0.4989    0.5230   19.1822   10.0277
+#> 2:    93.5157   -5.6781   -1.9742   -4.0546   -1.1333    0.1109    4.4678    1.5240    1.0939    2.3318    0.4740    0.6338   12.8885    7.4711
+#> 3:    93.2898   -5.7047   -1.8559   -4.1328   -1.0939    0.0438    5.0096    1.4478    1.1939    2.2152    0.4503    0.6021   11.0381    5.1444
+#> 4:    93.0426   -5.7814   -1.8501   -4.1839   -1.0410    0.0594    6.3802    1.4778    1.2229    2.1556    0.4278    0.5972   10.2381    4.4049
+#> 5:    92.9134   -5.8482   -1.8162   -4.2071   -1.0582    0.0732    6.9858    1.8242    1.1718    2.3151    0.4064    0.5822    9.8642    4.4088
+#> 6:    92.7655   -5.8047   -1.8535   -4.2041   -0.9870    0.0611    6.6365    1.7739    1.1619    2.2910    0.3861    0.5531    8.6374    4.0594
+#> 7:    93.0259   -5.8252   -1.9173   -4.2093   -0.9549    0.0995    6.3047    2.2731    1.1038    2.1765    0.3668    0.5255    9.0819    3.0678
+#> 8:    93.1406   -5.7510   -1.9019   -4.2213   -0.9559    0.1508    5.9894    2.5908    1.0919    2.0948    0.3484    0.4992    8.3332    2.3703
+#> 9:    93.3980   -5.5162   -1.9512   -4.2707   -0.9026    0.1570    5.6900    2.4612    1.0373    2.3579    0.3310    0.4742    7.7762    2.1692
+#> 10:    93.5148   -5.4966   -1.9184   -4.2482   -0.9045    0.1396    5.4055    2.3382    0.9855    2.2400    0.3145    0.4652    7.5796    1.9233
+#> 11:    93.1833   -5.5679   -1.9315   -4.2869   -0.9148    0.1713    5.1352    2.2213    0.9362    2.1465    0.2987    0.4622    7.5181    1.8003
+#> 12:    92.9902   -5.7249   -1.9741   -4.3054   -0.9148    0.1927    4.8784    2.9298    0.8975    2.3858    0.2838    0.5005    7.3638    1.7074
+#> 13:    92.5821   -5.7143   -1.9662   -4.3403   -0.8940    0.1595    4.6345    2.8035    0.9305    2.5370    0.2696    0.4755    7.1732    1.6333
+#> 14:    92.1385   -5.5571   -1.9874   -4.2935   -0.8815    0.1762    5.5000    2.6634    0.9011    2.4102    0.2561    0.5012    7.1920    1.7020
+#> 15:    92.1244   -5.5198   -1.9701   -4.3134   -0.8984    0.1704    5.2250    2.5302    0.9705    2.4401    0.2433    0.4839    7.4072    1.6160
+#> 16:    92.6306   -5.4666   -1.9776   -4.3023   -0.8906    0.1737    4.9638    2.4037    1.0278    2.3181    0.2312    0.5183    7.5105    1.6033
+#> 17:    92.5769   -5.4886   -2.0034   -4.3892   -0.8863    0.1967    5.6659    2.2835    1.0796    2.7700    0.2196    0.5138    7.6495    1.4656
+#> 18:    92.0321   -5.5257   -2.0086   -4.3651   -0.8914    0.1869    6.5345    2.1693    1.0771    2.6315    0.2086    0.4906    7.8248    1.4297
+#> 19:    92.5497   -5.5509   -1.9892   -4.3590   -0.8947    0.2148    6.2078    2.0936    1.0629    2.4999    0.1992    0.4847    7.8809    1.4881
+#> 20:    92.3638   -5.5322   -1.9943   -4.3507   -0.9153    0.1787    6.2176    2.1784    1.0242    2.5190    0.1923    0.4604    7.7900    1.5147
+#> 21:    92.3946   -5.5963   -1.9984   -4.3234   -0.9031    0.1961    5.9067    2.4305    0.9962    2.3930    0.1827    0.4374    7.6671    1.5182
+#> 22:    92.3389   -5.7757   -1.9686   -4.3485   -0.9054    0.1677    5.6113    3.2010    0.9650    2.4493    0.1907    0.4220    7.1305    1.5425
+#> 23:    92.5054   -5.7766   -1.9947   -4.3613   -0.9069    0.1781    5.3308    3.2506    0.9932    2.5478    0.1868    0.4217    7.6690    1.4526
+#> 24:    92.5865   -5.8597   -1.9691   -4.4676   -0.8950    0.1755    5.0642    4.0954    0.9471    3.5482    0.1891    0.4438    7.2397    1.6349
+#> 25:    92.3775   -5.8727   -1.9577   -4.4964   -0.8955    0.1477    4.8354    3.8906    0.9557    3.5054    0.2003    0.4216    6.7966    1.5576
+#> 26:    92.2427   -5.9696   -1.9672   -4.4384   -0.9063    0.1733    4.5937    4.0917    0.9924    3.3326    0.1918    0.4341    6.9377    1.5723
+#> 27:    92.7312   -5.8434   -1.9590   -4.3655   -0.9095    0.1669    4.4448    3.8871    1.0032    3.1660    0.2006    0.4320    7.1970    1.5118
+#> 28:    92.7033   -5.8759   -1.9827   -4.3776   -0.9145    0.1844    4.5885    3.6928    0.9750    3.0077    0.2093    0.4104    6.8745    1.4865
+#> 29:    92.5242   -5.8627   -1.9806   -4.4623   -0.9142    0.2069    5.2823    3.5081    0.9748    3.5849    0.2098    0.4120    6.9735    1.5115
+#> 30:    92.2312   -5.8332   -1.9739   -4.3699   -0.9100    0.1624    5.0182    3.5473    0.9553    3.4056    0.2102    0.3914    6.8547    1.5172
+#> 31:    92.1659   -5.7898   -1.9642   -4.3956   -0.9105    0.1625    4.7672    3.3700    0.9442    3.2571    0.2071    0.3795    6.5191    1.5452
+#> 32:    92.5436   -5.7968   -1.9642   -4.3987   -0.9179    0.1110    4.5289    3.2015    0.9382    3.0943    0.2024    0.3605    6.5921    1.5105
+#> 33:    92.7837   -5.8155   -1.9539   -4.3145   -0.9157    0.1398    4.3024    3.3616    0.9119    2.9395    0.1981    0.3494    6.2870    1.6036
+#> 34:    93.0500   -5.8853   -1.9587   -4.2507   -0.9146    0.1455    4.0873    4.3592    0.9129    2.7926    0.1961    0.3319    6.3493    1.6059
+#> 35:    93.1208   -5.8581   -1.9614   -4.2722   -0.9127    0.1255    4.0645    4.1413    0.9262    2.6529    0.1964    0.3157    6.1337    1.6010
+#> 36:    93.1002   -5.8598   -1.9886   -4.2092   -0.9076    0.1192    4.2392    3.9342    0.9566    2.5203    0.2015    0.3222    6.5326    1.4847
+#> 37:    92.8242   -5.6228   -1.9655   -4.2054   -0.9099    0.1010    6.8190    3.7375    0.9087    2.3943    0.1942    0.3141    6.2613    1.6015
+#> 38:    93.1512   -5.5747   -1.9736   -4.2054   -0.9115    0.0887    6.4781    3.5506    0.8904    2.2746    0.1930    0.3298    6.4960    1.5750
+#> 39:    92.9998   -5.5416   -1.9750   -4.2124   -0.9101    0.0953    6.1542    3.3731    0.9013    2.1608    0.1858    0.3204    6.6470    1.5705
+#> 40:    93.2158   -5.7057   -1.9587   -4.2101   -0.9122    0.0630    5.8464    3.2044    0.9350    2.1357    0.1851    0.3044    6.6842    1.5069
+#> 41:    93.0585   -5.5453   -1.9306   -4.2101   -0.9021    0.0531    5.5541    3.0442    0.9458    2.1673    0.1851    0.2892    6.3923    1.5949
+#> 42:    93.0958   -5.4512   -1.9484   -4.2227   -0.8959    0.0649    5.2764    2.8920    0.9571    2.1930    0.1829    0.2747    6.3082    1.5985
+#> 43:    93.2333   -5.5398   -1.9391   -4.2400   -0.8972    0.0870    5.0126    2.7474    0.9913    2.2830    0.1984    0.2720    6.0810    1.6131
+#> 44:    92.9479   -5.5648   -1.9227   -4.2468   -0.9104    0.0963    4.7620    2.6100    0.9682    2.2976    0.2038    0.2648    5.8461    1.6955
+#> 45:    93.0244   -5.6247   -1.9379   -4.2588   -0.9093    0.0865    5.2997    2.4894    0.9837    2.3100    0.2039    0.2844    5.9439    1.6121
+#> 46:    92.5959   -5.6240   -1.9513   -4.2588   -0.9172    0.0923    5.3111    2.5081    1.0158    2.3100    0.2050    0.2702    6.0141    1.6189
+#> 47:    92.8483   -5.5823   -1.9529   -4.2684   -0.9194    0.0770    6.2469    2.3827    1.0328    2.3567    0.2104    0.2567    6.0472    1.5858
+#> 48:    92.6210   -5.6336   -1.9379   -4.3049   -0.9054    0.0747    7.5721    2.3177    1.0379    2.5427    0.2103    0.2439    6.0431    1.5860
+#> 49:    92.6337   -5.6723   -1.9486   -4.2879   -0.8985    0.0773    7.1935    2.6572    1.0181    2.4515    0.2056    0.2559    6.0895    1.5217
+#> 50:    92.2413   -5.7138   -1.9587   -4.2804   -0.8926    0.0774    8.1551    2.9779    1.0282    2.4807    0.2090    0.2510    6.2355    1.5223
+#> 51:    92.2223   -5.6765   -1.9496   -4.2971   -0.8840    0.1034    7.7638    3.0625    1.0017    2.6024    0.2075    0.2384    6.3495    1.6621
+#> 52:    92.4242   -5.6573   -1.9408   -4.2943   -0.8993    0.1136    8.3190    2.9093    1.0044    2.4822    0.2163    0.2411    6.0611    1.5241
+#> 53:    92.6070   -5.5921   -1.9397   -4.2873   -0.9046    0.0904   10.3681    2.7639    1.0098    2.4895    0.2194    0.2393    6.1728    1.5264
+#> 54:    92.9339   -5.6194   -1.9292   -4.2950   -0.9006    0.1010    9.9150    2.6257    1.0088    2.4268    0.2346    0.2492    5.9203    1.5693
+#> 55:    93.4640   -5.5851   -1.8969   -4.2614   -0.9065    0.1058   10.3986    2.4944    1.0204    2.3055    0.2257    0.2403    5.7030    1.5717
+#> 56:    93.3646   -5.5851   -1.9127   -4.3130   -0.9196    0.1077    9.8787    2.3697    1.0067    2.6259    0.2261    0.2370    5.7389    1.5053
+#> 57:    93.5408   -5.4962   -1.9150   -4.3285   -0.9148    0.0880    9.3848    2.2512    0.9903    2.6118    0.2160    0.2494    5.7530    1.5780
+#> 58:    93.5195   -5.4358   -1.9459   -4.3041   -0.9076    0.1022    8.9155    2.1386    1.0220    2.5253    0.2220    0.2578    6.0138    1.4494
+#> 59:    93.5906   -5.4624   -1.9507   -4.3065   -0.9124    0.1374    8.4698    2.0317    1.0230    2.5539    0.2212    0.2449    5.7538    1.6021
+#> 60:    93.3308   -5.3784   -1.9540   -4.2417   -0.9173    0.1337    8.0463    1.9301    1.0298    2.4262    0.2173    0.2327    5.8841    1.4634
+#> 61:    93.3506   -5.4000   -1.9688   -4.2389   -0.9130    0.0942    7.6440    1.8336    1.0437    2.3049    0.2216    0.2210    6.0098    1.4243
+#> 62:    93.6969   -5.4175   -1.9467   -4.2389   -0.9135    0.1315    7.2618    1.7419    1.0213    2.2519    0.2250    0.2149    5.6278    1.4755
+#> 63:    93.6188   -5.3860   -1.9295   -4.2637   -0.9222    0.1196    7.8033    1.6548    1.0340    2.2699    0.2282    0.2282    5.6763    1.4755
+#> 64:    93.6782   -5.4118   -1.9518   -4.2655   -0.9298    0.1055    8.3519    1.5721    1.0227    2.4426    0.2317    0.2560    5.8006    1.4724
+#> 65:    93.5253   -5.4313   -1.9314   -4.2538   -0.9245    0.0919    7.9343    1.4980    1.0771    2.3486    0.2249    0.2635    5.8752    1.4850
+#> 66:    93.3192   -5.5672   -1.9715   -4.2575   -0.9224    0.1404    8.2293    1.9722    1.0233    2.3758    0.2365    0.2546    5.9462    1.5148
+#> 67:    93.0765   -5.4861   -1.9673   -4.2472   -0.9103    0.0935    8.3227    1.8736    0.9889    2.3305    0.2493    0.2419    5.7836    1.4946
+#> 68:    93.2666   -5.4963   -1.9635   -4.2435   -0.9093    0.0940    9.2911    1.7800    1.0050    2.3179    0.2495    0.2298    5.7104    1.4797
+#> 69:    93.3894   -5.5666   -1.9342   -4.2325   -0.9227    0.0957    9.0211    2.0287    1.0012    2.3052    0.2483    0.2348    5.8939    1.5158
+#> 70:    93.2671   -5.5710   -1.9486   -4.2723   -0.9323    0.1062    8.5700    2.1251    0.9714    2.3266    0.2498    0.2466    6.1562    1.5041
+#> 71:    92.9975   -5.5829   -1.9507   -4.2632   -0.9317    0.1166    8.1415    2.0322    0.9403    2.3654    0.2373    0.2454    5.8668    1.5122
+#> 72:    92.6364   -5.5255   -1.9888   -4.2605   -0.9255    0.1062    8.8866    1.9306    0.9680    2.4488    0.2314    0.2438    6.2101    1.5098
+#> 73:    92.4442   -5.5679   -1.9880   -4.3501   -0.9070    0.0972    9.1986    1.9203    0.9597    3.1091    0.2369    0.2412    6.1257    1.5029
+#> 74:    92.3866   -5.5447   -1.9895   -4.3137   -0.9004    0.0898   10.2222    1.8961    0.9573    2.9536    0.2494    0.2361    6.0474    1.4875
+#> 75:    92.2491   -5.6481   -1.9591   -4.3587   -0.8991    0.1028    9.7111    2.2694    1.0140    2.9121    0.2524    0.2243    6.0995    1.4780
+#> 76:    92.4656   -5.6014   -1.9860   -4.3538   -0.9015    0.0978   11.3121    2.1560    0.9861    2.9372    0.2489    0.2314    6.0996    1.4464
+#> 77:    92.5076   -5.5929   -1.9560   -4.3624   -0.9051    0.1008   12.0483    2.0482    1.0212    3.0132    0.2551    0.2378    5.9595    1.5081
+#> 78:    92.5987   -5.7000   -1.9592   -4.3611   -0.9131    0.0958   11.4458    2.3873    1.0062    2.9848    0.2549    0.2372    6.0385    1.4666
+#> 79:    92.4883   -5.7675   -1.9900   -4.4226   -0.9163    0.1153   10.8735    2.7867    0.9616    3.4984    0.2546    0.2309    5.9441    1.4722
+#> 80:    92.1716   -5.7782   -1.9810   -4.4398   -0.9122    0.1193   10.3299    3.0280    0.9642    3.6766    0.2520    0.2291    6.3013    1.4698
+#> 81:    92.1145   -5.8494   -1.9836   -4.3634   -0.9196    0.1013    9.8134    3.1850    0.9160    3.4927    0.2562    0.2409    6.2458    1.4664
+#> 82:    92.3761   -5.9668   -1.9722   -4.3888   -0.9240    0.1139    9.9738    3.9484    0.8923    3.3519    0.2434    0.2318    6.0987    1.4847
+#> 83:    92.7805   -6.1135   -1.9335   -4.3600   -0.9273    0.1027   11.2060    4.7684    0.8932    3.1843    0.2454    0.2202    5.9824    1.4920
+#> 84:    92.9601   -6.2190   -1.9374   -4.3187   -0.9376    0.1140   10.6457    5.6632    0.9077    3.0250    0.2464    0.2188    5.9979    1.5152
+#> 85:    92.4579   -6.1486   -1.9398   -4.3269   -0.9417    0.0979   10.1134    5.3800    0.9011    2.8738    0.2446    0.2330    5.7007    1.5648
+#> 86:    92.3580   -6.2177   -1.9549   -4.3287   -0.9510    0.1073    9.6077    5.1608    0.9318    2.7301    0.2497    0.2214    5.9916    1.5305
+#> 87:    92.8919   -6.3309   -1.9480   -4.3285   -0.9647    0.1009    9.1273    6.4577    0.9494    2.7023    0.2408    0.2126    5.9053    1.4313
+#> 88:    93.0621   -6.1220   -1.9623   -4.3341   -0.9624    0.1300    8.6710    6.1349    0.9563    2.6593    0.2404    0.2130    6.1925    1.4510
+#> 89:    92.7711   -6.2636   -1.9545   -4.3520   -0.9496    0.1227    8.2374    6.2143    0.9791    2.5862    0.2346    0.2333    5.9772    1.4523
+#> 90:    92.9148   -6.5481   -1.9586   -4.3275   -0.9496    0.1096    7.8255    8.2617    0.9787    2.4647    0.2346    0.2216    5.9136    1.4247
+#> 91:    92.8129   -6.4655   -1.9753   -4.3287   -0.9435    0.1210    9.1893    7.8487    0.9642    2.5304    0.2354    0.2268    5.9129    1.4229
+#> 92:    93.1090   -6.4752   -1.9841   -4.3533   -0.9428    0.1509   10.1133    7.7232    0.9160    2.6037    0.2457    0.2265    5.8601    1.4646
+#> 93:    93.4781   -6.3780   -1.9909   -4.3713   -0.9450    0.1544    9.6076    7.3370    0.9153    2.7656    0.2485    0.2499    5.9150    1.5180
+#> 94:    93.2125   -6.3021   -1.9798   -4.3459   -0.9470    0.1520    9.6738    6.9702    0.9314    2.6273    0.2428    0.2519    5.8752    1.4456
+#> 95:    93.0091   -5.9727   -1.9828   -4.3777   -0.9447    0.1370    9.6411    6.6217    0.9107    2.7137    0.2428    0.2556    5.8302    1.4477
+#> 96:    92.8731   -5.7813   -1.9952   -4.3343   -0.9352    0.1505    9.1590    6.2906    0.9011    2.5780    0.2366    0.2546    6.0545    1.4887
+#> 97:    92.7834   -5.8119   -1.9975   -4.3303   -0.9258    0.1231    8.8022    5.9760    0.9005    2.5331    0.2392    0.2419    5.9522    1.4754
+#> 98:    92.8447   -5.9773   -1.9940   -4.3353   -0.9301    0.1409    8.3621    5.6772    0.9244    2.4828    0.2426    0.2490    6.1027    1.4129
+#> 99:    93.1697   -5.8958   -1.9964   -4.3325   -0.9248    0.1411    7.9440    5.3934    0.9586    2.6138    0.2378    0.2545    6.2793    1.3719
+#> 100:    93.2536   -5.8481   -2.0009   -4.3408   -0.9304    0.1718    8.7965    5.1237    0.9290    2.6161    0.2398    0.2418    6.0908    1.4534
+#> 101:    93.2942   -5.8684   -1.9650   -4.3096   -0.9305    0.1496    9.7633    4.8675    0.9166    2.4853    0.2372    0.2565    5.9079    1.4948
+#> 102:    93.2636   -6.1363   -1.9517   -4.2653   -0.9235    0.1175   10.7772    5.1927    0.8944    2.3610    0.2448    0.2812    5.7748    1.5533
+#> 103:    92.6954   -5.9371   -1.9524   -4.2792   -0.9045    0.1288   10.2383    4.9331    0.8876    2.2429    0.2406    0.2720    5.5496    1.5601
+#> 104:    92.6149   -6.0650   -1.9532   -4.2752   -0.9048    0.0973   10.9914    4.6864    0.8845    2.1875    0.2475    0.2584    5.5593    1.4897
+#> 105:    92.8231   -5.9779   -1.9650   -4.2939   -0.9013    0.1112   10.4712    4.4521    0.9193    2.1985    0.2416    0.2455    5.4420    1.4910
+#> 106:    92.7599   -5.9602   -1.9594   -4.3018   -0.9026    0.1273   10.1396    4.2295    0.9308    2.1700    0.2453    0.2625    5.5458    1.4429
+#> 107:    93.1433   -5.9509   -1.9638   -4.2715   -0.9324    0.1385    9.6327    4.0415    0.9271    2.1026    0.2415    0.2626    5.4762    1.4286
+#> 108:    93.1354   -5.7359   -1.9691   -4.2962   -0.9256    0.1346   10.2794    3.8394    0.9387    2.1671    0.2412    0.2627    5.5107    1.4200
+#> 109:    92.9608   -5.8252   -1.9780   -4.3149   -0.9125    0.1564    9.7654    4.0619    0.9380    2.1731    0.2325    0.2657    5.8118    1.4379
+#> 110:    93.1043   -5.7632   -1.9874   -4.2868   -0.9113    0.1178    9.2771    3.8588    0.9420    2.1477    0.2214    0.2524    5.9352    1.4377
+#> 111:    92.8879   -5.7965   -1.9781   -4.2851   -0.9147    0.1107    8.8133    3.6659    0.9526    2.1891    0.2130    0.2398    5.6360    1.4461
+#> 112:    92.9347   -5.7484   -1.9460   -4.2825   -0.9195    0.1078    8.3726    3.4826    0.9710    2.2687    0.2051    0.2278    5.5771    1.5123
+#> 113:    92.7217   -5.7193   -1.9328   -4.2721   -0.9252    0.1021    7.9540    3.3085    1.0056    2.2848    0.2244    0.2164    5.7135    1.5082
+#> 114:    92.9944   -5.7382   -1.9414   -4.2835   -0.9210    0.1210    7.5563    3.1430    1.0184    2.2457    0.2260    0.2182    5.6799    1.4751
+#> 115:    93.1261   -5.8876   -1.9290   -4.2753   -0.9382    0.0960    9.7696    3.4406    1.0140    2.2745    0.2171    0.2073    5.3919    1.4919
+#> 116:    92.7669   -5.9842   -1.9484   -4.2828   -0.9504    0.1122    9.2811    4.1332    1.0202    2.2835    0.2160    0.2136    5.3651    1.5337
+#> 117:    92.9804   -5.9847   -1.9584   -4.2879   -0.9474    0.1234    9.2911    3.9265    0.9692    2.3115    0.2135    0.2163    5.1053    1.4774
+#> 118:    93.2853   -5.8443   -1.9494   -4.2700   -0.9400    0.1105    9.8572    3.7302    0.9736    2.2489    0.2192    0.2223    5.2416    1.4668
+#> 119:    93.2776   -5.8592   -1.9458   -4.2600   -0.9394    0.1072    9.3643    3.5437    0.9789    2.1964    0.2176    0.2205    5.2942    1.4847
+#> 120:    93.0335   -5.8156   -1.9453   -4.2623   -0.9437    0.1139    8.8961    3.3665    0.9698    2.2380    0.2206    0.2231    5.4427    1.4470
+#> 121:    93.0115   -5.8402   -1.9355   -4.2596   -0.9291    0.1138    8.4513    3.3018    0.9743    2.1463    0.2096    0.2120    5.1537    1.4487
+#> 122:    93.6277   -5.8852   -1.9276   -4.2787   -0.9419    0.1388    8.0287    3.4114    0.9438    2.1410    0.2072    0.2104    5.1198    1.5201
+#> 123:    93.4952   -6.0977   -1.9332   -4.2847   -0.9431    0.1412    7.6273    4.8225    0.9472    2.1335    0.2081    0.2129    5.2003    1.6193
+#> 124:    93.7207   -6.2280   -1.9105   -4.2692   -0.9551    0.1422    7.2459    5.4835    0.9657    2.0896    0.2148    0.2272    5.2901    1.5482
+#> 125:    93.6041   -6.0808   -1.9356   -4.2748   -0.9531    0.1184    7.0201    5.2094    0.9591    2.0421    0.2089    0.2158    5.3848    1.4896
+#> 126:    93.5193   -6.0164   -1.9296   -4.2890   -0.9600    0.1351    7.6848    4.9489    0.9931    2.1387    0.1989    0.2129    5.1988    1.4492
+#> 127:    93.7135   -5.9340   -1.9448   -4.2883   -0.9633    0.1428    8.3411    4.7014    0.9820    2.1192    0.1985    0.2046    5.3953    1.4985
+#> 128:    94.2312   -5.8849   -1.9404   -4.2754   -0.9633    0.1495    7.9240    4.4664    0.9884    2.0587    0.1902    0.2171    5.7113    1.4987
+#> 129:    94.0390   -5.8674   -1.9229   -4.3309   -0.9614    0.1472    8.5108    4.2430    1.0319    2.1023    0.1909    0.2154    5.5654    1.4294
+#> 130:    93.4178   -6.0458   -1.9224   -4.3364   -0.9560    0.1570    8.0852    4.4639    1.0184    2.2804    0.1869    0.2182    5.6585    1.4443
+#> 131:    93.5483   -6.2682   -1.9258   -4.3654   -0.9554    0.1449    7.6810    5.6020    1.0254    2.3477    0.1857    0.2230    5.4266    1.4324
+#> 132:    93.5180   -6.3297   -1.9204   -4.3577   -0.9640    0.1365    7.2969    5.5672    1.0354    2.3257    0.1788    0.2118    5.4913    1.4859
+#> 133:    93.4707   -6.0990   -1.9415   -4.3315   -0.9775    0.1232    6.9321    5.2888    1.0686    2.3421    0.1851    0.2012    5.8429    1.4618
+#> 134:    93.1012   -6.1236   -1.9308   -4.3409   -0.9654    0.1225    7.6471    5.0244    1.0517    2.4652    0.1947    0.2008    5.6902    1.5432
+#> 135:    93.2545   -6.1070   -1.9408   -4.3415   -0.9553    0.1228    9.2701    4.7732    1.0160    2.3607    0.1919    0.1907    5.5154    1.5317
+#> 136:    93.3338   -6.0321   -1.9336   -4.3074   -0.9598    0.1120    8.8066    4.5345    0.9652    2.2427    0.1999    0.2249    5.3667    1.6036
+#> 137:    93.5910   -6.0627   -1.9339   -4.3074   -0.9529    0.1407    8.3663    4.3078    0.9538    2.2128    0.1966    0.2195    5.2959    1.6015
+#> 138:    93.6338   -5.9702   -1.9252   -4.3105   -0.9615    0.1373    7.9480    4.0924    0.9875    2.2635    0.1964    0.2218    5.4532    1.5261
+#> 139:    93.6403   -5.8913   -1.9237   -4.2962   -0.9582    0.1165    8.0749    3.8878    0.9746    2.2457    0.1972    0.2125    5.9356    1.5173
+#> 140:    92.8503   -5.8314   -1.9452   -4.3180   -0.9487    0.1142    8.6356    3.6934    0.9933    2.2044    0.1961    0.2019    5.7908    1.5138
+#> 141:    93.1249   -6.0584   -1.9448   -4.3139   -0.9367    0.0950    8.9231    4.4196    1.0220    2.2246    0.2079    0.2077    6.0233    1.4339
+#> 142:    93.1846   -6.3026   -1.9152   -4.3093   -0.9392    0.0866   10.1508    5.9592    1.0562    2.3325    0.2082    0.2133    5.5285    1.4832
+#> 143:    92.4682   -6.1485   -1.9146   -4.2812   -0.9376    0.0260    9.6433    5.6613    1.0618    2.3594    0.2000    0.2027    6.0573    1.4428
+#> 144:    92.7792   -6.1108   -1.8939   -4.2740   -0.9341    0.0765    9.1611    5.3782    1.0917    2.3074    0.2057    0.2240    6.2141    1.4953
+#> 145:    93.1314   -6.2086   -1.8939   -4.3580   -0.9341    0.0741    8.7031    5.1093    1.0931    2.7164    0.2105    0.2229    5.8543    1.4855
+#> 146:    93.2254   -6.2170   -1.8998   -4.3724   -0.9311    0.0677    8.2679    5.0506    1.0811    2.8434    0.2049    0.2118    5.5455    1.4763
+#> 147:    93.3264   -6.0136   -1.8998   -4.3853   -0.9328    0.0817    9.4673    4.7980    1.0668    2.8512    0.2009    0.2114    5.5518    1.5225
+#> 148:    93.2298   -5.9143   -1.8921   -4.5001   -0.9296    0.1057    8.9939    4.5581    1.0563    3.8266    0.1982    0.2043    5.5242    1.5614
+#> 149:    93.3604   -5.9894   -1.8832   -4.5223   -0.9338    0.0858    8.5442    4.3302    1.0544    4.3930    0.1986    0.2003    5.4353    1.4957
+#> 150:    93.4715   -5.9630   -1.8833   -4.4796   -0.9335    0.0827    8.1170    4.1137    1.0912    4.1733    0.1984    0.1903    5.7477    1.4554
+#> 151:    93.3385   -5.8026   -1.9052   -4.4507   -0.9368    0.0684    8.7726    3.9080    1.1249    3.9647    0.2074    0.1808    5.7693    1.4400
+#> 152:    93.1682   -5.8529   -1.9441   -4.3545   -0.9309    0.0752    8.8042    3.1783    1.0496    3.0168    0.2069    0.1688    5.9161    1.4565
+#> 153:    93.0559   -6.0261   -1.9425   -4.3431   -0.9327    0.1016    9.1435    3.9939    1.0120    2.8470    0.1894    0.1509    5.4435    1.5486
+#> 154:    92.8582   -6.0887   -1.9278   -4.3094   -0.9352    0.1064    8.4316    4.2991    0.9819    2.6257    0.1907    0.1609    5.4587    1.5208
+#> 155:    93.3200   -5.8480   -1.9149   -4.3363   -0.9294    0.1143    9.6700    3.1734    0.9942    2.6441    0.1824    0.1906    5.5193    1.6410
+#> 156:    93.3199   -5.9053   -1.9213   -4.3163   -0.9369    0.1291    7.5899    3.5902    0.9823    2.4648    0.1770    0.1956    5.3816    1.5356
+#> 157:    93.2434   -5.8763   -1.9161   -4.3035   -0.9549    0.1075    8.4137    3.2576    0.9935    2.5007    0.1795    0.1852    5.4053    1.5706
+#> 158:    93.1494   -5.9243   -1.8929   -4.3162   -0.9680    0.1296    8.2959    3.3262    1.0029    2.4943    0.1866    0.1921    5.4369    1.5510
+#> 159:    93.5683   -6.0335   -1.9127   -4.3040   -0.9675    0.1271    7.7222    4.0079    0.9768    2.5765    0.1869    0.2028    5.7165    1.4968
+#> 160:    93.9417   -6.0018   -1.9085   -4.2818   -0.9611    0.1161    5.8791    4.4991    0.9658    2.4933    0.1878    0.1986    6.0579    1.5272
+#> 161:    94.1252   -5.9264   -1.8943   -4.2805   -0.9645    0.0860    4.9517    3.6307    0.9754    2.4988    0.1934    0.1785    5.7457    1.5878
+#> 162:    93.9389   -5.7613   -1.8946   -4.2410   -0.9752    0.0898    6.7269    2.5865    1.0184    2.4379    0.1933    0.1908    5.9052    1.5215
+#> 163:    93.5890   -5.7243   -1.8992   -4.2636   -0.9722    0.0759    8.4484    2.5137    1.0151    2.3869    0.1928    0.1889    5.4694    1.5048
+#> 164:    93.9751   -5.7314   -1.8786   -4.3271   -0.9702    0.1020    6.6884    2.5136    1.0133    2.8395    0.1907    0.1998    5.4625    1.4854
+#> 165:    93.9708   -5.7409   -1.8856   -4.3129   -0.9616    0.1094    5.8809    2.4589    1.0401    2.6662    0.1912    0.1998    5.4339    1.4549
+#> 166:    93.9265   -5.6937   -1.9134   -4.3080   -0.9702    0.1151    5.6940    2.4086    1.0065    2.6864    0.1983    0.1987    5.6907    1.4857
+#> 167:    93.4157   -5.7312   -1.9163   -4.3286   -0.9638    0.1216    5.1230    2.5468    1.0487    2.5930    0.1917    0.1940    5.5938    1.4267
+#> 168:    93.3701   -5.8757   -1.9196   -4.3493   -0.9579    0.1134    6.0802    3.3929    1.0517    2.6981    0.1888    0.2063    5.4125    1.4365
+#> 169:    93.4342   -6.0262   -1.9041   -4.3347   -0.9526    0.0997    6.0780    3.6349    1.0623    2.7344    0.1946    0.1978    5.4930    1.4594
+#> 170:    93.3751   -6.1195   -1.9093   -4.3541   -0.9872    0.0834    6.8972    4.0337    1.0763    2.8428    0.2077    0.2005    5.6759    1.4455
+#> 171:    93.3603   -6.0360   -1.9196   -4.4632   -0.9763    0.0866    7.4236    3.6025    1.0684    3.7611    0.2046    0.1894    5.6282    1.4414
+#> 172:    93.2776   -5.9538   -1.9031   -4.4815   -0.9779    0.1024    5.4751    3.2802    1.0599    3.9487    0.2115    0.1990    5.6116    1.4230
+#> 173:    93.4470   -5.8580   -1.9193   -4.4170   -0.9641    0.0957    5.6416    2.8005    1.0440    3.4509    0.2066    0.1863    5.5804    1.4485
+#> 174:    93.2952   -5.8590   -1.9010   -4.3600   -0.9640    0.0789    6.4314    2.9503    1.0808    2.9773    0.2045    0.1969    5.4423    1.4421
+#> 175:    93.3756   -5.7733   -1.8959   -4.3621   -0.9504    0.0609    6.1723    2.5287    1.0950    3.0019    0.2127    0.2053    5.4338    1.4470
+#> 176:    93.1450   -5.8266   -1.9053   -4.3401   -0.9457    0.0633    6.5237    3.0522    1.0942    2.9464    0.2134    0.2021    5.6501    1.3664
+#> 177:    92.7723   -5.9978   -1.9231   -4.3529   -0.9524    0.0658    7.4519    4.2374    1.0640    3.0260    0.2158    0.2146    5.9180    1.4100
+#> 178:    92.7261   -5.9836   -1.9189   -4.3349   -0.9576    0.0768    5.5211    4.2557    1.0611    2.8827    0.2169    0.2088    5.8872    1.4206
+#> 179:    92.9599   -6.0071   -1.9259   -4.3081   -0.9581    0.0657    6.0953    3.8205    1.0816    2.6709    0.2122    0.2014    5.8221    1.4026
+#> 180:    93.0831   -6.1544   -1.9400   -4.3018   -0.9496    0.0411    4.2312    4.9005    1.1064    2.6542    0.2143    0.2221    6.3264    1.3820
+#> 181:    92.8840   -6.0889   -1.9364   -4.3200   -0.9566    0.0861    4.2186    4.4615    1.0930    2.7270    0.2142    0.2424    6.0486    1.4035
+#> 182:    93.1913   -6.1457   -1.9384   -4.3085   -0.9606    0.0733    6.2878    4.6026    1.0917    2.6393    0.2131    0.2151    5.7042    1.4952
+#> 183:    93.1218   -6.3114   -1.9355   -4.2883   -0.9742    0.0741    7.2675    5.1377    1.0914    2.5060    0.2220    0.2111    5.5099    1.4097
+#> 184:    93.1462   -6.3147   -1.9068   -4.2880   -0.9653    0.0893    7.6928    5.6510    1.0563    2.5066    0.2256    0.2201    5.4138    1.5319
+#> 185:    93.1825   -6.3608   -1.9265   -4.2815   -0.9549    0.0873    7.1340    5.9801    1.0363    2.4788    0.2177    0.1958    5.4202    1.4569
+#> 186:    93.6270   -6.1413   -1.9278   -4.2702   -0.9696    0.1185    6.7652    4.5535    1.0400    2.3673    0.2163    0.1932    5.3005    1.5012
+#> 187:    93.9922   -6.3364   -1.9269   -4.2702   -0.9729    0.1197    7.7694    6.1592    0.9948    2.3673    0.2196    0.2091    5.3075    1.5105
+#> 188:    93.8884   -6.0236   -1.9207   -4.2928   -0.9900    0.1343    7.8090    4.2847    0.9840    2.4238    0.2195    0.1966    5.2861    1.5607
+#> 189:    94.3110   -6.0809   -1.9145   -4.2826   -0.9840    0.1224    8.5580    4.0998    0.9800    2.4505    0.2294    0.1840    5.7107    1.5180
+#> 190:    94.0039   -6.0996   -1.9140   -4.2793   -0.9782    0.1429   10.6594    4.1655    0.9796    2.4415    0.2297    0.1960    5.7533    1.5720
+#> 191:    93.9692   -6.1129   -1.9362   -4.3261   -0.9705    0.1462    8.8201    4.3146    1.0124    2.4625    0.2287    0.2049    5.5670    1.5206
+#> 192:    93.3178   -5.9759   -1.9192   -4.3378   -0.9664    0.1434    8.8047    3.7150    1.0282    2.4137    0.2243    0.1977    5.3858    1.4599
+#> 193:    93.1427   -5.9388   -1.9391   -4.3211   -0.9650    0.1401    7.1862    3.2835    1.0218    2.3216    0.2163    0.1866    5.3930    1.5017
+#> 194:    93.0588   -6.0605   -1.9361   -4.3350   -0.9462    0.1330    6.8930    4.0020    1.0166    2.3186    0.2057    0.1818    5.2535    1.5075
+#> 195:    93.1820   -6.1201   -1.9579   -4.3034   -0.9534    0.1557    8.1300    4.4218    0.9932    2.1873    0.2099    0.1834    5.4862    1.4698
+#> 196:    93.2230   -5.8879   -1.9725   -4.2965   -0.9584    0.1390    8.1307    3.0777    1.0051    2.1597    0.2089    0.1683    5.7058    1.3970
+#> 197:    93.3504   -5.8829   -1.9677   -4.3075   -0.9577    0.1638    6.7115    3.0660    1.0050    2.1377    0.2024    0.1642    5.4691    1.5016
+#> 198:    93.3016   -5.8771   -1.9885   -4.3241   -0.9605    0.1562    6.4722    3.0381    0.9727    2.2053    0.1975    0.1683    5.3434    1.4885
+#> 199:    93.2464   -5.8787   -1.9871   -4.3430   -0.9528    0.1751    4.5894    3.0445    0.9748    2.2247    0.1886    0.1780    5.4469    1.4405
+#> 200:    93.3474   -5.7995   -1.9767   -4.3298   -0.9480    0.1947    4.7024    2.8535    0.9895    2.2234    0.1951    0.2012    5.5130    1.4641
+#> 201:    93.3231   -5.8169   -1.9737   -4.3268   -0.9510    0.1804    4.4248    2.8913    0.9738    2.2141    0.1955    0.2057    5.5422    1.4843
+#> 202:    93.3484   -5.8009   -1.9732   -4.3240   -0.9519    0.1674    4.4068    2.8084    0.9736    2.2040    0.1959    0.2033    5.5843    1.4744
+#> 203:    93.2617   -5.7915   -1.9678   -4.3211   -0.9535    0.1629    4.5333    2.7678    0.9877    2.1980    0.1961    0.2023    5.6265    1.4811
+#> 204:    93.2210   -5.8071   -1.9647   -4.3220   -0.9504    0.1629    4.6144    2.8347    0.9922    2.1938    0.1937    0.2013    5.5745    1.4988
+#> 205:    93.1914   -5.8104   -1.9667   -4.3225   -0.9484    0.1593    4.5880    2.8639    0.9931    2.1952    0.1916    0.1979    5.5960    1.5057
+#> 206:    93.1827   -5.8348   -1.9697   -4.3236   -0.9498    0.1587    4.7189    3.0353    0.9929    2.2016    0.1922    0.1947    5.6096    1.5136
+#> 207:    93.2017   -5.8760   -1.9714   -4.3239   -0.9518    0.1592    4.8171    3.2659    0.9947    2.2042    0.1927    0.1910    5.6413    1.5078
+#> 208:    93.2226   -5.8819   -1.9736   -4.3261   -0.9532    0.1610    4.8241    3.2964    0.9957    2.2122    0.1938    0.1878    5.6704    1.5031
+#> 209:    93.2158   -5.8786   -1.9743   -4.3278   -0.9538    0.1595    4.6275    3.2763    0.9963    2.2279    0.1950    0.1848    5.6758    1.5038
+#> 210:    93.2216   -5.8798   -1.9746   -4.3286   -0.9535    0.1589    4.5667    3.2857    0.9974    2.2473    0.1948    0.1834    5.6707    1.5054
+#> 211:    93.2238   -5.8847   -1.9763   -4.3302   -0.9530    0.1591    4.5745    3.2932    0.9956    2.2576    0.1948    0.1823    5.6691    1.4990
+#> 212:    93.2242   -5.8893   -1.9777   -4.3323   -0.9532    0.1600    4.6203    3.2955    0.9938    2.2704    0.1958    0.1814    5.6732    1.4994
+#> 213:    93.2246   -5.8950   -1.9756   -4.3345   -0.9532    0.1588    4.7363    3.3106    0.9894    2.2864    0.1960    0.1791    5.6401    1.5015
+#> 214:    93.2056   -5.9070   -1.9740   -4.3368   -0.9532    0.1586    4.7814    3.3538    0.9888    2.3047    0.1960    0.1761    5.6265    1.5008
+#> 215:    93.2126   -5.9157   -1.9720   -4.3405   -0.9533    0.1580    4.9117    3.3916    0.9890    2.3191    0.1959    0.1742    5.6054    1.5015
+#> 216:    93.2161   -5.9242   -1.9716   -4.3423   -0.9533    0.1594    5.0163    3.4425    0.9897    2.3291    0.1959    0.1739    5.5975    1.5005
+#> 217:    93.2193   -5.9351   -1.9715   -4.3445   -0.9537    0.1614    4.9927    3.5085    0.9905    2.3309    0.1957    0.1739    5.5905    1.5024
+#> 218:    93.1973   -5.9314   -1.9725   -4.3479   -0.9548    0.1640    5.0502    3.4902    0.9918    2.3344    0.1952    0.1740    5.5909    1.5046
+#> 219:    93.1938   -5.9312   -1.9729   -4.3508   -0.9539    0.1664    5.0446    3.4901    0.9922    2.3365    0.1949    0.1746    5.5808    1.5046
+#> 220:    93.1994   -5.9424   -1.9734   -4.3531   -0.9536    0.1683    5.0462    3.5593    0.9917    2.3370    0.1945    0.1754    5.5831    1.5055
+#> 221:    93.2015   -5.9511   -1.9746   -4.3550   -0.9537    0.1702    5.1062    3.6002    0.9899    2.3368    0.1945    0.1762    5.5731    1.5043
+#> 222:    93.2057   -5.9653   -1.9756   -4.3571   -0.9541    0.1718    5.1727    3.6876    0.9886    2.3364    0.1943    0.1776    5.5813    1.5047
+#> 223:    93.1998   -5.9723   -1.9761   -4.3592   -0.9540    0.1726    5.1866    3.7239    0.9871    2.3428    0.1940    0.1791    5.5702    1.5047
+#> 224:    93.2042   -5.9799   -1.9768   -4.3615   -0.9540    0.1734    5.1516    3.7613    0.9849    2.3531    0.1934    0.1809    5.5705    1.5039
+#> 225:    93.1974   -5.9813   -1.9776   -4.3648   -0.9540    0.1740    5.1225    3.7676    0.9840    2.3663    0.1929    0.1834    5.5698    1.5030
+#> 226:    93.1963   -5.9807   -1.9777   -4.3679   -0.9535    0.1751    5.1632    3.7694    0.9839    2.3785    0.1927    0.1850    5.5676    1.5069
+#> 227:    93.1912   -5.9740   -1.9783   -4.3707   -0.9533    0.1768    5.1987    3.7421    0.9835    2.3931    0.1922    0.1855    5.5597    1.5091
+#> 228:    93.1902   -5.9799   -1.9792   -4.3745   -0.9533    0.1784    5.2070    3.7641    0.9825    2.4134    0.1917    0.1861    5.5502    1.5086
+#> 229:    93.1903   -5.9894   -1.9805   -4.3792   -0.9533    0.1796    5.2398    3.8109    0.9812    2.4382    0.1910    0.1870    5.5486    1.5075
+#> 230:    93.1833   -5.9946   -1.9816   -4.3836   -0.9530    0.1814    5.2357    3.8346    0.9800    2.4614    0.1904    0.1883    5.5515    1.5065
+#> 231:    93.1740   -6.0001   -1.9834   -4.3871   -0.9528    0.1833    5.2848    3.8635    0.9783    2.4814    0.1898    0.1893    5.5526    1.5057
+#> 232:    93.1581   -6.0071   -1.9852   -4.3904   -0.9523    0.1857    5.3056    3.8967    0.9766    2.5002    0.1891    0.1904    5.5571    1.5057
+#> 233:    93.1417   -6.0131   -1.9865   -4.3933   -0.9517    0.1869    5.3290    3.9227    0.9745    2.5129    0.1885    0.1909    5.5609    1.5069
+#> 234:    93.1245   -6.0198   -1.9878   -4.3961   -0.9514    0.1880    5.3062    3.9567    0.9731    2.5269    0.1886    0.1916    5.5645    1.5074
+#> 235:    93.1084   -6.0269   -1.9885   -4.3985   -0.9514    0.1892    5.3213    3.9969    0.9729    2.5390    0.1887    0.1931    5.5722    1.5065
+#> 236:    93.1037   -6.0382   -1.9897   -4.4009   -0.9517    0.1899    5.3601    4.0674    0.9744    2.5501    0.1886    0.1949    5.5811    1.5066
+#> 237:    93.0989   -6.0432   -1.9906   -4.4031   -0.9518    0.1909    5.3744    4.0877    0.9755    2.5623    0.1885    0.1964    5.5890    1.5051
+#> 238:    93.0932   -6.0433   -1.9912   -4.4041   -0.9521    0.1915    5.4192    4.0775    0.9772    2.5698    0.1886    0.1980    5.5980    1.5029
+#> 239:    93.0943   -6.0475   -1.9913   -4.4056   -0.9520    0.1921    5.4483    4.0960    0.9792    2.5785    0.1888    0.1997    5.5999    1.5011
+#> 240:    93.0904   -6.0498   -1.9909   -4.4070   -0.9520    0.1925    5.4921    4.1095    0.9814    2.5867    0.1887    0.2011    5.5974    1.5013
+#> 241:    93.0883   -6.0508   -1.9910   -4.4086   -0.9520    0.1931    5.5503    4.1140    0.9827    2.5966    0.1887    0.2023    5.6049    1.4997
+#> 242:    93.0884   -6.0487   -1.9916   -4.4102   -0.9517    0.1940    5.5634    4.1021    0.9831    2.6059    0.1886    0.2039    5.6116    1.5005
+#> 243:    93.0836   -6.0466   -1.9920   -4.4123   -0.9517    0.1950    5.5786    4.0878    0.9837    2.6204    0.1887    0.2054    5.6217    1.5000
+#> 244:    93.0756   -6.0477   -1.9926   -4.4149   -0.9517    0.1956    5.5827    4.0904    0.9843    2.6385    0.1887    0.2070    5.6306    1.4995
+#> 245:    93.0664   -6.0533   -1.9930   -4.4174   -0.9514    0.1963    5.6228    4.1208    0.9857    2.6549    0.1888    0.2086    5.6346    1.4996
+#> 246:    93.0643   -6.0543   -1.9931   -4.4200   -0.9511    0.1969    5.6236    4.1257    0.9872    2.6735    0.1886    0.2096    5.6381    1.4989
+#> 247:    93.0631   -6.0568   -1.9929   -4.4227   -0.9511    0.1974    5.6045    4.1389    0.9889    2.6910    0.1886    0.2107    5.6408    1.4984
+#> 248:    93.0636   -6.0567   -1.9924   -4.4264   -0.9513    0.1974    5.6016    4.1412    0.9906    2.7225    0.1886    0.2117    5.6424    1.4992
+#> 249:    93.0727   -6.0560   -1.9920   -4.4302   -0.9514    0.1973    5.6088    4.1383    0.9922    2.7584    0.1885    0.2125    5.6441    1.4992
+#> 250:    93.0865   -6.0551   -1.9915   -4.4337   -0.9512    0.1973    5.6127    4.1386    0.9941    2.7852    0.1884    0.2135    5.6522    1.4977
+#> 251:    93.0887   -6.0551   -1.9910   -4.4364   -0.9511    0.1967    5.5869    4.1455    0.9964    2.8060    0.1883    0.2146    5.6561    1.4968
+#> 252:    93.0877   -6.0522   -1.9904   -4.4376   -0.9511    0.1964    5.5778    4.1346    0.9987    2.8151    0.1883    0.2155    5.6583    1.4964
+#> 253:    93.0843   -6.0518   -1.9897   -4.4391   -0.9512    0.1961    5.5948    4.1323    1.0011    2.8253    0.1884    0.2164    5.6588    1.4972
+#> 254:    93.0818   -6.0518   -1.9896   -4.4399   -0.9512    0.1957    5.6122    4.1352    1.0016    2.8319    0.1882    0.2169    5.6573    1.4991
+#> 255:    93.0838   -6.0524   -1.9895   -4.4401   -0.9514    0.1954    5.6310    4.1366    1.0025    2.8408    0.1880    0.2174    5.6584    1.4996
+#> 256:    93.0850   -6.0579   -1.9892   -4.4400   -0.9515    0.1948    5.6526    4.1752    1.0043    2.8482    0.1879    0.2181    5.6611    1.4979
+#> 257:    93.0868   -6.0600   -1.9890   -4.4391   -0.9517    0.1940    5.6742    4.1941    1.0055    2.8499    0.1878    0.2189    5.6649    1.4985
+#> 258:    93.0873   -6.0606   -1.9888   -4.4391   -0.9518    0.1932    5.7088    4.2037    1.0066    2.8552    0.1877    0.2196    5.6668    1.4983
+#> 259:    93.0912   -6.0650   -1.9882   -4.4377   -0.9519    0.1925    5.7494    4.2300    1.0080    2.8537    0.1877    0.2204    5.6729    1.4977
+#> 260:    93.0964   -6.0699   -1.9874   -4.4362   -0.9519    0.1918    5.7609    4.2588    1.0100    2.8513    0.1877    0.2212    5.6792    1.4974
+#> 261:    93.1014   -6.0737   -1.9866   -4.4350   -0.9522    0.1913    5.7971    4.2807    1.0115    2.8496    0.1877    0.2220    5.6812    1.4969
+#> 262:    93.1064   -6.0734   -1.9859   -4.4346   -0.9526    0.1909    5.7936    4.2719    1.0129    2.8505    0.1877    0.2228    5.6824    1.4958
+#> 263:    93.1092   -6.0783   -1.9850   -4.4344   -0.9530    0.1906    5.8078    4.2973    1.0141    2.8525    0.1879    0.2233    5.6815    1.4954
+#> 264:    93.1128   -6.0830   -1.9842   -4.4338   -0.9535    0.1901    5.8245    4.3273    1.0146    2.8527    0.1880    0.2237    5.6768    1.4958
+#> 265:    93.1198   -6.0874   -1.9834   -4.4331   -0.9541    0.1895    5.8467    4.3490    1.0149    2.8522    0.1880    0.2238    5.6693    1.4965
+#> 266:    93.1284   -6.0890   -1.9828   -4.4327   -0.9546    0.1888    5.8350    4.3488    1.0149    2.8513    0.1881    0.2239    5.6650    1.4970
+#> 267:    93.1380   -6.0926   -1.9819   -4.4326   -0.9549    0.1883    5.8440    4.3677    1.0156    2.8526    0.1883    0.2240    5.6609    1.4974
+#> 268:    93.1480   -6.0915   -1.9810   -4.4321   -0.9552    0.1873    5.8565    4.3552    1.0163    2.8522    0.1886    0.2238    5.6537    1.4990
+#> 269:    93.1539   -6.0910   -1.9803   -4.4314   -0.9556    0.1866    5.8709    4.3438    1.0179    2.8503    0.1888    0.2237    5.6495    1.4989
+#> 270:    93.1620   -6.0898   -1.9798   -4.4311   -0.9561    0.1861    5.8678    4.3301    1.0197    2.8507    0.1890    0.2235    5.6466    1.4984
+#> 271:    93.1668   -6.0881   -1.9792   -4.4305   -0.9565    0.1857    5.8508    4.3147    1.0209    2.8487    0.1891    0.2234    5.6487    1.4997
+#> 272:    93.1725   -6.0848   -1.9787   -4.4300   -0.9569    0.1855    5.8431    4.2948    1.0217    2.8474    0.1894    0.2233    5.6488    1.5000
+#> 273:    93.1770   -6.0809   -1.9783   -4.4297   -0.9572    0.1850    5.8432    4.2739    1.0227    2.8470    0.1897    0.2235    5.6497    1.5000
+#> 274:    93.1797   -6.0774   -1.9776   -4.4299   -0.9574    0.1846    5.8549    4.2532    1.0243    2.8494    0.1901    0.2235    5.6511    1.5003
+#> 275:    93.1829   -6.0759   -1.9774   -4.4303   -0.9578    0.1845    5.8633    4.2387    1.0255    2.8514    0.1906    0.2234    5.6561    1.5010
+#> 276:    93.1846   -6.0764   -1.9771   -4.4303   -0.9581    0.1845    5.8738    4.2322    1.0267    2.8523    0.1911    0.2232    5.6554    1.5020
+#> 277:    93.1880   -6.0792   -1.9768   -4.4305   -0.9584    0.1844    5.8980    4.2423    1.0278    2.8541    0.1915    0.2229    5.6586    1.5019
+#> 278:    93.1920   -6.0791   -1.9766   -4.4307   -0.9586    0.1841    5.9368    4.2391    1.0289    2.8559    0.1919    0.2226    5.6600    1.5024
+#> 279:    93.1892   -6.0786   -1.9766   -4.4310   -0.9586    0.1839    5.9822    4.2309    1.0300    2.8584    0.1925    0.2226    5.6642    1.5015
+#> 280:    93.1868   -6.0782   -1.9765   -4.4311   -0.9587    0.1836    6.0381    4.2253    1.0311    2.8616    0.1930    0.2227    5.6686    1.5008
+#> 281:    93.1805   -6.0781   -1.9764   -4.4309   -0.9586    0.1832    6.0718    4.2228    1.0325    2.8626    0.1936    0.2227    5.6741    1.5002
+#> 282:    93.1780   -6.0768   -1.9762   -4.4318   -0.9585    0.1829    6.0867    4.2160    1.0341    2.8701    0.1941    0.2228    5.6740    1.4998
+#> 283:    93.1777   -6.0736   -1.9760   -4.4325   -0.9583    0.1825    6.1250    4.2003    1.0355    2.8768    0.1946    0.2228    5.6761    1.5010
+#> 284:    93.1745   -6.0726   -1.9757   -4.4337   -0.9582    0.1823    6.1509    4.1975    1.0370    2.8843    0.1951    0.2227    5.6764    1.5009
+#> 285:    93.1742   -6.0719   -1.9755   -4.4348   -0.9579    0.1820    6.1652    4.1936    1.0381    2.8910    0.1954    0.2225    5.6773    1.5011
+#> 286:    93.1706   -6.0698   -1.9754   -4.4356   -0.9576    0.1818    6.1840    4.1844    1.0394    2.8966    0.1958    0.2224    5.6780    1.5011
+#> 287:    93.1672   -6.0678   -1.9752   -4.4370   -0.9573    0.1816    6.2123    4.1767    1.0400    2.9079    0.1963    0.2224    5.6757    1.5015
+#> 288:    93.1628   -6.0658   -1.9753   -4.4379   -0.9572    0.1815    6.2355    4.1700    1.0407    2.9150    0.1967    0.2223    5.6742    1.5013
+#> 289:    93.1588   -6.0628   -1.9753   -4.4389   -0.9569    0.1818    6.2435    4.1565    1.0416    2.9217    0.1969    0.2218    5.6777    1.5007
+#> 290:    93.1560   -6.0590   -1.9754   -4.4399   -0.9565    0.1820    6.2564    4.1394    1.0425    2.9291    0.1971    0.2214    5.6778    1.5006
+#> 291:    93.1552   -6.0555   -1.9754   -4.4409   -0.9562    0.1821    6.2753    4.1246    1.0435    2.9375    0.1973    0.2210    5.6779    1.5009
+#> 292:    93.1546   -6.0541   -1.9754   -4.4415   -0.9558    0.1820    6.2881    4.1183    1.0444    2.9414    0.1975    0.2205    5.6762    1.5006
+#> 293:    93.1506   -6.0535   -1.9756   -4.4424   -0.9555    0.1821    6.2856    4.1182    1.0454    2.9474    0.1976    0.2200    5.6770    1.4994
+#> 294:    93.1453   -6.0520   -1.9758   -4.4424   -0.9553    0.1819    6.2733    4.1124    1.0463    2.9487    0.1979    0.2195    5.6792    1.4985
+#> 295:    93.1431   -6.0487   -1.9760   -4.4421   -0.9551    0.1820    6.2655    4.1009    1.0469    2.9498    0.1982    0.2190    5.6797    1.4989
+#> 296:    93.1425   -6.0460   -1.9760   -4.4432   -0.9548    0.1818    6.2801    4.0912    1.0478    2.9566    0.1984    0.2185    5.6795    1.4989
+#> 297:    93.1403   -6.0442   -1.9761   -4.4440   -0.9545    0.1818    6.2979    4.0836    1.0485    2.9626    0.1987    0.2182    5.6783    1.4978
+#> 298:    93.1400   -6.0438   -1.9763   -4.4440   -0.9543    0.1817    6.3069    4.0842    1.0492    2.9646    0.1989    0.2178    5.6783    1.4968
+#> 299:    93.1373   -6.0426   -1.9764   -4.4445   -0.9540    0.1813    6.3134    4.0790    1.0505    2.9694    0.1991    0.2175    5.6800    1.4953
+#> 300:    93.1340   -6.0412   -1.9764   -4.4450   -0.9538    0.1811    6.3192    4.0731    1.0516    2.9744    0.1993    0.2171    5.6782    1.4938
+#> 301:    93.1330   -6.0402   -1.9766   -4.4455   -0.9535    0.1808    6.3278    4.0685    1.0531    2.9784    0.1996    0.2167    5.6819    1.4925
+#> 302:    93.1308   -6.0402   -1.9768   -4.4457   -0.9534    0.1806    6.3417    4.0684    1.0549    2.9813    0.1998    0.2163    5.6824    1.4905
+#> 303:    93.1294   -6.0373   -1.9769   -4.4459   -0.9532    0.1804    6.3489    4.0538    1.0565    2.9838    0.2000    0.2159    5.6841    1.4890
+#> 304:    93.1304   -6.0345   -1.9771   -4.4461   -0.9530    0.1801    6.3543    4.0409    1.0581    2.9859    0.2002    0.2155    5.6869    1.4875
+#> 305:    93.1287   -6.0319   -1.9772   -4.4463   -0.9528    0.1800    6.3496    4.0293    1.0597    2.9882    0.2003    0.2151    5.6902    1.4867
+#> 306:    93.1261   -6.0301   -1.9775   -4.4474   -0.9527    0.1802    6.3479    4.0231    1.0614    2.9989    0.2003    0.2145    5.6963    1.4856
+#> 307:    93.1232   -6.0284   -1.9777   -4.4479   -0.9526    0.1802    6.3507    4.0135    1.0629    3.0036    0.2004    0.2141    5.6987    1.4849
+#> 308:    93.1192   -6.0264   -1.9779   -4.4483   -0.9524    0.1802    6.3641    4.0019    1.0644    3.0084    0.2004    0.2135    5.6991    1.4837
+#> 309:    93.1137   -6.0253   -1.9783   -4.4487   -0.9522    0.1803    6.3579    3.9953    1.0658    3.0133    0.2004    0.2130    5.7035    1.4826
+#> 310:    93.1100   -6.0223   -1.9787   -4.4489   -0.9520    0.1804    6.3423    3.9800    1.0665    3.0171    0.2005    0.2126    5.7061    1.4822
+#> 311:    93.1044   -6.0215   -1.9791   -4.4496   -0.9517    0.1804    6.3365    3.9744    1.0675    3.0251    0.2005    0.2121    5.7092    1.4816
+#> 312:    93.1006   -6.0206   -1.9795   -4.4501   -0.9516    0.1806    6.3317    3.9681    1.0688    3.0321    0.2006    0.2115    5.7128    1.4805
+#> 313:    93.0951   -6.0194   -1.9797   -4.4499   -0.9516    0.1805    6.3297    3.9609    1.0702    3.0333    0.2008    0.2109    5.7137    1.4805
+#> 314:    93.0922   -6.0192   -1.9800   -4.4497   -0.9515    0.1804    6.3486    3.9570    1.0715    3.0345    0.2009    0.2104    5.7144    1.4800
+#> 315:    93.0883   -6.0186   -1.9804   -4.4495   -0.9515    0.1803    6.3712    3.9528    1.0726    3.0351    0.2011    0.2100    5.7156    1.4794
+#> 316:    93.0808   -6.0182   -1.9808   -4.4492   -0.9514    0.1802    6.3979    3.9483    1.0738    3.0345    0.2013    0.2097    5.7164    1.4792
+#> 317:    93.0758   -6.0174   -1.9813   -4.4487   -0.9513    0.1801    6.4377    3.9428    1.0747    3.0327    0.2015    0.2094    5.7175    1.4787
+#> 318:    93.0713   -6.0166   -1.9816   -4.4484   -0.9513    0.1801    6.4856    3.9375    1.0757    3.0316    0.2017    0.2091    5.7197    1.4778
+#> 319:    93.0659   -6.0176   -1.9819   -4.4482   -0.9511    0.1800    6.5263    3.9425    1.0768    3.0313    0.2018    0.2088    5.7218    1.4772
+#> 320:    93.0607   -6.0165   -1.9822   -4.4484   -0.9510    0.1798    6.5554    3.9372    1.0777    3.0329    0.2019    0.2087    5.7236    1.4771
+#> 321:    93.0551   -6.0145   -1.9825   -4.4487   -0.9509    0.1797    6.5844    3.9275    1.0787    3.0368    0.2021    0.2085    5.7256    1.4766
+#> 322:    93.0531   -6.0130   -1.9827   -4.4491   -0.9507    0.1797    6.6073    3.9201    1.0797    3.0400    0.2021    0.2082    5.7250    1.4759
+#> 323:    93.0477   -6.0123   -1.9828   -4.4493   -0.9506    0.1794    6.6255    3.9149    1.0804    3.0420    0.2021    0.2080    5.7249    1.4756
+#> 324:    93.0425   -6.0107   -1.9829   -4.4498   -0.9504    0.1792    6.6282    3.9060    1.0813    3.0457    0.2022    0.2078    5.7250    1.4754
+#> 325:    93.0389   -6.0090   -1.9830   -4.4504   -0.9503    0.1792    6.6252    3.8965    1.0819    3.0496    0.2022    0.2077    5.7246    1.4749
+#> 326:    93.0411   -6.0093   -1.9832   -4.4509   -0.9503    0.1795    6.6358    3.8976    1.0827    3.0516    0.2022    0.2076    5.7248    1.4738
+#> 327:    93.0418   -6.0095   -1.9834   -4.4514   -0.9503    0.1797    6.6415    3.8962    1.0834    3.0533    0.2022    0.2075    5.7237    1.4737
+#> 328:    93.0434   -6.0093   -1.9835   -4.4520   -0.9503    0.1798    6.6621    3.8957    1.0841    3.0550    0.2022    0.2074    5.7247    1.4731
+#> 329:    93.0446   -6.0109   -1.9836   -4.4522   -0.9503    0.1798    6.6763    3.9048    1.0847    3.0543    0.2022    0.2072    5.7259    1.4725
+#> 330:    93.0451   -6.0133   -1.9838   -4.4518   -0.9503    0.1799    6.6859    3.9192    1.0852    3.0521    0.2022    0.2070    5.7252    1.4719
+#> 331:    93.0456   -6.0136   -1.9838   -4.4516   -0.9503    0.1799    6.6773    3.9217    1.0858    3.0505    0.2022    0.2067    5.7250    1.4715
+#> 332:    93.0463   -6.0133   -1.9839   -4.4515   -0.9504    0.1799    6.6560    3.9195    1.0863    3.0494    0.2022    0.2063    5.7255    1.4710
+#> 333:    93.0496   -6.0122   -1.9839   -4.4513   -0.9505    0.1800    6.6484    3.9134    1.0869    3.0474    0.2022    0.2060    5.7253    1.4705
+#> 334:    93.0520   -6.0105   -1.9838   -4.4513   -0.9505    0.1801    6.6314    3.9035    1.0877    3.0462    0.2022    0.2056    5.7259    1.4702
+#> 335:    93.0550   -6.0088   -1.9836   -4.4510   -0.9507    0.1800    6.6194    3.8941    1.0887    3.0451    0.2022    0.2051    5.7263    1.4702
+#> 336:    93.0554   -6.0081   -1.9834   -4.4509   -0.9508    0.1800    6.6100    3.8896    1.0896    3.0444    0.2022    0.2048    5.7266    1.4705
+#> 337:    93.0582   -6.0067   -1.9832   -4.4507   -0.9509    0.1800    6.6089    3.8805    1.0904    3.0445    0.2021    0.2044    5.7260    1.4706
+#> 338:    93.0631   -6.0073   -1.9831   -4.4507   -0.9511    0.1801    6.5993    3.8798    1.0908    3.0443    0.2021    0.2040    5.7250    1.4711
+#> 339:    93.0689   -6.0071   -1.9831   -4.4508   -0.9513    0.1803    6.5976    3.8749    1.0911    3.0442    0.2021    0.2037    5.7240    1.4714
+#> 340:    93.0694   -6.0085   -1.9831   -4.4507   -0.9516    0.1804    6.5915    3.8779    1.0914    3.0436    0.2022    0.2032    5.7227    1.4711
+#> 341:    93.0709   -6.0097   -1.9830   -4.4508   -0.9518    0.1804    6.5862    3.8803    1.0915    3.0429    0.2023    0.2026    5.7213    1.4715
+#> 342:    93.0741   -6.0104   -1.9829   -4.4507   -0.9521    0.1804    6.5894    3.8812    1.0918    3.0417    0.2024    0.2022    5.7204    1.4714
+#> 343:    93.0781   -6.0122   -1.9829   -4.4505   -0.9523    0.1804    6.5907    3.8870    1.0921    3.0410    0.2024    0.2016    5.7202    1.4712
+#> 344:    93.0818   -6.0134   -1.9829   -4.4503   -0.9525    0.1804    6.5908    3.8895    1.0926    3.0400    0.2025    0.2011    5.7182    1.4712
+#> 345:    93.0850   -6.0148   -1.9829   -4.4500   -0.9528    0.1806    6.5984    3.8931    1.0926    3.0387    0.2026    0.2006    5.7169    1.4712
+#> 346:    93.0849   -6.0155   -1.9831   -4.4502   -0.9529    0.1807    6.6079    3.8986    1.0931    3.0401    0.2028    0.2002    5.7172    1.4716
+#> 347:    93.0859   -6.0161   -1.9832   -4.4503   -0.9530    0.1809    6.6307    3.9028    1.0941    3.0404    0.2029    0.1998    5.7170    1.4712
+#> 348:    93.0885   -6.0173   -1.9833   -4.4503   -0.9532    0.1809    6.6470    3.9096    1.0951    3.0404    0.2030    0.1993    5.7174    1.4708
+#> 349:    93.0894   -6.0189   -1.9835   -4.4503   -0.9534    0.1810    6.6443    3.9190    1.0955    3.0410    0.2031    0.1989    5.7175    1.4707
+#> 350:    93.0924   -6.0196   -1.9836   -4.4502   -0.9535    0.1813    6.6543    3.9218    1.0957    3.0409    0.2032    0.1983    5.7182    1.4705
+#> 351:    93.0938   -6.0203   -1.9838   -4.4503   -0.9536    0.1814    6.6630    3.9233    1.0963    3.0417    0.2032    0.1977    5.7189    1.4703
+#> 352:    93.0946   -6.0210   -1.9838   -4.4505   -0.9537    0.1816    6.6698    3.9263    1.0968    3.0432    0.2033    0.1973    5.7196    1.4701
+#> 353:    93.0969   -6.0214   -1.9839   -4.4505   -0.9538    0.1818    6.6837    3.9270    1.0973    3.0442    0.2034    0.1968    5.7199    1.4701
+#> 354:    93.1014   -6.0199   -1.9839   -4.4504   -0.9539    0.1817    6.7040    3.9204    1.0978    3.0438    0.2034    0.1962    5.7191    1.4703
+#> 355:    93.1035   -6.0197   -1.9838   -4.4502   -0.9539    0.1816    6.7119    3.9222    1.0983    3.0433    0.2034    0.1957    5.7194    1.4706
+#> 356:    93.1055   -6.0198   -1.9839   -4.4496   -0.9539    0.1815    6.7302    3.9277    1.0989    3.0409    0.2035    0.1952    5.7206    1.4707
+#> 357:    93.1080   -6.0188   -1.9837   -4.4490   -0.9540    0.1813    6.7558    3.9243    1.0997    3.0386    0.2035    0.1948    5.7217    1.4706
+#> 358:    93.1111   -6.0182   -1.9835   -4.4484   -0.9541    0.1812    6.7733    3.9204    1.1005    3.0365    0.2035    0.1944    5.7209    1.4700
+#> 359:    93.1148   -6.0175   -1.9834   -4.4481   -0.9542    0.1811    6.7997    3.9151    1.1012    3.0355    0.2035    0.1940    5.7191    1.4696
+#> 360:    93.1157   -6.0176   -1.9832   -4.4478   -0.9543    0.1810    6.8133    3.9155    1.1017    3.0340    0.2035    0.1937    5.7158    1.4691
+#> 361:    93.1169   -6.0185   -1.9830   -4.4476   -0.9544    0.1808    6.8098    3.9232    1.1022    3.0328    0.2035    0.1934    5.7143    1.4690
+#> 362:    93.1173   -6.0205   -1.9829   -4.4472   -0.9545    0.1805    6.8125    3.9361    1.1024    3.0319    0.2035    0.1931    5.7137    1.4693
+#> 363:    93.1162   -6.0230   -1.9828   -4.4467   -0.9545    0.1801    6.8240    3.9524    1.1025    3.0312    0.2035    0.1928    5.7125    1.4695
+#> 364:    93.1173   -6.0240   -1.9826   -4.4464   -0.9546    0.1799    6.8341    3.9575    1.1027    3.0307    0.2035    0.1924    5.7092    1.4695
+#> 365:    93.1199   -6.0259   -1.9824   -4.4462   -0.9547    0.1796    6.8476    3.9687    1.1028    3.0316    0.2036    0.1920    5.7073    1.4695
+#> 366:    93.1220   -6.0277   -1.9821   -4.4461   -0.9548    0.1793    6.8542    3.9777    1.1032    3.0319    0.2037    0.1916    5.7060    1.4694
+#> 367:    93.1230   -6.0287   -1.9819   -4.4460   -0.9548    0.1791    6.8633    3.9829    1.1038    3.0331    0.2038    0.1914    5.7056    1.4693
+#> 368:    93.1255   -6.0276   -1.9816   -4.4459   -0.9549    0.1789    6.8734    3.9764    1.1038    3.0341    0.2038    0.1912    5.7050    1.4695
+#> 369:    93.1258   -6.0263   -1.9814   -4.4461   -0.9549    0.1787    6.8756    3.9698    1.1039    3.0357    0.2039    0.1910    5.7031    1.4697
+#> 370:    93.1288   -6.0252   -1.9811   -4.4463   -0.9548    0.1785    6.8892    3.9639    1.1039    3.0375    0.2040    0.1909    5.7029    1.4701
+#> 371:    93.1317   -6.0245   -1.9810   -4.4467   -0.9548    0.1784    6.8974    3.9601    1.1037    3.0391    0.2040    0.1907    5.7037    1.4700
+#> 372:    93.1346   -6.0233   -1.9811   -4.4465   -0.9548    0.1781    6.9042    3.9536    1.1035    3.0386    0.2040    0.1905    5.7038    1.4700
+#> 373:    93.1340   -6.0234   -1.9810   -4.4461   -0.9547    0.1778    6.9034    3.9548    1.1034    3.0371    0.2039    0.1903    5.7040    1.4698
+#> 374:    93.1324   -6.0230   -1.9811   -4.4456   -0.9547    0.1775    6.9080    3.9527    1.1034    3.0349    0.2038    0.1901    5.7055    1.4691
+#> 375:    93.1309   -6.0226   -1.9812   -4.4451   -0.9546    0.1773    6.9093    3.9493    1.1034    3.0334    0.2037    0.1899    5.7063    1.4683
+#> 376:    93.1298   -6.0215   -1.9811   -4.4447   -0.9546    0.1770    6.9039    3.9432    1.1035    3.0319    0.2036    0.1897    5.7064    1.4678
+#> 377:    93.1296   -6.0209   -1.9811   -4.4443   -0.9546    0.1768    6.8932    3.9390    1.1036    3.0305    0.2035    0.1895    5.7056    1.4672
+#> 378:    93.1292   -6.0200   -1.9810   -4.4438   -0.9545    0.1764    6.8850    3.9349    1.1037    3.0288    0.2034    0.1892    5.7068    1.4667
+#> 379:    93.1284   -6.0196   -1.9808   -4.4432   -0.9544    0.1760    6.8766    3.9318    1.1038    3.0266    0.2033    0.1890    5.7072    1.4665
+#> 380:    93.1304   -6.0182   -1.9806   -4.4425   -0.9543    0.1756    6.8737    3.9249    1.1040    3.0236    0.2033    0.1888    5.7074    1.4662
+#> 381:    93.1315   -6.0169   -1.9804   -4.4417   -0.9542    0.1754    6.8707    3.9193    1.1040    3.0210    0.2032    0.1886    5.7066    1.4661
+#> 382:    93.1331   -6.0160   -1.9801   -4.4409   -0.9542    0.1750    6.8645    3.9150    1.1040    3.0187    0.2032    0.1885    5.7063    1.4664
+#> 383:    93.1334   -6.0153   -1.9800   -4.4403   -0.9542    0.1746    6.8599    3.9123    1.1037    3.0167    0.2032    0.1882    5.7074    1.4665
+#> 384:    93.1328   -6.0140   -1.9801   -4.4397   -0.9540    0.1742    6.8600    3.9074    1.1034    3.0149    0.2031    0.1879    5.7072    1.4667
+#> 385:    93.1306   -6.0137   -1.9801   -4.4392   -0.9539    0.1739    6.8449    3.9073    1.1031    3.0137    0.2030    0.1876    5.7084    1.4665
+#> 386:    93.1281   -6.0134   -1.9801   -4.4388   -0.9539    0.1735    6.8356    3.9088    1.1028    3.0123    0.2029    0.1872    5.7087    1.4667
+#> 387:    93.1267   -6.0141   -1.9801   -4.4384   -0.9537    0.1732    6.8364    3.9150    1.1025    3.0110    0.2028    0.1869    5.7101    1.4669
+#> 388:    93.1252   -6.0142   -1.9801   -4.4380   -0.9536    0.1730    6.8374    3.9192    1.1022    3.0097    0.2028    0.1866    5.7110    1.4670
+#> 389:    93.1223   -6.0140   -1.9801   -4.4375   -0.9535    0.1728    6.8334    3.9209    1.1019    3.0083    0.2028    0.1862    5.7105    1.4674
+#> 390:    93.1221   -6.0144   -1.9800   -4.4371   -0.9534    0.1726    6.8248    3.9256    1.1014    3.0068    0.2028    0.1859    5.7098    1.4675
+#> 391:    93.1210   -6.0149   -1.9799   -4.4365   -0.9533    0.1725    6.8339    3.9293    1.1011    3.0054    0.2028    0.1856    5.7109    1.4678
+#> 392:    93.1193   -6.0145   -1.9799   -4.4360   -0.9532    0.1724    6.8360    3.9279    1.1009    3.0040    0.2028    0.1852    5.7107    1.4678
+#> 393:    93.1200   -6.0149   -1.9799   -4.4357   -0.9532    0.1723    6.8461    3.9287    1.1005    3.0019    0.2028    0.1849    5.7100    1.4678
+#> 394:    93.1202   -6.0138   -1.9799   -4.4355   -0.9532    0.1723    6.8520    3.9229    1.1006    3.0003    0.2028    0.1846    5.7085    1.4679
+#> 395:    93.1203   -6.0134   -1.9800   -4.4354   -0.9532    0.1723    6.8583    3.9200    1.1005    2.9987    0.2027    0.1844    5.7072    1.4680
+#> 396:    93.1195   -6.0131   -1.9800   -4.4353   -0.9532    0.1724    6.8593    3.9169    1.1004    2.9969    0.2027    0.1842    5.7062    1.4676
+#> 397:    93.1195   -6.0130   -1.9801   -4.4352   -0.9532    0.1724    6.8591    3.9143    1.1004    2.9958    0.2027    0.1839    5.7046    1.4675
+#> 398:    93.1200   -6.0128   -1.9801   -4.4352   -0.9532    0.1725    6.8522    3.9125    1.1004    2.9945    0.2028    0.1836    5.7032    1.4675
+#> 399:    93.1200   -6.0135   -1.9803   -4.4351   -0.9531    0.1726    6.8471    3.9166    1.1003    2.9933    0.2028    0.1833    5.7032    1.4673
+#> 400:    93.1204   -6.0139   -1.9803   -4.4351   -0.9531    0.1727    6.8438    3.9191    1.1003    2.9918    0.2027    0.1832    5.7026    1.4671
+#> 401:    93.1198   -6.0139   -1.9804   -4.4351   -0.9530    0.1728    6.8373    3.9186    1.1004    2.9901    0.2027    0.1831    5.7015    1.4670
+#> 402:    93.1199   -6.0141   -1.9804   -4.4351   -0.9530    0.1729    6.8357    3.9194    1.1005    2.9882    0.2027    0.1830    5.7003    1.4671
+#> 403:    93.1196   -6.0155   -1.9804   -4.4350   -0.9530    0.1730    6.8285    3.9255    1.1007    2.9863    0.2026    0.1829    5.7001    1.4671
+#> 404:    93.1183   -6.0164   -1.9805   -4.4350   -0.9531    0.1732    6.8204    3.9308    1.1009    2.9843    0.2026    0.1829    5.7008    1.4670
+#> 405:    93.1178   -6.0161   -1.9805   -4.4350   -0.9532    0.1733    6.8205    3.9286    1.1012    2.9823    0.2025    0.1829    5.7013    1.4669
+#> 406:    93.1176   -6.0171   -1.9806   -4.4348   -0.9533    0.1735    6.8253    3.9319    1.1013    2.9801    0.2025    0.1828    5.7026    1.4666
+#> 407:    93.1168   -6.0185   -1.9807   -4.4348   -0.9533    0.1736    6.8290    3.9373    1.1015    2.9788    0.2024    0.1830    5.7033    1.4664
+#> 408:    93.1165   -6.0198   -1.9808   -4.4349   -0.9534    0.1738    6.8217    3.9428    1.1017    2.9773    0.2023    0.1830    5.7047    1.4663
+#> 409:    93.1165   -6.0210   -1.9809   -4.4350   -0.9534    0.1741    6.8208    3.9505    1.1019    2.9761    0.2021    0.1830    5.7055    1.4661
+#> 410:    93.1169   -6.0230   -1.9810   -4.4351   -0.9535    0.1745    6.8239    3.9617    1.1020    2.9751    0.2020    0.1829    5.7052    1.4658
+#> 411:    93.1166   -6.0237   -1.9811   -4.4353   -0.9536    0.1748    6.8234    3.9664    1.1020    2.9741    0.2019    0.1829    5.7043    1.4657
+#> 412:    93.1164   -6.0235   -1.9812   -4.4355   -0.9536    0.1751    6.8205    3.9643    1.1020    2.9735    0.2017    0.1827    5.7053    1.4654
+#> 413:    93.1182   -6.0232   -1.9814   -4.4356   -0.9537    0.1755    6.8133    3.9615    1.1020    2.9726    0.2016    0.1825    5.7070    1.4650
+#> 414:    93.1190   -6.0226   -1.9815   -4.4360   -0.9537    0.1760    6.8113    3.9578    1.1021    2.9726    0.2015    0.1825    5.7081    1.4648
+#> 415:    93.1183   -6.0226   -1.9817   -4.4364   -0.9538    0.1765    6.8081    3.9557    1.1021    2.9725    0.2014    0.1824    5.7085    1.4646
+#> 416:    93.1185   -6.0238   -1.9818   -4.4369   -0.9538    0.1768    6.8134    3.9617    1.1020    2.9734    0.2013    0.1822    5.7103    1.4645
+#> 417:    93.1190   -6.0245   -1.9819   -4.4373   -0.9540    0.1770    6.8164    3.9664    1.1022    2.9743    0.2012    0.1819    5.7102    1.4650
+#> 418:    93.1219   -6.0256   -1.9818   -4.4376   -0.9542    0.1773    6.8206    3.9710    1.1026    2.9745    0.2011    0.1816    5.7110    1.4655
+#> 419:    93.1255   -6.0261   -1.9817   -4.4381   -0.9543    0.1776    6.8183    3.9714    1.1030    2.9759    0.2010    0.1814    5.7134    1.4659
+#> 420:    93.1294   -6.0262   -1.9816   -4.4385   -0.9546    0.1779    6.8113    3.9704    1.1033    2.9768    0.2009    0.1810    5.7156    1.4666
+#> 421:    93.1319   -6.0259   -1.9815   -4.4392   -0.9547    0.1781    6.7989    3.9685    1.1036    2.9786    0.2008    0.1808    5.7171    1.4676
+#> 422:    93.1338   -6.0263   -1.9814   -4.4398   -0.9548    0.1783    6.7922    3.9681    1.1038    2.9806    0.2006    0.1808    5.7179    1.4681
+#> 423:    93.1353   -6.0266   -1.9813   -4.4406   -0.9550    0.1786    6.7868    3.9674    1.1040    2.9837    0.2006    0.1808    5.7181    1.4687
+#> 424:    93.1374   -6.0270   -1.9811   -4.4414   -0.9550    0.1787    6.7758    3.9674    1.1043    2.9866    0.2004    0.1807    5.7198    1.4693
+#> 425:    93.1383   -6.0270   -1.9811   -4.4420   -0.9551    0.1787    6.7547    3.9674    1.1042    2.9887    0.2003    0.1806    5.7211    1.4702
+#> 426:    93.1400   -6.0268   -1.9811   -4.4427   -0.9551    0.1789    6.7376    3.9654    1.1043    2.9917    0.2002    0.1805    5.7241    1.4706
+#> 427:    93.1391   -6.0268   -1.9811   -4.4433   -0.9552    0.1790    6.7196    3.9634    1.1045    2.9951    0.2001    0.1805    5.7271    1.4710
+#> 428:    93.1404   -6.0268   -1.9810   -4.4442   -0.9552    0.1792    6.7104    3.9628    1.1044    2.9999    0.2000    0.1803    5.7282    1.4712
+#> 429:    93.1431   -6.0265   -1.9810   -4.4450   -0.9553    0.1793    6.7029    3.9612    1.1045    3.0043    0.1999    0.1803    5.7293    1.4716
+#> 430:    93.1464   -6.0263   -1.9809   -4.4457   -0.9554    0.1795    6.6962    3.9606    1.1046    3.0074    0.1999    0.1802    5.7291    1.4724
+#> 431:    93.1485   -6.0267   -1.9809   -4.4460   -0.9555    0.1797    6.6865    3.9623    1.1046    3.0082    0.1998    0.1802    5.7287    1.4726
+#> 432:    93.1509   -6.0277   -1.9808   -4.4462   -0.9556    0.1798    6.6843    3.9658    1.1047    3.0086    0.1998    0.1801    5.7280    1.4727
+#> 433:    93.1528   -6.0289   -1.9806   -4.4464   -0.9557    0.1798    6.6840    3.9714    1.1049    3.0087    0.1998    0.1801    5.7282    1.4729
+#> 434:    93.1555   -6.0286   -1.9804   -4.4467   -0.9557    0.1798    6.6870    3.9693    1.1052    3.0094    0.1997    0.1800    5.7277    1.4729
+#> 435:    93.1574   -6.0290   -1.9803   -4.4467   -0.9558    0.1798    6.6893    3.9712    1.1055    3.0095    0.1996    0.1800    5.7278    1.4727
+#> 436:    93.1594   -6.0299   -1.9802   -4.4468   -0.9558    0.1798    6.6934    3.9749    1.1059    3.0103    0.1996    0.1801    5.7271    1.4727
+#> 437:    93.1600   -6.0311   -1.9800   -4.4469   -0.9558    0.1797    6.7010    3.9812    1.1065    3.0110    0.1996    0.1801    5.7275    1.4727
+#> 438:    93.1617   -6.0318   -1.9799   -4.4471   -0.9559    0.1796    6.7120    3.9865    1.1069    3.0121    0.1995    0.1801    5.7271    1.4727
+#> 439:    93.1634   -6.0329   -1.9798   -4.4472   -0.9559    0.1795    6.7279    3.9930    1.1075    3.0127    0.1995    0.1802    5.7268    1.4727
+#> 440:    93.1644   -6.0332   -1.9797   -4.4473   -0.9559    0.1794    6.7338    3.9962    1.1080    3.0136    0.1994    0.1803    5.7270    1.4726
+#> 441:    93.1654   -6.0335   -1.9795   -4.4477   -0.9558    0.1794    6.7435    3.9988    1.1085    3.0155    0.1994    0.1805    5.7274    1.4728
+#> 442:    93.1670   -6.0340   -1.9792   -4.4480   -0.9558    0.1794    6.7493    4.0028    1.1091    3.0173    0.1993    0.1808    5.7282    1.4729
+#> 443:    93.1685   -6.0346   -1.9790   -4.4485   -0.9558    0.1793    6.7577    4.0073    1.1092    3.0202    0.1992    0.1811    5.7267    1.4732
+#> 444:    93.1671   -6.0346   -1.9789   -4.4491   -0.9558    0.1792    6.7559    4.0069    1.1093    3.0238    0.1992    0.1813    5.7258    1.4733
+#> 445:    93.1655   -6.0355   -1.9789   -4.4497   -0.9557    0.1790    6.7552    4.0127    1.1094    3.0276    0.1992    0.1814    5.7262    1.4733
+#> 446:    93.1641   -6.0361   -1.9787   -4.4501   -0.9557    0.1789    6.7579    4.0169    1.1096    3.0306    0.1991    0.1816    5.7262    1.4732
+#> 447:    93.1628   -6.0363   -1.9786   -4.4503   -0.9556    0.1787    6.7680    4.0196    1.1099    3.0318    0.1991    0.1818    5.7258    1.4729
+#> 448:    93.1629   -6.0371   -1.9787   -4.4509   -0.9556    0.1786    6.7705    4.0248    1.1100    3.0358    0.1990    0.1820    5.7267    1.4725
+#> 449:    93.1626   -6.0381   -1.9785   -4.4510   -0.9556    0.1784    6.7800    4.0298    1.1101    3.0368    0.1989    0.1822    5.7266    1.4722
+#> 450:    93.1614   -6.0386   -1.9782   -4.4514   -0.9556    0.1782    6.7796    4.0316    1.1103    3.0392    0.1989    0.1824    5.7260    1.4720
+#> 451:    93.1603   -6.0397   -1.9779   -4.4518   -0.9556    0.1780    6.7799    4.0381    1.1107    3.0416    0.1988    0.1827    5.7264    1.4720
+#> 452:    93.1610   -6.0406   -1.9775   -4.4522   -0.9556    0.1777    6.7813    4.0424    1.1111    3.0443    0.1988    0.1828    5.7268    1.4719
+#> 453:    93.1618   -6.0414   -1.9771   -4.4523   -0.9556    0.1774    6.7814    4.0490    1.1115    3.0456    0.1987    0.1830    5.7262    1.4721
+#> 454:    93.1625   -6.0415   -1.9767   -4.4525   -0.9555    0.1771    6.7799    4.0499    1.1118    3.0473    0.1986    0.1831    5.7260    1.4723
+#> 455:    93.1636   -6.0412   -1.9765   -4.4528   -0.9555    0.1769    6.7778    4.0489    1.1123    3.0496    0.1985    0.1832    5.7268    1.4722
+#> 456:    93.1653   -6.0401   -1.9762   -4.4532   -0.9554    0.1768    6.7703    4.0441    1.1127    3.0517    0.1983    0.1834    5.7282    1.4725
+#> 457:    93.1672   -6.0396   -1.9760   -4.4535   -0.9554    0.1766    6.7683    4.0427    1.1129    3.0539    0.1982    0.1835    5.7281    1.4727
+#> 458:    93.1692   -6.0398   -1.9757   -4.4539   -0.9554    0.1765    6.7627    4.0450    1.1132    3.0570    0.1981    0.1835    5.7294    1.4729
+#> 459:    93.1708   -6.0402   -1.9756   -4.4542   -0.9554    0.1763    6.7615    4.0483    1.1133    3.0596    0.1980    0.1836    5.7320    1.4728
+#> 460:    93.1710   -6.0401   -1.9755   -4.4544   -0.9553    0.1762    6.7629    4.0487    1.1135    3.0615    0.1979    0.1835    5.7323    1.4730
+#> 461:    93.1708   -6.0403   -1.9755   -4.4546   -0.9552    0.1762    6.7639    4.0492    1.1136    3.0631    0.1978    0.1834    5.7321    1.4729
+#> 462:    93.1707   -6.0405   -1.9755   -4.4548   -0.9552    0.1760    6.7657    4.0506    1.1136    3.0647    0.1977    0.1833    5.7323    1.4727
+#> 463:    93.1690   -6.0403   -1.9755   -4.4548   -0.9551    0.1759    6.7607    4.0494    1.1136    3.0651    0.1976    0.1832    5.7332    1.4726
+#> 464:    93.1673   -6.0400   -1.9755   -4.4548   -0.9551    0.1758    6.7588    4.0480    1.1138    3.0652    0.1975    0.1832    5.7344    1.4724
+#> 465:    93.1657   -6.0399   -1.9755   -4.4548   -0.9550    0.1756    6.7601    4.0474    1.1138    3.0652    0.1974    0.1831    5.7350    1.4724
+#> 466:    93.1656   -6.0406   -1.9754   -4.4548   -0.9549    0.1755    6.7589    4.0514    1.1139    3.0658    0.1973    0.1831    5.7355    1.4723
+#> 467:    93.1657   -6.0408   -1.9753   -4.4548   -0.9549    0.1754    6.7558    4.0525    1.1139    3.0664    0.1972    0.1831    5.7358    1.4725
+#> 468:    93.1664   -6.0411   -1.9752   -4.4551   -0.9548    0.1753    6.7546    4.0551    1.1140    3.0679    0.1971    0.1832    5.7358    1.4723
+#> 469:    93.1667   -6.0412   -1.9751   -4.4552   -0.9547    0.1752    6.7547    4.0554    1.1141    3.0676    0.1970    0.1833    5.7354    1.4721
+#> 470:    93.1664   -6.0413   -1.9750   -4.4552   -0.9546    0.1751    6.7579    4.0564    1.1143    3.0676    0.1969    0.1833    5.7352    1.4718
+#> 471:    93.1656   -6.0411   -1.9750   -4.4553   -0.9545    0.1750    6.7611    4.0555    1.1142    3.0681    0.1968    0.1834    5.7354    1.4715
+#> 472:    93.1644   -6.0408   -1.9751   -4.4554   -0.9544    0.1749    6.7577    4.0542    1.1142    3.0686    0.1968    0.1834    5.7362    1.4712
+#> 473:    93.1632   -6.0405   -1.9751   -4.4554   -0.9543    0.1749    6.7527    4.0526    1.1141    3.0686    0.1967    0.1835    5.7363    1.4708
+#> 474:    93.1619   -6.0405   -1.9752   -4.4555   -0.9542    0.1748    6.7479    4.0521    1.1140    3.0689    0.1967    0.1835    5.7366    1.4705
+#> 475:    93.1609   -6.0413   -1.9753   -4.4557   -0.9542    0.1748    6.7469    4.0558    1.1139    3.0698    0.1967    0.1835    5.7379    1.4702
+#> 476:    93.1607   -6.0411   -1.9754   -4.4556   -0.9542    0.1747    6.7414    4.0549    1.1139    3.0697    0.1966    0.1835    5.7388    1.4698
+#> 477:    93.1597   -6.0413   -1.9754   -4.4560   -0.9542    0.1747    6.7321    4.0560    1.1137    3.0733    0.1966    0.1836    5.7392    1.4697
+#> 478:    93.1591   -6.0421   -1.9754   -4.4563   -0.9542    0.1745    6.7239    4.0608    1.1137    3.0765    0.1965    0.1836    5.7399    1.4697
+#> 479:    93.1589   -6.0438   -1.9754   -4.4564   -0.9542    0.1744    6.7150    4.0719    1.1136    3.0785    0.1964    0.1838    5.7421    1.4695
+#> 480:    93.1594   -6.0459   -1.9754   -4.4566   -0.9542    0.1742    6.7102    4.0895    1.1135    3.0807    0.1964    0.1839    5.7446    1.4695
+#> 481:    93.1604   -6.0472   -1.9754   -4.4570   -0.9542    0.1741    6.7104    4.1016    1.1135    3.0848    0.1964    0.1841    5.7456    1.4693
+#> 482:    93.1584   -6.0486   -1.9754   -4.4573   -0.9542    0.1739    6.7061    4.1152    1.1136    3.0877    0.1964    0.1842    5.7464    1.4690
+#> 483:    93.1561   -6.0501   -1.9754   -4.4576   -0.9541    0.1737    6.7067    4.1286    1.1135    3.0903    0.1963    0.1843    5.7475    1.4688
+#> 484:    93.1545   -6.0507   -1.9754   -4.4578   -0.9541    0.1737    6.7113    4.1362    1.1134    3.0918    0.1963    0.1845    5.7488    1.4687
+#> 485:    93.1524   -6.0507   -1.9754   -4.4583   -0.9540    0.1736    6.7094    4.1381    1.1134    3.0970    0.1964    0.1847    5.7496    1.4685
+#> 486:    93.1510   -6.0508   -1.9754   -4.4586   -0.9540    0.1735    6.7118    4.1405    1.1134    3.0996    0.1964    0.1847    5.7502    1.4682
+#> 487:    93.1495   -6.0507   -1.9755   -4.4591   -0.9539    0.1734    6.7128    4.1406    1.1134    3.1037    0.1965    0.1848    5.7510    1.4680
+#> 488:    93.1494   -6.0502   -1.9756   -4.4597   -0.9538    0.1734    6.7171    4.1384    1.1135    3.1081    0.1965    0.1848    5.7508    1.4677
+#> 489:    93.1497   -6.0497   -1.9756   -4.4604   -0.9538    0.1734    6.7188    4.1358    1.1135    3.1133    0.1966    0.1847    5.7499    1.4675
+#> 490:    93.1507   -6.0486   -1.9757   -4.4607   -0.9538    0.1735    6.7206    4.1319    1.1136    3.1157    0.1967    0.1847    5.7498    1.4672
+#> 491:    93.1507   -6.0476   -1.9757   -4.4612   -0.9537    0.1735    6.7141    4.1270    1.1136    3.1187    0.1968    0.1846    5.7503    1.4672
+#> 492:    93.1507   -6.0470   -1.9758   -4.4618   -0.9536    0.1735    6.7140    4.1238    1.1139    3.1218    0.1969    0.1846    5.7511    1.4669
+#> 493:    93.1513   -6.0468   -1.9758   -4.4623   -0.9535    0.1736    6.7214    4.1232    1.1141    3.1246    0.1970    0.1845    5.7514    1.4668
+#> 494:    93.1511   -6.0467   -1.9759   -4.4629   -0.9534    0.1737    6.7332    4.1232    1.1144    3.1278    0.1971    0.1845    5.7512    1.4664
+#> 495:    93.1511   -6.0464   -1.9761   -4.4635   -0.9533    0.1738    6.7377    4.1218    1.1145    3.1309    0.1972    0.1845    5.7515    1.4661
+#> 496:    93.1498   -6.0465   -1.9762   -4.4639   -0.9532    0.1739    6.7412    4.1241    1.1147    3.1325    0.1974    0.1845    5.7514    1.4657
+#> 497:    93.1482   -6.0467   -1.9764   -4.4644   -0.9532    0.1741    6.7506    4.1259    1.1149    3.1346    0.1975    0.1846    5.7513    1.4652
+#> 498:    93.1479   -6.0465   -1.9765   -4.4647   -0.9531    0.1743    6.7588    4.1263    1.1150    3.1357    0.1977    0.1846    5.7511    1.4648
+#> 499:    93.1462   -6.0455   -1.9766   -4.4651   -0.9530    0.1745    6.7659    4.1219    1.1152    3.1374    0.1978    0.1847    5.7515    1.4645
+#> 500:    93.1455   -6.0439   -1.9768   -4.4657   -0.9529    0.1747    6.7747    4.1151    1.1154    3.1404    0.1980    0.1848    5.7516    1.4641
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |
+#> |.....................|    log_k2 |  g_qlogis |sigma_parent |  sigma_A1 |
+#> |.....................|        o1 |        o2 |        o3 |        o4 |
+#> |.....................|        o5 |        o6 |...........|...........|
+#> |    1|     488.12318 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     488.12318 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     488.12318 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    G|    Gill Diff. |     52.24 |     2.364 |   -0.1419 |   0.08101 |
+#> |.....................|   -0.5200 |   0.08781 |    -28.20 |    -16.37 |
+#> |.....................|     14.83 |     13.24 |    -12.01 |    -2.482 |
+#> |.....................|     5.466 |    -10.09 |...........|...........|
+#> |    2|     2642.5634 |    0.2192 |    -1.035 |   -0.9096 |   -0.9332 |
+#> |.....................|   -0.9743 |   -0.8898 |   -0.4296 |   -0.6255 |
+#> |.....................|    -1.099 |    -1.073 |   -0.6891 |   -0.8357 |
+#> |.....................|   -0.9567 |   -0.7180 |...........|...........|
+#> |    U|     2642.5634 |     20.48 |    -5.348 |   -0.9517 |    -1.954 |
+#> |.....................|    -4.421 |    0.1928 |     2.469 |     1.224 |
+#> |.....................|    0.5606 |    0.7036 |     1.386 |     1.005 |
+#> |.....................|    0.7896 |     1.336 |...........|...........|
+#> |    X|     2642.5634 |     20.48 |  0.004759 |    0.2785 |    0.1417 |
+#> |.....................|   0.01202 |    0.5480 |     2.469 |     1.224 |
+#> |.....................|    0.5606 |    0.7036 |     1.386 |     1.005 |
+#> |.....................|    0.7896 |     1.336 |...........|...........|
+#> |    3|     546.98314 |    0.9219 |    -1.004 |   -0.9115 |   -0.9321 |
+#> |.....................|   -0.9813 |   -0.8886 |   -0.8089 |   -0.8458 |
+#> |.....................|   -0.9000 |   -0.8944 |   -0.8506 |   -0.8691 |
+#> |.....................|   -0.8831 |   -0.8538 |...........|...........|
+#> |    U|     546.98314 |     86.13 |    -5.316 |   -0.9535 |    -1.953 |
+#> |.....................|    -4.428 |    0.1930 |     2.082 |     1.104 |
+#> |.....................|    0.7044 |    0.8599 |     1.196 |    0.9723 |
+#> |.....................|    0.8529 |     1.178 |...........|...........|
+#> |    X|     546.98314 |     86.13 |  0.004913 |    0.2782 |    0.1419 |
+#> |.....................|   0.01193 |    0.5481 |     2.082 |     1.104 |
+#> |.....................|    0.7044 |    0.8599 |     1.196 |    0.9723 |
+#> |.....................|    0.8529 |     1.178 |...........|...........|
+#> |    4|     506.37737 |    0.9922 |    -1.000 |   -0.9117 |   -0.9320 |
+#> |.....................|   -0.9820 |   -0.8885 |   -0.8469 |   -0.8679 |
+#> |.....................|   -0.8800 |   -0.8766 |   -0.8668 |   -0.8724 |
+#> |.....................|   -0.8758 |   -0.8674 |...........|...........|
+#> |    U|     506.37737 |     92.70 |    -5.313 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.043 |     1.092 |
+#> |.....................|    0.7187 |    0.8755 |     1.177 |    0.9691 |
+#> |.....................|    0.8592 |     1.163 |...........|...........|
+#> |    X|     506.37737 |     92.70 |  0.004928 |    0.2781 |    0.1419 |
+#> |.....................|   0.01193 |    0.5481 |     2.043 |     1.092 |
+#> |.....................|    0.7187 |    0.8755 |     1.177 |    0.9691 |
+#> |.....................|    0.8592 |     1.163 |...........|...........|
+#> |    5|     506.42840 |    0.9992 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8507 |   -0.8701 |
+#> |.....................|   -0.8780 |   -0.8748 |   -0.8684 |   -0.8727 |
+#> |.....................|   -0.8751 |   -0.8687 |...........|...........|
+#> |    U|      506.4284 |     93.35 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.091 |
+#> |.....................|    0.7202 |    0.8771 |     1.175 |    0.9688 |
+#> |.....................|    0.8598 |     1.161 |...........|...........|
+#> |    X|      506.4284 |     93.35 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.091 |
+#> |.....................|    0.7202 |    0.8771 |     1.175 |    0.9688 |
+#> |.....................|    0.8598 |     1.161 |...........|...........|
+#> |    6|     506.47762 |    0.9999 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.47762 |     93.42 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.47762 |     93.42 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    7|     506.48298 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48298 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48298 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    8|     506.48363 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48363 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48363 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    9|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |   10|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |   11|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |   12|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |   13|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |   14|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |   15|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |   16|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |   17|     506.48371 |     1.000 |    -1.000 |   -0.9117 |   -0.9319 |
+#> |.....................|   -0.9821 |   -0.8885 |   -0.8511 |   -0.8703 |
+#> |.....................|   -0.8778 |   -0.8746 |   -0.8686 |   -0.8728 |
+#> |.....................|   -0.8750 |   -0.8689 |...........|...........|
+#> |    U|     506.48371 |     93.43 |    -5.312 |   -0.9537 |    -1.953 |
+#> |.....................|    -4.429 |    0.1930 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> |    X|     506.48371 |     93.43 |  0.004930 |    0.2781 |    0.1419 |
+#> |.....................|   0.01192 |    0.5481 |     2.039 |     1.090 |
+#> |.....................|    0.7203 |    0.8772 |     1.175 |    0.9687 |
+#> |.....................|    0.8599 |     1.161 |...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                
+# Identical two-component error for all variables is only possible with
+# est = 'focei' in nlmixr
+f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |
+#> |.....................|  log_beta | sigma_low |  rsd_high |        o1 |
+#> |.....................|        o2 |        o3 |        o4 |        o5 |
+#> |    1|     504.82714 |     1.000 |    -1.000 |   -0.9114 |   -0.8944 |
+#> |.....................|   -0.8457 |   -0.8687 |   -0.8916 |   -0.8768 |
+#> |.....................|   -0.8745 |   -0.8676 |   -0.8705 |   -0.8704 |
+#> |    U|     504.82714 |     93.12 |    -5.303 |   -0.9442 |   -0.1065 |
+#> |.....................|     2.291 |     1.160 |   0.03005 |    0.7578 |
+#> |.....................|    0.8738 |     1.213 |     1.069 |     1.072 |
+#> |    X|     504.82714 |     93.12 |  0.004975 |    0.2801 |    0.8989 |
+#> |.....................|     9.884 |     1.160 |   0.03005 |    0.7578 |
+#> |.....................|    0.8738 |     1.213 |     1.069 |     1.072 |
+#> |    G|    Gill Diff. |     73.79 |     2.406 |   0.05615 |    0.2285 |
+#> |.....................|  0.009051 |    -72.42 |    -25.46 |     1.201 |
+#> |.....................|     11.89 |    -10.88 |    -9.982 |    -10.81 |
+#> |    2|     4107.3121 |    0.3213 |    -1.022 |   -0.9119 |   -0.8965 |
+#> |.....................|   -0.8458 |   -0.2026 |   -0.6574 |   -0.8879 |
+#> |.....................|   -0.9839 |   -0.7675 |   -0.7787 |   -0.7710 |
+#> |    U|     4107.3121 |     29.92 |    -5.326 |   -0.9447 |   -0.1086 |
+#> |.....................|     2.291 |     1.546 |   0.03357 |    0.7494 |
+#> |.....................|    0.7782 |     1.335 |     1.167 |     1.179 |
+#> |    X|     4107.3121 |     29.92 |  0.004866 |    0.2800 |    0.8971 |
+#> |.....................|     9.883 |     1.546 |   0.03357 |    0.7494 |
+#> |.....................|    0.7782 |     1.335 |     1.167 |     1.179 |
+#> |    3|     528.17103 |    0.9321 |    -1.002 |   -0.9115 |   -0.8946 |
+#> |.....................|   -0.8457 |   -0.8021 |   -0.8682 |   -0.8779 |
+#> |.....................|   -0.8854 |   -0.8576 |   -0.8613 |   -0.8605 |
+#> |    U|     528.17103 |     86.80 |    -5.306 |   -0.9442 |   -0.1067 |
+#> |.....................|     2.291 |     1.198 |   0.03041 |    0.7570 |
+#> |.....................|    0.8642 |     1.226 |     1.079 |     1.083 |
+#> |    X|     528.17103 |     86.80 |  0.004964 |    0.2800 |    0.8988 |
+#> |.....................|     9.884 |     1.198 |   0.03041 |    0.7570 |
+#> |.....................|    0.8642 |     1.226 |     1.079 |     1.083 |
+#> |    4|     503.95550 |    0.9892 |    -1.000 |   -0.9114 |   -0.8944 |
+#> |.....................|   -0.8457 |   -0.8581 |   -0.8879 |   -0.8770 |
+#> |.....................|   -0.8762 |   -0.8660 |   -0.8691 |   -0.8689 |
+#> |    U|      503.9555 |     92.11 |    -5.304 |   -0.9442 |   -0.1066 |
+#> |.....................|     2.291 |     1.166 |   0.03011 |    0.7577 |
+#> |.....................|    0.8723 |     1.215 |     1.070 |     1.074 |
+#> |    X|      503.9555 |     92.11 |  0.004973 |    0.2801 |    0.8989 |
+#> |.....................|     9.884 |     1.166 |   0.03011 |    0.7577 |
+#> |.....................|    0.8723 |     1.215 |     1.070 |     1.074 |
+#> |    F| Forward Diff. |    -82.12 |     2.266 |   -0.2557 |    0.1457 |
+#> |.....................|   -0.3150 |    -70.09 |    -26.27 |     1.274 |
+#> |.....................|     9.305 |    -11.84 |    -9.592 |    -10.45 |
+#> |    5|     503.06948 |     1.000 |    -1.001 |   -0.9114 |   -0.8944 |
+#> |.....................|   -0.8456 |   -0.8479 |   -0.8841 |   -0.8772 |
+#> |.....................|   -0.8776 |   -0.8643 |   -0.8677 |   -0.8674 |
+#> |    U|     503.06948 |     93.16 |    -5.304 |   -0.9442 |   -0.1066 |
+#> |.....................|     2.291 |     1.172 |   0.03017 |    0.7575 |
+#> |.....................|    0.8711 |     1.217 |     1.072 |     1.075 |
+#> |    X|     503.06948 |     93.16 |  0.004971 |    0.2801 |    0.8989 |
+#> |.....................|     9.884 |     1.172 |   0.03017 |    0.7575 |
+#> |.....................|    0.8711 |     1.217 |     1.072 |     1.075 |
+#> |    F| Forward Diff. |     78.20 |     2.380 |   0.07920 |    0.2489 |
+#> |.....................|   0.04185 |    -69.32 |    -24.13 |     1.306 |
+#> |.....................|     9.997 |    -11.88 |    -9.541 |    -10.51 |
+#> |    6|     502.21512 |    0.9895 |    -1.001 |   -0.9114 |   -0.8945 |
+#> |.....................|   -0.8456 |   -0.8375 |   -0.8805 |   -0.8774 |
+#> |.....................|   -0.8791 |   -0.8625 |   -0.8662 |   -0.8658 |
+#> |    U|     502.21512 |     92.14 |    -5.304 |   -0.9442 |   -0.1066 |
+#> |.....................|     2.291 |     1.178 |   0.03022 |    0.7574 |
+#> |.....................|    0.8698 |     1.220 |     1.073 |     1.077 |
+#> |    X|     502.21512 |     92.14 |  0.004969 |    0.2801 |    0.8989 |
+#> |.....................|     9.884 |     1.178 |   0.03022 |    0.7574 |
+#> |.....................|    0.8698 |     1.220 |     1.073 |     1.077 |
+#> |    F| Forward Diff. |    -79.18 |     2.245 |   -0.2400 |    0.1569 |
+#> |.....................|   -0.2882 |    -67.02 |    -25.09 |     1.000 |
+#> |.....................|     9.365 |    -11.67 |    -9.440 |    -10.32 |
+#> |    7|     501.33312 |     1.000 |    -1.001 |   -0.9114 |   -0.8945 |
+#> |.....................|   -0.8456 |   -0.8270 |   -0.8765 |   -0.8775 |
+#> |.....................|   -0.8805 |   -0.8607 |   -0.8647 |   -0.8642 |
+#> |    U|     501.33312 |     93.14 |    -5.305 |   -0.9441 |   -0.1067 |
+#> |.....................|     2.291 |     1.184 |   0.03028 |    0.7573 |
+#> |.....................|    0.8685 |     1.222 |     1.075 |     1.079 |
+#> |    X|     501.33312 |     93.14 |  0.004968 |    0.2801 |    0.8988 |
+#> |.....................|     9.884 |     1.184 |   0.03028 |    0.7573 |
+#> |.....................|    0.8685 |     1.222 |     1.075 |     1.079 |
+#> |    F| Forward Diff. |     73.96 |     2.351 |   0.08380 |    0.2565 |
+#> |.....................|   0.05289 |    -66.42 |    -23.08 |    0.9343 |
+#> |.....................|     11.48 |    -11.71 |    -9.377 |    -10.38 |
+#> |    8|     500.50460 |    0.9897 |    -1.002 |   -0.9114 |   -0.8946 |
+#> |.....................|   -0.8456 |   -0.8163 |   -0.8728 |   -0.8777 |
+#> |.....................|   -0.8824 |   -0.8588 |   -0.8632 |   -0.8625 |
+#> |    U|      500.5046 |     92.16 |    -5.305 |   -0.9442 |   -0.1067 |
+#> |.....................|     2.291 |     1.190 |   0.03034 |    0.7572 |
+#> |.....................|    0.8669 |     1.224 |     1.077 |     1.081 |
+#> |    X|      500.5046 |     92.16 |  0.004966 |    0.2801 |    0.8988 |
+#> |.....................|     9.884 |     1.190 |   0.03034 |    0.7572 |
+#> |.....................|    0.8669 |     1.224 |     1.077 |     1.081 |
+#> |    F| Forward Diff. |    -76.85 |     2.219 |   -0.2273 |    0.1675 |
+#> |.....................|   -0.2752 |    -63.09 |    -23.56 |     1.068 |
+#> |.....................|     8.794 |    -11.52 |    -9.279 |    -10.19 |
+#> |    9|     499.65692 |     1.000 |    -1.002 |   -0.9113 |   -0.8946 |
+#> |.....................|   -0.8456 |   -0.8056 |   -0.8689 |   -0.8779 |
+#> |.....................|   -0.8839 |   -0.8568 |   -0.8617 |   -0.8608 |
+#> |    U|     499.65692 |     93.14 |    -5.306 |   -0.9441 |   -0.1067 |
+#> |.....................|     2.291 |     1.196 |   0.03040 |    0.7570 |
+#> |.....................|    0.8655 |     1.226 |     1.078 |     1.082 |
+#> |    X|     499.65692 |     93.14 |  0.004964 |    0.2801 |    0.8988 |
+#> |.....................|     9.885 |     1.196 |   0.03040 |    0.7570 |
+#> |.....................|    0.8655 |     1.226 |     1.078 |     1.082 |
+#> |    F| Forward Diff. |     72.32 |     2.320 |   0.09176 |    0.2615 |
+#> |.....................|   0.06934 |    -62.36 |    -21.54 |     1.140 |
+#> |.....................|     9.404 |    -11.56 |    -9.216 |    -10.24 |
+#> |   10|     498.81870 |    0.9902 |    -1.003 |   -0.9114 |   -0.8946 |
+#> |.....................|   -0.8456 |   -0.7946 |   -0.8650 |   -0.8781 |
+#> |.....................|   -0.8856 |   -0.8548 |   -0.8600 |   -0.8589 |
+#> |    U|      498.8187 |     92.21 |    -5.306 |   -0.9441 |   -0.1068 |
+#> |.....................|     2.291 |     1.203 |   0.03045 |    0.7569 |
+#> |.....................|    0.8641 |     1.229 |     1.080 |     1.084 |
+#> |    X|      498.8187 |     92.21 |  0.004962 |    0.2801 |    0.8987 |
+#> |.....................|     9.885 |     1.203 |   0.03045 |    0.7569 |
+#> |.....................|    0.8641 |     1.229 |     1.080 |     1.084 |
+#> |    F| Forward Diff. |    -70.56 |     2.198 |   -0.2057 |    0.1798 |
+#> |.....................|   -0.2468 |    -59.74 |    -22.28 |    0.8150 |
+#> |.....................|     7.180 |    -11.33 |    -9.109 |    -10.05 |
+#> |   11|     497.99655 |     1.000 |    -1.003 |   -0.9113 |   -0.8947 |
+#> |.....................|   -0.8455 |   -0.7835 |   -0.8609 |   -0.8782 |
+#> |.....................|   -0.8869 |   -0.8527 |   -0.8583 |   -0.8571 |
+#> |    U|     497.99655 |     93.13 |    -5.306 |   -0.9441 |   -0.1068 |
+#> |.....................|     2.291 |     1.209 |   0.03052 |    0.7568 |
+#> |.....................|    0.8629 |     1.231 |     1.082 |     1.086 |
+#> |    X|     497.99655 |     93.13 |  0.004960 |    0.2801 |    0.8987 |
+#> |.....................|     9.885 |     1.209 |   0.03052 |    0.7568 |
+#> |.....................|    0.8629 |     1.231 |     1.082 |     1.086 |
+#> |    F| Forward Diff. |     69.16 |     2.293 |    0.1087 |    0.2725 |
+#> |.....................|   0.08752 |    -59.63 |    -20.54 |    0.7584 |
+#> |.....................|     10.86 |    -11.45 |    -9.094 |    -10.13 |
+#> |   12|     497.16410 |    0.9907 |    -1.003 |   -0.9113 |   -0.8947 |
+#> |.....................|   -0.8455 |   -0.7720 |   -0.8569 |   -0.8784 |
+#> |.....................|   -0.8889 |   -0.8505 |   -0.8566 |   -0.8551 |
+#> |    U|      497.1641 |     92.25 |    -5.307 |   -0.9441 |   -0.1069 |
+#> |.....................|     2.291 |     1.216 |   0.03058 |    0.7566 |
+#> |.....................|    0.8612 |     1.234 |     1.084 |     1.088 |
+#> |    X|      497.1641 |     92.25 |  0.004958 |    0.2801 |    0.8987 |
+#> |.....................|     9.885 |     1.216 |   0.03058 |    0.7566 |
+#> |.....................|    0.8612 |     1.234 |     1.084 |     1.088 |
+#> |    F| Forward Diff. |    -65.09 |     2.175 |   -0.1829 |    0.1920 |
+#> |.....................|   -0.2233 |    -56.76 |    -21.02 |    0.6415 |
+#> |.....................|     9.983 |    -11.18 |    -8.930 |    -9.895 |
+#> |   13|     496.40281 |     1.000 |    -1.004 |   -0.9113 |   -0.8948 |
+#> |.....................|   -0.8455 |   -0.7609 |   -0.8528 |   -0.8785 |
+#> |.....................|   -0.8909 |   -0.8483 |   -0.8548 |   -0.8532 |
+#> |    U|     496.40281 |     93.15 |    -5.307 |   -0.9441 |   -0.1069 |
+#> |.....................|     2.291 |     1.222 |   0.03064 |    0.7566 |
+#> |.....................|    0.8594 |     1.237 |     1.086 |     1.091 |
+#> |    X|     496.40281 |     93.15 |  0.004955 |    0.2801 |    0.8986 |
+#> |.....................|     9.885 |     1.222 |   0.03064 |    0.7566 |
+#> |.....................|    0.8594 |     1.237 |     1.086 |     1.091 |
+#> |    F| Forward Diff. |     70.05 |     2.265 |    0.1236 |    0.2851 |
+#> |.....................|    0.1152 |    -55.71 |    -19.12 |    0.8701 |
+#> |.....................|     7.394 |    -11.22 |    -8.890 |    -9.949 |
+#> |   14|     495.59236 |    0.9910 |    -1.004 |   -0.9113 |   -0.8948 |
+#> |.....................|   -0.8455 |   -0.7494 |   -0.8488 |   -0.8787 |
+#> |.....................|   -0.8926 |   -0.8459 |   -0.8530 |   -0.8511 |
+#> |    U|     495.59236 |     92.28 |    -5.308 |   -0.9441 |   -0.1070 |
+#> |.....................|     2.291 |     1.229 |   0.03070 |    0.7564 |
+#> |.....................|    0.8580 |     1.240 |     1.088 |     1.093 |
+#> |    X|     495.59236 |     92.28 |  0.004953 |    0.2801 |    0.8986 |
+#> |.....................|     9.885 |     1.229 |   0.03070 |    0.7564 |
+#> |.....................|    0.8580 |     1.240 |     1.088 |     1.093 |
+#> |    F| Forward Diff. |    -61.97 |     2.150 |   -0.1619 |    0.2028 |
+#> |.....................|   -0.2007 |    -53.46 |    -19.76 |    0.5341 |
+#> |.....................|     9.715 |    -10.96 |    -8.745 |    -9.729 |
+#> |   15|     494.82198 |     1.000 |    -1.005 |   -0.9113 |   -0.8949 |
+#> |.....................|   -0.8455 |   -0.7378 |   -0.8446 |   -0.8788 |
+#> |.....................|   -0.8946 |   -0.8435 |   -0.8510 |   -0.8489 |
+#> |    U|     494.82198 |     93.11 |    -5.308 |   -0.9441 |   -0.1070 |
+#> |.....................|     2.291 |     1.235 |   0.03076 |    0.7563 |
+#> |.....................|    0.8562 |     1.243 |     1.090 |     1.095 |
+#> |    X|     494.82198 |     93.11 |  0.004951 |    0.2801 |    0.8985 |
+#> |.....................|     9.886 |     1.235 |   0.03076 |    0.7563 |
+#> |.....................|    0.8562 |     1.243 |     1.090 |     1.095 |
+#> |    F| Forward Diff. |     62.35 |     2.229 |    0.1203 |    0.2879 |
+#> |.....................|    0.1180 |    -52.16 |    -17.88 |    0.7550 |
+#> |.....................|     8.431 |    -10.99 |    -8.665 |    -9.736 |
+#> |   16|     494.07821 |    0.9910 |    -1.005 |   -0.9113 |   -0.8949 |
+#> |.....................|   -0.8455 |   -0.7261 |   -0.8406 |   -0.8789 |
+#> |.....................|   -0.8966 |   -0.8410 |   -0.8490 |   -0.8467 |
+#> |    U|     494.07821 |     92.28 |    -5.309 |   -0.9441 |   -0.1071 |
+#> |.....................|     2.291 |     1.242 |   0.03082 |    0.7562 |
+#> |.....................|    0.8544 |     1.246 |     1.092 |     1.098 |
+#> |    X|     494.07821 |     92.28 |  0.004948 |    0.2801 |    0.8985 |
+#> |.....................|     9.885 |     1.242 |   0.03082 |    0.7562 |
+#> |.....................|    0.8544 |     1.246 |     1.092 |     1.098 |
+#> |    F| Forward Diff. |    -62.97 |     2.119 |   -0.1628 |    0.2103 |
+#> |.....................|   -0.1835 |    -49.97 |    -18.50 |    0.4855 |
+#> |.....................|     6.275 |    -10.75 |    -8.529 |    -9.546 |
+#> |   17|     493.31030 |    0.9997 |    -1.006 |   -0.9113 |   -0.8950 |
+#> |.....................|   -0.8455 |   -0.7143 |   -0.8363 |   -0.8790 |
+#> |.....................|   -0.8981 |   -0.8383 |   -0.8469 |   -0.8443 |
+#> |    U|      493.3103 |     93.08 |    -5.309 |   -0.9441 |   -0.1071 |
+#> |.....................|     2.291 |     1.249 |   0.03089 |    0.7561 |
+#> |.....................|    0.8531 |     1.249 |     1.094 |     1.100 |
+#> |    X|      493.3103 |     93.08 |  0.004946 |    0.2801 |    0.8984 |
+#> |.....................|     9.886 |     1.249 |   0.03089 |    0.7561 |
+#> |.....................|    0.8531 |     1.249 |     1.094 |     1.100 |
+#> |    F| Forward Diff. |     56.08 |     2.195 |    0.1067 |    0.2931 |
+#> |.....................|    0.1254 |    -49.64 |    -16.98 |    0.3491 |
+#> |.....................|     8.549 |    -10.78 |    -8.455 |    -9.552 |
+#> |   18|     492.59068 |    0.9914 |    -1.006 |   -0.9113 |   -0.8951 |
+#> |.....................|   -0.8455 |   -0.7023 |   -0.8321 |   -0.8791 |
+#> |.....................|   -0.9000 |   -0.8355 |   -0.8448 |   -0.8419 |
+#> |    U|     492.59068 |     92.32 |    -5.310 |   -0.9441 |   -0.1072 |
+#> |.....................|     2.291 |     1.256 |   0.03095 |    0.7561 |
+#> |.....................|    0.8514 |     1.252 |     1.096 |     1.103 |
+#> |    X|     492.59068 |     92.32 |  0.004943 |    0.2801 |    0.8983 |
+#> |.....................|     9.885 |     1.256 |   0.03095 |    0.7561 |
+#> |.....................|    0.8514 |     1.252 |     1.096 |     1.103 |
+#> |    F| Forward Diff. |    -58.13 |     2.097 |   -0.1289 |    0.2246 |
+#> |.....................|   -0.1582 |    -47.13 |    -17.33 |    0.3097 |
+#> |.....................|     7.738 |    -10.54 |    -8.304 |    -9.345 |
+#> |   19|     491.88063 |    0.9998 |    -1.007 |   -0.9113 |   -0.8951 |
+#> |.....................|   -0.8455 |   -0.6905 |   -0.8279 |   -0.8791 |
+#> |.....................|   -0.9022 |   -0.8327 |   -0.8426 |   -0.8394 |
+#> |    U|     491.88063 |     93.10 |    -5.310 |   -0.9441 |   -0.1073 |
+#> |.....................|     2.291 |     1.263 |   0.03101 |    0.7561 |
+#> |.....................|    0.8496 |     1.256 |     1.099 |     1.105 |
+#> |    X|     491.88063 |     93.10 |  0.004940 |    0.2801 |    0.8983 |
+#> |.....................|     9.886 |     1.263 |   0.03101 |    0.7561 |
+#> |.....................|    0.8496 |     1.256 |     1.099 |     1.105 |
+#> |    F| Forward Diff. |     56.71 |     2.166 |    0.1292 |    0.3076 |
+#> |.....................|    0.1542 |    -45.57 |    -15.60 |    0.4873 |
+#> |.....................|     6.413 |    -10.51 |    -8.202 |    -9.332 |
+#> |   20|     491.19020 |    0.9917 |    -1.008 |   -0.9113 |   -0.8952 |
+#> |.....................|   -0.8455 |   -0.6785 |   -0.8237 |   -0.8792 |
+#> |.....................|   -0.9039 |   -0.8296 |   -0.8402 |   -0.8366 |
+#> |    U|      491.1902 |     92.34 |    -5.311 |   -0.9441 |   -0.1074 |
+#> |.....................|     2.291 |     1.270 |   0.03107 |    0.7560 |
+#> |.....................|    0.8481 |     1.259 |     1.101 |     1.108 |
+#> |    X|      491.1902 |     92.34 |  0.004937 |    0.2801 |    0.8982 |
+#> |.....................|     9.885 |     1.270 |   0.03107 |    0.7560 |
+#> |.....................|    0.8481 |     1.259 |     1.101 |     1.108 |
+#> |    F| Forward Diff. |    -55.56 |     2.070 |   -0.1130 |    0.2359 |
+#> |.....................|   -0.1346 |    -44.07 |    -16.23 |    0.1008 |
+#> |.....................|     7.464 |    -10.26 |    -8.060 |    -9.125 |
+#> |   21|     490.47868 |    0.9993 |    -1.008 |   -0.9113 |   -0.8953 |
+#> |.....................|   -0.8455 |   -0.6665 |   -0.8194 |   -0.8791 |
+#> |.....................|   -0.9059 |   -0.8264 |   -0.8377 |   -0.8337 |
+#> |    U|     490.47868 |     93.05 |    -5.312 |   -0.9441 |   -0.1075 |
+#> |.....................|     2.291 |     1.277 |   0.03114 |    0.7561 |
+#> |.....................|    0.8463 |     1.263 |     1.104 |     1.111 |
+#> |    X|     490.47868 |     93.05 |  0.004934 |    0.2801 |    0.8981 |
+#> |.....................|     9.885 |     1.277 |   0.03114 |    0.7561 |
+#> |.....................|    0.8463 |     1.263 |     1.104 |     1.111 |
+#> |    F| Forward Diff. |     47.93 |     2.132 |    0.1269 |    0.3117 |
+#> |.....................|    0.1562 |    -43.27 |    -14.78 |   0.06906 |
+#> |.....................|     9.295 |    -10.26 |    -7.955 |    -9.092 |
+#> |   22|     489.84765 |    0.9918 |    -1.009 |   -0.9114 |   -0.8954 |
+#> |.....................|   -0.8456 |   -0.6545 |   -0.8153 |   -0.8790 |
+#> |.....................|   -0.9090 |   -0.8231 |   -0.8352 |   -0.8308 |
+#> |    U|     489.84765 |     92.35 |    -5.312 |   -0.9441 |   -0.1076 |
+#> |.....................|     2.291 |     1.284 |   0.03120 |    0.7562 |
+#> |.....................|    0.8436 |     1.267 |     1.107 |     1.115 |
+#> |    X|     489.84765 |     92.35 |  0.004930 |    0.2801 |    0.8980 |
+#> |.....................|     9.885 |     1.284 |   0.03120 |    0.7562 |
+#> |.....................|    0.8436 |     1.267 |     1.107 |     1.115 |
+#> |    F| Forward Diff. |    -55.71 |     2.038 |   -0.1283 |    0.2328 |
+#> |.....................|   -0.1164 |    -41.15 |    -15.14 |  0.009736 |
+#> |.....................|     8.505 |    -10.03 |    -7.805 |    -8.885 |
+#> |   23|     489.17644 |    0.9994 |    -1.010 |   -0.9113 |   -0.8955 |
+#> |.....................|   -0.8456 |   -0.6429 |   -0.8112 |   -0.8788 |
+#> |.....................|   -0.9126 |   -0.8197 |   -0.8325 |   -0.8278 |
+#> |    U|     489.17644 |     93.06 |    -5.313 |   -0.9441 |   -0.1077 |
+#> |.....................|     2.291 |     1.290 |   0.03126 |    0.7563 |
+#> |.....................|    0.8405 |     1.272 |     1.109 |     1.118 |
+#> |    X|     489.17644 |     93.06 |  0.004927 |    0.2801 |    0.8979 |
+#> |.....................|     9.885 |     1.290 |   0.03126 |    0.7563 |
+#> |.....................|    0.8405 |     1.272 |     1.109 |     1.118 |
+#> |    F| Forward Diff. |     46.87 |     2.093 |    0.1493 |    0.3243 |
+#> |.....................|    0.1838 |    -40.03 |    -13.57 |    0.1411 |
+#> |.....................|     5.593 |    -9.957 |    -7.669 |    -8.831 |
+#> |   24|     488.58015 |    0.9920 |    -1.011 |   -0.9114 |   -0.8957 |
+#> |.....................|   -0.8457 |   -0.6309 |   -0.8071 |   -0.8787 |
+#> |.....................|   -0.9147 |   -0.8159 |   -0.8297 |   -0.8244 |
+#> |    U|     488.58015 |     92.37 |    -5.314 |   -0.9442 |   -0.1078 |
+#> |.....................|     2.291 |     1.297 |   0.03132 |    0.7564 |
+#> |.....................|    0.8386 |     1.276 |     1.112 |     1.121 |
+#> |    X|     488.58015 |     92.37 |  0.004923 |    0.2801 |    0.8978 |
+#> |.....................|     9.884 |     1.297 |   0.03132 |    0.7564 |
+#> |.....................|    0.8386 |     1.276 |     1.112 |     1.121 |
+#> |    F| Forward Diff. |    -53.50 |     2.005 |   -0.1078 |    0.2446 |
+#> |.....................|  -0.09190 |    -37.89 |    -13.87 |   0.05672 |
+#> |.....................|     4.909 |    -9.713 |    -7.511 |    -8.606 |
+#> |   25|     487.93833 |    0.9991 |    -1.011 |   -0.9114 |   -0.8958 |
+#> |.....................|   -0.8457 |   -0.6190 |   -0.8030 |   -0.8785 |
+#> |.....................|   -0.9153 |   -0.8117 |   -0.8266 |   -0.8207 |
+#> |    U|     487.93833 |     93.04 |    -5.315 |   -0.9442 |   -0.1080 |
+#> |.....................|     2.291 |     1.304 |   0.03139 |    0.7566 |
+#> |.....................|    0.8381 |     1.281 |     1.116 |     1.125 |
+#> |    X|     487.93833 |     93.04 |  0.004918 |    0.2801 |    0.8977 |
+#> |.....................|     9.883 |     1.304 |   0.03139 |    0.7566 |
+#> |.....................|    0.8381 |     1.281 |     1.116 |     1.125 |
+#> |    F| Forward Diff. |     41.92 |     2.065 |    0.1569 |    0.3320 |
+#> |.....................|    0.1961 |    -37.34 |    -12.63 |   0.01172 |
+#> |.....................|     5.301 |    -9.646 |    -7.360 |    -8.530 |
+#> |   26|     487.37063 |    0.9925 |    -1.012 |   -0.9115 |   -0.8960 |
+#> |.....................|   -0.8458 |   -0.6069 |   -0.7990 |   -0.8783 |
+#> |.....................|   -0.9161 |   -0.8073 |   -0.8233 |   -0.8168 |
+#> |    U|     487.37063 |     92.42 |    -5.316 |   -0.9443 |   -0.1081 |
+#> |.....................|     2.291 |     1.311 |   0.03145 |    0.7567 |
+#> |.....................|    0.8374 |     1.287 |     1.119 |     1.130 |
+#> |    X|     487.37063 |     92.42 |  0.004913 |    0.2800 |    0.8975 |
+#> |.....................|     9.882 |     1.311 |   0.03145 |    0.7567 |
+#> |.....................|    0.8374 |     1.287 |     1.119 |     1.130 |
+#> |    F| Forward Diff. |    -47.84 |     1.989 |  -0.08553 |    0.2559 |
+#> |.....................|  -0.06263 |    -35.59 |    -12.91 |  -0.09336 |
+#> |.....................|     8.020 |    -9.356 |    -7.180 |    -8.291 |
+#> |   27|     486.76802 |    0.9991 |    -1.014 |   -0.9115 |   -0.8962 |
+#> |.....................|   -0.8459 |   -0.5954 |   -0.7952 |   -0.8779 |
+#> |.....................|   -0.9197 |   -0.8027 |   -0.8200 |   -0.8127 |
+#> |    U|     486.76802 |     93.03 |    -5.317 |   -0.9443 |   -0.1083 |
+#> |.....................|     2.291 |     1.318 |   0.03150 |    0.7570 |
+#> |.....................|    0.8342 |     1.292 |     1.123 |     1.134 |
+#> |    X|     486.76802 |     93.03 |  0.004908 |    0.2800 |    0.8973 |
+#> |.....................|     9.881 |     1.318 |   0.03150 |    0.7570 |
+#> |.....................|    0.8342 |     1.292 |     1.123 |     1.134 |
+#> |    F| Forward Diff. |     39.28 |     2.032 |    0.1697 |    0.3409 |
+#> |.....................|    0.2161 |    -34.26 |    -11.60 |  -0.04206 |
+#> |.....................|     6.414 |    -9.258 |    -7.014 |    -8.183 |
+#> |   28|     486.25961 |    0.9924 |    -1.015 |   -0.9116 |   -0.8964 |
+#> |.....................|   -0.8461 |   -0.5843 |   -0.7916 |   -0.8775 |
+#> |.....................|   -0.9242 |   -0.7980 |   -0.8166 |   -0.8086 |
+#> |    U|     486.25961 |     92.41 |    -5.318 |   -0.9444 |   -0.1086 |
+#> |.....................|     2.290 |     1.324 |   0.03156 |    0.7573 |
+#> |.....................|    0.8303 |     1.298 |     1.126 |     1.138 |
+#> |    X|     486.25961 |     92.41 |  0.004902 |    0.2800 |    0.8971 |
+#> |.....................|     9.880 |     1.324 |   0.03156 |    0.7573 |
+#> |.....................|    0.8303 |     1.298 |     1.126 |     1.138 |
+#> |    F| Forward Diff. |    -50.63 |     1.945 |  -0.07307 |    0.2626 |
+#> |.....................|  -0.04930 |    -33.11 |    -12.03 |   -0.1686 |
+#> |.....................|     7.510 |    -8.984 |    -6.802 |    -7.934 |
+#> |   29|     485.66844 |    0.9985 |    -1.016 |   -0.9117 |   -0.8967 |
+#> |.....................|   -0.8462 |   -0.5738 |   -0.7881 |   -0.8769 |
+#> |.....................|   -0.9293 |   -0.7927 |   -0.8129 |   -0.8039 |
+#> |    U|     485.66844 |     92.98 |    -5.319 |   -0.9445 |   -0.1089 |
+#> |.....................|     2.290 |     1.331 |   0.03161 |    0.7578 |
+#> |.....................|    0.8259 |     1.304 |     1.130 |     1.143 |
+#> |    X|     485.66844 |     92.98 |  0.004895 |    0.2800 |    0.8969 |
+#> |.....................|     9.878 |     1.331 |   0.03161 |    0.7578 |
+#> |.....................|    0.8259 |     1.304 |     1.130 |     1.143 |
+#> |    F| Forward Diff. |     30.24 |     1.977 |    0.1746 |    0.3455 |
+#> |.....................|    0.2218 |    -32.22 |    -10.87 |   -0.2249 |
+#> |.....................|     4.336 |    -8.820 |    -6.615 |    -7.812 |
+#> |   30|     485.23968 |    0.9921 |    -1.017 |   -0.9119 |   -0.8970 |
+#> |.....................|   -0.8465 |   -0.5622 |   -0.7845 |   -0.8762 |
+#> |.....................|   -0.9314 |   -0.7876 |   -0.8094 |   -0.7994 |
+#> |    U|     485.23968 |     92.38 |    -5.321 |   -0.9447 |   -0.1091 |
+#> |.....................|     2.290 |     1.337 |   0.03166 |    0.7583 |
+#> |.....................|    0.8240 |     1.310 |     1.134 |     1.148 |
+#> |    X|     485.23968 |     92.38 |  0.004889 |    0.2800 |    0.8966 |
+#> |.....................|     9.876 |     1.337 |   0.03166 |    0.7583 |
+#> |.....................|    0.8240 |     1.310 |     1.134 |     1.148 |
+#> |    F| Forward Diff. |    -56.59 |     1.902 |  -0.07536 |    0.2678 |
+#> |.....................|  -0.04797 |    -30.46 |    -11.14 |  -0.09043 |
+#> |.....................|     3.742 |    -8.533 |    -6.412 |    -7.541 |
+#> |   31|     484.69662 |    0.9984 |    -1.019 |   -0.9121 |   -0.8974 |
+#> |.....................|   -0.8467 |   -0.5517 |   -0.7813 |   -0.8754 |
+#> |.....................|   -0.9289 |   -0.7816 |   -0.8053 |   -0.7941 |
+#> |    U|     484.69662 |     92.97 |    -5.322 |   -0.9448 |   -0.1095 |
+#> |.....................|     2.290 |     1.343 |   0.03171 |    0.7589 |
+#> |.....................|    0.8262 |     1.318 |     1.138 |     1.154 |
+#> |    X|     484.69662 |     92.97 |  0.004881 |    0.2799 |    0.8963 |
+#> |.....................|     9.873 |     1.343 |   0.03171 |    0.7589 |
+#> |.....................|    0.8262 |     1.318 |     1.138 |     1.154 |
+#> |    F| Forward Diff. |     27.47 |     1.960 |    0.1737 |    0.3487 |
+#> |.....................|    0.2320 |    -29.84 |    -10.04 |   -0.2714 |
+#> |.....................|     5.731 |    -8.337 |    -6.228 |    -7.371 |
+#> |   32|     484.27605 |    0.9928 |    -1.021 |   -0.9123 |   -0.8978 |
+#> |.....................|   -0.8471 |   -0.5404 |   -0.7779 |   -0.8746 |
+#> |.....................|   -0.9302 |   -0.7757 |   -0.8014 |   -0.7889 |
+#> |    U|     484.27605 |     92.45 |    -5.324 |   -0.9451 |   -0.1099 |
+#> |.....................|     2.289 |     1.350 |   0.03176 |    0.7595 |
+#> |.....................|    0.8251 |     1.325 |     1.143 |     1.159 |
+#> |    X|     484.27605 |     92.45 |  0.004872 |    0.2799 |    0.8959 |
+#> |.....................|     9.870 |     1.350 |   0.03176 |    0.7595 |
+#> |.....................|    0.8251 |     1.325 |     1.143 |     1.159 |
+#> |    F| Forward Diff. |    -48.28 |     1.894 |  -0.05804 |    0.2769 |
+#> |.....................|  -0.01457 |    -28.21 |    -10.24 |   -0.1977 |
+#> |.....................|     5.253 |    -8.027 |    -5.998 |    -7.085 |
+#> |   33|     483.77365 |    0.9986 |    -1.023 |   -0.9126 |   -0.8983 |
+#> |.....................|   -0.8475 |   -0.5309 |   -0.7752 |   -0.8734 |
+#> |.....................|   -0.9343 |   -0.7690 |   -0.7970 |   -0.7831 |
+#> |    U|     483.77365 |     92.99 |    -5.326 |   -0.9453 |   -0.1104 |
+#> |.....................|     2.289 |     1.355 |   0.03180 |    0.7604 |
+#> |.....................|    0.8215 |     1.333 |     1.147 |     1.166 |
+#> |    X|     483.77365 |     92.99 |  0.004861 |    0.2798 |    0.8954 |
+#> |.....................|     9.866 |     1.355 |   0.03180 |    0.7604 |
+#> |.....................|    0.8215 |     1.333 |     1.147 |     1.166 |
+#> |    F| Forward Diff. |     28.59 |     1.923 |    0.1952 |    0.3548 |
+#> |.....................|    0.2608 |    -27.76 |    -9.333 |   -0.3645 |
+#> |.....................|     3.958 |    -7.814 |    -5.777 |    -6.894 |
+#> |   34|     483.37086 |    0.9934 |    -1.025 |   -0.9129 |   -0.8989 |
+#> |.....................|   -0.8480 |   -0.5203 |   -0.7721 |   -0.8720 |
+#> |.....................|   -0.9349 |   -0.7624 |   -0.7928 |   -0.7774 |
+#> |    U|     483.37086 |     92.51 |    -5.329 |   -0.9456 |   -0.1110 |
+#> |.....................|     2.289 |     1.362 |   0.03185 |    0.7615 |
+#> |.....................|    0.8209 |     1.341 |     1.152 |     1.172 |
+#> |    X|     483.37086 |     92.51 |  0.004850 |    0.2798 |    0.8949 |
+#> |.....................|     9.861 |     1.362 |   0.03185 |    0.7615 |
+#> |.....................|    0.8209 |     1.341 |     1.152 |     1.172 |
+#> |    F| Forward Diff. |    -41.16 |     1.862 |  -0.03265 |    0.2828 |
+#> |.....................|   0.01951 |    -26.43 |    -9.488 |   -0.2833 |
+#> |.....................|     3.545 |    -7.469 |    -5.528 |    -6.584 |
+#> |   35|     482.96272 |    0.9987 |    -1.028 |   -0.9132 |   -0.8995 |
+#> |.....................|   -0.8485 |   -0.5103 |   -0.7694 |   -0.8702 |
+#> |.....................|   -0.9315 |   -0.7558 |   -0.7888 |   -0.7716 |
+#> |    U|     482.96272 |     92.99 |    -5.332 |   -0.9459 |   -0.1117 |
+#> |.....................|     2.288 |     1.367 |   0.03189 |    0.7629 |
+#> |.....................|    0.8240 |     1.349 |     1.156 |     1.178 |
+#> |    X|     482.96272 |     92.99 |  0.004836 |    0.2797 |    0.8943 |
+#> |.....................|     9.856 |     1.367 |   0.03189 |    0.7629 |
+#> |.....................|    0.8240 |     1.349 |     1.156 |     1.178 |
+#> |    F| Forward Diff. |     28.21 |     1.908 |    0.1917 |    0.3504 |
+#> |.....................|    0.2712 |    -25.82 |    -8.599 |   -0.3385 |
+#> |.....................|     4.050 |    -7.278 |    -5.334 |    -6.398 |
+#> |   36|     482.60011 |    0.9939 |    -1.032 |   -0.9136 |   -0.9003 |
+#> |.....................|   -0.8492 |   -0.4998 |   -0.7669 |   -0.8684 |
+#> |.....................|   -0.9296 |   -0.7490 |   -0.7849 |   -0.7659 |
+#> |    U|     482.60011 |     92.55 |    -5.335 |   -0.9462 |   -0.1124 |
+#> |.....................|     2.287 |     1.373 |   0.03193 |    0.7642 |
+#> |.....................|    0.8256 |     1.357 |     1.160 |     1.184 |
+#> |    X|     482.60011 |     92.55 |  0.004820 |    0.2796 |    0.8937 |
+#> |.....................|     9.849 |     1.373 |   0.03193 |    0.7642 |
+#> |.....................|    0.8256 |     1.357 |     1.160 |     1.184 |
+#> |    F| Forward Diff. |    -36.31 |     1.855 |  -0.03781 |    0.2769 |
+#> |.....................|   0.03076 |    -24.99 |    -8.890 |   -0.4685 |
+#> |.....................|     7.176 |    -6.892 |    -5.117 |    -6.081 |
+#> |   37|     482.21198 |    0.9982 |    -1.035 |   -0.9138 |   -0.9009 |
+#> |.....................|   -0.8497 |   -0.4920 |   -0.7653 |   -0.8661 |
+#> |.....................|   -0.9399 |   -0.7441 |   -0.7821 |   -0.7617 |
+#> |    U|     482.21198 |     92.95 |    -5.338 |   -0.9465 |   -0.1130 |
+#> |.....................|     2.287 |     1.378 |   0.03195 |    0.7659 |
+#> |.....................|    0.8166 |     1.363 |     1.163 |     1.189 |
+#> |    X|     482.21198 |     92.95 |  0.004805 |    0.2796 |    0.8931 |
+#> |.....................|     9.844 |     1.378 |   0.03195 |    0.7659 |
+#> |.....................|    0.8166 |     1.363 |     1.163 |     1.189 |
+#> |    F| Forward Diff. |     20.01 |     1.850 |    0.1852 |    0.3312 |
+#> |.....................|    0.2616 |    -23.95 |    -7.997 |   -0.3393 |
+#> |.....................|     4.985 |    -6.711 |    -4.923 |    -5.940 |
+#> |   38|     481.96846 |    0.9924 |    -1.037 |   -0.9141 |   -0.9014 |
+#> |.....................|   -0.8503 |   -0.4828 |   -0.7630 |   -0.8646 |
+#> |.....................|   -0.9490 |   -0.7399 |   -0.7795 |   -0.7579 |
+#> |    U|     481.96846 |     92.41 |    -5.341 |   -0.9468 |   -0.1136 |
+#> |.....................|     2.286 |     1.383 |   0.03199 |    0.7671 |
+#> |.....................|    0.8087 |     1.368 |     1.166 |     1.193 |
+#> |    X|     481.96846 |     92.41 |  0.004793 |    0.2795 |    0.8927 |
+#> |.....................|     9.838 |     1.383 |   0.03199 |    0.7671 |
+#> |.....................|    0.8087 |     1.368 |     1.166 |     1.193 |
+#> |    F| Forward Diff. |    -59.26 |     1.761 |  -0.08116 |    0.2547 |
+#> |.....................|  -0.02692 |    -22.78 |    -8.366 |   -0.2344 |
+#> |.....................|     4.087 |    -6.524 |    -4.792 |    -5.748 |
+#> |   39|     481.52549 |    0.9980 |    -1.042 |   -0.9148 |   -0.9024 |
+#> |.....................|   -0.8514 |   -0.4755 |   -0.7621 |   -0.8625 |
+#> |.....................|   -0.9558 |   -0.7333 |   -0.7761 |   -0.7520 |
+#> |    U|     481.52549 |     92.93 |    -5.345 |   -0.9474 |   -0.1146 |
+#> |.....................|     2.285 |     1.388 |   0.03200 |    0.7686 |
+#> |.....................|    0.8027 |     1.376 |     1.170 |     1.199 |
+#> |    X|     481.52549 |     92.93 |  0.004770 |    0.2794 |    0.8917 |
+#> |.....................|     9.827 |     1.388 |   0.03200 |    0.7686 |
+#> |.....................|    0.8027 |     1.376 |     1.170 |     1.199 |
+#> |    F| Forward Diff. |     14.56 |     1.771 |    0.1903 |    0.3270 |
+#> |.....................|    0.2641 |    -22.44 |    -7.508 |   -0.4496 |
+#> |.....................|     2.566 |    -6.373 |    -4.622 |    -5.584 |
+#> |   40|     481.26396 |    0.9932 |    -1.045 |   -0.9155 |   -0.9032 |
+#> |.....................|   -0.8523 |   -0.4642 |   -0.7593 |   -0.8605 |
+#> |.....................|   -0.9543 |   -0.7272 |   -0.7727 |   -0.7469 |
+#> |    U|     481.26396 |     92.49 |    -5.349 |   -0.9480 |   -0.1154 |
+#> |.....................|     2.284 |     1.394 |   0.03204 |    0.7702 |
+#> |.....................|    0.8040 |     1.384 |     1.173 |     1.205 |
+#> |    X|     481.26396 |     92.49 |  0.004753 |    0.2793 |    0.8910 |
+#> |.....................|     9.818 |     1.394 |   0.03204 |    0.7702 |
+#> |.....................|    0.8040 |     1.384 |     1.173 |     1.205 |
+#> |    F| Forward Diff. |    -49.84 |     1.721 |  -0.06329 |    0.2500 |
+#> |.....................|  0.003387 |    -21.58 |    -7.808 |   -0.4470 |
+#> |.....................|     3.805 |    -6.020 |    -4.412 |    -5.292 |
+#> |   41|     480.91101 |    0.9981 |    -1.051 |   -0.9163 |   -0.9044 |
+#> |.....................|   -0.8537 |   -0.4552 |   -0.7584 |   -0.8559 |
+#> |.....................|   -0.9510 |   -0.7207 |   -0.7698 |   -0.7416 |
+#> |    U|     480.91101 |     92.94 |    -5.355 |   -0.9488 |   -0.1166 |
+#> |.....................|     2.283 |     1.399 |   0.03206 |    0.7737 |
+#> |.....................|    0.8069 |     1.392 |     1.176 |     1.210 |
+#> |    X|     480.91101 |     92.94 |  0.004727 |    0.2791 |    0.8900 |
+#> |.....................|     9.804 |     1.399 |   0.03206 |    0.7737 |
+#> |.....................|    0.8069 |     1.392 |     1.176 |     1.210 |
+#> |    F| Forward Diff. |     16.05 |     1.751 |    0.1631 |    0.3020 |
+#> |.....................|    0.2540 |    -20.90 |    -6.928 |   -0.3893 |
+#> |.....................|     4.288 |    -5.817 |    -4.263 |    -5.144 |
+#> |   42|     480.64341 |    0.9941 |    -1.056 |   -0.9169 |   -0.9053 |
+#> |.....................|   -0.8549 |   -0.4456 |   -0.7571 |   -0.8527 |
+#> |.....................|   -0.9585 |   -0.7158 |   -0.7673 |   -0.7373 |
+#> |    U|     480.64341 |     92.57 |    -5.360 |   -0.9493 |   -0.1175 |
+#> |.....................|     2.282 |     1.405 |   0.03208 |    0.7761 |
+#> |.....................|    0.8004 |     1.398 |     1.179 |     1.215 |
+#> |    X|     480.64341 |     92.57 |  0.004703 |    0.2790 |    0.8892 |
+#> |.....................|     9.793 |     1.405 |   0.03208 |    0.7761 |
+#> |.....................|    0.8004 |     1.398 |     1.179 |     1.215 |
+#> |    F| Forward Diff. |    -40.16 |     1.680 |  -0.01378 |    0.2424 |
+#> |.....................|   0.03021 |    -20.27 |    -7.228 |   -0.4675 |
+#> |.....................|     4.140 |    -5.523 |    -4.100 |    -4.903 |
+#> |   43|     480.34062 |    0.9982 |    -1.062 |   -0.9177 |   -0.9064 |
+#> |.....................|   -0.8561 |   -0.4387 |   -0.7572 |   -0.8486 |
+#> |.....................|   -0.9687 |   -0.7122 |   -0.7655 |   -0.7338 |
+#> |    U|     480.34062 |     92.95 |    -5.365 |   -0.9501 |   -0.1185 |
+#> |.....................|     2.280 |     1.409 |   0.03207 |    0.7792 |
+#> |.....................|    0.7914 |     1.402 |     1.181 |     1.219 |
+#> |    X|     480.34062 |     92.95 |  0.004675 |    0.2789 |    0.8883 |
+#> |.....................|     9.781 |     1.409 |   0.03207 |    0.7792 |
+#> |.....................|    0.7914 |     1.402 |     1.181 |     1.219 |
+#> |   44|     480.11354 |    0.9982 |    -1.069 |   -0.9186 |   -0.9075 |
+#> |.....................|   -0.8576 |   -0.4327 |   -0.7582 |   -0.8437 |
+#> |.....................|   -0.9807 |   -0.7086 |   -0.7639 |   -0.7301 |
+#> |    U|     480.11354 |     92.95 |    -5.372 |   -0.9510 |   -0.1197 |
+#> |.....................|     2.279 |     1.412 |   0.03206 |    0.7829 |
+#> |.....................|    0.7810 |     1.406 |     1.183 |     1.223 |
+#> |    X|     480.11354 |     92.95 |  0.004643 |    0.2787 |    0.8872 |
+#> |.....................|     9.767 |     1.412 |   0.03206 |    0.7829 |
+#> |.....................|    0.7810 |     1.406 |     1.183 |     1.223 |
+#> |   45|     479.24256 |    0.9982 |    -1.100 |   -0.9228 |   -0.9129 |
+#> |.....................|   -0.8642 |   -0.4061 |   -0.7626 |   -0.8221 |
+#> |.....................|    -1.034 |   -0.6924 |   -0.7565 |   -0.7138 |
+#> |    U|     479.24256 |     92.95 |    -5.404 |   -0.9550 |   -0.1250 |
+#> |.....................|     2.272 |     1.428 |   0.03199 |    0.7993 |
+#> |.....................|    0.7344 |     1.426 |     1.191 |     1.240 |
+#> |    X|     479.24256 |     92.95 |  0.004500 |    0.2779 |    0.8825 |
+#> |.....................|     9.702 |     1.428 |   0.03199 |    0.7993 |
+#> |.....................|    0.7344 |     1.426 |     1.191 |     1.240 |
+#> |   46|     477.60836 |     1.003 |    -1.228 |   -0.9400 |   -0.9346 |
+#> |.....................|   -0.8912 |   -0.2901 |   -0.7784 |   -0.7332 |
+#> |.....................|    -1.206 |   -0.6258 |   -0.7257 |   -0.6466 |
+#> |    U|     477.60836 |     93.40 |    -5.531 |   -0.9712 |   -0.1467 |
+#> |.....................|     2.245 |     1.495 |   0.03176 |    0.8667 |
+#> |.....................|    0.5843 |     1.507 |     1.224 |     1.312 |
+#> |    X|     477.60836 |     93.40 |  0.003961 |    0.2746 |    0.8635 |
+#> |.....................|     9.444 |     1.495 |   0.03176 |    0.8667 |
+#> |.....................|    0.5843 |     1.507 |     1.224 |     1.312 |
+#> |    F| Forward Diff. |     50.81 |    0.8332 |    0.6263 |   0.04339 |
+#> |.....................|    0.5543 |    -9.740 |    -2.969 |    0.1978 |
+#> |.....................|    -10.28 |    -2.761 |    -1.505 |    -1.849 |
+#> |   47|     476.77966 |     1.006 |    -1.398 |   -0.9862 |   -0.9532 |
+#> |.....................|   -0.9413 |  -0.07616 |   -0.7687 |   -0.6374 |
+#> |.....................|   -0.9573 |   -0.5395 |   -0.7103 |   -0.5930 |
+#> |    U|     476.77966 |     93.71 |    -5.701 |    -1.015 |   -0.1654 |
+#> |.....................|     2.195 |     1.619 |   0.03190 |    0.9393 |
+#> |.....................|    0.8014 |     1.612 |     1.240 |     1.369 |
+#> |    X|     476.77966 |     93.71 |  0.003342 |    0.2660 |    0.8476 |
+#> |.....................|     8.982 |     1.619 |   0.03190 |    0.9393 |
+#> |.....................|    0.8014 |     1.612 |     1.240 |     1.369 |
+#> |    F| Forward Diff. |     100.8 |    0.5681 |    -2.148 |   -0.2910 |
+#> |.....................|   -0.6169 |    0.8458 |    0.8586 |    0.3650 |
+#> |.....................|     3.820 |     1.443 |   -0.7364 |    0.2440 |
+#> |   48|     478.65806 |    0.9952 |    -1.512 |   -0.6913 |   -0.9031 |
+#> |.....................|   -0.8317 |  -0.01918 |   -0.7109 |   -0.6555 |
+#> |.....................|   -0.9083 |   -0.7021 |   -0.6121 |   -0.6260 |
+#> |    U|     478.65806 |     92.67 |    -5.815 |   -0.7363 |   -0.1152 |
+#> |.....................|     2.305 |     1.652 |   0.03277 |    0.9255 |
+#> |.....................|    0.8442 |     1.414 |     1.345 |     1.334 |
+#> |    X|     478.65806 |     92.67 |  0.002982 |    0.3238 |    0.8912 |
+#> |.....................|     10.02 |     1.652 |   0.03277 |    0.9255 |
+#> |.....................|    0.8442 |     1.414 |     1.345 |     1.334 |
+#> |   49|     476.83500 |    0.9931 |    -1.426 |   -0.9118 |   -0.9406 |
+#> |.....................|   -0.9137 |  -0.06192 |   -0.7543 |   -0.6420 |
+#> |.....................|   -0.9454 |   -0.5805 |   -0.6855 |   -0.6013 |
+#> |    U|       476.835 |     92.48 |    -5.730 |   -0.9445 |   -0.1527 |
+#> |.....................|     2.223 |     1.627 |   0.03212 |    0.9358 |
+#> |.....................|    0.8118 |     1.562 |     1.267 |     1.361 |
+#> |    X|       476.835 |     92.48 |  0.003247 |    0.2800 |    0.8584 |
+#> |.....................|     9.234 |     1.627 |   0.03212 |    0.9358 |
+#> |.....................|    0.8118 |     1.562 |     1.267 |     1.361 |
+#> |   50|     476.86775 |    0.9928 |    -1.411 |   -0.9513 |   -0.9473 |
+#> |.....................|   -0.9284 |  -0.06958 |   -0.7620 |   -0.6396 |
+#> |.....................|   -0.9520 |   -0.5587 |   -0.6987 |   -0.5969 |
+#> |    U|     476.86775 |     92.44 |    -5.715 |   -0.9819 |   -0.1595 |
+#> |.....................|     2.208 |     1.623 |   0.03200 |    0.9376 |
+#> |.....................|    0.8060 |     1.588 |     1.252 |     1.365 |
+#> |    X|     476.86775 |     92.44 |  0.003297 |    0.2725 |    0.8526 |
+#> |.....................|     9.099 |     1.623 |   0.03200 |    0.9376 |
+#> |.....................|    0.8060 |     1.588 |     1.252 |     1.365 |
+#> |   51|     476.94436 |    0.9926 |    -1.403 |   -0.9724 |   -0.9509 |
+#> |.....................|   -0.9362 |  -0.07366 |   -0.7662 |   -0.6383 |
+#> |.....................|   -0.9556 |   -0.5471 |   -0.7057 |   -0.5945 |
+#> |    U|     476.94436 |     92.42 |    -5.706 |    -1.002 |   -0.1630 |
+#> |.....................|     2.200 |     1.621 |   0.03194 |    0.9386 |
+#> |.....................|    0.8029 |     1.602 |     1.245 |     1.368 |
+#> |    X|     476.94436 |     92.42 |  0.003324 |    0.2686 |    0.8496 |
+#> |.....................|     9.028 |     1.621 |   0.03194 |    0.9386 |
+#> |.....................|    0.8029 |     1.602 |     1.245 |     1.368 |
+#> |   52|     476.64580 |    0.9959 |    -1.398 |   -0.9860 |   -0.9532 |
+#> |.....................|   -0.9413 |  -0.07625 |   -0.7688 |   -0.6374 |
+#> |.....................|   -0.9577 |   -0.5396 |   -0.7102 |   -0.5930 |
+#> |    U|      476.6458 |     92.74 |    -5.701 |    -1.015 |   -0.1653 |
+#> |.....................|     2.195 |     1.619 |   0.03190 |    0.9392 |
+#> |.....................|    0.8011 |     1.611 |     1.240 |     1.369 |
+#> |    X|      476.6458 |     92.74 |  0.003342 |    0.2661 |    0.8476 |
+#> |.....................|     8.983 |     1.619 |   0.03190 |    0.9392 |
+#> |.....................|    0.8011 |     1.611 |     1.240 |     1.369 |
+#> |    F| Forward Diff. |    -76.03 |    0.4748 |    -3.401 |   -0.5335 |
+#> |.....................|    -1.858 |     1.570 |   -0.1336 |    0.2990 |
+#> |.....................|     3.107 |     1.921 |   -0.6340 |    0.6252 |
+#> |   53|     476.45477 |     1.000 |    -1.400 |   -0.9787 |   -0.9521 |
+#> |.....................|   -0.9380 |  -0.07508 |   -0.7683 |   -0.6381 |
+#> |.....................|   -0.9567 |   -0.5427 |   -0.7079 |   -0.5935 |
+#> |    U|     476.45477 |     93.14 |    -5.704 |    -1.008 |   -0.1642 |
+#> |.....................|     2.199 |     1.620 |   0.03191 |    0.9387 |
+#> |.....................|    0.8019 |     1.608 |     1.243 |     1.369 |
+#> |    X|     476.45477 |     93.14 |  0.003334 |    0.2674 |    0.8486 |
+#> |.....................|     9.012 |     1.620 |   0.03191 |    0.9387 |
+#> |.....................|    0.8019 |     1.608 |     1.243 |     1.369 |
+#> |    F| Forward Diff. |    0.2803 |    0.4975 |    -2.426 |   -0.4122 |
+#> |.....................|    -1.237 |     1.245 |    0.3711 |    0.1250 |
+#> |.....................|     4.601 |     1.480 |   -0.5654 |    0.4236 |
+#> |   54|     476.38303 |    0.9998 |    -1.401 |   -0.9743 |   -0.9513 |
+#> |.....................|   -0.9358 |  -0.07732 |   -0.7690 |   -0.6383 |
+#> |.....................|   -0.9650 |   -0.5454 |   -0.7069 |   -0.5943 |
+#> |    U|     476.38303 |     93.10 |    -5.704 |    -1.004 |   -0.1635 |
+#> |.....................|     2.201 |     1.618 |   0.03190 |    0.9385 |
+#> |.....................|    0.7947 |     1.604 |     1.244 |     1.368 |
+#> |    X|     476.38303 |     93.10 |  0.003331 |    0.2682 |    0.8492 |
+#> |.....................|     9.032 |     1.618 |   0.03190 |    0.9385 |
+#> |.....................|    0.7947 |     1.604 |     1.244 |     1.368 |
+#> |   55|     476.22864 |    0.9983 |    -1.404 |   -0.9612 |   -0.9491 |
+#> |.....................|   -0.9291 |  -0.08404 |   -0.7710 |   -0.6390 |
+#> |.....................|   -0.9898 |   -0.5533 |   -0.7039 |   -0.5966 |
+#> |    U|     476.22864 |     92.96 |    -5.707 |   -0.9912 |   -0.1612 |
+#> |.....................|     2.207 |     1.614 |   0.03187 |    0.9380 |
+#> |.....................|    0.7730 |     1.595 |     1.247 |     1.366 |
+#> |    X|     476.22864 |     92.96 |  0.003322 |    0.2707 |    0.8511 |
+#> |.....................|     9.093 |     1.614 |   0.03187 |    0.9380 |
+#> |.....................|    0.7730 |     1.595 |     1.247 |     1.366 |
+#> |   56|     476.57199 |    0.9958 |    -1.445 |   -0.8532 |   -0.9271 |
+#> |.....................|   -0.8725 |  -0.06353 |   -0.7679 |   -0.6421 |
+#> |.....................|   -0.9751 |   -0.5970 |   -0.6712 |   -0.6082 |
+#> |    U|     476.57199 |     92.73 |    -5.749 |   -0.8892 |   -0.1393 |
+#> |.....................|     2.264 |     1.626 |   0.03191 |    0.9357 |
+#> |.....................|    0.7859 |     1.542 |     1.282 |     1.353 |
+#> |    X|     476.57199 |     92.73 |  0.003186 |    0.2913 |    0.8700 |
+#> |.....................|     9.623 |     1.626 |   0.03191 |    0.9357 |
+#> |.....................|    0.7859 |     1.542 |     1.282 |     1.353 |
+#> |    F| Forward Diff. |    -32.75 |    0.5399 |    -1.515 |   -0.3941 |
+#> |.....................|    -1.151 |     1.245 |   0.03890 |    0.2327 |
+#> |.....................|     2.518 |    0.9004 |   -0.2852 |    0.3306 |
+#> |   57|     476.21990 |    0.9982 |    -1.515 |   -0.9538 |   -0.8974 |
+#> |.....................|   -0.8289 |   -0.1020 |   -0.7526 |   -0.6734 |
+#> |.....................|   -0.9899 |   -0.5334 |   -0.6863 |   -0.5986 |
+#> |    U|      476.2199 |     92.95 |    -5.819 |   -0.9842 |   -0.1096 |
+#> |.....................|     2.308 |     1.604 |   0.03214 |    0.9120 |
+#> |.....................|    0.7729 |     1.619 |     1.266 |     1.364 |
+#> |    X|      476.2199 |     92.95 |  0.002972 |    0.2721 |    0.8962 |
+#> |.....................|     10.05 |     1.604 |   0.03214 |    0.9120 |
+#> |.....................|    0.7729 |     1.619 |     1.266 |     1.364 |
+#> |    F| Forward Diff. |    -17.29 |    0.1752 |    -1.213 |    0.7541 |
+#> |.....................|     1.907 |    0.8055 |   -0.1948 |  -0.02118 |
+#> |.....................|     1.522 |     1.784 |    0.5826 |    0.3001 |
+#> |   58|     476.15328 |    0.9997 |    -1.587 |   -0.9380 |   -0.8926 |
+#> |.....................|   -0.8393 |   -0.1057 |   -0.7294 |   -0.6920 |
+#> |.....................|   -0.9908 |   -0.5546 |   -0.6943 |   -0.5998 |
+#> |    U|     476.15328 |     93.09 |    -5.890 |   -0.9693 |   -0.1048 |
+#> |.....................|     2.297 |     1.602 |   0.03249 |    0.8979 |
+#> |.....................|    0.7721 |     1.593 |     1.257 |     1.362 |
+#> |    X|     476.15328 |     93.09 |  0.002766 |    0.2750 |    0.9005 |
+#> |.....................|     9.947 |     1.602 |   0.03249 |    0.8979 |
+#> |.....................|    0.7721 |     1.593 |     1.257 |     1.362 |
+#> |    F| Forward Diff. |     9.478 |  -0.04668 |  -0.07764 |    0.8847 |
+#> |.....................|     1.686 |     1.059 |    0.2200 |  -0.09397 |
+#> |.....................|     3.078 |    0.7416 |    0.1570 |    0.2315 |
+#> |   59|     476.01802 |     1.000 |    -1.651 |   -0.9570 |   -0.8992 |
+#> |.....................|   -0.8607 |   -0.1274 |   -0.7088 |   -0.7141 |
+#> |.....................|    -1.015 |   -0.5543 |   -0.6984 |   -0.6027 |
+#> |    U|     476.01802 |     93.12 |    -5.954 |   -0.9872 |   -0.1113 |
+#> |.....................|     2.276 |     1.589 |   0.03280 |    0.8811 |
+#> |.....................|    0.7512 |     1.594 |     1.253 |     1.359 |
+#> |    X|     476.01802 |     93.12 |  0.002594 |    0.2715 |    0.8947 |
+#> |.....................|     9.736 |     1.589 |   0.03280 |    0.8811 |
+#> |.....................|    0.7512 |     1.594 |     1.253 |     1.359 |
+#> |   60|     476.22711 |     1.004 |    -1.844 |    -1.014 |   -0.9185 |
+#> |.....................|   -0.9244 |   -0.1921 |   -0.6470 |   -0.7805 |
+#> |.....................|    -1.085 |   -0.5529 |   -0.7106 |   -0.6114 |
+#> |    U|     476.22711 |     93.52 |    -6.147 |    -1.041 |   -0.1307 |
+#> |.....................|     2.212 |     1.552 |   0.03373 |    0.8308 |
+#> |.....................|    0.6895 |     1.595 |     1.240 |     1.350 |
+#> |    X|     476.22711 |     93.52 |  0.002140 |    0.2610 |    0.8775 |
+#> |.....................|     9.136 |     1.552 |   0.03373 |    0.8308 |
+#> |.....................|    0.6895 |     1.595 |     1.240 |     1.350 |
+#> |    F| Forward Diff. |     11.37 |   -0.1053 |    -1.010 |    0.7448 |
+#> |.....................|     1.048 |    0.2820 |    0.2022 |   -0.3140 |
+#> |.....................|    0.8239 |    0.7199 |  -0.08354 |   0.05077 |
+#> |   61|     477.73164 |    0.9986 |    -1.783 |   -0.8482 |    -1.092 |
+#> |.....................|   -0.9355 |   -0.2068 |   -0.7199 |   -0.6608 |
+#> |.....................|    -1.022 |   -0.4554 |   -0.5612 |   -0.5707 |
+#> |    U|     477.73164 |     92.99 |    -6.086 |   -0.8845 |   -0.3044 |
+#> |.....................|     2.201 |     1.543 |   0.03264 |    0.9215 |
+#> |.....................|    0.7445 |     1.714 |     1.399 |     1.393 |
+#> |    X|     477.73164 |     92.99 |  0.002274 |    0.2922 |    0.7376 |
+#> |.....................|     9.035 |     1.543 |   0.03264 |    0.9215 |
+#> |.....................|    0.7445 |     1.714 |     1.399 |     1.393 |
+#> |   62|     476.07192 |    0.9962 |    -1.664 |   -0.9459 |   -0.9184 |
+#> |.....................|   -0.8684 |   -0.1353 |   -0.7100 |   -0.7087 |
+#> |.....................|    -1.016 |   -0.5448 |   -0.6848 |   -0.5995 |
+#> |    U|     476.07192 |     92.76 |    -5.967 |   -0.9768 |   -0.1306 |
+#> |.....................|     2.268 |     1.585 |   0.03278 |    0.8852 |
+#> |.....................|    0.7503 |     1.605 |     1.267 |     1.362 |
+#> |    X|     476.07192 |     92.76 |  0.002561 |    0.2735 |    0.8776 |
+#> |.....................|     9.662 |     1.585 |   0.03278 |    0.8852 |
+#> |.....................|    0.7503 |     1.605 |     1.267 |     1.362 |
+#> |   63|     476.10587 |    0.9957 |    -1.654 |   -0.9539 |   -0.9043 |
+#> |.....................|   -0.8630 |   -0.1295 |   -0.7092 |   -0.7126 |
+#> |.....................|    -1.015 |   -0.5521 |   -0.6949 |   -0.6019 |
+#> |    U|     476.10587 |     92.72 |    -5.958 |   -0.9843 |   -0.1164 |
+#> |.....................|     2.274 |     1.588 |   0.03280 |    0.8822 |
+#> |.....................|    0.7508 |     1.596 |     1.257 |     1.360 |
+#> |    X|     476.10587 |     92.72 |  0.002586 |    0.2720 |    0.8901 |
+#> |.....................|     9.714 |     1.588 |   0.03280 |    0.8822 |
+#> |.....................|    0.7508 |     1.596 |     1.257 |     1.360 |
+#> |   64|     476.02413 |    0.9981 |    -1.651 |   -0.9568 |   -0.8993 |
+#> |.....................|   -0.8609 |   -0.1274 |   -0.7088 |   -0.7140 |
+#> |.....................|    -1.015 |   -0.5544 |   -0.6984 |   -0.6027 |
+#> |    U|     476.02413 |     92.94 |    -5.954 |   -0.9870 |   -0.1114 |
+#> |.....................|     2.276 |     1.589 |   0.03280 |    0.8812 |
+#> |.....................|    0.7511 |     1.593 |     1.253 |     1.359 |
+#> |    X|     476.02413 |     92.94 |  0.002594 |    0.2715 |    0.8946 |
+#> |.....................|     9.735 |     1.589 |   0.03280 |    0.8812 |
+#> |.....................|    0.7511 |     1.593 |     1.253 |     1.359 |
+#> |   65|     476.01367 |    0.9993 |    -1.651 |   -0.9569 |   -0.8992 |
+#> |.....................|   -0.8608 |   -0.1274 |   -0.7088 |   -0.7141 |
+#> |.....................|    -1.015 |   -0.5543 |   -0.6984 |   -0.6027 |
+#> |    U|     476.01367 |     93.05 |    -5.954 |   -0.9871 |   -0.1114 |
+#> |.....................|     2.276 |     1.589 |   0.03280 |    0.8812 |
+#> |.....................|    0.7512 |     1.594 |     1.253 |     1.359 |
+#> |    X|     476.01367 |     93.05 |  0.002594 |    0.2715 |    0.8946 |
+#> |.....................|     9.736 |     1.589 |   0.03280 |    0.8812 |
+#> |.....................|    0.7512 |     1.594 |     1.253 |     1.359 |
+#> |    F| Forward Diff. |   -0.2880 |   -0.1104 |    -1.088 |    0.7255 |
+#> |.....................|    0.9655 |  -0.09765 |   0.02713 |   -0.4308 |
+#> |.....................|     1.898 |    0.6709 |  -0.08067 |   0.06084 |
+#> |   66|     476.01068 |    0.9993 |    -1.651 |   -0.9566 |   -0.8994 |
+#> |.....................|   -0.8610 |   -0.1274 |   -0.7088 |   -0.7139 |
+#> |.....................|    -1.015 |   -0.5545 |   -0.6983 |   -0.6027 |
+#> |    U|     476.01068 |     93.06 |    -5.954 |   -0.9868 |   -0.1116 |
+#> |.....................|     2.276 |     1.589 |   0.03280 |    0.8813 |
+#> |.....................|    0.7507 |     1.593 |     1.253 |     1.359 |
+#> |    X|     476.01068 |     93.06 |  0.002595 |    0.2715 |    0.8944 |
+#> |.....................|     9.733 |     1.589 |   0.03280 |    0.8813 |
+#> |.....................|    0.7507 |     1.593 |     1.253 |     1.359 |
+#> |   67|     476.00249 |    0.9996 |    -1.651 |   -0.9556 |   -0.9000 |
+#> |.....................|   -0.8619 |   -0.1273 |   -0.7089 |   -0.7136 |
+#> |.....................|    -1.017 |   -0.5551 |   -0.6983 |   -0.6027 |
+#> |    U|     476.00249 |     93.08 |    -5.954 |   -0.9860 |   -0.1122 |
+#> |.....................|     2.275 |     1.589 |   0.03280 |    0.8815 |
+#> |.....................|    0.7493 |     1.593 |     1.253 |     1.359 |
+#> |    X|     476.00249 |     93.08 |  0.002595 |    0.2717 |    0.8939 |
+#> |.....................|     9.725 |     1.589 |   0.03280 |    0.8815 |
+#> |.....................|    0.7493 |     1.593 |     1.253 |     1.359 |
+#> |   68|     475.98648 |    0.9997 |    -1.654 |   -0.9518 |   -0.9062 |
+#> |.....................|   -0.8643 |   -0.1288 |   -0.7101 |   -0.7095 |
+#> |.....................|    -1.019 |   -0.5521 |   -0.6956 |   -0.6031 |
+#> |    U|     475.98648 |     93.09 |    -5.957 |   -0.9823 |   -0.1183 |
+#> |.....................|     2.272 |     1.589 |   0.03278 |    0.8846 |
+#> |.....................|    0.7477 |     1.596 |     1.256 |     1.359 |
+#> |    X|     475.98648 |     93.09 |  0.002587 |    0.2724 |    0.8884 |
+#> |.....................|     9.702 |     1.589 |   0.03278 |    0.8846 |
+#> |.....................|    0.7477 |     1.596 |     1.256 |     1.359 |
+#> |   69|     475.97179 |    0.9994 |    -1.666 |   -0.9399 |   -0.9282 |
+#> |.....................|   -0.8710 |   -0.1347 |   -0.7147 |   -0.6948 |
+#> |.....................|    -1.020 |   -0.5387 |   -0.6854 |   -0.6045 |
+#> |    U|     475.97179 |     93.06 |    -5.969 |   -0.9711 |   -0.1404 |
+#> |.....................|     2.266 |     1.585 |   0.03271 |    0.8957 |
+#> |.....................|    0.7463 |     1.612 |     1.267 |     1.357 |
+#> |    X|     475.97179 |     93.06 |  0.002557 |    0.2747 |    0.8690 |
+#> |.....................|     9.637 |     1.585 |   0.03271 |    0.8957 |
+#> |.....................|    0.7463 |     1.612 |     1.267 |     1.357 |
+#> |    F| Forward Diff. |     1.543 |   -0.1187 |  -0.09427 |   0.04746 |
+#> |.....................|    0.7019 |    0.1743 |  0.004057 |   -0.1664 |
+#> |.....................|     1.824 |     1.487 |    0.8060 |   -0.1087 |
+#> |   70|     475.93640 |    0.9984 |    -1.664 |   -0.9398 |   -0.9470 |
+#> |.....................|   -0.8662 |   -0.1315 |   -0.7271 |   -0.6595 |
+#> |.....................|    -1.030 |   -0.5499 |   -0.6986 |   -0.5913 |
+#> |    U|      475.9364 |     92.96 |    -5.967 |   -0.9710 |   -0.1592 |
+#> |.....................|     2.270 |     1.587 |   0.03253 |    0.9225 |
+#> |.....................|    0.7382 |     1.599 |     1.253 |     1.371 |
+#> |    X|      475.9364 |     92.96 |  0.002561 |    0.2747 |    0.8529 |
+#> |.....................|     9.682 |     1.587 |   0.03253 |    0.9225 |
+#> |.....................|    0.7382 |     1.599 |     1.253 |     1.371 |
+#> |    F| Forward Diff. |    -18.02 |  -0.07507 |   -0.1675 |   -0.4306 |
+#> |.....................|    0.8222 |   -0.4249 |   -0.3576 |  -0.06909 |
+#> |.....................|   -0.1553 |    0.7789 |  -0.06902 |    0.4423 |
+#> |   71|     475.93449 |    0.9995 |    -1.655 |   -0.9484 |   -0.9330 |
+#> |.....................|   -0.8784 |   -0.1258 |   -0.7357 |   -0.6330 |
+#> |.....................|    -1.033 |   -0.5716 |   -0.6758 |   -0.5988 |
+#> |    U|     475.93449 |     93.07 |    -5.959 |   -0.9791 |   -0.1451 |
+#> |.....................|     2.258 |     1.590 |   0.03240 |    0.9426 |
+#> |.....................|    0.7351 |     1.573 |     1.277 |     1.363 |
+#> |    X|     475.93449 |     93.07 |  0.002583 |    0.2731 |    0.8649 |
+#> |.....................|     9.566 |     1.590 |   0.03240 |    0.9426 |
+#> |.....................|    0.7351 |     1.573 |     1.277 |     1.363 |
+#> |    F| Forward Diff. |    -1.432 |  -0.03245 |   -0.4539 |  -0.04331 |
+#> |.....................|    0.5695 |  -0.03993 |   -0.2223 |    0.1396 |
+#> |.....................|   -0.3709 |  -0.08203 |     1.409 |   0.03273 |
+#> |   72|     475.92305 |     1.001 |    -1.648 |   -0.9418 |   -0.9189 |
+#> |.....................|   -0.8867 |   -0.1240 |   -0.7358 |   -0.6284 |
+#> |.....................|    -1.035 |   -0.5652 |   -0.6857 |   -0.6066 |
+#> |    U|     475.92305 |     93.18 |    -5.952 |   -0.9729 |   -0.1311 |
+#> |.....................|     2.250 |     1.591 |   0.03240 |    0.9461 |
+#> |.....................|    0.7335 |     1.580 |     1.266 |     1.355 |
+#> |    X|     475.92305 |     93.18 |  0.002602 |    0.2743 |    0.8772 |
+#> |.....................|     9.486 |     1.591 |   0.03240 |    0.9461 |
+#> |.....................|    0.7335 |     1.580 |     1.266 |     1.355 |
+#> |    F| Forward Diff. |     18.31 |  0.001701 |   0.03033 |    0.3531 |
+#> |.....................|    0.4204 |   0.05655 |  -0.08057 |    0.1734 |
+#> |.....................|   -0.4632 |    0.1099 |    0.8178 |   -0.3689 |
+#> |   73|     475.91938 |    0.9986 |    -1.638 |   -0.9366 |   -0.9070 |
+#> |.....................|   -0.8945 |   -0.1236 |   -0.7244 |   -0.6267 |
+#> |.....................|    -1.037 |   -0.5623 |   -0.6914 |   -0.6147 |
+#> |    U|     475.91938 |     92.99 |    -5.941 |   -0.9680 |   -0.1192 |
+#> |.....................|     2.242 |     1.592 |   0.03257 |    0.9474 |
+#> |.....................|    0.7320 |     1.584 |     1.260 |     1.346 |
+#> |    X|     475.91938 |     92.99 |  0.002629 |    0.2753 |    0.8877 |
+#> |.....................|     9.412 |     1.592 |   0.03257 |    0.9474 |
+#> |.....................|    0.7320 |     1.584 |     1.260 |     1.346 |
+#> |    F| Forward Diff. |    -15.99 |   0.01876 |   0.07238 |    0.5908 |
+#> |.....................|  -0.09055 |    0.2914 |   -0.2119 |    0.1409 |
+#> |.....................|    0.4365 |    0.1061 |    0.4376 |   -0.5157 |
+#> |   74|     475.91938 |    0.9986 |    -1.638 |   -0.9366 |   -0.9070 |
+#> |.....................|   -0.8945 |   -0.1236 |   -0.7244 |   -0.6267 |
+#> |.....................|    -1.037 |   -0.5623 |   -0.6914 |   -0.6147 |
+#> |    U|     475.91938 |     92.99 |    -5.941 |   -0.9680 |   -0.1192 |
+#> |.....................|     2.242 |     1.592 |   0.03257 |    0.9474 |
+#> |.....................|    0.7320 |     1.584 |     1.260 |     1.346 |
+#> |    X|     475.91938 |     92.99 |  0.002629 |    0.2753 |    0.8877 |
+#> |.....................|     9.412 |     1.592 |   0.03257 |    0.9474 |
+#> |.....................|    0.7320 |     1.584 |     1.260 |     1.346 |
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |
+#> |.....................|    log_k2 |  g_qlogis | sigma_low |  rsd_high |
+#> |.....................|        o1 |        o2 |        o3 |        o4 |
+#> |.....................|        o5 |        o6 |...........|...........|
+#> |    1|     495.80376 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     495.80376 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     495.80376 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    G|    Gill Diff. |     40.10 |     2.344 |  -0.09792 |   0.01304 |
+#> |.....................|   -0.4854 |    0.6353 |    -29.93 |    -20.00 |
+#> |.....................|     1.261 |     9.993 |    -12.68 |   -0.7774 |
+#> |.....................|     8.106 |    -12.55 |...........|...........|
+#> |    2|     2936.2793 |    0.3119 |    -1.040 |   -0.9093 |   -0.9382 |
+#> |.....................|   -0.9801 |   -0.8941 |   -0.3619 |   -0.5483 |
+#> |.....................|   -0.8992 |    -1.046 |   -0.6506 |   -0.8594 |
+#> |.....................|    -1.014 |   -0.6521 |...........|...........|
+#> |    U|     2936.2793 |     28.54 |    -5.229 |   -0.8860 |    -2.190 |
+#> |.....................|    -4.622 |    0.4539 |     1.041 |   0.06759 |
+#> |.....................|    0.7138 |    0.7431 |     1.443 |    0.9756 |
+#> |.....................|    0.7388 |     1.478 |...........|...........|
+#> |    X|     2936.2793 |     28.54 |  0.005360 |    0.2919 |    0.1119 |
+#> |.....................|  0.009832 |    0.6116 |     1.041 |   0.06759 |
+#> |.....................|    0.7138 |    0.7431 |     1.443 |    0.9756 |
+#> |.....................|    0.7388 |     1.478 |...........|...........|
+#> |    3|     515.54714 |    0.9312 |    -1.004 |   -0.9108 |   -0.9380 |
+#> |.....................|   -0.9876 |   -0.8843 |   -0.8242 |   -0.8571 |
+#> |.....................|   -0.8797 |   -0.8912 |   -0.8464 |   -0.8714 |
+#> |.....................|   -0.8888 |   -0.8460 |...........|...........|
+#> |    U|     515.54714 |     85.19 |    -5.193 |   -0.8873 |    -2.190 |
+#> |.....................|    -4.630 |    0.4584 |    0.8493 |   0.05868 |
+#> |.....................|    0.7280 |    0.8815 |     1.211 |    0.9641 |
+#> |.....................|    0.8462 |     1.242 |...........|...........|
+#> |    X|     515.54714 |     85.19 |  0.005557 |    0.2917 |    0.1119 |
+#> |.....................|  0.009758 |    0.6126 |    0.8493 |   0.05868 |
+#> |.....................|    0.7280 |    0.8815 |     1.211 |    0.9641 |
+#> |.....................|    0.8462 |     1.242 |...........|...........|
+#> |    4|     501.46574 |    0.9922 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9884 |   -0.8833 |   -0.8697 |   -0.8876 |
+#> |.....................|   -0.8778 |   -0.8761 |   -0.8657 |   -0.8726 |
+#> |.....................|   -0.8765 |   -0.8650 |...........|...........|
+#> |    U|     501.46574 |     90.77 |    -5.189 |   -0.8874 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8304 |   0.05781 |
+#> |.....................|    0.7294 |    0.8952 |     1.188 |    0.9629 |
+#> |.....................|    0.8568 |     1.219 |...........|...........|
+#> |    X|     501.46574 |     90.77 |  0.005577 |    0.2916 |    0.1119 |
+#> |.....................|  0.009751 |    0.6127 |    0.8304 |   0.05781 |
+#> |.....................|    0.7294 |    0.8952 |     1.188 |    0.9629 |
+#> |.....................|    0.8568 |     1.219 |...........|...........|
+#> |    5|     501.84206 |    0.9992 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9884 |   -0.8832 |   -0.8749 |   -0.8911 |
+#> |.....................|   -0.8776 |   -0.8743 |   -0.8679 |   -0.8727 |
+#> |.....................|   -0.8751 |   -0.8673 |...........|...........|
+#> |    U|     501.84206 |     91.41 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8283 |   0.05771 |
+#> |.....................|    0.7296 |    0.8967 |     1.185 |    0.9628 |
+#> |.....................|    0.8580 |     1.216 |...........|...........|
+#> |    X|     501.84206 |     91.41 |  0.005579 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8283 |   0.05771 |
+#> |.....................|    0.7296 |    0.8967 |     1.185 |    0.9628 |
+#> |.....................|    0.8580 |     1.216 |...........|...........|
+#> |    6|     501.90183 |    0.9999 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8914 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90183 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05770 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90183 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05770 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    7|     501.90808 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90808 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90808 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    8|     501.90873 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90873 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90873 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    9|     501.90880 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|      501.9088 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|      501.9088 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |   10|     501.90881 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90881 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90881 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |   11|     501.90882 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90882 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90882 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |   12|     501.90882 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90882 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90882 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |   13|     501.90882 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90882 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90882 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |   14|     501.90882 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90882 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90882 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |   15|     501.90882 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90882 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90882 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |   16|     501.90883 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90883 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90883 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |   17|     501.90883 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8776 |   -0.8741 |   -0.8681 |   -0.8727 |
+#> |.....................|   -0.8749 |   -0.8675 |...........|...........|
+#> |    U|     501.90883 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> |    X|     501.90883 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.7296 |    0.8969 |     1.185 |    0.9628 |
+#> |.....................|    0.8582 |     1.216 |...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                
+# Two-component error by variable is possible with both estimation methods
+# Variance by variable is supported by 'saem' and 'focei'
+f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem",
+  error_model = "obs_tc")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> 1:    92.2740   -5.2361    0.2113    1.9393   -2.0029    2.8805    1.6298    0.7279    0.7192    0.4382    6.7264    0.4769    7.2363    0.6178
+#> 2:    93.1532   -5.3060    0.0602    2.0735   -2.0177    2.7365    1.5483    0.6915    0.8577    0.4163    7.5229    0.0003    8.5494    0.0006
+#> 3:    9.3232e+01  -5.5491e+00   5.1555e-02   2.4627e+00  -1.4981e+00   2.5997e+00   1.4709e+00   6.5697e-01   8.1480e-01   3.9549e-01   4.6581e+00   4.3492e-05   5.3112e+00   1.7818e-04
+#> 4:    9.3109e+01  -5.6749e+00   3.7928e-02   2.4274e+00  -1.3355e+00   2.4697e+00   1.3973e+00   6.2412e-01   7.7406e-01   3.7572e-01   3.5252e+00   9.5643e-05   4.0990e+00   4.6584e-05
+#> 5:    9.3327e+01  -5.8341e+00  -1.6798e-02   2.4024e+00  -1.2129e+00   2.3462e+00   1.3274e+00   5.9292e-01   7.3536e-01   3.5693e-01   3.3259e+00   1.6901e-05   3.5218e+00   4.0075e-05
+#> 6:    9.3449e+01  -6.0745e+00  -6.1031e-02   2.3458e+00  -1.2034e+00   2.2289e+00   1.8700e+00   5.6327e-01   6.9859e-01   3.3908e-01   2.9533e+00   6.5587e-07   3.1056e+00   2.1346e-02
+#> 7:    93.2519   -6.0564   -0.0590    2.3588   -1.1293    2.1174    1.8910    0.5351    0.6637    0.3221    2.8211    0.0082    2.8507    0.0251
+#> 8:    93.0343   -5.9362   -0.0851    2.2949   -1.0760    2.0116    1.7964    0.5084    0.6305    0.3060    2.5340    0.0181    2.6368    0.0243
+#> 9:    93.1444   -6.1910   -0.1199    2.2709   -1.1077    1.9110    1.8664    0.4829    0.5990    0.2907    2.3768    0.0191    2.3601    0.0284
+#> 10:    93.2748   -6.4970   -0.1598    2.2235   -1.1034    2.1024    3.1968    0.4588    0.5690    0.2762    2.1991    0.0255    2.2790    0.0316
+#> 11:    93.4141   -6.4463   -0.1698    2.1876   -1.0890    1.9973    3.0370    0.4358    0.5406    0.2624    2.1469    0.0266    2.1681    0.0325
+#> 12:    93.4935   -6.5467   -0.1715    2.1666   -1.0952    1.8974    3.7848    0.4141    0.5135    0.2493    1.9137    0.0292    2.0701    0.0331
+#> 13:    93.6730   -6.4173   -0.1752    2.1387   -1.0753    1.8026    3.7278    0.3934    0.4879    0.2368    1.9084    0.0272    2.0289    0.0369
+#> 14:    93.5721   -6.2146   -0.1738    2.1854   -1.0740    2.0902    3.5415    0.3737    0.4635    0.2250    1.9861    0.0239    2.0052    0.0347
+#> 15:    93.6638   -6.3103   -0.1693    2.1828   -1.0327    2.0702    3.3644    0.3720    0.4403    0.2137    1.8947    0.0247    1.9865    0.0375
+#> 16:    93.4156   -6.0957   -0.1666    2.1755   -1.0737    2.6391    3.1962    0.3691    0.4183    0.2030    1.9089    0.0241    2.0159    0.0360
+#> 17:    93.4257   -6.1494   -0.1705    2.1664   -1.0589    2.5072    3.0714    0.3697    0.3974    0.1929    1.8253    0.0268    2.0391    0.0301
+#> 18:    93.5593   -6.1696   -0.1780    2.1670   -1.0129    2.3818    3.7604    0.3725    0.3775    0.1832    1.8529    0.0304    1.8784    0.0298
+#> 19:    93.5027   -6.2960   -0.1791    2.1543   -1.0325    2.6052    4.5501    0.3942    0.3586    0.1741    1.8082    0.0328    1.8654    0.0335
+#> 20:    93.4480   -6.4389   -0.1776    2.1772   -1.0485    2.6607    5.1881    0.3894    0.3554    0.1654    1.8032    0.0322    1.9018    0.0312
+#> 21:    93.6411   -6.2893   -0.1750    2.1759   -1.0350    2.5276    4.9287    0.3817    0.3386    0.1605    1.8533    0.0264    1.9317    0.0301
+#> 22:    93.9320   -6.1469   -0.1750    2.1910   -1.0527    2.4013    4.6823    0.3720    0.3642    0.1525    1.8949    0.0273    1.8977    0.0310
+#> 23:    93.6074   -6.3097   -0.1502    2.2111   -1.0155    2.2812    4.6643    0.3832    0.4236    0.1449    1.7075    0.0340    1.7367    0.0337
+#> 24:    93.7425   -6.4598   -0.1446    2.2249   -1.0011    2.7056    6.0597    0.3949    0.4075    0.1479    1.7180    0.0360    1.7786    0.0302
+#> 25:    94.1822   -6.3674   -0.1496    2.1917   -1.0011    3.4724    5.7567    0.3897    0.4355    0.1465    1.6977    0.0356    1.8373    0.0328
+#> 26:    94.0446   -6.3235   -0.1496    2.2004   -1.0414    3.5912    5.4688    0.3897    0.4438    0.1405    1.6765    0.0344    1.8262    0.0355
+#> 27:    94.4454   -6.2148   -0.1370    2.2360   -1.0220    4.6238    5.1954    0.3702    0.4216    0.1335    1.7209    0.0349    1.7702    0.0336
+#> 28:    94.1837   -6.1301   -0.1376    2.2253   -1.0261    4.3926    4.9356    0.3644    0.4005    0.1345    1.6968    0.0290    1.8540    0.0316
+#> 29:    94.0681   -5.8726   -0.1440    2.2237   -1.0400    4.1730    4.6889    0.3750    0.4055    0.1464    1.7084    0.0329    1.7379    0.0407
+#> 30:    94.5866   -5.9141   -0.1416    2.2045   -1.0350    3.9896    4.4544    0.3770    0.3852    0.1769    1.6009    0.0326    1.8718    0.0350
+#> 31:    94.1640   -6.0370   -0.1382    2.2140   -1.0189    5.4942    4.2317    0.3759    0.3809    0.1680    1.5887    0.0386    1.8918    0.0286
+#> 32:    94.5952   -5.8349   -0.1373    2.2374   -1.0283    5.2195    4.0201    0.3745    0.3835    0.1636    1.6451    0.0375    1.7459    0.0382
+#> 33:    95.0936   -5.8145   -0.1356    2.2325   -1.0037    4.9634    3.8191    0.3614    0.3644    0.1677    1.6313    0.0414    1.6809    0.0399
+#> 34:    94.7033   -5.8916   -0.1208    2.2687   -0.9896    5.4935    3.6281    0.3741    0.3536    0.1701    1.5923    0.0376    1.2962    0.0644
+#> 35:    94.8127   -5.9839   -0.1122    2.2615   -0.9983    5.2188    3.7348    0.3817    0.3661    0.1712    1.5848    0.0313    1.1651    0.0752
+#> 36:    94.6798   -5.8938   -0.1203    2.2441   -1.0009    4.9578    3.5480    0.3835    0.3478    0.1708    1.5525    0.0313    1.1527    0.0712
+#> 37:    93.9759   -5.8017   -0.1274    2.2346   -1.0021    4.7100    3.3706    0.3868    0.3350    0.1622    1.6278    0.0256    1.7263    0.0372
+#> 38:    94.2013   -5.8617   -0.1206    2.2570   -1.0125    4.4745    3.2021    0.3754    0.3520    0.1574    1.5396    0.0290    1.0653    0.0746
+#> 39:    94.1314   -5.7645   -0.1261    2.2381   -1.0361    4.2507    3.0420    0.3804    0.3521    0.1543    1.6280    0.0267    1.1461    0.0755
+#> 40:    93.7934   -5.8654   -0.1206    2.2417   -1.0503    4.0382    2.8899    0.3624    0.3413    0.1747    1.6231    0.0239    1.5698    0.0513
+#> 41:    93.8756   -6.0150   -0.1171    2.2581   -1.0313    3.8363    3.3629    0.3809    0.3369    0.1944    1.6461    0.0217    1.7762    0.0345
+#> 42:    94.0644   -5.9723   -0.1136    2.2769   -1.0295    3.6445    3.2171    0.3702    0.3394    0.1920    1.5035    0.0416    1.5148    0.0475
+#> 43:    93.7394   -5.9927   -0.1233    2.2650   -1.0374    3.4622    3.0562    0.3735    0.3370    0.1824    1.6022    0.0379    1.5080    0.0468
+#> 44:    93.5428   -5.9784   -0.1187    2.2780   -1.0279    3.2891    2.9495    0.3732    0.3289    0.1742    1.5456    0.0471    1.4361    0.0517
+#> 45:    93.2885   -5.9836   -0.1273    2.2650   -1.0100    3.1247    3.2884    0.3768    0.3719    0.1655    1.6579    0.0336    1.4031    0.0585
+#> 46:    93.4080   -5.9261   -0.1371    2.2513   -1.0159    3.4180    3.1630    0.3709    0.3762    0.1711    1.7365    0.0269    1.4612    0.0530
+#> 47:    93.4548   -5.8101   -0.1372    2.2650   -1.0058    3.2471    3.0049    0.3703    0.3921    0.1797    1.7161    0.0300    1.4813    0.0524
+#> 48:    93.1829   -5.6877   -0.1391    2.2594   -1.0035    3.0848    2.8546    0.3690    0.3901    0.1707    1.7558    0.0292    1.5856    0.0487
+#> 49:    93.1860   -5.8153   -0.1349    2.2793   -0.9905    2.9305    2.7119    0.3619    0.3877    0.1690    1.7255    0.0299    1.6143    0.0465
+#> 50:    93.5597   -5.7551   -0.1334    2.2669   -0.9808    2.7840    2.5763    0.3652    0.3795    0.1716    1.6690    0.0290    1.4895    0.0536
+#> 51:    93.5952   -5.8089   -0.1358    2.2626   -1.0100    2.6448    2.4475    0.3640    0.4246    0.1630    1.5892    0.0344    1.3958    0.0604
+#> 52:    93.3111   -5.9181   -0.1323    2.2489   -0.9909    2.5126    2.8739    0.3695    0.4337    0.1549    1.5200    0.0329    1.2246    0.0685
+#> 53:    93.4921   -6.0837   -0.1307    2.2513   -1.0031    2.3869    3.6029    0.3678    0.4363    0.1682    1.4683    0.0336    1.2917    0.0665
+#> 54:    93.4808   -6.2019   -0.1488    2.2068   -1.0207    2.2676    4.1833    0.3952    0.4145    0.1598    1.6478    0.0325    1.2418    0.0659
+#> 55:    93.5453   -6.2747   -0.1411    2.2297   -1.0122    2.1542    4.5107    0.3941    0.4044    0.1556    1.5685    0.0358    1.3236    0.0654
+#> 56:    94.0212   -6.2713   -0.1355    2.2228   -1.0205    2.0465    5.1718    0.3901    0.4101    0.1516    1.5568    0.0341    1.1952    0.0736
+#> 57:    93.7155   -6.2511   -0.1574    2.1899   -1.0374    1.9442    4.9132    0.3991    0.3974    0.1442    1.5528    0.0364    1.5497    0.0485
+#> 58:    93.9064   -6.2021   -0.1543    2.1935   -1.0277    1.8470    4.6676    0.3935    0.3944    0.1458    1.5590    0.0354    1.3512    0.0613
+#> 59:    93.9059   -6.3971   -0.1550    2.1899   -1.0124    1.7546    5.8885    0.3925    0.3943    0.1446    1.5641    0.0373    1.4293    0.0550
+#> 60:    93.8600   -6.2474   -0.1552    2.1978   -0.9930    1.7661    5.5941    0.3905    0.4078    0.1532    1.5235    0.0364    1.5442    0.0477
+#> 61:    93.8936   -6.3077   -0.1568    2.2022   -1.0084    1.7122    5.3507    0.3946    0.4146    0.1455    1.5154    0.0342    1.3664    0.0587
+#> 62:    93.6133   -6.1446   -0.1473    2.2277   -1.0195    1.6266    5.0832    0.3794    0.4254    0.1383    1.5586    0.0330    1.1663    0.0705
+#> 63:    93.5549   -6.3005   -0.1437    2.2302   -1.0096    1.5452    5.0969    0.3651    0.4262    0.1349    1.5730    0.0323    1.2501    0.0668
+#> 64:    93.3212   -6.1190   -0.1428    2.2309   -1.0005    1.4826    4.8421    0.3661    0.4181    0.1443    1.6657    0.0259    1.3409    0.0627
+#> 65:    93.2534   -5.9614   -0.1492    2.2310   -0.9865    1.4084    4.6000    0.3735    0.4186    0.1695    1.6883    0.0235    1.4446    0.0563
+#> 66:    93.3429   -5.9786   -0.1401    2.2198   -0.9934    1.3380    4.3700    0.3807    0.4094    0.1610    1.6697    0.0270    1.1164    0.0778
+#> 67:    93.5657   -6.2158   -0.1405    2.2326   -0.9891    1.2711    4.4653    0.3827    0.4063    0.1530    1.5851    0.0316    1.3581    0.0590
+#> 68:    93.4898   -5.9763   -0.1375    2.2431   -0.9837    1.2076    4.2420    0.3771    0.4127    0.1453    1.6134    0.0325    1.1459    0.0744
+#> 69:    93.4995   -6.1375   -0.1412    2.2423   -1.0003    1.3178    4.3907    0.3746    0.4202    0.1403    1.6223    0.0304    1.3354    0.0608
+#> 70:    93.4369   -6.1690   -0.1395    2.2472   -1.0047    1.6239    4.5654    0.3793    0.4087    0.1400    1.6317    0.0349    1.4812    0.0494
+#> 71:    93.4041   -6.3637   -0.1489    2.2348   -1.0125    1.5427    5.3897    0.3603    0.3883    0.1330    1.5954    0.0303    1.3502    0.0612
+#> 72:    93.1755   -6.4067   -0.1441    2.2492   -0.9859    1.4656    6.3554    0.3423    0.3688    0.1388    1.6135    0.0287    1.6402    0.0435
+#> 73:    93.0023   -6.7319   -0.1526    2.2550   -0.9800    1.3923    7.6438    0.3341    0.3504    0.1462    1.5491    0.0312    1.3997    0.0554
+#> 74:    92.8952   -6.7189   -0.1530    2.2393   -0.9936    1.5478    7.2616    0.3344    0.3329    0.1503    1.5626    0.0326    1.3340    0.0634
+#> 75:    93.0812   -6.8015   -0.1546    2.2265   -0.9751    1.4704    8.9537    0.3501    0.3162    0.1438    1.6019    0.0268    1.1663    0.0715
+#> 76:    93.1080   -6.1728   -0.1515    2.2259   -1.0010    1.3969    8.5060    0.3407    0.3015    0.1398    1.6484    0.0279    1.3118    0.0637
+#> 77:    92.9248   -6.3432   -0.1573    2.2221   -0.9819    1.4456    8.0807    0.3506    0.3002    0.1442    1.5947    0.0294    1.6368    0.0407
+#> 78:    93.0194   -6.1448   -0.1611    2.2228   -0.9831    1.3733    7.6767    0.3487    0.3046    0.1369    1.6471    0.0254    1.4261    0.0529
+#> 79:    92.9378   -6.6970   -0.1593    2.2313   -0.9910    1.3046   10.0158    0.3460    0.2999    0.1386    1.6108    0.0267    1.5818    0.0420
+#> 80:    93.0293   -6.3275   -0.1579    2.2290   -0.9753    1.3191    9.5150    0.3543    0.2960    0.1490    1.6570    0.0259    1.5435    0.0431
+#> 81:    93.1417   -6.2258   -0.1607    2.2285   -0.9399    1.4131    9.0393    0.3514    0.3020    0.1415    1.6990    0.0236    1.6875    0.0364
+#> 82:    92.9115   -6.1764   -0.1555    2.2204   -0.9471    1.3424    8.5873    0.3502    0.2954    0.1540    1.6780    0.0216    1.2280    0.0687
+#> 83:    93.0528   -6.3505   -0.1559    2.2391   -0.9651    1.2753    8.1579    0.3499    0.2903    0.1706    1.6924    0.0242    1.6807    0.0465
+#> 84:    93.0032   -6.2300   -0.1596    2.2300   -0.9232    1.2115    7.9391    0.3470    0.2995    0.1858    1.7153    0.0259    1.7160    0.0406
+#> 85:    93.0518   -6.3704   -0.1434    2.2696   -0.9330    1.1510    8.3071    0.3504    0.2916    0.1765    1.7072    0.0275    1.5494    0.0490
+#> 86:    93.1344   -6.3566   -0.1424    2.2595   -0.9512    1.0934    9.2972    0.3520    0.2869    0.1677    1.6609    0.0253    1.5022    0.0508
+#> 87:    93.2468   -6.3860   -0.1449    2.2505   -0.9601    1.0387    8.8323    0.3474    0.3046    0.1593    1.6326    0.0262    1.3048    0.0626
+#> 88:    93.2286   -6.3886   -0.1466    2.2452   -0.9870    0.9868    8.3907    0.3474    0.2894    0.1513    1.6554    0.0245    1.6330    0.0376
+#> 89:    93.2892   -6.0277   -0.1469    2.2403   -0.9694    0.9375    7.9712    0.3451    0.2904    0.1438    1.6795    0.0251    1.6691    0.0365
+#> 90:    93.1766   -6.1076   -0.1460    2.2502   -0.9729    0.8906    7.5726    0.3458    0.2932    0.1481    1.6182    0.0331    1.5854    0.0401
+#> 91:    93.3300   -6.0932   -0.1559    2.2356   -0.9551    0.8461    7.1940    0.3771    0.2883    0.1512    1.6728    0.0272    1.6098    0.0401
+#> 92:    93.2470   -6.4839   -0.1592    2.2265   -1.0016    0.8038    6.8343    0.3813    0.2923    0.1597    1.7017    0.0300    1.6084    0.0423
+#> 93:    93.2272   -6.2819   -0.1612    2.2356   -1.0073    0.7636    6.4926    0.3849    0.2816    0.1722    1.5422    0.0420    1.4772    0.0493
+#> 94:    93.1441   -6.1805   -0.1571    2.2274   -1.0106    0.7254    6.1680    0.3878    0.2811    0.1636    1.5998    0.0403    1.4386    0.0535
+#> 95:    92.7747   -6.2274   -0.1709    2.2191   -1.0042    0.6891    5.8596    0.3909    0.2905    0.1591    1.7184    0.0282    1.6086    0.0519
+#> 96:    92.9830   -6.3291   -0.1603    2.2297   -1.0053    0.6547    5.5666    0.3774    0.2850    0.1512    1.7427    0.0284    1.7548    0.0384
+#> 97:    92.9302   -6.3943   -0.1608    2.2211   -0.9643    0.6219    5.2882    0.3817    0.2828    0.1589    1.7080    0.0295    1.7102    0.0398
+#> 98:    92.7704   -6.3554   -0.1679    2.1894   -0.9736    0.5908    5.4196    0.3864    0.2813    0.1560    1.7234    0.0240    1.2269    0.0685
+#> 99:    92.7596   -6.2138   -0.1687    2.2088   -0.9744    0.5613    5.1486    0.3939    0.2983    0.1482    1.6732    0.0250    1.5718    0.0497
+#> 100:    92.6608   -6.2662   -0.1687    2.2180   -1.0107    0.5332    5.1471    0.3939    0.2927    0.1408    1.8434    0.0232    1.7316    0.0413
+#> 101:    92.7024   -6.1288   -0.1643    2.2096   -1.0032    0.5066    4.8898    0.3934    0.2807    0.1349    1.7055    0.0253    1.5883    0.0439
+#> 102:    92.8885   -6.3175   -0.1697    2.2208   -0.9967    0.4812    4.9699    0.3888    0.2912    0.1371    1.7311    0.0284    1.6455    0.0402
+#> 103:    92.9487   -6.2493   -0.1677    2.1861   -0.9874    0.4572    4.9605    0.3907    0.2844    0.1626    1.6898    0.0279    1.6252    0.0409
+#> 104:    92.9633   -6.2534   -0.1731    2.1797   -0.9790    0.4343    4.8675    0.4015    0.2784    0.1758    1.6516    0.0268    1.6901    0.0360
+#> 105:    93.0513   -6.0656   -0.1748    2.1802   -0.9876    0.4126    4.6241    0.4041    0.2801    0.1670    1.6863    0.0269    1.6208    0.0366
+#> 106:    93.0600   -6.2162   -0.1860    2.1783   -0.9702    0.4570    4.5504    0.4451    0.2761    0.1586    1.6859    0.0274    1.5273    0.0437
+#> 107:    93.1856   -6.1826   -0.1801    2.1796   -0.9813    0.4341    4.7286    0.4517    0.2807    0.1575    1.6268    0.0341    1.2548    0.0630
+#> 108:    93.2401   -6.2943   -0.1783    2.1808   -0.9806    0.4124    5.3114    0.4502    0.2786    0.1496    1.6676    0.0291    1.4627    0.0484
+#> 109:    93.0988   -6.1669   -0.1655    2.2018   -0.9682    0.4036    5.0458    0.4302    0.3195    0.1435    1.6524    0.0295    1.5759    0.0447
+#> 110:    93.2129   -6.3104   -0.1748    2.1876   -0.9837    0.4825    5.6408    0.4430    0.3306    0.1595    1.6068    0.0326    1.6295    0.0388
+#> 111:    93.1292   -5.9096   -0.1740    2.1932   -0.9674    0.5262    5.3587    0.4444    0.3233    0.1646    1.5777    0.0334    1.6590    0.0374
+#> 112:    93.2723   -5.8153   -0.1706    2.1920   -0.9761    0.5109    5.0908    0.4486    0.3180    0.1634    1.6128    0.0321    1.6551    0.0396
+#> 113:    93.3171   -6.0458   -0.1666    2.1879   -0.9740    0.5530    4.8362    0.4508    0.3303    0.1607    1.5862    0.0325    1.2705    0.0643
+#> 114:    93.1717   -5.9615   -0.1655    2.1638   -0.9773    0.5254    4.5944    0.4472    0.3283    0.1657    1.6307    0.0287    1.2995    0.0677
+#> 115:    93.1917   -6.0856   -0.1592    2.1576   -1.0269    0.4991    4.3647    0.4349    0.3464    0.1574    1.6430    0.0354    1.2812    0.0714
+#> 116:    93.1287   -5.9635   -0.1609    2.1640   -0.9985    0.4741    4.1465    0.4237    0.3408    0.1495    1.6910    0.0269    1.2338    0.0738
+#> 117:    93.1184   -5.8768   -0.1603    2.1842   -0.9557    0.4504    3.9392    0.4211    0.3293    0.1420    1.6447    0.0257    1.2680    0.0705
+#> 118:    93.2207   -5.7436   -0.1654    2.1709   -0.9816    0.4279    3.7422    0.4158    0.3298    0.1349    1.6860    0.0238    1.1436    0.0780
+#> 119:    93.3064   -5.8397   -0.1713    2.1722   -1.0093    0.4065    3.5551    0.4100    0.3429    0.1384    1.6612    0.0262    1.6491    0.0458
+#> 120:    93.2749   -5.8221   -0.1737    2.1643   -1.0166    0.3862    3.3773    0.4044    0.3305    0.1527    1.6516    0.0232    1.7832    0.0410
+#> 121:    93.1620   -5.9756   -0.1579    2.2018   -1.0007    0.3818    3.2992    0.3841    0.3433    0.1620    1.6648    0.0251    1.3408    0.0665
+#> 122:    93.2070   -6.0164   -0.1540    2.2154   -1.0196    0.4217    3.5598    0.3649    0.3436    0.1539    1.6757    0.0287    1.3019    0.0652
+#> 123:    93.1588   -5.7424   -0.1581    2.2142   -0.9985    0.5270    3.3818    0.3491    0.3584    0.1655    1.6321    0.0237    1.3494    0.0644
+#> 124:    93.1496   -5.6257   -0.1463    2.2264   -0.9767    0.5914    3.2127    0.3347    0.3738    0.1573    1.6553    0.0226    1.5964    0.0544
+#> 125:    93.0224   -5.8536   -0.1742    2.1859   -0.9939    0.6381    3.0521    0.3840    0.3692    0.1664    1.6009    0.0246    1.4169    0.0652
+#> 126:    93.0788   -5.6973   -0.1778    2.1772   -0.9574    0.6062    2.8995    0.3710    0.3630    0.1839    1.5256    0.0312    1.5566    0.0518
+#> 127:    93.1613   -5.5833   -0.1729    2.1806   -0.9588    0.5759    2.7545    0.3532    0.3464    0.1878    1.5708    0.0307    1.6405    0.0476
+#> 128:    93.2043   -5.6742   -0.1746    2.1919   -0.9814    0.7099    2.6168    0.3569    0.3422    0.1848    1.6236    0.0312    1.5066    0.0517
+#> 129:    93.1963   -5.7026   -0.1770    2.1853   -0.9814    0.6744    2.4859    0.3544    0.3390    0.1774    1.6150    0.0293    1.5712    0.0479
+#> 130:    93.1669   -5.7260   -0.1826    2.1565   -0.9959    0.6407    2.3616    0.3750    0.3249    0.1685    1.6347    0.0215    1.5556    0.0535
+#> 131:    93.0792   -5.7201   -0.1971    2.1339   -1.0057    0.7376    2.2436    0.3901    0.3086    0.1616    1.7653    0.0206    1.6640    0.0458
+#> 132:    92.8580   -5.8266   -0.1877    2.1512   -0.9940    0.7008    2.3272    0.3895    0.3161    0.1863    1.6050    0.0231    1.5123    0.0558
+#> 133:    92.8479   -5.8397   -0.1834    2.1637   -0.9815    0.7195    2.4732    0.3875    0.3060    0.1877    1.6197    0.0217    1.4131    0.0617
+#> 134:    92.9218   -5.8317   -0.1903    2.1709   -0.9903    0.6835    2.5070    0.3808    0.3147    0.1857    1.7298    0.0225    1.5493    0.0521
+#> 135:    92.7533   -5.7287   -0.1909    2.1670   -0.9674    0.6493    2.3817    0.3792    0.3156    0.1981    1.7074    0.0222    1.2776    0.0718
+#> 136:    92.7255   -5.9071   -0.1787    2.1826   -0.9826    0.6169    2.8147    0.3603    0.3172    0.1882    1.6242    0.0288    1.2313    0.0682
+#> 137:    92.7882   -5.9574   -0.1847    2.1549   -0.9848    0.5860    3.0538    0.3651    0.3206    0.1787    1.5640    0.0277    1.1609    0.0716
+#> 138:    92.8155   -5.9445   -0.1719    2.1750   -0.9838    0.5567    3.3525    0.3568    0.3390    0.1698    1.5507    0.0259    1.0634    0.0816
+#> 139:    92.9393   -6.0638   -0.1726    2.1840   -0.9888    0.5289    4.1627    0.3562    0.3453    0.1613    1.5792    0.0259    1.5189    0.0533
+#> 140:    93.0330   -6.1823   -0.1726    2.1984   -0.9850    0.5024    4.3153    0.3562    0.3506    0.1533    1.6467    0.0248    1.5734    0.0459
+#> 141:    93.0651   -6.1847   -0.1702    2.2183   -0.9749    0.4773    4.1656    0.3604    0.3626    0.1527    1.5887    0.0272    1.5613    0.0433
+#> 142:    93.0350   -5.9581   -0.1641    2.2133   -0.9707    0.4535    3.9574    0.3642    0.3541    0.1662    1.5904    0.0246    1.4665    0.0556
+#> 143:    92.9215   -5.7798   -0.1642    2.2269   -0.9665    0.5015    3.7595    0.3665    0.3626    0.1667    1.6019    0.0275    1.3379    0.0563
+#> 144:    93.0132   -5.6752   -0.1629    2.2273   -0.9468    0.4764    3.5715    0.3648    0.3555    0.1648    1.5218    0.0320    1.1736    0.0695
+#> 145:    92.9596   -5.8104   -0.1449    2.2498   -0.9730    0.4526    3.3929    0.3465    0.3524    0.1670    1.5918    0.0284    1.3067    0.0630
+#> 146:    92.7925   -5.7223   -0.1458    2.2463   -0.9569    0.5591    3.2233    0.3443    0.3492    0.1587    1.6175    0.0260    1.0691    0.0729
+#> 147:    92.8399   -5.8322   -0.1478    2.2485   -0.9474    0.5312    3.2015    0.3422    0.3536    0.1507    1.6257    0.0255    1.2184    0.0622
+#> 148:    92.8390   -5.9554   -0.1498    2.2490   -0.9550    0.5046    3.6305    0.3387    0.3597    0.1615    1.5994    0.0263    1.2274    0.0638
+#> 149:    92.8158   -5.9697   -0.1511    2.2337   -0.9812    0.4794    3.8244    0.3386    0.3894    0.1559    1.5723    0.0255    1.0661    0.0760
+#> 150:    92.8379   -6.0841   -0.1532    2.2323   -0.9832    0.4554    4.3416    0.3340    0.3840    0.1575    1.5375    0.0272    1.1589    0.0677
+#> 151:    92.6741   -6.3268   -0.1572    2.2252   -0.9782    0.4327    5.9395    0.3389    0.3859    0.1584    1.5384    0.0252    1.2809    0.0638
+#> 152:    92.7165   -6.3594   -0.1527    2.2233   -1.0007    0.4210    5.8433    0.3384    0.3915    0.1324    1.5861    0.0254    1.0728    0.0756
+#> 153:    92.6823   -6.2114   -0.1640    2.2160   -0.9861    0.5285    5.4117    0.3473    0.3878    0.1376    1.6150    0.0255    1.2105    0.0659
+#> 154:    92.4787   -6.1829   -0.1622    2.2055   -0.9571    0.5031    5.7087    0.3490    0.3748    0.1345    1.5749    0.0250    1.0579    0.0741
+#> 155:    92.4780   -6.4925   -0.1675    2.2190   -0.9301    0.4020    7.4764    0.3587    0.3785    0.1287    1.5959    0.0258    1.1342    0.0709
+#> 156:    92.5151   -6.2825   -0.1673    2.2194   -0.9174    0.3603    5.6463    0.3589    0.3848    0.1202    1.5413    0.0301    1.1866    0.0674
+#> 157:    92.5140   -6.0058   -0.1644    2.2312   -0.9298    0.3857    4.2481    0.3610    0.3706    0.1281    1.5944    0.0292    1.2712    0.0631
+#> 158:    92.5669   -5.8692   -0.1673    2.2493   -0.9413    0.4751    3.7632    0.3600    0.3572    0.1383    1.6202    0.0323    1.4797    0.0499
+#> 159:    92.4844   -6.0078   -0.1540    2.2464   -0.9423    0.4626    4.6774    0.3587    0.3603    0.1450    1.6404    0.0280    1.3577    0.0587
+#> 160:    92.5182   -6.1231   -0.1504    2.2518   -0.9274    0.4153    5.0466    0.3616    0.3633    0.1373    1.5891    0.0297    1.2392    0.0653
+#> 161:    92.5665   -5.9062   -0.1569    2.2563   -0.9412    0.3989    4.3594    0.3541    0.3719    0.1433    1.6242    0.0314    1.2822    0.0627
+#> 162:    92.5749   -6.0936   -0.1507    2.2752   -0.9474    0.3140    4.4065    0.3438    0.3921    0.1320    1.5013    0.0378    1.1647    0.0662
+#> 163:    92.6248   -6.1392   -0.1565    2.2499   -0.9499    0.2129    4.6022    0.3512    0.3890    0.1425    1.4936    0.0336    1.4339    0.0494
+#> 164:    92.6486   -6.3898   -0.1590    2.2519   -0.9574    0.1948    5.7817    0.3564    0.3925    0.1308    1.5218    0.0326    1.2197    0.0630
+#> 165:    92.6600   -6.3261   -0.1606    2.2464   -0.9815    0.3054    5.9162    0.3611    0.3979    0.1433    1.5747    0.0316    1.2062    0.0632
+#> 166:    92.7951   -6.3068   -0.1630    2.2428   -0.9542    0.3144    5.7041    0.3597    0.3766    0.1612    1.5464    0.0317    1.2649    0.0617
+#> 167:    92.8541   -6.4919   -0.1642    2.2275   -0.9505    0.3509    6.3858    0.3639    0.3713    0.1581    1.5543    0.0315    1.3546    0.0574
+#> 168:    92.6848   -6.3299   -0.1618    2.2329   -0.9494    0.4645    5.7127    0.3700    0.3698    0.1544    1.5058    0.0340    1.1747    0.0685
+#> 169:    92.5817   -6.0236   -0.1572    2.2583   -0.9510    0.6725    3.9864    0.3672    0.3812    0.1763    1.4445    0.0386    1.3230    0.0583
+#> 170:    92.7223   -5.9170   -0.1609    2.2456   -0.9485    0.5137    3.7991    0.3712    0.3714    0.1601    1.5502    0.0385    1.3393    0.0547
+#> 171:    92.6532   -5.9417   -0.1544    2.2294   -0.9448    0.6206    3.9052    0.3789    0.3634    0.1487    1.5809    0.0314    1.1226    0.0711
+#> 172:    92.4803   -5.7302   -0.1414    2.2679   -0.9255    0.7853    2.7901    0.3598    0.3666    0.1508    1.5531    0.0341    1.1785    0.0667
+#> 173:    92.3172   -5.7462   -0.1405    2.2823   -0.9193    1.2505    2.9155    0.3579    0.3678    0.1480    1.4894    0.0434    1.2288    0.0618
+#> 174:    92.4674   -5.6638   -0.1415    2.2775   -0.9054    1.0653    2.8138    0.3623    0.3740    0.1371    1.5301    0.0393    1.0790    0.0669
+#> 175:    92.5581   -5.6388   -0.1338    2.2878   -0.9154    0.6617    2.5216    0.3471    0.3719    0.1546    1.5231    0.0361    1.0672    0.0723
+#> 176:    92.7218   -5.7548   -0.1249    2.3099   -0.9203    0.4464    2.8226    0.3570    0.3978    0.1570    1.4938    0.0354    1.1125    0.0655
+#> 177:    92.7655   -5.6769   -0.1232    2.3114   -0.9257    0.5291    2.5249    0.3571    0.4023    0.1657    1.4392    0.0386    1.1149    0.0663
+#> 178:    92.7966   -5.6766   -0.1219    2.3202   -0.9142    0.4897    2.3359    0.3605    0.3944    0.1720    1.4792    0.0401    1.1665    0.0637
+#> 179:    92.8304   -5.7678   -0.1133    2.3352   -0.9262    0.5428    2.8512    0.3552    0.4191    0.1716    1.4994    0.0410    1.0651    0.0701
+#> 180:    92.8413   -5.7485   -0.1124    2.3452   -0.9494    0.5179    2.6552    0.3555    0.4025    0.1778    1.5102    0.0383    1.1541    0.0670
+#> 181:    92.7078   -5.7437   -0.1145    2.3257   -0.9482    0.6237    2.5673    0.3564    0.3851    0.1897    1.5373    0.0335    1.1413    0.0698
+#> 182:    92.6278   -5.7965   -0.1115    2.3341   -0.9763    0.7558    2.7421    0.3541    0.3850    0.1625    1.5720    0.0309    1.1164    0.0758
+#> 183:    92.4359   -5.7826   -0.1211    2.3204   -0.9481    1.2089    3.0954    0.3598    0.3813    0.1384    1.6391    0.0333    1.2142    0.0646
+#> 184:    92.4840   -5.9143   -0.1218    2.2965   -0.9330    1.2610    4.0248    0.3752    0.3549    0.1597    1.6019    0.0292    1.0945    0.0767
+#> 185:    92.5659   -5.8333   -0.1223    2.2914   -0.9090    1.0578    3.9752    0.3706    0.3640    0.1769    1.5858    0.0287    1.7070    0.0404
+#> 186:    92.5157   -5.9540   -0.1274    2.2967   -0.9678    1.0199    3.7413    0.3625    0.3766    0.1354    1.5905    0.0321    1.2521    0.0660
+#> 187:    92.6988   -5.8607   -0.1193    2.2922   -0.9685    1.1721    2.9764    0.3511    0.3823    0.1347    1.5790    0.0352    1.1477    0.0746
+#> 188:    92.7427   -5.9073   -0.1166    2.3166   -0.9529    1.3606    2.9747    0.3487    0.3981    0.1322    1.5315    0.0344    1.3014    0.0594
+#> 189:    92.6288   -5.8326   -0.1075    2.3268   -0.9543    1.3459    3.2341    0.3388    0.3983    0.1622    1.5374    0.0334    1.5390    0.0504
+#> 190:    92.8047   -5.6198   -0.1064    2.3212   -0.9148    1.6280    2.5774    0.3319    0.4086    0.1656    1.5159    0.0321    1.5423    0.0515
+#> 191:    92.7642   -5.5780   -0.1105    2.3041   -0.9414    1.5723    2.6038    0.3402    0.4111    0.1612    1.5254    0.0321    1.1206    0.0792
+#> 192:    92.7137   -5.5650   -0.1087    2.3014   -0.9399    1.1968    2.0552    0.3412    0.4267    0.1418    1.4910    0.0332    0.9683    0.0834
+#> 193:    93.0503   -5.6414   -0.1060    2.3050   -0.9563    1.0067    2.2362    0.3434    0.4179    0.1371    1.5947    0.0279    1.0349    0.0813
+#> 194:    93.1071   -5.6349   -0.1048    2.3170   -0.9613    1.1495    2.6224    0.3451    0.4086    0.1419    1.6235    0.0276    1.0558    0.0792
+#> 195:    93.0741   -5.7863   -0.1052    2.3293   -0.9605    1.1597    3.0814    0.3440    0.4342    0.1394    1.5248    0.0348    1.0554    0.0771
+#> 196:    93.0768   -5.6986   -0.0911    2.3395   -0.9537    1.1388    2.7165    0.3463    0.4303    0.1467    1.5960    0.0324    1.1195    0.0755
+#> 197:    92.8638   -5.7840   -0.1009    2.3420   -0.9699    1.0231    2.8293    0.3625    0.4272    0.1849    1.5366    0.0360    1.3691    0.0602
+#> 198:    92.8979   -5.8328   -0.0905    2.3497   -0.9668    0.8847    2.7469    0.3509    0.4357    0.1842    1.5501    0.0361    1.1744    0.0715
+#> 199:    92.7817   -6.0173   -0.0946    2.3477   -0.9729    0.8131    3.4886    0.3517    0.4471    0.1906    1.4350    0.0393    1.2311    0.0693
+#> 200:    92.6353   -6.0362   -0.0924    2.3396   -0.9621    0.8259    3.3916    0.3556    0.4569    0.1867    1.4397    0.0350    1.0910    0.0793
+#> 201:    92.6908   -6.0423   -0.0917    2.3400   -0.9564    0.6766    3.6159    0.3552    0.4565    0.1735    1.4506    0.0362    1.0646    0.0794
+#> 202:    92.6302   -6.0238   -0.0919    2.3443   -0.9546    0.5824    3.6723    0.3555    0.4576    0.1716    1.4800    0.0363    1.0519    0.0791
+#> 203:    92.6040   -6.0387   -0.0944    2.3405   -0.9579    0.5710    3.9080    0.3583    0.4476    0.1752    1.4934    0.0373    1.0842    0.0762
+#> 204:    92.6042   -6.0088   -0.0965    2.3351   -0.9580    0.6145    3.8412    0.3608    0.4413    0.1720    1.5047    0.0374    1.0694    0.0760
+#> 205:    92.5887   -6.0107   -0.0968    2.3362   -0.9576    0.6432    3.8854    0.3606    0.4405    0.1711    1.4896    0.0380    1.0615    0.0750
+#> 206:    92.6452   -5.9990   -0.0992    2.3311   -0.9581    0.6728    3.8231    0.3636    0.4339    0.1683    1.4904    0.0379    1.0630    0.0747
+#> 207:    92.6867   -5.9760   -0.1012    2.3283   -0.9606    0.6907    3.6867    0.3665    0.4303    0.1665    1.4908    0.0376    1.0656    0.0739
+#> 208:    92.6867   -5.9652   -0.1033    2.3252   -0.9611    0.6656    3.6185    0.3680    0.4271    0.1656    1.4972    0.0369    1.0944    0.0724
+#> 209:    92.6807   -5.9535   -0.1051    2.3225   -0.9621    0.6532    3.5653    0.3669    0.4249    0.1641    1.4992    0.0366    1.1029    0.0721
+#> 210:    92.6772   -5.9392   -0.1067    2.3185   -0.9611    0.6492    3.4774    0.3661    0.4220    0.1620    1.5034    0.0360    1.0982    0.0723
+#> 211:    92.6803   -5.9099   -0.1089    2.3129   -0.9619    0.6462    3.3783    0.3656    0.4218    0.1622    1.5094    0.0354    1.1060    0.0725
+#> 212:    92.7033   -5.9046   -0.1110    2.3085   -0.9606    0.6467    3.3879    0.3653    0.4222    0.1602    1.5099    0.0350    1.1004    0.0726
+#> 213:    92.7143   -5.9026   -0.1135    2.3046   -0.9594    0.6326    3.3887    0.3646    0.4214    0.1585    1.5139    0.0347    1.1050    0.0722
+#> 214:    92.7156   -5.9151   -0.1157    2.3011   -0.9590    0.6186    3.4587    0.3637    0.4205    0.1571    1.5149    0.0344    1.1060    0.0720
+#> 215:    92.7185   -5.9240   -0.1177    2.2984   -0.9585    0.6226    3.5192    0.3630    0.4190    0.1564    1.5155    0.0342    1.1159    0.0713
+#> 216:    92.7133   -5.9331   -0.1197    2.2953   -0.9575    0.6253    3.5505    0.3630    0.4179    0.1552    1.5199    0.0338    1.1276    0.0708
+#> 217:    92.7111   -5.9341   -0.1215    2.2924   -0.9579    0.6200    3.5565    0.3627    0.4170    0.1542    1.5238    0.0337    1.1409    0.0702
+#> 218:    92.7142   -5.9390   -0.1226    2.2901   -0.9588    0.6110    3.5792    0.3623    0.4162    0.1541    1.5236    0.0335    1.1378    0.0704
+#> 219:    92.7121   -5.9351   -0.1233    2.2891   -0.9587    0.6083    3.5562    0.3617    0.4154    0.1535    1.5280    0.0335    1.1518    0.0697
+#> 220:    92.7133   -5.9467   -0.1244    2.2876   -0.9591    0.6158    3.6036    0.3614    0.4147    0.1542    1.5273    0.0334    1.1572    0.0693
+#> 221:    92.7206   -5.9543   -0.1253    2.2856   -0.9602    0.6252    3.6357    0.3610    0.4131    0.1540    1.5272    0.0335    1.1591    0.0692
+#> 222:    92.7267   -5.9436   -0.1262    2.2840   -0.9608    0.6377    3.5725    0.3608    0.4118    0.1540    1.5302    0.0334    1.1735    0.0683
+#> 223:    92.7364   -5.9346   -0.1268    2.2825   -0.9619    0.6430    3.5288    0.3606    0.4117    0.1542    1.5327    0.0332    1.1883    0.0676
+#> 224:    92.7464   -5.9269   -0.1274    2.2822   -0.9621    0.6394    3.4906    0.3604    0.4107    0.1541    1.5342    0.0334    1.2022    0.0667
+#> 225:    92.7572   -5.9244   -0.1278    2.2813   -0.9616    0.6340    3.4677    0.3603    0.4100    0.1535    1.5345    0.0334    1.2129    0.0661
+#> 226:    92.7662   -5.9237   -0.1282    2.2803   -0.9615    0.6336    3.4532    0.3603    0.4101    0.1532    1.5326    0.0334    1.2151    0.0661
+#> 227:    92.7778   -5.9193   -0.1286    2.2792   -0.9628    0.6280    3.4339    0.3604    0.4096    0.1527    1.5323    0.0334    1.2217    0.0658
+#> 228:    92.7824   -5.9112   -0.1289    2.2782   -0.9636    0.6217    3.3964    0.3607    0.4091    0.1525    1.5316    0.0335    1.2255    0.0658
+#> 229:    92.7895   -5.9077   -0.1291    2.2770   -0.9646    0.6178    3.3717    0.3607    0.4096    0.1521    1.5326    0.0334    1.2247    0.0660
+#> 230:    92.7987   -5.9153   -0.1297    2.2758   -0.9648    0.6177    3.4004    0.3603    0.4098    0.1517    1.5333    0.0334    1.2321    0.0656
+#> 231:    92.8081   -5.9176   -0.1308    2.2735   -0.9654    0.6185    3.4195    0.3596    0.4086    0.1513    1.5361    0.0331    1.2359    0.0656
+#> 232:    92.8119   -5.9161   -0.1318    2.2715   -0.9658    0.6140    3.4221    0.3590    0.4075    0.1513    1.5387    0.0330    1.2434    0.0653
+#> 233:    92.8117   -5.9111   -0.1329    2.2694   -0.9662    0.6096    3.4008    0.3586    0.4065    0.1511    1.5410    0.0328    1.2426    0.0654
+#> 234:    92.8132   -5.9040   -0.1339    2.2672   -0.9660    0.6097    3.3787    0.3583    0.4059    0.1506    1.5425    0.0325    1.2463    0.0654
+#> 235:    92.8117   -5.8978   -0.1347    2.2653   -0.9661    0.6020    3.3558    0.3579    0.4051    0.1502    1.5443    0.0324    1.2439    0.0657
+#> 236:    92.8050   -5.8967   -0.1355    2.2638   -0.9663    0.5963    3.3466    0.3575    0.4046    0.1495    1.5453    0.0322    1.2377    0.0661
+#> 237:    92.7975   -5.9004   -0.1362    2.2625   -0.9668    0.5891    3.3624    0.3571    0.4043    0.1491    1.5460    0.0321    1.2334    0.0664
+#> 238:    92.7965   -5.9036   -0.1371    2.2613   -0.9670    0.5828    3.3683    0.3569    0.4037    0.1488    1.5486    0.0320    1.2405    0.0662
+#> 239:    92.8006   -5.9067   -0.1376    2.2607   -0.9677    0.5767    3.3801    0.3568    0.4027    0.1490    1.5487    0.0319    1.2478    0.0658
+#> 240:    92.8061   -5.9102   -0.1382    2.2597   -0.9678    0.5697    3.3876    0.3566    0.4014    0.1489    1.5499    0.0319    1.2545    0.0654
+#> 241:    92.8111   -5.9132   -0.1388    2.2589   -0.9684    0.5647    3.3986    0.3567    0.4004    0.1489    1.5507    0.0319    1.2607    0.0651
+#> 242:    92.8157   -5.9119   -0.1395    2.2577   -0.9686    0.5610    3.3902    0.3568    0.3995    0.1490    1.5524    0.0319    1.2673    0.0647
+#> 243:    92.8204   -5.9142   -0.1401    2.2567   -0.9689    0.5597    3.3991    0.3570    0.3983    0.1492    1.5526    0.0319    1.2728    0.0646
+#> 244:    92.8272   -5.9129   -0.1408    2.2558   -0.9689    0.5598    3.3989    0.3574    0.3972    0.1493    1.5542    0.0319    1.2805    0.0642
+#> 245:    92.8361   -5.9152   -0.1414    2.2548   -0.9693    0.5617    3.4133    0.3580    0.3959    0.1500    1.5541    0.0318    1.2876    0.0638
+#> 246:    92.8432   -5.9122   -0.1420    2.2536   -0.9695    0.5627    3.4039    0.3584    0.3946    0.1507    1.5546    0.0318    1.2944    0.0633
+#> 247:    92.8481   -5.9125   -0.1426    2.2524   -0.9695    0.5574    3.4087    0.3588    0.3931    0.1515    1.5556    0.0318    1.3003    0.0629
+#> 248:    92.8486   -5.9123   -0.1433    2.2515   -0.9693    0.5545    3.4095    0.3594    0.3916    0.1519    1.5583    0.0317    1.3043    0.0626
+#> 249:    92.8515   -5.9123   -0.1439    2.2505   -0.9694    0.5547    3.4088    0.3600    0.3904    0.1523    1.5605    0.0316    1.3087    0.0623
+#> 250:    92.8521   -5.9139   -0.1443    2.2493   -0.9691    0.5589    3.4212    0.3604    0.3894    0.1525    1.5617    0.0316    1.3081    0.0624
+#> 251:    92.8530   -5.9118   -0.1450    2.2484   -0.9683    0.5562    3.4138    0.3612    0.3884    0.1528    1.5615    0.0316    1.3066    0.0625
+#> 252:    92.8568   -5.9075   -0.1457    2.2474   -0.9681    0.5506    3.3889    0.3619    0.3875    0.1531    1.5620    0.0315    1.3067    0.0625
+#> 253:    92.8603   -5.9070   -0.1464    2.2467   -0.9682    0.5476    3.3746    0.3622    0.3867    0.1539    1.5640    0.0314    1.3122    0.0622
+#> 254:    92.8653   -5.9077   -0.1470    2.2457   -0.9688    0.5448    3.3656    0.3626    0.3858    0.1546    1.5641    0.0314    1.3147    0.0620
+#> 255:    92.8686   -5.9059   -0.1477    2.2445   -0.9688    0.5406    3.3533    0.3630    0.3850    0.1549    1.5637    0.0314    1.3155    0.0619
+#> 256:    92.8706   -5.9011   -0.1483    2.2435   -0.9685    0.5384    3.3300    0.3634    0.3841    0.1550    1.5644    0.0313    1.3161    0.0617
+#> 257:    92.8721   -5.8957   -0.1488    2.2426   -0.9683    0.5398    3.3084    0.3638    0.3833    0.1552    1.5647    0.0313    1.3158    0.0617
+#> 258:    92.8725   -5.8928   -0.1493    2.2419   -0.9680    0.5392    3.2921    0.3641    0.3822    0.1552    1.5665    0.0312    1.3184    0.0614
+#> 259:    92.8718   -5.8915   -0.1498    2.2411   -0.9680    0.5367    3.2850    0.3644    0.3815    0.1553    1.5668    0.0312    1.3202    0.0613
+#> 260:    92.8701   -5.8928   -0.1499    2.2409   -0.9679    0.5339    3.2888    0.3652    0.3802    0.1552    1.5675    0.0312    1.3215    0.0612
+#> 261:    92.8700   -5.8961   -0.1499    2.2407   -0.9679    0.5302    3.2976    0.3659    0.3789    0.1551    1.5677    0.0312    1.3197    0.0613
+#> 262:    92.8683   -5.9013   -0.1500    2.2407   -0.9678    0.5282    3.3236    0.3666    0.3778    0.1549    1.5684    0.0312    1.3184    0.0613
+#> 263:    92.8662   -5.9021   -0.1498    2.2407   -0.9677    0.5271    3.3285    0.3670    0.3767    0.1547    1.5682    0.0313    1.3156    0.0615
+#> 264:    92.8631   -5.9059   -0.1495    2.2409   -0.9675    0.5244    3.3527    0.3673    0.3755    0.1547    1.5677    0.0313    1.3139    0.0616
+#> 265:    92.8635   -5.9042   -0.1492    2.2411   -0.9675    0.5220    3.3541    0.3675    0.3745    0.1545    1.5676    0.0313    1.3098    0.0618
+#> 266:    92.8636   -5.9033   -0.1490    2.2411   -0.9673    0.5208    3.3523    0.3680    0.3735    0.1546    1.5679    0.0312    1.3087    0.0619
+#> 267:    92.8639   -5.9035   -0.1489    2.2413   -0.9673    0.5208    3.3566    0.3685    0.3726    0.1546    1.5676    0.0312    1.3072    0.0621
+#> 268:    92.8620   -5.9065   -0.1487    2.2413   -0.9674    0.5191    3.3797    0.3689    0.3717    0.1545    1.5676    0.0312    1.3103    0.0620
+#> 269:    92.8593   -5.9073   -0.1486    2.2416   -0.9672    0.5192    3.3885    0.3693    0.3710    0.1545    1.5685    0.0312    1.3136    0.0618
+#> 270:    92.8549   -5.9087   -0.1487    2.2418   -0.9672    0.5209    3.4007    0.3695    0.3703    0.1544    1.5703    0.0312    1.3177    0.0615
+#> 271:    92.8519   -5.9089   -0.1487    2.2416   -0.9671    0.5227    3.4043    0.3696    0.3697    0.1545    1.5705    0.0312    1.3216    0.0613
+#> 272:    92.8493   -5.9084   -0.1488    2.2416   -0.9669    0.5223    3.3999    0.3698    0.3693    0.1543    1.5707    0.0311    1.3206    0.0614
+#> 273:    92.8479   -5.9090   -0.1486    2.2416   -0.9667    0.5230    3.3980    0.3701    0.3689    0.1544    1.5699    0.0311    1.3192    0.0615
+#> 274:    92.8456   -5.9108   -0.1485    2.2417   -0.9667    0.5249    3.4024    0.3705    0.3684    0.1544    1.5688    0.0311    1.3169    0.0617
+#> 275:    92.8440   -5.9131   -0.1483    2.2422   -0.9666    0.5253    3.4117    0.3707    0.3677    0.1542    1.5690    0.0311    1.3166    0.0616
+#> 276:    92.8425   -5.9132   -0.1482    2.2426   -0.9662    0.5241    3.4171    0.3709    0.3670    0.1540    1.5689    0.0311    1.3142    0.0617
+#> 277:    92.8412   -5.9139   -0.1481    2.2430   -0.9660    0.5214    3.4228    0.3711    0.3663    0.1540    1.5687    0.0311    1.3173    0.0615
+#> 278:    92.8398   -5.9139   -0.1479    2.2432   -0.9659    0.5184    3.4254    0.3712    0.3654    0.1540    1.5684    0.0311    1.3148    0.0617
+#> 279:    92.8386   -5.9156   -0.1478    2.2433   -0.9661    0.5157    3.4338    0.3713    0.3649    0.1539    1.5682    0.0311    1.3136    0.0618
+#> 280:    92.8378   -5.9173   -0.1478    2.2428   -0.9663    0.5127    3.4381    0.3714    0.3643    0.1537    1.5679    0.0311    1.3104    0.0621
+#> 281:    92.8364   -5.9188   -0.1479    2.2423   -0.9666    0.5089    3.4418    0.3716    0.3634    0.1533    1.5674    0.0311    1.3071    0.0623
+#> 282:    92.8377   -5.9179   -0.1481    2.2418   -0.9668    0.5045    3.4355    0.3717    0.3626    0.1530    1.5686    0.0311    1.3055    0.0624
+#> 283:    92.8385   -5.9157   -0.1485    2.2410   -0.9667    0.5014    3.4260    0.3720    0.3616    0.1527    1.5699    0.0311    1.3072    0.0622
+#> 284:    92.8388   -5.9156   -0.1489    2.2403   -0.9666    0.4977    3.4274    0.3723    0.3605    0.1525    1.5705    0.0310    1.3081    0.0621
+#> 285:    92.8374   -5.9156   -0.1492    2.2395   -0.9668    0.4944    3.4215    0.3727    0.3594    0.1525    1.5716    0.0310    1.3103    0.0619
+#> 286:    92.8376   -5.9168   -0.1496    2.2388   -0.9672    0.4915    3.4197    0.3731    0.3583    0.1526    1.5724    0.0310    1.3141    0.0617
+#> 287:    92.8393   -5.9176   -0.1498    2.2380   -0.9673    0.4886    3.4177    0.3735    0.3572    0.1523    1.5737    0.0309    1.3155    0.0615
+#> 288:    92.8400   -5.9206   -0.1502    2.2372   -0.9675    0.4873    3.4259    0.3739    0.3562    0.1523    1.5739    0.0309    1.3160    0.0614
+#> 289:    92.8404   -5.9217   -0.1506    2.2362   -0.9678    0.4845    3.4269    0.3744    0.3552    0.1524    1.5735    0.0309    1.3165    0.0614
+#> 290:    92.8395   -5.9255   -0.1510    2.2354   -0.9680    0.4830    3.4395    0.3748    0.3543    0.1521    1.5737    0.0308    1.3159    0.0615
+#> 291:    92.8384   -5.9274   -0.1513    2.2345   -0.9680    0.4841    3.4460    0.3752    0.3533    0.1518    1.5742    0.0309    1.3173    0.0613
+#> 292:    92.8384   -5.9276   -0.1515    2.2342   -0.9681    0.4865    3.4437    0.3755    0.3525    0.1516    1.5738    0.0309    1.3163    0.0614
+#> 293:    92.8385   -5.9281   -0.1517    2.2338   -0.9681    0.4882    3.4446    0.3757    0.3516    0.1513    1.5738    0.0308    1.3143    0.0614
+#> 294:    92.8400   -5.9277   -0.1519    2.2335   -0.9680    0.4871    3.4449    0.3758    0.3508    0.1512    1.5736    0.0308    1.3149    0.0614
+#> 295:    92.8414   -5.9279   -0.1520    2.2331   -0.9680    0.4842    3.4523    0.3760    0.3502    0.1510    1.5740    0.0308    1.3153    0.0614
+#> 296:    92.8424   -5.9282   -0.1521    2.2329   -0.9681    0.4835    3.4589    0.3760    0.3496    0.1509    1.5743    0.0307    1.3180    0.0613
+#> 297:    92.8409   -5.9281   -0.1522    2.2325   -0.9683    0.4827    3.4636    0.3760    0.3491    0.1509    1.5745    0.0307    1.3216    0.0611
+#> 298:    92.8395   -5.9276   -0.1522    2.2322   -0.9684    0.4819    3.4641    0.3761    0.3486    0.1508    1.5744    0.0307    1.3226    0.0612
+#> 299:    92.8388   -5.9305   -0.1524    2.2321   -0.9686    0.4800    3.4829    0.3761    0.3481    0.1507    1.5745    0.0307    1.3218    0.0612
+#> 300:    92.8375   -5.9329   -0.1524    2.2321   -0.9683    0.4792    3.4982    0.3761    0.3477    0.1505    1.5745    0.0307    1.3205    0.0613
+#> 301:    92.8359   -5.9337   -0.1524    2.2321   -0.9680    0.4788    3.5056    0.3762    0.3473    0.1503    1.5746    0.0306    1.3182    0.0614
+#> 302:    92.8346   -5.9360   -0.1524    2.2322   -0.9678    0.4800    3.5237    0.3763    0.3470    0.1500    1.5744    0.0306    1.3174    0.0614
+#> 303:    92.8338   -5.9387   -0.1524    2.2324   -0.9674    0.4795    3.5444    0.3764    0.3467    0.1501    1.5738    0.0307    1.3181    0.0613
+#> 304:    92.8318   -5.9436   -0.1524    2.2327   -0.9673    0.4787    3.5819    0.3766    0.3464    0.1502    1.5735    0.0307    1.3191    0.0612
+#> 305:    92.8300   -5.9486   -0.1524    2.2327   -0.9673    0.4794    3.6200    0.3766    0.3460    0.1502    1.5726    0.0308    1.3198    0.0611
+#> 306:    92.8294   -5.9540   -0.1524    2.2328   -0.9673    0.4788    3.6681    0.3766    0.3456    0.1502    1.5723    0.0309    1.3214    0.0610
+#> 307:    92.8287   -5.9579   -0.1525    2.2330   -0.9669    0.4779    3.7052    0.3766    0.3452    0.1498    1.5735    0.0309    1.3235    0.0609
+#> 308:    92.8290   -5.9624   -0.1524    2.2332   -0.9669    0.4775    3.7470    0.3766    0.3448    0.1500    1.5737    0.0309    1.3265    0.0607
+#> 309:    92.8293   -5.9653   -0.1524    2.2333   -0.9668    0.4774    3.7756    0.3766    0.3443    0.1499    1.5736    0.0309    1.3290    0.0605
+#> 310:    92.8289   -5.9672   -0.1523    2.2335   -0.9669    0.4762    3.7957    0.3767    0.3438    0.1499    1.5736    0.0309    1.3316    0.0603
+#> 311:    92.8301   -5.9702   -0.1521    2.2337   -0.9670    0.4755    3.8172    0.3767    0.3432    0.1498    1.5737    0.0309    1.3324    0.0603
+#> 312:    92.8322   -5.9715   -0.1520    2.2341   -0.9670    0.4742    3.8229    0.3767    0.3427    0.1496    1.5734    0.0309    1.3309    0.0603
+#> 313:    92.8338   -5.9713   -0.1517    2.2342   -0.9672    0.4737    3.8202    0.3766    0.3422    0.1494    1.5733    0.0309    1.3306    0.0604
+#> 314:    92.8360   -5.9711   -0.1515    2.2343   -0.9675    0.4725    3.8154    0.3767    0.3417    0.1493    1.5733    0.0309    1.3322    0.0603
+#> 315:    92.8378   -5.9694   -0.1514    2.2343   -0.9680    0.4714    3.8051    0.3767    0.3414    0.1494    1.5734    0.0309    1.3352    0.0601
+#> 316:    92.8400   -5.9683   -0.1514    2.2343   -0.9682    0.4705    3.7984    0.3767    0.3410    0.1495    1.5735    0.0309    1.3354    0.0602
+#> 317:    92.8422   -5.9689   -0.1513    2.2344   -0.9686    0.4695    3.7961    0.3768    0.3406    0.1497    1.5735    0.0309    1.3362    0.0602
+#> 318:    92.8440   -5.9696   -0.1510    2.2347   -0.9689    0.4681    3.7934    0.3769    0.3403    0.1499    1.5731    0.0309    1.3381    0.0601
+#> 319:    92.8458   -5.9710   -0.1508    2.2350   -0.9692    0.4668    3.7913    0.3769    0.3401    0.1500    1.5723    0.0309    1.3403    0.0599
+#> 320:    92.8474   -5.9719   -0.1506    2.2353   -0.9695    0.4667    3.7876    0.3769    0.3400    0.1502    1.5714    0.0309    1.3423    0.0598
+#> 321:    92.8494   -5.9710   -0.1503    2.2355   -0.9696    0.4673    3.7790    0.3769    0.3397    0.1503    1.5709    0.0309    1.3439    0.0597
+#> 322:    92.8511   -5.9693   -0.1501    2.2359   -0.9698    0.4690    3.7674    0.3769    0.3395    0.1503    1.5708    0.0309    1.3451    0.0596
+#> 323:    92.8528   -5.9700   -0.1498    2.2364   -0.9699    0.4696    3.7641    0.3768    0.3394    0.1504    1.5701    0.0310    1.3470    0.0594
+#> 324:    92.8547   -5.9695   -0.1495    2.2369   -0.9699    0.4703    3.7567    0.3767    0.3392    0.1505    1.5698    0.0310    1.3485    0.0593
+#> 325:    92.8563   -5.9678   -0.1490    2.2376   -0.9702    0.4701    3.7473    0.3769    0.3395    0.1505    1.5702    0.0311    1.3494    0.0592
+#> 326:    92.8582   -5.9676   -0.1486    2.2382   -0.9703    0.4709    3.7434    0.3771    0.3397    0.1506    1.5700    0.0311    1.3479    0.0593
+#> 327:    92.8603   -5.9665   -0.1481    2.2389   -0.9704    0.4716    3.7361    0.3769    0.3399    0.1507    1.5699    0.0311    1.3471    0.0594
+#> 328:    92.8622   -5.9671   -0.1477    2.2397   -0.9704    0.4726    3.7379    0.3767    0.3398    0.1507    1.5698    0.0311    1.3481    0.0593
+#> 329:    92.8639   -5.9667   -0.1473    2.2405   -0.9707    0.4735    3.7366    0.3766    0.3398    0.1506    1.5696    0.0311    1.3482    0.0593
+#> 330:    92.8663   -5.9673   -0.1469    2.2413   -0.9708    0.4736    3.7382    0.3765    0.3397    0.1506    1.5691    0.0312    1.3492    0.0592
+#> 331:    92.8674   -5.9670   -0.1464    2.2420   -0.9710    0.4740    3.7350    0.3763    0.3397    0.1507    1.5689    0.0312    1.3512    0.0591
+#> 332:    92.8681   -5.9664   -0.1460    2.2428   -0.9710    0.4737    3.7311    0.3762    0.3396    0.1509    1.5687    0.0312    1.3527    0.0590
+#> 333:    92.8683   -5.9649   -0.1456    2.2436   -0.9708    0.4727    3.7232    0.3760    0.3397    0.1509    1.5686    0.0312    1.3505    0.0591
+#> 334:    92.8690   -5.9642   -0.1452    2.2444   -0.9707    0.4723    3.7194    0.3758    0.3399    0.1511    1.5682    0.0312    1.3490    0.0592
+#> 335:    92.8698   -5.9656   -0.1447    2.2454   -0.9707    0.4722    3.7289    0.3756    0.3400    0.1512    1.5674    0.0313    1.3476    0.0592
+#> 336:    92.8691   -5.9664   -0.1443    2.2463   -0.9706    0.4724    3.7333    0.3753    0.3401    0.1511    1.5669    0.0313    1.3455    0.0593
+#> 337:    92.8687   -5.9670   -0.1440    2.2471   -0.9705    0.4742    3.7378    0.3749    0.3402    0.1510    1.5665    0.0314    1.3433    0.0594
+#> 338:    92.8683   -5.9663   -0.1435    2.2480   -0.9703    0.4747    3.7370    0.3746    0.3405    0.1510    1.5663    0.0313    1.3402    0.0595
+#> 339:    92.8682   -5.9650   -0.1431    2.2488   -0.9701    0.4760    3.7332    0.3743    0.3408    0.1509    1.5661    0.0313    1.3374    0.0597
+#> 340:    92.8684   -5.9639   -0.1427    2.2496   -0.9699    0.4774    3.7283    0.3739    0.3411    0.1510    1.5658    0.0313    1.3358    0.0597
+#> 341:    92.8685   -5.9610   -0.1423    2.2504   -0.9696    0.4782    3.7169    0.3735    0.3413    0.1510    1.5661    0.0313    1.3338    0.0598
+#> 342:    92.8681   -5.9581   -0.1419    2.2512   -0.9696    0.4802    3.7060    0.3731    0.3416    0.1511    1.5661    0.0313    1.3316    0.0599
+#> 343:    92.8671   -5.9557   -0.1414    2.2521   -0.9697    0.4821    3.6971    0.3726    0.3419    0.1510    1.5667    0.0313    1.3292    0.0601
+#> 344:    92.8662   -5.9550   -0.1409    2.2531   -0.9696    0.4825    3.6931    0.3722    0.3424    0.1509    1.5660    0.0314    1.3269    0.0602
+#> 345:    92.8651   -5.9542   -0.1405    2.2542   -0.9696    0.4825    3.6886    0.3717    0.3429    0.1511    1.5645    0.0315    1.3252    0.0602
+#> 346:    92.8636   -5.9534   -0.1401    2.2549   -0.9696    0.4822    3.6821    0.3714    0.3432    0.1510    1.5638    0.0315    1.3231    0.0603
+#> 347:    92.8622   -5.9532   -0.1397    2.2557   -0.9696    0.4815    3.6782    0.3712    0.3435    0.1509    1.5636    0.0315    1.3220    0.0604
+#> 348:    92.8593   -5.9538   -0.1394    2.2566   -0.9697    0.4813    3.6787    0.3709    0.3438    0.1508    1.5634    0.0315    1.3202    0.0605
+#> 349:    92.8574   -5.9532   -0.1389    2.2574   -0.9697    0.4808    3.6739    0.3706    0.3440    0.1506    1.5630    0.0316    1.3179    0.0606
+#> 350:    92.8561   -5.9528   -0.1385    2.2583   -0.9697    0.4801    3.6705    0.3703    0.3443    0.1505    1.5625    0.0316    1.3161    0.0607
+#> 351:    92.8541   -5.9518   -0.1381    2.2591   -0.9697    0.4804    3.6650    0.3700    0.3446    0.1505    1.5619    0.0316    1.3141    0.0608
+#> 352:    92.8528   -5.9516   -0.1377    2.2599   -0.9700    0.4818    3.6626    0.3698    0.3449    0.1504    1.5614    0.0316    1.3122    0.0609
+#> 353:    92.8506   -5.9518   -0.1373    2.2607   -0.9700    0.4836    3.6601    0.3697    0.3451    0.1506    1.5604    0.0317    1.3116    0.0610
+#> 354:    92.8482   -5.9507   -0.1369    2.2615   -0.9700    0.4852    3.6520    0.3696    0.3451    0.1506    1.5595    0.0317    1.3099    0.0611
+#> 355:    92.8459   -5.9500   -0.1365    2.2624   -0.9699    0.4873    3.6467    0.3695    0.3454    0.1505    1.5589    0.0318    1.3090    0.0611
+#> 356:    92.8441   -5.9494   -0.1361    2.2632   -0.9700    0.4893    3.6407    0.3696    0.3456    0.1505    1.5581    0.0319    1.3083    0.0612
+#> 357:    92.8425   -5.9492   -0.1356    2.2641   -0.9700    0.4906    3.6359    0.3696    0.3459    0.1506    1.5568    0.0320    1.3082    0.0612
+#> 358:    92.8414   -5.9487   -0.1351    2.2649   -0.9700    0.4914    3.6300    0.3697    0.3460    0.1506    1.5559    0.0321    1.3064    0.0613
+#> 359:    92.8395   -5.9487   -0.1346    2.2657   -0.9700    0.4923    3.6262    0.3699    0.3462    0.1507    1.5558    0.0321    1.3050    0.0614
+#> 360:    92.8373   -5.9478   -0.1341    2.2666   -0.9700    0.4922    3.6206    0.3700    0.3465    0.1509    1.5553    0.0322    1.3061    0.0614
+#> 361:    92.8353   -5.9475   -0.1337    2.2673   -0.9699    0.4912    3.6183    0.3700    0.3469    0.1510    1.5549    0.0322    1.3051    0.0614
+#> 362:    92.8339   -5.9474   -0.1333    2.2681   -0.9699    0.4896    3.6164    0.3700    0.3472    0.1510    1.5549    0.0322    1.3041    0.0616
+#> 363:    92.8318   -5.9470   -0.1328    2.2690   -0.9696    0.4882    3.6136    0.3700    0.3476    0.1510    1.5541    0.0323    1.3035    0.0616
+#> 364:    92.8305   -5.9460   -0.1325    2.2697   -0.9695    0.4863    3.6099    0.3701    0.3477    0.1510    1.5533    0.0324    1.3028    0.0616
+#> 365:    92.8300   -5.9451   -0.1320    2.2705   -0.9693    0.4851    3.6083    0.3703    0.3479    0.1511    1.5535    0.0324    1.3017    0.0617
+#> 366:    92.8290   -5.9444   -0.1317    2.2710   -0.9691    0.4841    3.6062    0.3707    0.3476    0.1512    1.5534    0.0325    1.3013    0.0617
+#> 367:    92.8279   -5.9438   -0.1313    2.2715   -0.9688    0.4829    3.6026    0.3711    0.3473    0.1513    1.5537    0.0325    1.2996    0.0618
+#> 368:    92.8270   -5.9437   -0.1310    2.2721   -0.9687    0.4824    3.6015    0.3715    0.3471    0.1513    1.5535    0.0325    1.2984    0.0619
+#> 369:    92.8268   -5.9444   -0.1306    2.2726   -0.9686    0.4829    3.6042    0.3718    0.3469    0.1514    1.5530    0.0325    1.2983    0.0619
+#> 370:    92.8268   -5.9455   -0.1303    2.2732   -0.9686    0.4833    3.6099    0.3721    0.3466    0.1513    1.5526    0.0326    1.2971    0.0619
+#> 371:    92.8269   -5.9462   -0.1300    2.2737   -0.9686    0.4842    3.6169    0.3723    0.3465    0.1512    1.5516    0.0326    1.2961    0.0619
+#> 372:    92.8272   -5.9465   -0.1297    2.2741   -0.9685    0.4852    3.6242    0.3726    0.3463    0.1512    1.5507    0.0327    1.2950    0.0620
+#> 373:    92.8275   -5.9456   -0.1294    2.2746   -0.9686    0.4861    3.6219    0.3729    0.3461    0.1511    1.5501    0.0328    1.2946    0.0620
+#> 374:    92.8278   -5.9445   -0.1291    2.2750   -0.9687    0.4867    3.6175    0.3730    0.3461    0.1509    1.5496    0.0328    1.2942    0.0620
+#> 375:    92.8285   -5.9438   -0.1289    2.2753   -0.9689    0.4874    3.6118    0.3731    0.3459    0.1509    1.5491    0.0329    1.2938    0.0620
+#> 376:    92.8286   -5.9439   -0.1287    2.2755   -0.9689    0.4876    3.6100    0.3733    0.3458    0.1508    1.5488    0.0329    1.2930    0.0621
+#> 377:    92.8289   -5.9431   -0.1285    2.2758   -0.9690    0.4870    3.6054    0.3735    0.3456    0.1508    1.5487    0.0329    1.2921    0.0621
+#> 378:    92.8293   -5.9428   -0.1284    2.2760   -0.9689    0.4865    3.6019    0.3737    0.3454    0.1508    1.5484    0.0329    1.2910    0.0622
+#> 379:    92.8294   -5.9441   -0.1282    2.2763   -0.9688    0.4857    3.6077    0.3739    0.3451    0.1507    1.5480    0.0329    1.2907    0.0622
+#> 380:    92.8296   -5.9448   -0.1281    2.2766   -0.9688    0.4844    3.6104    0.3741    0.3448    0.1506    1.5475    0.0329    1.2901    0.0622
+#> 381:    92.8301   -5.9461   -0.1280    2.2767   -0.9689    0.4833    3.6194    0.3743    0.3444    0.1505    1.5476    0.0329    1.2893    0.0622
+#> 382:    92.8312   -5.9464   -0.1278    2.2768   -0.9689    0.4823    3.6237    0.3745    0.3441    0.1505    1.5476    0.0329    1.2881    0.0622
+#> 383:    92.8317   -5.9459   -0.1277    2.2770   -0.9687    0.4817    3.6282    0.3747    0.3438    0.1504    1.5479    0.0329    1.2875    0.0622
+#> 384:    92.8325   -5.9458   -0.1276    2.2772   -0.9686    0.4818    3.6293    0.3749    0.3434    0.1503    1.5481    0.0329    1.2863    0.0623
+#> 385:    92.8337   -5.9449   -0.1275    2.2773   -0.9685    0.4832    3.6263    0.3751    0.3431    0.1503    1.5481    0.0330    1.2860    0.0622
+#> 386:    92.8346   -5.9455   -0.1274    2.2773   -0.9682    0.4834    3.6283    0.3754    0.3427    0.1501    1.5483    0.0330    1.2851    0.0623
+#> 387:    92.8353   -5.9460   -0.1273    2.2775   -0.9681    0.4831    3.6303    0.3756    0.3424    0.1499    1.5486    0.0330    1.2836    0.0623
+#> 388:    92.8365   -5.9462   -0.1272    2.2777   -0.9680    0.4831    3.6294    0.3759    0.3420    0.1498    1.5486    0.0330    1.2830    0.0624
+#> 389:    92.8378   -5.9456   -0.1271    2.2779   -0.9678    0.4830    3.6260    0.3762    0.3416    0.1497    1.5486    0.0330    1.2816    0.0624
+#> 390:    92.8397   -5.9454   -0.1270    2.2779   -0.9678    0.4835    3.6245    0.3765    0.3413    0.1496    1.5488    0.0330    1.2805    0.0625
+#> 391:    92.8416   -5.9461   -0.1269    2.2780   -0.9679    0.4841    3.6273    0.3768    0.3409    0.1497    1.5486    0.0330    1.2816    0.0624
+#> 392:    92.8430   -5.9471   -0.1269    2.2779   -0.9679    0.4844    3.6293    0.3771    0.3408    0.1498    1.5483    0.0330    1.2830    0.0623
+#> 393:    92.8444   -5.9478   -0.1269    2.2779   -0.9680    0.4841    3.6310    0.3774    0.3407    0.1500    1.5485    0.0330    1.2842    0.0623
+#> 394:    92.8458   -5.9492   -0.1268    2.2779   -0.9680    0.4839    3.6370    0.3775    0.3407    0.1502    1.5484    0.0330    1.2847    0.0622
+#> 395:    92.8474   -5.9501   -0.1268    2.2780   -0.9681    0.4830    3.6391    0.3777    0.3406    0.1503    1.5485    0.0330    1.2849    0.0622
+#> 396:    92.8484   -5.9500   -0.1267    2.2781   -0.9682    0.4820    3.6369    0.3778    0.3406    0.1504    1.5490    0.0330    1.2850    0.0622
+#> 397:    92.8497   -5.9490   -0.1267    2.2782   -0.9680    0.4813    3.6308    0.3779    0.3407    0.1504    1.5494    0.0330    1.2848    0.0622
+#> 398:    92.8511   -5.9478   -0.1267    2.2782   -0.9679    0.4811    3.6256    0.3780    0.3407    0.1505    1.5498    0.0330    1.2844    0.0622
+#> 399:    92.8531   -5.9467   -0.1266    2.2782   -0.9680    0.4804    3.6208    0.3781    0.3407    0.1505    1.5505    0.0330    1.2842    0.0623
+#> 400:    92.8545   -5.9465   -0.1266    2.2782   -0.9679    0.4793    3.6175    0.3783    0.3406    0.1505    1.5506    0.0329    1.2833    0.0623
+#> 401:    92.8558   -5.9458   -0.1266    2.2781   -0.9679    0.4787    3.6135    0.3784    0.3406    0.1506    1.5506    0.0329    1.2836    0.0623
+#> 402:    92.8571   -5.9454   -0.1266    2.2780   -0.9678    0.4788    3.6122    0.3786    0.3405    0.1506    1.5508    0.0329    1.2841    0.0623
+#> 403:    92.8583   -5.9454   -0.1267    2.2778   -0.9679    0.4794    3.6115    0.3790    0.3402    0.1507    1.5508    0.0330    1.2859    0.0622
+#> 404:    92.8593   -5.9466   -0.1268    2.2776   -0.9681    0.4787    3.6149    0.3793    0.3401    0.1508    1.5507    0.0330    1.2875    0.0621
+#> 405:    92.8598   -5.9475   -0.1269    2.2774   -0.9681    0.4781    3.6208    0.3796    0.3399    0.1509    1.5507    0.0330    1.2888    0.0620
+#> 406:    92.8596   -5.9480   -0.1269    2.2773   -0.9680    0.4776    3.6238    0.3798    0.3397    0.1509    1.5508    0.0330    1.2895    0.0619
+#> 407:    92.8588   -5.9487   -0.1270    2.2773   -0.9679    0.4773    3.6289    0.3801    0.3395    0.1508    1.5510    0.0331    1.2887    0.0619
+#> 408:    92.8587   -5.9489   -0.1271    2.2771   -0.9677    0.4777    3.6323    0.3804    0.3391    0.1508    1.5513    0.0331    1.2878    0.0620
+#> 409:    92.8585   -5.9498   -0.1272    2.2770   -0.9677    0.4791    3.6383    0.3806    0.3389    0.1506    1.5512    0.0331    1.2865    0.0621
+#> 410:    92.8574   -5.9522   -0.1272    2.2769   -0.9676    0.4810    3.6538    0.3809    0.3387    0.1507    1.5509    0.0331    1.2855    0.0621
+#> 411:    92.8568   -5.9532   -0.1272    2.2767   -0.9675    0.4817    3.6651    0.3811    0.3385    0.1507    1.5508    0.0332    1.2842    0.0622
+#> 412:    92.8562   -5.9535   -0.1273    2.2767   -0.9674    0.4819    3.6756    0.3812    0.3383    0.1507    1.5509    0.0332    1.2851    0.0621
+#> 413:    92.8559   -5.9542   -0.1274    2.2766   -0.9672    0.4824    3.6881    0.3814    0.3381    0.1507    1.5514    0.0332    1.2848    0.0621
+#> 414:    92.8556   -5.9550   -0.1274    2.2765   -0.9670    0.4835    3.6990    0.3815    0.3379    0.1507    1.5519    0.0332    1.2838    0.0622
+#> 415:    92.8551   -5.9566   -0.1274    2.2764   -0.9669    0.4838    3.7133    0.3816    0.3377    0.1506    1.5522    0.0332    1.2828    0.0623
+#> 416:    92.8547   -5.9581   -0.1275    2.2764   -0.9668    0.4848    3.7276    0.3818    0.3374    0.1504    1.5526    0.0332    1.2814    0.0623
+#> 417:    92.8538   -5.9581   -0.1274    2.2764   -0.9667    0.4856    3.7321    0.3818    0.3372    0.1503    1.5532    0.0332    1.2800    0.0624
+#> 418:    92.8527   -5.9590   -0.1273    2.2766   -0.9665    0.4869    3.7398    0.3817    0.3372    0.1502    1.5532    0.0332    1.2787    0.0625
+#> 419:    92.8524   -5.9596   -0.1272    2.2768   -0.9663    0.4869    3.7467    0.3817    0.3372    0.1501    1.5531    0.0332    1.2779    0.0625
+#> 420:    92.8520   -5.9598   -0.1271    2.2771   -0.9662    0.4863    3.7494    0.3817    0.3372    0.1501    1.5528    0.0332    1.2774    0.0625
+#> 421:    92.8516   -5.9601   -0.1270    2.2772   -0.9661    0.4855    3.7541    0.3817    0.3372    0.1500    1.5527    0.0333    1.2763    0.0625
+#> 422:    92.8509   -5.9602   -0.1270    2.2775   -0.9659    0.4855    3.7554    0.3818    0.3371    0.1499    1.5525    0.0333    1.2753    0.0626
+#> 423:    92.8497   -5.9608   -0.1269    2.2777   -0.9658    0.4855    3.7590    0.3819    0.3371    0.1499    1.5524    0.0334    1.2746    0.0626
+#> 424:    92.8490   -5.9620   -0.1269    2.2779   -0.9658    0.4852    3.7657    0.3820    0.3370    0.1498    1.5521    0.0334    1.2740    0.0626
+#> 425:    92.8481   -5.9615   -0.1268    2.2780   -0.9657    0.4852    3.7639    0.3819    0.3369    0.1497    1.5520    0.0334    1.2741    0.0625
+#> 426:    92.8471   -5.9611   -0.1267    2.2783   -0.9656    0.4859    3.7632    0.3819    0.3369    0.1495    1.5520    0.0335    1.2744    0.0625
+#> 427:    92.8470   -5.9605   -0.1266    2.2784   -0.9655    0.4856    3.7616    0.3819    0.3368    0.1494    1.5522    0.0335    1.2739    0.0625
+#> 428:    92.8464   -5.9602   -0.1266    2.2786   -0.9653    0.4851    3.7603    0.3820    0.3367    0.1493    1.5522    0.0335    1.2731    0.0625
+#> 429:    92.8450   -5.9593   -0.1265    2.2788   -0.9652    0.4852    3.7573    0.3820    0.3366    0.1493    1.5525    0.0335    1.2720    0.0626
+#> 430:    92.8440   -5.9590   -0.1264    2.2789   -0.9651    0.4862    3.7586    0.3821    0.3365    0.1493    1.5524    0.0335    1.2710    0.0627
+#> 431:    92.8428   -5.9583   -0.1263    2.2791   -0.9649    0.4868    3.7575    0.3821    0.3365    0.1493    1.5522    0.0335    1.2698    0.0627
+#> 432:    92.8417   -5.9583   -0.1262    2.2793   -0.9649    0.4881    3.7580    0.3821    0.3365    0.1493    1.5518    0.0335    1.2683    0.0628
+#> 433:    92.8404   -5.9589   -0.1261    2.2796   -0.9648    0.4888    3.7614    0.3821    0.3364    0.1494    1.5513    0.0335    1.2681    0.0628
+#> 434:    92.8392   -5.9585   -0.1260    2.2798   -0.9646    0.4900    3.7602    0.3821    0.3363    0.1494    1.5509    0.0336    1.2686    0.0627
+#> 435:    92.8376   -5.9587   -0.1260    2.2801   -0.9645    0.4913    3.7622    0.3822    0.3362    0.1494    1.5506    0.0336    1.2677    0.0627
+#> 436:    92.8367   -5.9581   -0.1259    2.2802   -0.9646    0.4912    3.7594    0.3821    0.3361    0.1494    1.5504    0.0336    1.2684    0.0627
+#> 437:    92.8352   -5.9588   -0.1259    2.2803   -0.9647    0.4910    3.7634    0.3821    0.3360    0.1494    1.5501    0.0337    1.2695    0.0626
+#> 438:    92.8332   -5.9592   -0.1259    2.2804   -0.9648    0.4913    3.7649    0.3821    0.3358    0.1494    1.5498    0.0337    1.2705    0.0625
+#> 439:    92.8310   -5.9589   -0.1258    2.2805   -0.9648    0.4916    3.7630    0.3821    0.3357    0.1494    1.5497    0.0337    1.2713    0.0625
+#> 440:    92.8292   -5.9590   -0.1258    2.2806   -0.9649    0.4915    3.7620    0.3821    0.3355    0.1493    1.5494    0.0338    1.2712    0.0625
+#> 441:    92.8276   -5.9590   -0.1258    2.2808   -0.9650    0.4915    3.7619    0.3822    0.3353    0.1493    1.5493    0.0338    1.2712    0.0625
+#> 442:    92.8258   -5.9587   -0.1257    2.2809   -0.9650    0.4927    3.7592    0.3822    0.3351    0.1493    1.5493    0.0338    1.2707    0.0625
+#> 443:    92.8241   -5.9586   -0.1256    2.2811   -0.9651    0.4941    3.7563    0.3822    0.3350    0.1493    1.5491    0.0338    1.2704    0.0625
+#> 444:    92.8228   -5.9591   -0.1256    2.2812   -0.9651    0.4954    3.7566    0.3822    0.3349    0.1493    1.5488    0.0339    1.2703    0.0625
+#> 445:    92.8210   -5.9596   -0.1256    2.2813   -0.9652    0.4972    3.7573    0.3821    0.3348    0.1493    1.5484    0.0339    1.2702    0.0625
+#> 446:    92.8193   -5.9595   -0.1255    2.2815   -0.9652    0.4989    3.7551    0.3821    0.3348    0.1494    1.5482    0.0339    1.2708    0.0624
+#> 447:    92.8183   -5.9598   -0.1255    2.2817   -0.9652    0.5002    3.7548    0.3820    0.3347    0.1494    1.5478    0.0339    1.2710    0.0624
+#> 448:    92.8177   -5.9607   -0.1255    2.2818   -0.9653    0.5019    3.7585    0.3819    0.3347    0.1495    1.5475    0.0340    1.2711    0.0624
+#> 449:    92.8171   -5.9613   -0.1254    2.2819   -0.9654    0.5040    3.7592    0.3819    0.3347    0.1495    1.5474    0.0340    1.2711    0.0624
+#> 450:    92.8164   -5.9621   -0.1253    2.2821   -0.9655    0.5060    3.7632    0.3818    0.3346    0.1495    1.5470    0.0340    1.2704    0.0624
+#> 451:    92.8157   -5.9628   -0.1253    2.2822   -0.9655    0.5082    3.7655    0.3816    0.3346    0.1495    1.5469    0.0340    1.2699    0.0625
+#> 452:    92.8157   -5.9633   -0.1252    2.2824   -0.9656    0.5092    3.7657    0.3815    0.3346    0.1495    1.5468    0.0340    1.2691    0.0625
+#> 453:    92.8155   -5.9631   -0.1252    2.2823   -0.9657    0.5099    3.7646    0.3815    0.3347    0.1494    1.5470    0.0340    1.2684    0.0625
+#> 454:    92.8149   -5.9627   -0.1252    2.2823   -0.9656    0.5110    3.7623    0.3815    0.3347    0.1495    1.5470    0.0340    1.2678    0.0626
+#> 455:    92.8147   -5.9626   -0.1253    2.2822   -0.9656    0.5118    3.7610    0.3816    0.3347    0.1495    1.5471    0.0340    1.2675    0.0626
+#> 456:    92.8146   -5.9631   -0.1253    2.2821   -0.9657    0.5124    3.7612    0.3817    0.3348    0.1495    1.5473    0.0340    1.2684    0.0625
+#> 457:    92.8146   -5.9639   -0.1253    2.2820   -0.9658    0.5131    3.7636    0.3817    0.3347    0.1494    1.5471    0.0340    1.2683    0.0625
+#> 458:    92.8142   -5.9641   -0.1254    2.2818   -0.9658    0.5143    3.7637    0.3817    0.3347    0.1493    1.5472    0.0340    1.2679    0.0626
+#> 459:    92.8129   -5.9636   -0.1254    2.2818   -0.9660    0.5155    3.7609    0.3817    0.3347    0.1493    1.5474    0.0340    1.2692    0.0625
+#> 460:    92.8118   -5.9630   -0.1254    2.2817   -0.9660    0.5155    3.7563    0.3818    0.3347    0.1493    1.5476    0.0340    1.2703    0.0624
+#> 461:    92.8102   -5.9625   -0.1255    2.2816   -0.9661    0.5159    3.7525    0.3818    0.3347    0.1493    1.5478    0.0340    1.2711    0.0624
+#> 462:    92.8090   -5.9628   -0.1255    2.2814   -0.9661    0.5163    3.7520    0.3819    0.3347    0.1492    1.5481    0.0340    1.2708    0.0624
+#> 463:    92.8075   -5.9633   -0.1256    2.2813   -0.9660    0.5180    3.7534    0.3819    0.3347    0.1491    1.5484    0.0340    1.2705    0.0624
+#> 464:    92.8066   -5.9628   -0.1256    2.2812   -0.9659    0.5194    3.7507    0.3820    0.3347    0.1490    1.5485    0.0340    1.2702    0.0624
+#> 465:    92.8058   -5.9627   -0.1257    2.2811   -0.9658    0.5212    3.7506    0.3820    0.3347    0.1490    1.5484    0.0340    1.2696    0.0625
+#> 466:    92.8055   -5.9624   -0.1258    2.2808   -0.9656    0.5227    3.7510    0.3821    0.3347    0.1489    1.5487    0.0340    1.2704    0.0624
+#> 467:    92.8052   -5.9624   -0.1260    2.2805   -0.9656    0.5242    3.7518    0.3822    0.3346    0.1488    1.5488    0.0340    1.2715    0.0623
+#> 468:    92.8054   -5.9623   -0.1261    2.2803   -0.9654    0.5260    3.7545    0.3823    0.3346    0.1487    1.5493    0.0340    1.2730    0.0623
+#> 469:    92.8052   -5.9629   -0.1262    2.2803   -0.9654    0.5278    3.7617    0.3824    0.3346    0.1486    1.5495    0.0340    1.2737    0.0622
+#> 470:    92.8055   -5.9638   -0.1263    2.2802   -0.9653    0.5290    3.7667    0.3825    0.3347    0.1486    1.5494    0.0341    1.2729    0.0623
+#> 471:    92.8061   -5.9645   -0.1263    2.2801   -0.9653    0.5293    3.7702    0.3825    0.3347    0.1485    1.5494    0.0341    1.2724    0.0623
+#> 472:    92.8057   -5.9645   -0.1264    2.2800   -0.9653    0.5288    3.7699    0.3826    0.3347    0.1484    1.5495    0.0341    1.2728    0.0623
+#> 473:    92.8053   -5.9643   -0.1265    2.2799   -0.9652    0.5282    3.7701    0.3827    0.3347    0.1483    1.5494    0.0341    1.2721    0.0623
+#> 474:    92.8049   -5.9638   -0.1266    2.2798   -0.9653    0.5273    3.7676    0.3828    0.3347    0.1483    1.5495    0.0341    1.2722    0.0623
+#> 475:    92.8041   -5.9639   -0.1267    2.2796   -0.9654    0.5269    3.7668    0.3829    0.3347    0.1482    1.5495    0.0341    1.2721    0.0623
+#> 476:    92.8032   -5.9641   -0.1269    2.2794   -0.9653    0.5260    3.7681    0.3830    0.3347    0.1481    1.5496    0.0341    1.2716    0.0623
+#> 477:    92.8026   -5.9634   -0.1270    2.2792   -0.9653    0.5249    3.7647    0.3831    0.3347    0.1480    1.5500    0.0341    1.2716    0.0623
+#> 478:    92.8021   -5.9627   -0.1271    2.2789   -0.9653    0.5241    3.7606    0.3832    0.3346    0.1480    1.5500    0.0341    1.2718    0.0623
+#> 479:    92.8019   -5.9623   -0.1272    2.2787   -0.9654    0.5241    3.7581    0.3833    0.3345    0.1480    1.5502    0.0342    1.2714    0.0624
+#> 480:    92.8017   -5.9631   -0.1274    2.2784   -0.9654    0.5241    3.7606    0.3835    0.3344    0.1479    1.5503    0.0342    1.2711    0.0624
+#> 481:    92.8020   -5.9638   -0.1275    2.2781   -0.9654    0.5237    3.7659    0.3837    0.3343    0.1478    1.5508    0.0342    1.2720    0.0624
+#> 482:    92.8024   -5.9640   -0.1278    2.2777   -0.9654    0.5228    3.7668    0.3838    0.3342    0.1478    1.5512    0.0342    1.2729    0.0623
+#> 483:    92.8017   -5.9645   -0.1280    2.2773   -0.9654    0.5224    3.7676    0.3840    0.3341    0.1478    1.5515    0.0342    1.2741    0.0622
+#> 484:    92.8012   -5.9642   -0.1281    2.2771   -0.9653    0.5221    3.7649    0.3841    0.3340    0.1478    1.5521    0.0341    1.2747    0.0622
+#> 485:    92.8009   -5.9642   -0.1283    2.2769   -0.9653    0.5214    3.7635    0.3842    0.3339    0.1479    1.5523    0.0341    1.2752    0.0622
+#> 486:    92.8002   -5.9639   -0.1284    2.2767   -0.9652    0.5213    3.7609    0.3842    0.3339    0.1480    1.5523    0.0341    1.2760    0.0621
+#> 487:    92.7998   -5.9636   -0.1285    2.2767   -0.9652    0.5212    3.7603    0.3842    0.3339    0.1480    1.5525    0.0341    1.2762    0.0621
+#> 488:    92.7995   -5.9634   -0.1285    2.2766   -0.9652    0.5218    3.7592    0.3841    0.3339    0.1480    1.5530    0.0341    1.2773    0.0621
+#> 489:    92.7996   -5.9630   -0.1286    2.2765   -0.9653    0.5220    3.7578    0.3841    0.3339    0.1480    1.5532    0.0341    1.2778    0.0621
+#> 490:    92.8001   -5.9629   -0.1287    2.2764   -0.9652    0.5226    3.7573    0.3841    0.3339    0.1479    1.5533    0.0341    1.2788    0.0620
+#> 491:    92.8001   -5.9629   -0.1287    2.2762   -0.9651    0.5225    3.7568    0.3841    0.3338    0.1479    1.5533    0.0341    1.2790    0.0620
+#> 492:    92.8005   -5.9625   -0.1288    2.2761   -0.9651    0.5228    3.7544    0.3840    0.3339    0.1479    1.5536    0.0341    1.2797    0.0619
+#> 493:    92.8010   -5.9626   -0.1289    2.2759   -0.9651    0.5228    3.7544    0.3840    0.3339    0.1479    1.5537    0.0340    1.2795    0.0620
+#> 494:    92.8014   -5.9623   -0.1290    2.2757   -0.9651    0.5239    3.7523    0.3839    0.3340    0.1479    1.5540    0.0340    1.2790    0.0620
+#> 495:    92.8017   -5.9617   -0.1291    2.2755   -0.9652    0.5244    3.7491    0.3838    0.3341    0.1480    1.5540    0.0340    1.2787    0.0621
+#> 496:    92.8019   -5.9613   -0.1291    2.2754   -0.9652    0.5246    3.7459    0.3837    0.3341    0.1481    1.5539    0.0340    1.2802    0.0620
+#> 497:    92.8023   -5.9611   -0.1292    2.2753   -0.9653    0.5252    3.7447    0.3836    0.3340    0.1482    1.5539    0.0340    1.2814    0.0620
+#> 498:    92.8025   -5.9615   -0.1292    2.2752   -0.9653    0.5254    3.7446    0.3836    0.3339    0.1483    1.5539    0.0340    1.2825    0.0619
+#> 499:    92.8033   -5.9616   -0.1292    2.2751   -0.9654    0.5254    3.7447    0.3836    0.3338    0.1483    1.5538    0.0340    1.2834    0.0619
+#> 500:    92.8041   -5.9630   -0.1292    2.2752   -0.9655    0.5248    3.7529    0.3836    0.3337    0.1484    1.5538    0.0340    1.2841    0.0619
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei",
+  error_model = "obs_tc")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis | log_alpha |
+#> |.....................|  log_beta |sigma_low_parent |rsd_high_parent |sigma_low_A1 |
+#> |.....................|rsd_high_A1 |        o1 |        o2 |        o3 |
+#> |.....................|        o4 |        o5 |...........|...........|
+#> |    1|     504.82714 |     1.000 |    -1.000 |   -0.9114 |   -0.8944 |
+#> |.....................|   -0.8457 |   -0.8687 |   -0.8916 |   -0.8687 |
+#> |.....................|   -0.8916 |   -0.8768 |   -0.8745 |   -0.8676 |
+#> |.....................|   -0.8705 |   -0.8704 |...........|...........|
+#> |    U|     504.82714 |     93.12 |    -5.303 |   -0.9442 |   -0.1065 |
+#> |.....................|     2.291 |     1.160 |   0.03005 |     1.160 |
+#> |.....................|   0.03005 |    0.7578 |    0.8738 |     1.213 |
+#> |.....................|     1.069 |     1.072 |...........|...........|
+#> |    X|     504.82714 |     93.12 |  0.004975 |    0.2801 |    0.8989 |
+#> |.....................|     9.884 |     1.160 |   0.03005 |     1.160 |
+#> |.....................|   0.03005 |    0.7578 |    0.8738 |     1.213 |
+#> |.....................|     1.069 |     1.072 |...........|...........|
+#> |    G|    Gill Diff. |     73.79 |     2.406 |   0.05615 |    0.2285 |
+#> |.....................|  0.009051 |    -73.50 |    -23.10 |    0.2441 |
+#> |.....................|    -2.663 |     1.201 |     11.89 |    -10.88 |
+#> |.....................|    -9.982 |    -10.81 |...........|...........|
+#> |    2|     4109.9562 |    0.3228 |    -1.022 |   -0.9119 |   -0.8965 |
+#> |.....................|   -0.8458 |   -0.1941 |   -0.6796 |   -0.8709 |
+#> |.....................|   -0.8672 |   -0.8879 |   -0.9836 |   -0.7677 |
+#> |.....................|   -0.7789 |   -0.7712 |...........|...........|
+#> |    U|     4109.9562 |     30.05 |    -5.326 |   -0.9447 |   -0.1086 |
+#> |.....................|     2.291 |     1.551 |   0.03324 |     1.158 |
+#> |.....................|   0.03042 |    0.7495 |    0.7784 |     1.335 |
+#> |.....................|     1.167 |     1.178 |...........|...........|
+#> |    X|     4109.9562 |     30.05 |  0.004866 |    0.2800 |    0.8971 |
+#> |.....................|     9.883 |     1.551 |   0.03324 |     1.158 |
+#> |.....................|   0.03042 |    0.7495 |    0.7784 |     1.335 |
+#> |.....................|     1.167 |     1.178 |...........|...........|
+#> |    3|     527.72868 |    0.9323 |    -1.002 |   -0.9115 |   -0.8946 |
+#> |.....................|   -0.8457 |   -0.8012 |   -0.8704 |   -0.8689 |
+#> |.....................|   -0.8892 |   -0.8779 |   -0.8854 |   -0.8576 |
+#> |.....................|   -0.8613 |   -0.8605 |...........|...........|
+#> |    U|     527.72868 |     86.81 |    -5.306 |   -0.9442 |   -0.1067 |
+#> |.....................|     2.291 |     1.199 |   0.03037 |     1.159 |
+#> |.....................|   0.03009 |    0.7570 |    0.8642 |     1.226 |
+#> |.....................|     1.079 |     1.083 |...........|...........|
+#> |    X|     527.72868 |     86.81 |  0.004964 |    0.2800 |    0.8988 |
+#> |.....................|     9.884 |     1.199 |   0.03037 |     1.159 |
+#> |.....................|   0.03009 |    0.7570 |    0.8642 |     1.226 |
+#> |.....................|     1.079 |     1.083 |...........|...........|
+#> |    4|     503.94655 |    0.9891 |    -1.000 |   -0.9114 |   -0.8944 |
+#> |.....................|   -0.8457 |   -0.8578 |   -0.8882 |   -0.8687 |
+#> |.....................|   -0.8912 |   -0.8770 |   -0.8762 |   -0.8660 |
+#> |.....................|   -0.8690 |   -0.8688 |...........|...........|
+#> |    U|     503.94655 |     92.10 |    -5.304 |   -0.9442 |   -0.1066 |
+#> |.....................|     2.291 |     1.166 |   0.03011 |     1.160 |
+#> |.....................|   0.03006 |    0.7577 |    0.8722 |     1.215 |
+#> |.....................|     1.070 |     1.074 |...........|...........|
+#> |    X|     503.94655 |     92.10 |  0.004973 |    0.2801 |    0.8989 |
+#> |.....................|     9.884 |     1.166 |   0.03011 |     1.160 |
+#> |.....................|   0.03006 |    0.7577 |    0.8722 |     1.215 |
+#> |.....................|     1.070 |     1.074 |...........|...........|
+#> |    F| Forward Diff. |    -83.20 |     2.270 |   -0.2572 |    0.1460 |
+#> |.....................|   -0.3233 |    -71.29 |    -24.25 |    0.7297 |
+#> |.....................|    -2.130 |     1.329 |     9.332 |    -11.82 |
+#> |.....................|    -9.604 |    -10.42 |...........|...........|
+#> |    5|     503.03407 |     1.000 |    -1.001 |   -0.9114 |   -0.8944 |
+#> |.....................|   -0.8456 |   -0.8473 |   -0.8847 |   -0.8688 |
+#> |.....................|   -0.8909 |   -0.8772 |   -0.8776 |   -0.8642 |
+#> |.....................|   -0.8676 |   -0.8673 |...........|...........|
+#> |    U|     503.03407 |     93.15 |    -5.304 |   -0.9442 |   -0.1066 |
+#> |.....................|     2.291 |     1.172 |   0.03016 |     1.159 |
+#> |.....................|   0.03007 |    0.7575 |    0.8710 |     1.217 |
+#> |.....................|     1.072 |     1.075 |...........|...........|
+#> |    X|     503.03407 |     93.15 |  0.004971 |    0.2801 |    0.8989 |
+#> |.....................|     9.884 |     1.172 |   0.03016 |     1.159 |
+#> |.....................|   0.03007 |    0.7575 |    0.8710 |     1.217 |
+#> |.....................|     1.072 |     1.075 |...........|...........|
+#> |    F| Forward Diff. |     79.23 |     2.386 |   0.06830 |    0.2424 |
+#> |.....................|   0.02121 |    -70.84 |    -22.28 |   -0.5289 |
+#> |.....................|    -2.713 |     1.149 |     11.82 |    -11.86 |
+#> |.....................|    -9.567 |    -10.47 |...........|...........|
+#> |    6|     502.12413 |    0.9895 |    -1.001 |   -0.9114 |   -0.8945 |
+#> |.....................|   -0.8456 |   -0.8365 |   -0.8812 |   -0.8687 |
+#> |.....................|   -0.8905 |   -0.8774 |   -0.8794 |   -0.8624 |
+#> |.....................|   -0.8662 |   -0.8657 |...........|...........|
+#> |    U|     502.12413 |     92.14 |    -5.304 |   -0.9442 |   -0.1066 |
+#> |.....................|     2.291 |     1.178 |   0.03021 |     1.160 |
+#> |.....................|   0.03007 |    0.7574 |    0.8695 |     1.220 |
+#> |.....................|     1.073 |     1.077 |...........|...........|
+#> |    X|     502.12413 |     92.14 |  0.004969 |    0.2801 |    0.8989 |
+#> |.....................|     9.884 |     1.178 |   0.03021 |     1.160 |
+#> |.....................|   0.03007 |    0.7574 |    0.8695 |     1.220 |
+#> |.....................|     1.073 |     1.077 |...........|...........|
+#> |    F| Forward Diff. |    -77.28 |     2.252 |   -0.2503 |    0.1427 |
+#> |.....................|   -0.3238 |    -69.21 |    -23.25 |    0.3943 |
+#> |.....................|    -2.493 |     1.092 |     10.79 |    -11.67 |
+#> |.....................|    -9.485 |    -10.25 |...........|...........|
+#> |    7|     501.24651 |     1.000 |    -1.001 |   -0.9114 |   -0.8945 |
+#> |.....................|   -0.8456 |   -0.8257 |   -0.8776 |   -0.8688 |
+#> |.....................|   -0.8901 |   -0.8775 |   -0.8811 |   -0.8606 |
+#> |.....................|   -0.8647 |   -0.8641 |...........|...........|
+#> |    U|     501.24651 |     93.15 |    -5.305 |   -0.9441 |   -0.1067 |
+#> |.....................|     2.291 |     1.184 |   0.03026 |     1.160 |
+#> |.....................|   0.03008 |    0.7573 |    0.8680 |     1.222 |
+#> |.....................|     1.075 |     1.079 |...........|...........|
+#> |    X|     501.24651 |     93.15 |  0.004968 |    0.2801 |    0.8988 |
+#> |.....................|     9.885 |     1.184 |   0.03026 |     1.160 |
+#> |.....................|   0.03008 |    0.7573 |    0.8680 |     1.222 |
+#> |.....................|     1.075 |     1.079 |...........|...........|
+#> |    F| Forward Diff. |     78.96 |     2.363 |   0.07229 |    0.2390 |
+#> |.....................|   0.02239 |    -67.81 |    -20.97 |    0.1381 |
+#> |.....................|    -2.125 |     1.379 |     9.797 |    -11.70 |
+#> |.....................|    -9.438 |    -10.29 |...........|...........|
+#> |    8|     500.35160 |    0.9896 |    -1.002 |   -0.9114 |   -0.8945 |
+#> |.....................|   -0.8456 |   -0.8148 |   -0.8742 |   -0.8688 |
+#> |.....................|   -0.8898 |   -0.8778 |   -0.8827 |   -0.8587 |
+#> |.....................|   -0.8632 |   -0.8625 |...........|...........|
+#> |    U|      500.3516 |     92.15 |    -5.305 |   -0.9441 |   -0.1067 |
+#> |.....................|     2.291 |     1.191 |   0.03032 |     1.159 |
+#> |.....................|   0.03008 |    0.7571 |    0.8666 |     1.224 |
+#> |.....................|     1.077 |     1.081 |...........|...........|
+#> |    X|      500.3516 |     92.15 |  0.004966 |    0.2801 |    0.8988 |
+#> |.....................|     9.885 |     1.191 |   0.03032 |     1.159 |
+#> |.....................|   0.03008 |    0.7571 |    0.8666 |     1.224 |
+#> |.....................|     1.077 |     1.081 |...........|...........|
+#> |    F| Forward Diff. |    -75.23 |     2.232 |   -0.2459 |    0.1501 |
+#> |.....................|   -0.3253 |    -66.87 |    -22.19 |    0.4436 |
+#> |.....................|    -2.150 |    0.9434 |     9.182 |    -11.49 |
+#> |.....................|    -9.350 |    -10.07 |...........|...........|
+#> |    9|     499.45361 |     1.000 |    -1.002 |   -0.9113 |   -0.8946 |
+#> |.....................|   -0.8455 |   -0.8036 |   -0.8705 |   -0.8689 |
+#> |.....................|   -0.8894 |   -0.8779 |   -0.8842 |   -0.8568 |
+#> |.....................|   -0.8616 |   -0.8608 |...........|...........|
+#> |    U|     499.45361 |     93.12 |    -5.306 |   -0.9441 |   -0.1067 |
+#> |.....................|     2.291 |     1.197 |   0.03037 |     1.159 |
+#> |.....................|   0.03009 |    0.7570 |    0.8653 |     1.226 |
+#> |.....................|     1.078 |     1.082 |...........|...........|
+#> |    X|     499.45361 |     93.12 |  0.004964 |    0.2801 |    0.8988 |
+#> |.....................|     9.885 |     1.197 |   0.03037 |     1.159 |
+#> |.....................|   0.03009 |    0.7570 |    0.8653 |     1.226 |
+#> |.....................|     1.078 |     1.082 |...........|...........|
+#> |    F| Forward Diff. |     73.21 |     2.337 |   0.06584 |    0.2472 |
+#> |.....................|  0.008903 |    -65.96 |    -20.21 |   -0.3457 |
+#> |.....................|    -2.677 |     1.048 |     11.29 |    -11.53 |
+#> |.....................|    -9.311 |    -10.11 |...........|...........|
+#> |   10|     498.59105 |    0.9896 |    -1.003 |   -0.9113 |   -0.8946 |
+#> |.....................|   -0.8455 |   -0.7924 |   -0.8671 |   -0.8688 |
+#> |.....................|   -0.8890 |   -0.8781 |   -0.8861 |   -0.8548 |
+#> |.....................|   -0.8600 |   -0.8591 |...........|...........|
+#> |    U|     498.59105 |     92.15 |    -5.306 |   -0.9441 |   -0.1068 |
+#> |.....................|     2.291 |     1.204 |   0.03042 |     1.159 |
+#> |.....................|   0.03009 |    0.7568 |    0.8636 |     1.229 |
+#> |.....................|     1.080 |     1.084 |...........|...........|
+#> |    X|     498.59105 |     92.15 |  0.004962 |    0.2801 |    0.8987 |
+#> |.....................|     9.885 |     1.204 |   0.03042 |     1.159 |
+#> |.....................|   0.03009 |    0.7568 |    0.8636 |     1.229 |
+#> |.....................|     1.080 |     1.084 |...........|...........|
+#> |    F| Forward Diff. |    -74.43 |     2.211 |   -0.2431 |    0.1502 |
+#> |.....................|   -0.3305 |    -64.40 |    -21.08 |    0.5329 |
+#> |.....................|    -2.487 |    0.9319 |     8.926 |    -11.33 |
+#> |.....................|    -9.217 |    -9.888 |...........|...........|
+#> |   11|     497.71590 |     1.000 |    -1.003 |   -0.9113 |   -0.8946 |
+#> |.....................|   -0.8455 |   -0.7811 |   -0.8634 |   -0.8689 |
+#> |.....................|   -0.8885 |   -0.8783 |   -0.8877 |   -0.8529 |
+#> |.....................|   -0.8584 |   -0.8573 |...........|...........|
+#> |    U|      497.7159 |     93.11 |    -5.306 |   -0.9441 |   -0.1068 |
+#> |.....................|     2.291 |     1.210 |   0.03048 |     1.159 |
+#> |.....................|   0.03010 |    0.7567 |    0.8622 |     1.231 |
+#> |.....................|     1.082 |     1.086 |...........|...........|
+#> |    X|      497.7159 |     93.11 |  0.004960 |    0.2801 |    0.8987 |
+#> |.....................|     9.886 |     1.210 |   0.03048 |     1.159 |
+#> |.....................|   0.03010 |    0.7567 |    0.8622 |     1.231 |
+#> |.....................|     1.082 |     1.086 |...........|...........|
+#> |    F| Forward Diff. |     71.79 |     2.312 |   0.07434 |    0.2557 |
+#> |.....................|  0.006614 |    -63.04 |    -18.95 |    0.3164 |
+#> |.....................|    -2.117 |     1.342 |     9.274 |    -11.35 |
+#> |.....................|    -9.172 |    -9.924 |...........|...........|
+#> |   12|     496.86264 |    0.9898 |    -1.003 |   -0.9113 |   -0.8947 |
+#> |.....................|   -0.8455 |   -0.7696 |   -0.8599 |   -0.8690 |
+#> |.....................|   -0.8881 |   -0.8785 |   -0.8894 |   -0.8508 |
+#> |.....................|   -0.8567 |   -0.8555 |...........|...........|
+#> |    U|     496.86264 |     92.17 |    -5.307 |   -0.9441 |   -0.1068 |
+#> |.....................|     2.291 |     1.217 |   0.03053 |     1.159 |
+#> |.....................|   0.03011 |    0.7565 |    0.8607 |     1.234 |
+#> |.....................|     1.084 |     1.088 |...........|...........|
+#> |    X|     496.86264 |     92.17 |  0.004958 |    0.2801 |    0.8987 |
+#> |.....................|     9.886 |     1.217 |   0.03053 |     1.159 |
+#> |.....................|   0.03011 |    0.7565 |    0.8607 |     1.234 |
+#> |.....................|     1.084 |     1.088 |...........|...........|
+#> |    F| Forward Diff. |    -71.54 |     2.190 |   -0.2371 |    0.1482 |
+#> |.....................|   -0.3369 |    -61.67 |    -19.90 |    0.9419 |
+#> |.....................|    -2.139 |     1.041 |     7.036 |    -11.13 |
+#> |.....................|    -9.064 |    -9.692 |...........|...........|
+#> |   13|     495.99097 |    0.9997 |    -1.004 |   -0.9113 |   -0.8947 |
+#> |.....................|   -0.8454 |   -0.7580 |   -0.8562 |   -0.8692 |
+#> |.....................|   -0.8877 |   -0.8787 |   -0.8907 |   -0.8487 |
+#> |.....................|   -0.8550 |   -0.8537 |...........|...........|
+#> |    U|     495.99097 |     93.09 |    -5.307 |   -0.9441 |   -0.1069 |
+#> |.....................|     2.291 |     1.224 |   0.03059 |     1.159 |
+#> |.....................|   0.03011 |    0.7564 |    0.8596 |     1.236 |
+#> |.....................|     1.085 |     1.090 |...........|...........|
+#> |    X|     495.99097 |     93.09 |  0.004956 |    0.2801 |    0.8987 |
+#> |.....................|     9.886 |     1.224 |   0.03059 |     1.159 |
+#> |.....................|   0.03011 |    0.7564 |    0.8596 |     1.236 |
+#> |.....................|     1.085 |     1.090 |...........|...........|
+#> |    F| Forward Diff. |     67.48 |     2.282 |   0.05510 |    0.2442 |
+#> |.....................|  -0.01700 |    -60.62 |    -17.93 |    0.4372 |
+#> |.....................|    -2.100 |     1.212 |     9.042 |    -11.17 |
+#> |.....................|    -9.025 |    -9.723 |...........|...........|
+#> |   14|     495.15472 |    0.9899 |    -1.004 |   -0.9113 |   -0.8948 |
+#> |.....................|   -0.8454 |   -0.7463 |   -0.8527 |   -0.8693 |
+#> |.....................|   -0.8873 |   -0.8789 |   -0.8924 |   -0.8465 |
+#> |.....................|   -0.8533 |   -0.8518 |...........|...........|
+#> |    U|     495.15472 |     92.18 |    -5.308 |   -0.9441 |   -0.1069 |
+#> |.....................|     2.291 |     1.231 |   0.03064 |     1.159 |
+#> |.....................|   0.03012 |    0.7562 |    0.8581 |     1.239 |
+#> |.....................|     1.087 |     1.092 |...........|...........|
+#> |    X|     495.15472 |     92.18 |  0.004954 |    0.2801 |    0.8986 |
+#> |.....................|     9.886 |     1.231 |   0.03064 |     1.159 |
+#> |.....................|   0.03012 |    0.7562 |    0.8581 |     1.239 |
+#> |.....................|     1.087 |     1.092 |...........|...........|
+#> |    F| Forward Diff. |    -68.93 |     2.171 |   -0.2257 |    0.1488 |
+#> |.....................|   -0.3348 |    -59.34 |    -18.81 |     1.070 |
+#> |.....................|    -2.082 |     1.016 |     8.208 |    -10.96 |
+#> |.....................|    -8.930 |    -9.498 |...........|...........|
+#> |   15|     494.30065 |    0.9995 |    -1.005 |   -0.9112 |   -0.8948 |
+#> |.....................|   -0.8453 |   -0.7344 |   -0.8490 |   -0.8695 |
+#> |.....................|   -0.8869 |   -0.8792 |   -0.8941 |   -0.8443 |
+#> |.....................|   -0.8515 |   -0.8499 |...........|...........|
+#> |    U|     494.30065 |     93.07 |    -5.308 |   -0.9440 |   -0.1069 |
+#> |.....................|     2.291 |     1.237 |   0.03069 |     1.159 |
+#> |.....................|   0.03013 |    0.7561 |    0.8567 |     1.242 |
+#> |.....................|     1.089 |     1.094 |...........|...........|
+#> |    X|     494.30065 |     93.07 |  0.004951 |    0.2801 |    0.8986 |
+#> |.....................|     9.887 |     1.237 |   0.03069 |     1.159 |
+#> |.....................|   0.03013 |    0.7561 |    0.8567 |     1.242 |
+#> |.....................|     1.089 |     1.094 |...........|...........|
+#> |    F| Forward Diff. |     65.20 |     2.260 |   0.06851 |    0.2416 |
+#> |.....................|  -0.02143 |    -58.42 |    -17.03 |    0.3665 |
+#> |.....................|    -2.202 |     1.112 |     7.377 |    -10.96 |
+#> |.....................|    -8.866 |    -9.510 |...........|...........|
+#> |   16|     493.48608 |    0.9901 |    -1.005 |   -0.9112 |   -0.8948 |
+#> |.....................|   -0.8453 |   -0.7225 |   -0.8455 |   -0.8696 |
+#> |.....................|   -0.8865 |   -0.8794 |   -0.8956 |   -0.8421 |
+#> |.....................|   -0.8496 |   -0.8479 |...........|...........|
+#> |    U|     493.48608 |     92.19 |    -5.309 |   -0.9440 |   -0.1070 |
+#> |.....................|     2.291 |     1.244 |   0.03075 |     1.159 |
+#> |.....................|   0.03013 |    0.7559 |    0.8553 |     1.244 |
+#> |.....................|     1.091 |     1.096 |...........|...........|
+#> |    X|     493.48608 |     92.19 |  0.004949 |    0.2801 |    0.8985 |
+#> |.....................|     9.887 |     1.244 |   0.03075 |     1.159 |
+#> |.....................|   0.03013 |    0.7559 |    0.8553 |     1.244 |
+#> |.....................|     1.091 |     1.096 |...........|...........|
+#> |    F| Forward Diff. |    -66.94 |     2.152 |   -0.2367 |    0.1452 |
+#> |.....................|   -0.3412 |    -57.13 |    -17.84 |     1.057 |
+#> |.....................|    -2.129 |    0.9540 |     6.557 |    -10.77 |
+#> |.....................|    -8.770 |    -9.285 |...........|...........|
+#> |   17|     492.64670 |    0.9993 |    -1.006 |   -0.9112 |   -0.8949 |
+#> |.....................|   -0.8453 |   -0.7105 |   -0.8419 |   -0.8698 |
+#> |.....................|   -0.8860 |   -0.8796 |   -0.8969 |   -0.8398 |
+#> |.....................|   -0.8478 |   -0.8460 |...........|...........|
+#> |    U|      492.6467 |     93.06 |    -5.309 |   -0.9440 |   -0.1070 |
+#> |.....................|     2.291 |     1.251 |   0.03080 |     1.159 |
+#> |.....................|   0.03014 |    0.7557 |    0.8542 |     1.247 |
+#> |.....................|     1.093 |     1.098 |...........|...........|
+#> |    X|      492.6467 |     93.06 |  0.004947 |    0.2801 |    0.8985 |
+#> |.....................|     9.888 |     1.251 |   0.03080 |     1.159 |
+#> |.....................|   0.03014 |    0.7557 |    0.8542 |     1.247 |
+#> |.....................|     1.093 |     1.098 |...........|...........|
+#> |    F| Forward Diff. |     62.51 |     2.244 |   0.07930 |    0.2506 |
+#> |.....................|  -0.02305 |    -56.21 |    -16.10 |    0.4420 |
+#> |.....................|    -2.202 |     1.071 |     7.160 |    -10.75 |
+#> |.....................|    -8.705 |    -9.292 |...........|...........|
+#> |   18|     491.85024 |    0.9902 |    -1.006 |   -0.9112 |   -0.8949 |
+#> |.....................|   -0.8453 |   -0.6983 |   -0.8384 |   -0.8699 |
+#> |.....................|   -0.8855 |   -0.8798 |   -0.8984 |   -0.8374 |
+#> |.....................|   -0.8459 |   -0.8439 |...........|...........|
+#> |    U|     491.85024 |     92.21 |    -5.310 |   -0.9440 |   -0.1071 |
+#> |.....................|     2.291 |     1.258 |   0.03085 |     1.159 |
+#> |.....................|   0.03015 |    0.7556 |    0.8529 |     1.250 |
+#> |.....................|     1.095 |     1.100 |...........|...........|
+#> |    X|     491.85024 |     92.21 |  0.004944 |    0.2801 |    0.8985 |
+#> |.....................|     9.888 |     1.258 |   0.03085 |     1.159 |
+#> |.....................|   0.03015 |    0.7556 |    0.8529 |     1.250 |
+#> |.....................|     1.095 |     1.100 |...........|...........|
+#> |    F| Forward Diff. |    -64.39 |     2.132 |   -0.2231 |    0.1507 |
+#> |.....................|   -0.3455 |    -54.91 |    -16.84 |     1.107 |
+#> |.....................|    -2.130 |    0.9153 |     6.361 |    -10.56 |
+#> |.....................|    -8.604 |    -9.065 |...........|...........|
+#> |   19|     491.03181 |    0.9992 |    -1.007 |   -0.9112 |   -0.8950 |
+#> |.....................|   -0.8452 |   -0.6860 |   -0.8347 |   -0.8702 |
+#> |.....................|   -0.8850 |   -0.8800 |   -0.8997 |   -0.8350 |
+#> |.....................|   -0.8439 |   -0.8419 |...........|...........|
+#> |    U|     491.03181 |     93.04 |    -5.310 |   -0.9440 |   -0.1071 |
+#> |.....................|     2.291 |     1.265 |   0.03091 |     1.159 |
+#> |.....................|   0.03015 |    0.7554 |    0.8517 |     1.253 |
+#> |.....................|     1.097 |     1.103 |...........|...........|
+#> |    X|     491.03181 |     93.04 |  0.004942 |    0.2801 |    0.8984 |
+#> |.....................|     9.888 |     1.265 |   0.03091 |     1.159 |
+#> |.....................|   0.03015 |    0.7554 |    0.8517 |     1.253 |
+#> |.....................|     1.097 |     1.103 |...........|...........|
+#> |    F| Forward Diff. |     59.97 |     2.217 |   0.06954 |    0.2512 |
+#> |.....................|  -0.03854 |    -54.10 |    -15.21 |    0.3955 |
+#> |.....................|    -2.336 |     1.047 |     8.162 |    -10.81 |
+#> |.....................|    -8.706 |    -9.233 |...........|...........|
+#> |   20|     490.24998 |    0.9904 |    -1.007 |   -0.9112 |   -0.8950 |
+#> |.....................|   -0.8452 |   -0.6737 |   -0.8313 |   -0.8703 |
+#> |.....................|   -0.8845 |   -0.8803 |   -0.9015 |   -0.8325 |
+#> |.....................|   -0.8419 |   -0.8397 |...........|...........|
+#> |    U|     490.24998 |     92.22 |    -5.311 |   -0.9440 |   -0.1072 |
+#> |.....................|     2.291 |     1.273 |   0.03096 |     1.159 |
+#> |.....................|   0.03016 |    0.7552 |    0.8502 |     1.256 |
+#> |.....................|     1.099 |     1.105 |...........|...........|
+#> |    X|     490.24998 |     92.22 |  0.004939 |    0.2801 |    0.8984 |
+#> |.....................|     9.889 |     1.273 |   0.03096 |     1.159 |
+#> |.....................|   0.03016 |    0.7552 |    0.8502 |     1.256 |
+#> |.....................|     1.099 |     1.105 |...........|...........|
+#> |    F| Forward Diff. |    -61.40 |     2.114 |   -0.2172 |    0.1580 |
+#> |.....................|   -0.3477 |    -53.15 |    -16.02 |    0.7982 |
+#> |.....................|    -2.483 |    0.7215 |     9.240 |    -10.34 |
+#> |.....................|    -8.435 |    -8.843 |...........|...........|
+#> |   21|     489.45580 |    0.9991 |    -1.008 |   -0.9111 |   -0.8951 |
+#> |.....................|   -0.8451 |   -0.6614 |   -0.8278 |   -0.8705 |
+#> |.....................|   -0.8839 |   -0.8804 |   -0.9038 |   -0.8300 |
+#> |.....................|   -0.8398 |   -0.8376 |...........|...........|
+#> |    U|      489.4558 |     93.03 |    -5.311 |   -0.9439 |   -0.1072 |
+#> |.....................|     2.291 |     1.280 |   0.03101 |     1.159 |
+#> |.....................|   0.03017 |    0.7551 |    0.8482 |     1.259 |
+#> |.....................|     1.102 |     1.107 |...........|...........|
+#> |    X|      489.4558 |     93.03 |  0.004937 |    0.2801 |    0.8983 |
+#> |.....................|     9.889 |     1.280 |   0.03101 |     1.159 |
+#> |.....................|   0.03017 |    0.7551 |    0.8482 |     1.259 |
+#> |.....................|     1.102 |     1.107 |...........|...........|
+#> |    F| Forward Diff. |     58.20 |     2.191 |   0.07193 |    0.2543 |
+#> |.....................|  -0.04201 |    -51.69 |    -14.22 |    0.6968 |
+#> |.....................|    -2.088 |     1.024 |     8.024 |    -10.34 |
+#> |.....................|    -8.364 |    -8.845 |...........|...........|
+#> |   22|     488.71859 |    0.9903 |    -1.008 |   -0.9111 |   -0.8951 |
+#> |.....................|   -0.8451 |   -0.6491 |   -0.8245 |   -0.8707 |
+#> |.....................|   -0.8833 |   -0.8807 |   -0.9059 |   -0.8275 |
+#> |.....................|   -0.8378 |   -0.8354 |...........|...........|
+#> |    U|     488.71859 |     92.21 |    -5.312 |   -0.9439 |   -0.1073 |
+#> |.....................|     2.291 |     1.287 |   0.03106 |     1.158 |
+#> |.....................|   0.03018 |    0.7549 |    0.8463 |     1.262 |
+#> |.....................|     1.104 |     1.110 |...........|...........|
+#> |    X|     488.71859 |     92.21 |  0.004934 |    0.2801 |    0.8983 |
+#> |.....................|     9.890 |     1.287 |   0.03106 |     1.158 |
+#> |.....................|   0.03018 |    0.7549 |    0.8463 |     1.262 |
+#> |.....................|     1.104 |     1.110 |...........|...........|
+#> |    F| Forward Diff. |    -62.72 |     2.087 |   -0.2158 |    0.1536 |
+#> |.....................|   -0.3560 |    -50.59 |    -14.96 |     1.289 |
+#> |.....................|    -2.066 |    0.8753 |     7.259 |    -10.12 |
+#> |.....................|    -8.247 |    -8.604 |...........|...........|
+#> |   23|     487.91801 |    0.9987 |    -1.009 |   -0.9111 |   -0.8952 |
+#> |.....................|   -0.8450 |   -0.6366 |   -0.8210 |   -0.8711 |
+#> |.....................|   -0.8828 |   -0.8809 |   -0.9078 |   -0.8248 |
+#> |.....................|   -0.8356 |   -0.8332 |...........|...........|
+#> |    U|     487.91801 |     93.00 |    -5.312 |   -0.9439 |   -0.1073 |
+#> |.....................|     2.292 |     1.294 |   0.03112 |     1.158 |
+#> |.....................|   0.03019 |    0.7547 |    0.8446 |     1.265 |
+#> |.....................|     1.106 |     1.112 |...........|...........|
+#> |    X|     487.91801 |     93.00 |  0.004931 |    0.2801 |    0.8982 |
+#> |.....................|     9.890 |     1.294 |   0.03112 |     1.158 |
+#> |.....................|   0.03019 |    0.7547 |    0.8446 |     1.265 |
+#> |.....................|     1.106 |     1.112 |...........|...........|
+#> |    F| Forward Diff. |     52.73 |     2.162 |   0.07610 |    0.2481 |
+#> |.....................|  -0.05835 |    -50.28 |    -13.63 |    0.1991 |
+#> |.....................|    -2.681 |    0.6961 |     9.479 |    -10.12 |
+#> |.....................|    -8.180 |    -8.607 |...........|...........|
+#> |   24|     487.19380 |    0.9906 |    -1.009 |   -0.9111 |   -0.8952 |
+#> |.....................|   -0.8450 |   -0.6240 |   -0.8177 |   -0.8712 |
+#> |.....................|   -0.8820 |   -0.8811 |   -0.9103 |   -0.8222 |
+#> |.....................|   -0.8335 |   -0.8310 |...........|...........|
+#> |    U|      487.1938 |     92.24 |    -5.313 |   -0.9439 |   -0.1074 |
+#> |.....................|     2.292 |     1.301 |   0.03116 |     1.158 |
+#> |.....................|   0.03020 |    0.7546 |    0.8424 |     1.269 |
+#> |.....................|     1.108 |     1.114 |...........|...........|
+#> |    X|      487.1938 |     92.24 |  0.004929 |    0.2801 |    0.8982 |
+#> |.....................|     9.891 |     1.301 |   0.03116 |     1.158 |
+#> |.....................|   0.03020 |    0.7546 |    0.8424 |     1.269 |
+#> |.....................|     1.108 |     1.114 |...........|...........|
+#> |    F| Forward Diff. |    -58.70 |     2.065 |   -0.2024 |    0.1592 |
+#> |.....................|   -0.3563 |    -48.58 |    -14.05 |     1.280 |
+#> |.....................|    -2.114 |    0.8980 |     5.535 |    -9.882 |
+#> |.....................|    -8.046 |    -8.364 |...........|...........|
+#> |   25|     486.45861 |    0.9990 |    -1.010 |   -0.9111 |   -0.8953 |
+#> |.....................|   -0.8449 |   -0.6115 |   -0.8144 |   -0.8715 |
+#> |.....................|   -0.8813 |   -0.8813 |   -0.9121 |   -0.8195 |
+#> |.....................|   -0.8313 |   -0.8287 |...........|...........|
+#> |    U|     486.45861 |     93.03 |    -5.313 |   -0.9439 |   -0.1074 |
+#> |.....................|     2.292 |     1.309 |   0.03121 |     1.158 |
+#> |.....................|   0.03021 |    0.7545 |    0.8409 |     1.272 |
+#> |.....................|     1.111 |     1.117 |...........|...........|
+#> |    X|     486.45861 |     93.03 |  0.004926 |    0.2801 |    0.8981 |
+#> |.....................|     9.892 |     1.309 |   0.03121 |     1.158 |
+#> |.....................|   0.03021 |    0.7545 |    0.8409 |     1.272 |
+#> |.....................|     1.111 |     1.117 |...........|...........|
+#> |    F| Forward Diff. |     56.64 |     2.141 |   0.09518 |    0.2574 |
+#> |.....................|  -0.04938 |    -48.45 |    -12.81 |    0.1110 |
+#> |.....................|    -2.819 |    0.7463 |     7.804 |    -9.858 |
+#> |.....................|    -7.976 |    -8.366 |...........|...........|
+#> |   26|     485.70463 |    0.9912 |    -1.011 |   -0.9111 |   -0.8954 |
+#> |.....................|   -0.8448 |   -0.5987 |   -0.8113 |   -0.8717 |
+#> |.....................|   -0.8805 |   -0.8815 |   -0.9139 |   -0.8166 |
+#> |.....................|   -0.8290 |   -0.8264 |...........|...........|
+#> |    U|     485.70463 |     92.30 |    -5.314 |   -0.9439 |   -0.1075 |
+#> |.....................|     2.292 |     1.316 |   0.03126 |     1.158 |
+#> |.....................|   0.03022 |    0.7543 |    0.8393 |     1.275 |
+#> |.....................|     1.113 |     1.119 |...........|...........|
+#> |    X|     485.70463 |     92.30 |  0.004923 |    0.2801 |    0.8981 |
+#> |.....................|     9.892 |     1.316 |   0.03126 |     1.158 |
+#> |.....................|   0.03022 |    0.7543 |    0.8393 |     1.275 |
+#> |.....................|     1.113 |     1.119 |...........|...........|
+#> |    F| Forward Diff. |    -49.75 |     2.049 |   -0.1896 |    0.1657 |
+#> |.....................|   -0.3394 |    -47.06 |    -13.27 |    0.8968 |
+#> |.....................|    -2.558 |    0.5259 |     7.006 |    -9.655 |
+#> |.....................|    -7.860 |    -8.128 |...........|...........|
+#> |   27|     485.03383 |    0.9993 |    -1.011 |   -0.9111 |   -0.8954 |
+#> |.....................|   -0.8447 |   -0.5860 |   -0.8081 |   -0.8719 |
+#> |.....................|   -0.8796 |   -0.8816 |   -0.9160 |   -0.8138 |
+#> |.....................|   -0.8267 |   -0.8240 |...........|...........|
+#> |    U|     485.03383 |     93.05 |    -5.315 |   -0.9439 |   -0.1076 |
+#> |.....................|     2.292 |     1.323 |   0.03131 |     1.158 |
+#> |.....................|   0.03024 |    0.7542 |    0.8375 |     1.279 |
+#> |.....................|     1.116 |     1.122 |...........|...........|
+#> |    X|     485.03383 |     93.05 |  0.004920 |    0.2801 |    0.8980 |
+#> |.....................|     9.893 |     1.323 |   0.03131 |     1.158 |
+#> |.....................|   0.03024 |    0.7542 |    0.8375 |     1.279 |
+#> |.....................|     1.116 |     1.122 |...........|...........|
+#> |    F| Forward Diff. |     59.36 |     2.117 |    0.1128 |    0.2587 |
+#> |.....................|  -0.03694 |    -45.49 |    -11.65 |    0.8714 |
+#> |.....................|    -2.196 |    0.9711 |     7.208 |    -9.629 |
+#> |.....................|    -7.785 |    -8.123 |...........|...........|
+#> |   28|     484.30050 |    0.9913 |    -1.012 |   -0.9111 |   -0.8955 |
+#> |.....................|   -0.8447 |   -0.5733 |   -0.8052 |   -0.8723 |
+#> |.....................|   -0.8788 |   -0.8818 |   -0.9181 |   -0.8109 |
+#> |.....................|   -0.8243 |   -0.8216 |...........|...........|
+#> |    U|      484.3005 |     92.30 |    -5.315 |   -0.9439 |   -0.1077 |
+#> |.....................|     2.292 |     1.331 |   0.03135 |     1.157 |
+#> |.....................|   0.03025 |    0.7541 |    0.8357 |     1.282 |
+#> |.....................|     1.118 |     1.124 |...........|...........|
+#> |    X|      484.3005 |     92.30 |  0.004916 |    0.2801 |    0.8979 |
+#> |.....................|     9.894 |     1.331 |   0.03135 |     1.157 |
+#> |.....................|   0.03025 |    0.7541 |    0.8357 |     1.282 |
+#> |.....................|     1.118 |     1.124 |...........|...........|
+#> |    F| Forward Diff. |    -49.13 |     2.024 |   -0.1788 |    0.1668 |
+#> |.....................|   -0.3408 |    -44.74 |    -12.30 |     1.348 |
+#> |.....................|    -2.137 |    0.7757 |     5.010 |    -9.393 |
+#> |.....................|    -7.651 |    -7.866 |...........|...........|
+#> |   29|     483.61888 |    0.9988 |    -1.013 |   -0.9110 |   -0.8956 |
+#> |.....................|   -0.8446 |   -0.5603 |   -0.8022 |   -0.8729 |
+#> |.....................|   -0.8781 |   -0.8821 |   -0.9194 |   -0.8078 |
+#> |.....................|   -0.8218 |   -0.8191 |...........|...........|
+#> |    U|     483.61888 |     93.00 |    -5.316 |   -0.9438 |   -0.1077 |
+#> |.....................|     2.292 |     1.338 |   0.03140 |     1.157 |
+#> |.....................|   0.03026 |    0.7539 |    0.8345 |     1.286 |
+#> |.....................|     1.121 |     1.127 |...........|...........|
+#> |    X|     483.61888 |     93.00 |  0.004913 |    0.2801 |    0.8979 |
+#> |.....................|     9.895 |     1.338 |   0.03140 |     1.157 |
+#> |.....................|   0.03026 |    0.7539 |    0.8345 |     1.286 |
+#> |.....................|     1.121 |     1.127 |...........|...........|
+#> |    F| Forward Diff. |     51.77 |     2.082 |   0.08733 |    0.2462 |
+#> |.....................|  -0.07383 |    -44.60 |    -11.22 |    0.3023 |
+#> |.....................|    -2.722 |    0.5489 |     8.672 |    -9.371 |
+#> |.....................|    -7.562 |    -7.848 |...........|...........|
+#> |   30|     482.91165 |    0.9915 |    -1.013 |   -0.9110 |   -0.8957 |
+#> |.....................|   -0.8445 |   -0.5473 |   -0.7995 |   -0.8732 |
+#> |.....................|   -0.8770 |   -0.8822 |   -0.9219 |   -0.8047 |
+#> |.....................|   -0.8192 |   -0.8165 |...........|...........|
+#> |    U|     482.91165 |     92.33 |    -5.317 |   -0.9438 |   -0.1078 |
+#> |.....................|     2.292 |     1.346 |   0.03144 |     1.157 |
+#> |.....................|   0.03027 |    0.7538 |    0.8323 |     1.290 |
+#> |.....................|     1.124 |     1.130 |...........|...........|
+#> |    X|     482.91165 |     92.33 |  0.004909 |    0.2801 |    0.8978 |
+#> |.....................|     9.895 |     1.346 |   0.03144 |     1.157 |
+#> |.....................|   0.03027 |    0.7538 |    0.8323 |     1.290 |
+#> |.....................|     1.124 |     1.130 |...........|...........|
+#> |    F| Forward Diff. |    -45.50 |     2.003 |   -0.1660 |    0.1702 |
+#> |.....................|   -0.3374 |    -43.33 |    -11.63 |    0.9930 |
+#> |.....................|    -2.511 |    0.4656 |     7.949 |    -9.128 |
+#> |.....................|    -7.427 |    -7.608 |...........|...........|
+#> |   31|     482.28997 |    0.9991 |    -1.014 |   -0.9110 |   -0.8957 |
+#> |.....................|   -0.8444 |   -0.5346 |   -0.7968 |   -0.8735 |
+#> |.....................|   -0.8759 |   -0.8822 |   -0.9253 |   -0.8017 |
+#> |.....................|   -0.8168 |   -0.8141 |...........|...........|
+#> |    U|     482.28997 |     93.03 |    -5.317 |   -0.9438 |   -0.1079 |
+#> |.....................|     2.292 |     1.353 |   0.03148 |     1.157 |
+#> |.....................|   0.03029 |    0.7538 |    0.8294 |     1.293 |
+#> |.....................|     1.126 |     1.132 |...........|...........|
+#> |    X|     482.28997 |     93.03 |  0.004906 |    0.2801 |    0.8977 |
+#> |.....................|     9.896 |     1.353 |   0.03148 |     1.157 |
+#> |.....................|   0.03029 |    0.7538 |    0.8294 |     1.293 |
+#> |.....................|     1.126 |     1.132 |...........|...........|
+#> |    F| Forward Diff. |     55.95 |     2.054 |    0.1106 |    0.2465 |
+#> |.....................|  -0.05340 |    -42.18 |    -10.21 |    0.8261 |
+#> |.....................|    -2.234 |    0.9104 |     5.096 |    -9.114 |
+#> |.....................|    -7.334 |    -7.590 |...........|...........|
+#> |   32|     481.60550 |    0.9915 |    -1.015 |   -0.9110 |   -0.8958 |
+#> |.....................|   -0.8443 |   -0.5217 |   -0.7945 |   -0.8740 |
+#> |.....................|   -0.8749 |   -0.8824 |   -0.9274 |   -0.7984 |
+#> |.....................|   -0.8142 |   -0.8115 |...........|...........|
+#> |    U|      481.6055 |     92.33 |    -5.318 |   -0.9438 |   -0.1080 |
+#> |.....................|     2.292 |     1.361 |   0.03151 |     1.156 |
+#> |.....................|   0.03031 |    0.7536 |    0.8276 |     1.297 |
+#> |.....................|     1.129 |     1.135 |...........|...........|
+#> |    X|      481.6055 |     92.33 |  0.004902 |    0.2801 |    0.8977 |
+#> |.....................|     9.897 |     1.361 |   0.03151 |     1.156 |
+#> |.....................|   0.03031 |    0.7536 |    0.8276 |     1.297 |
+#> |.....................|     1.129 |     1.135 |...........|...........|
+#> |    F| Forward Diff. |    -45.82 |     1.973 |   -0.1624 |    0.1674 |
+#> |.....................|   -0.3387 |    -41.15 |    -10.74 |     1.410 |
+#> |.....................|    -2.130 |    0.6088 |     4.422 |    -8.852 |
+#> |.....................|    -7.186 |    -7.335 |...........|...........|
+#> |   33|     480.97343 |    0.9986 |    -1.016 |   -0.9110 |   -0.8959 |
+#> |.....................|   -0.8442 |   -0.5084 |   -0.7922 |   -0.8748 |
+#> |.....................|   -0.8740 |   -0.8826 |   -0.9278 |   -0.7950 |
+#> |.....................|   -0.8114 |   -0.8088 |...........|...........|
+#> |    U|     480.97343 |     92.98 |    -5.319 |   -0.9438 |   -0.1081 |
+#> |.....................|     2.292 |     1.368 |   0.03155 |     1.156 |
+#> |.....................|   0.03032 |    0.7534 |    0.8272 |     1.301 |
+#> |.....................|     1.132 |     1.138 |...........|...........|
+#> |    X|     480.97343 |     92.98 |  0.004897 |    0.2801 |    0.8976 |
+#> |.....................|     9.898 |     1.368 |   0.03155 |     1.156 |
+#> |.....................|   0.03032 |    0.7534 |    0.8272 |     1.301 |
+#> |.....................|     1.132 |     1.138 |...........|...........|
+#> |    F| Forward Diff. |     47.76 |     2.024 |   0.09167 |    0.2404 |
+#> |.....................|  -0.07393 |    -40.22 |    -9.470 |     1.031 |
+#> |.....................|    -2.098 |    0.8752 |     6.346 |    -8.797 |
+#> |.....................|    -7.089 |    -7.296 |...........|...........|
+#> |   34|     480.33235 |    0.9916 |    -1.017 |   -0.9110 |   -0.8960 |
+#> |.....................|   -0.8441 |   -0.4952 |   -0.7903 |   -0.8757 |
+#> |.....................|   -0.8731 |   -0.8830 |   -0.9294 |   -0.7914 |
+#> |.....................|   -0.8086 |   -0.8060 |...........|...........|
+#> |    U|     480.33235 |     92.33 |    -5.320 |   -0.9438 |   -0.1082 |
+#> |.....................|     2.292 |     1.376 |   0.03158 |     1.155 |
+#> |.....................|   0.03033 |    0.7532 |    0.8258 |     1.306 |
+#> |.....................|     1.135 |     1.141 |...........|...........|
+#> |    X|     480.33235 |     92.33 |  0.004893 |    0.2801 |    0.8975 |
+#> |.....................|     9.899 |     1.376 |   0.03158 |     1.155 |
+#> |.....................|   0.03033 |    0.7532 |    0.8258 |     1.306 |
+#> |.....................|     1.135 |     1.141 |...........|...........|
+#> |    F| Forward Diff. |    -44.82 |     1.956 |   -0.1640 |    0.1653 |
+#> |.....................|   -0.3374 |    -39.36 |    -9.982 |     1.432 |
+#> |.....................|    -2.136 |    0.6770 |     5.747 |    -8.552 |
+#> |.....................|    -6.943 |    -7.038 |...........|...........|
+#> |   35|     479.71253 |    0.9984 |    -1.018 |   -0.9110 |   -0.8961 |
+#> |.....................|   -0.8439 |   -0.4821 |   -0.7885 |   -0.8768 |
+#> |.....................|   -0.8721 |   -0.8833 |   -0.9319 |   -0.7879 |
+#> |.....................|   -0.8057 |   -0.8033 |...........|...........|
+#> |    U|     479.71253 |     92.97 |    -5.321 |   -0.9438 |   -0.1083 |
+#> |.....................|     2.293 |     1.384 |   0.03160 |     1.155 |
+#> |.....................|   0.03035 |    0.7529 |    0.8236 |     1.310 |
+#> |.....................|     1.138 |     1.144 |...........|...........|
+#> |    X|     479.71253 |     92.97 |  0.004888 |    0.2801 |    0.8974 |
+#> |.....................|     9.901 |     1.384 |   0.03160 |     1.155 |
+#> |.....................|   0.03035 |    0.7529 |    0.8236 |     1.310 |
+#> |.....................|     1.138 |     1.144 |...........|...........|
+#> |    F| Forward Diff. |     45.27 |     2.001 |   0.09802 |    0.2411 |
+#> |.....................|  -0.07361 |    -39.48 |    -9.147 |    0.2467 |
+#> |.....................|    -2.886 |    0.4583 |     7.836 |    -8.475 |
+#> |.....................|    -6.831 |    -7.001 |...........|...........|
+#> |   36|     479.08241 |    0.9920 |    -1.019 |   -0.9110 |   -0.8962 |
+#> |.....................|   -0.8438 |   -0.4691 |   -0.7871 |   -0.8771 |
+#> |.....................|   -0.8704 |   -0.8833 |   -0.9359 |   -0.7844 |
+#> |.....................|   -0.8029 |   -0.8006 |...........|...........|
+#> |    U|     479.08241 |     92.37 |    -5.322 |   -0.9438 |   -0.1084 |
+#> |.....................|     2.293 |     1.391 |   0.03163 |     1.155 |
+#> |.....................|   0.03037 |    0.7529 |    0.8201 |     1.314 |
+#> |.....................|     1.141 |     1.147 |...........|...........|
+#> |    X|     479.08241 |     92.37 |  0.004883 |    0.2801 |    0.8973 |
+#> |.....................|     9.902 |     1.391 |   0.03163 |     1.155 |
+#> |.....................|   0.03037 |    0.7529 |    0.8201 |     1.314 |
+#> |.....................|     1.141 |     1.147 |...........|...........|
+#> |    F| Forward Diff. |    -39.48 |     1.926 |   -0.1378 |    0.1752 |
+#> |.....................|   -0.3206 |    -38.45 |    -9.498 |    0.8453 |
+#> |.....................|    -2.699 |    0.3871 |     5.589 |    -8.242 |
+#> |.....................|    -6.674 |    -6.762 |...........|...........|
+#> |   37|     478.53604 |    0.9990 |    -1.019 |   -0.9110 |   -0.8964 |
+#> |.....................|   -0.8437 |   -0.4561 |   -0.7854 |   -0.8772 |
+#> |.....................|   -0.8684 |   -0.8832 |   -0.9392 |   -0.7811 |
+#> |.....................|   -0.8002 |   -0.7981 |...........|...........|
+#> |    U|     478.53604 |     93.02 |    -5.323 |   -0.9438 |   -0.1085 |
+#> |.....................|     2.293 |     1.399 |   0.03165 |     1.155 |
+#> |.....................|   0.03040 |    0.7530 |    0.8172 |     1.318 |
+#> |.....................|     1.144 |     1.150 |...........|...........|
+#> |    X|     478.53604 |     93.02 |  0.004879 |    0.2801 |    0.8972 |
+#> |.....................|     9.903 |     1.399 |   0.03165 |     1.155 |
+#> |.....................|   0.03040 |    0.7530 |    0.8172 |     1.318 |
+#> |.....................|     1.144 |     1.150 |...........|...........|
+#> |    F| Forward Diff. |     52.06 |     1.969 |    0.1359 |    0.2508 |
+#> |.....................|  -0.04337 |    -37.95 |    -8.435 |    0.2680 |
+#> |.....................|    -2.930 |    0.5186 |     5.955 |    -8.188 |
+#> |.....................|    -6.576 |    -6.741 |...........|...........|
+#> |   38|     477.90297 |    0.9924 |    -1.021 |   -0.9111 |   -0.8965 |
+#> |.....................|   -0.8436 |   -0.4428 |   -0.7846 |   -0.8771 |
+#> |.....................|   -0.8659 |   -0.8830 |   -0.9416 |   -0.7776 |
+#> |.....................|   -0.7975 |   -0.7955 |...........|...........|
+#> |    U|     477.90297 |     92.41 |    -5.324 |   -0.9439 |   -0.1086 |
+#> |.....................|     2.293 |     1.406 |   0.03166 |     1.155 |
+#> |.....................|   0.03044 |    0.7531 |    0.8151 |     1.323 |
+#> |.....................|     1.147 |     1.152 |...........|...........|
+#> |    X|     477.90297 |     92.41 |  0.004873 |    0.2801 |    0.8971 |
+#> |.....................|     9.904 |     1.406 |   0.03166 |     1.155 |
+#> |.....................|   0.03044 |    0.7531 |    0.8151 |     1.323 |
+#> |.....................|     1.147 |     1.152 |...........|...........|
+#> |    F| Forward Diff. |    -35.48 |     1.900 |   -0.1171 |    0.1805 |
+#> |.....................|   -0.3013 |    -36.12 |    -8.554 |     1.521 |
+#> |.....................|    -2.082 |    0.5139 |     5.057 |    -7.934 |
+#> |.....................|    -6.421 |    -6.501 |...........|...........|
+#> |   39|     477.39487 |    0.9991 |    -1.022 |   -0.9111 |   -0.8966 |
+#> |.....................|   -0.8434 |   -0.4296 |   -0.7836 |   -0.8780 |
+#> |.....................|   -0.8642 |   -0.8831 |   -0.9436 |   -0.7740 |
+#> |.....................|   -0.7946 |   -0.7928 |...........|...........|
+#> |    U|     477.39487 |     93.04 |    -5.325 |   -0.9439 |   -0.1088 |
+#> |.....................|     2.293 |     1.414 |   0.03168 |     1.154 |
+#> |.....................|   0.03047 |    0.7531 |    0.8134 |     1.327 |
+#> |.....................|     1.150 |     1.155 |...........|...........|
+#> |    X|     477.39487 |     93.04 |  0.004868 |    0.2801 |    0.8969 |
+#> |.....................|     9.906 |     1.414 |   0.03168 |     1.154 |
+#> |.....................|   0.03047 |    0.7531 |    0.8134 |     1.327 |
+#> |.....................|     1.150 |     1.155 |...........|...........|
+#> |    F| Forward Diff. |     53.22 |     1.947 |    0.1564 |    0.2562 |
+#> |.....................|  -0.02756 |    -35.38 |    -7.440 |     1.129 |
+#> |.....................|    -2.109 |    0.8531 |     5.389 |    -7.888 |
+#> |.....................|    -6.311 |    -6.462 |...........|...........|
+#> |   40|     476.77835 |    0.9927 |    -1.023 |   -0.9112 |   -0.8968 |
+#> |.....................|   -0.8433 |   -0.4165 |   -0.7840 |   -0.8801 |
+#> |.....................|   -0.8630 |   -0.8835 |   -0.9455 |   -0.7699 |
+#> |.....................|   -0.7913 |   -0.7897 |...........|...........|
+#> |    U|     476.77835 |     92.44 |    -5.326 |   -0.9439 |   -0.1090 |
+#> |.....................|     2.293 |     1.422 |   0.03167 |     1.153 |
+#> |.....................|   0.03048 |    0.7527 |    0.8117 |     1.332 |
+#> |.....................|     1.153 |     1.159 |...........|...........|
+#> |    X|     476.77835 |     92.44 |  0.004861 |    0.2801 |    0.8968 |
+#> |.....................|     9.907 |     1.422 |   0.03167 |     1.153 |
+#> |.....................|   0.03048 |    0.7527 |    0.8117 |     1.332 |
+#> |.....................|     1.153 |     1.159 |...........|...........|
+#> |    F| Forward Diff. |    -31.48 |     1.878 |  -0.09989 |    0.1868 |
+#> |.....................|   -0.2862 |    -34.69 |    -7.934 |     1.303 |
+#> |.....................|    -2.230 |    0.5238 |     3.299 |    -7.623 |
+#> |.....................|    -6.137 |    -6.207 |...........|...........|
+#> |   41|     476.29140 |    0.9988 |    -1.024 |   -0.9112 |   -0.8970 |
+#> |.....................|   -0.8432 |   -0.4030 |   -0.7837 |   -0.8817 |
+#> |.....................|   -0.8615 |   -0.8839 |   -0.9453 |   -0.7660 |
+#> |.....................|   -0.7883 |   -0.7869 |...........|...........|
+#> |    U|      476.2914 |     93.01 |    -5.328 |   -0.9440 |   -0.1091 |
+#> |.....................|     2.293 |     1.430 |   0.03168 |     1.152 |
+#> |.....................|   0.03051 |    0.7524 |    0.8119 |     1.337 |
+#> |.....................|     1.157 |     1.162 |...........|...........|
+#> |    X|      476.2914 |     93.01 |  0.004855 |    0.2801 |    0.8966 |
+#> |.....................|     9.909 |     1.430 |   0.03168 |     1.152 |
+#> |.....................|   0.03051 |    0.7524 |    0.8119 |     1.337 |
+#> |.....................|     1.157 |     1.162 |...........|...........|
+#> |    F| Forward Diff. |     48.73 |     1.930 |    0.1514 |    0.2545 |
+#> |.....................|  -0.03521 |    -34.01 |    -6.934 |     1.004 |
+#> |.....................|    -2.133 |    0.7968 |     5.252 |    -7.528 |
+#> |.....................|    -6.021 |    -6.137 |...........|...........|
+#> |   42|     475.72593 |    0.9927 |    -1.026 |   -0.9113 |   -0.8972 |
+#> |.....................|   -0.8430 |   -0.3897 |   -0.7848 |   -0.8834 |
+#> |.....................|   -0.8598 |   -0.8844 |   -0.9451 |   -0.7619 |
+#> |.....................|   -0.7851 |   -0.7840 |...........|...........|
+#> |    U|     475.72593 |     92.44 |    -5.329 |   -0.9441 |   -0.1094 |
+#> |.....................|     2.294 |     1.437 |   0.03166 |     1.151 |
+#> |.....................|   0.03053 |    0.7521 |    0.8121 |     1.342 |
+#> |.....................|     1.160 |     1.165 |...........|...........|
+#> |    X|     475.72593 |     92.44 |  0.004847 |    0.2801 |    0.8964 |
+#> |.....................|     9.910 |     1.437 |   0.03166 |     1.151 |
+#> |.....................|   0.03053 |    0.7521 |    0.8121 |     1.342 |
+#> |.....................|     1.160 |     1.165 |...........|...........|
+#> |    F| Forward Diff. |    -31.62 |     1.868 |   -0.1026 |    0.1833 |
+#> |.....................|   -0.2884 |    -33.06 |    -7.282 |     1.547 |
+#> |.....................|    -2.194 |    0.5347 |     3.320 |    -7.249 |
+#> |.....................|    -5.852 |    -5.889 |...........|...........|
+#> |   43|     475.25217 |    0.9986 |    -1.027 |   -0.9113 |   -0.8974 |
+#> |.....................|   -0.8428 |   -0.3762 |   -0.7856 |   -0.8854 |
+#> |.....................|   -0.8580 |   -0.8849 |   -0.9453 |   -0.7580 |
+#> |.....................|   -0.7821 |   -0.7812 |...........|...........|
+#> |    U|     475.25217 |     92.99 |    -5.331 |   -0.9441 |   -0.1096 |
+#> |.....................|     2.294 |     1.445 |   0.03165 |     1.150 |
+#> |.....................|   0.03056 |    0.7517 |    0.8119 |     1.346 |
+#> |.....................|     1.163 |     1.168 |...........|...........|
+#> |    X|     475.25217 |     92.99 |  0.004840 |    0.2801 |    0.8962 |
+#> |.....................|     9.912 |     1.445 |   0.03165 |     1.150 |
+#> |.....................|   0.03056 |    0.7517 |    0.8119 |     1.346 |
+#> |.....................|     1.163 |     1.168 |...........|...........|
+#> |    F| Forward Diff. |     45.01 |     1.918 |    0.1424 |    0.2472 |
+#> |.....................|  -0.04139 |    -32.61 |    -6.424 |    0.9161 |
+#> |.....................|    -2.151 |    0.6354 |     5.209 |    -7.174 |
+#> |.....................|    -5.746 |    -5.822 |...........|...........|
+#> |   44|     474.72079 |    0.9929 |    -1.029 |   -0.9114 |   -0.8977 |
+#> |.....................|   -0.8427 |   -0.3629 |   -0.7879 |   -0.8876 |
+#> |.....................|   -0.8559 |   -0.8852 |   -0.9458 |   -0.7541 |
+#> |.....................|   -0.7790 |   -0.7785 |...........|...........|
+#> |    U|     474.72079 |     92.46 |    -5.333 |   -0.9442 |   -0.1098 |
+#> |.....................|     2.294 |     1.453 |   0.03161 |     1.149 |
+#> |.....................|   0.03059 |    0.7515 |    0.8114 |     1.351 |
+#> |.....................|     1.167 |     1.171 |...........|...........|
+#> |    X|     474.72079 |     92.46 |  0.004831 |    0.2800 |    0.8960 |
+#> |.....................|     9.913 |     1.453 |   0.03161 |     1.149 |
+#> |.....................|   0.03059 |    0.7515 |    0.8114 |     1.351 |
+#> |.....................|     1.167 |     1.171 |...........|...........|
+#> |    F| Forward Diff. |    -29.98 |     1.856 |  -0.09377 |    0.1852 |
+#> |.....................|   -0.2753 |    -32.15 |    -6.889 |     1.072 |
+#> |.....................|    -2.266 |    0.4091 |     3.274 |    -6.876 |
+#> |.....................|    -5.564 |    -5.585 |...........|...........|
+#> |   45|     474.26379 |    0.9985 |    -1.031 |   -0.9115 |   -0.8979 |
+#> |.....................|   -0.8425 |   -0.3491 |   -0.7895 |   -0.8887 |
+#> |.....................|   -0.8536 |   -0.8852 |   -0.9464 |   -0.7506 |
+#> |.....................|   -0.7762 |   -0.7761 |...........|...........|
+#> |    U|     474.26379 |     92.98 |    -5.335 |   -0.9443 |   -0.1101 |
+#> |.....................|     2.294 |     1.461 |   0.03159 |     1.148 |
+#> |.....................|   0.03063 |    0.7515 |    0.8109 |     1.355 |
+#> |.....................|     1.170 |     1.173 |...........|...........|
+#> |    X|     474.26379 |     92.98 |  0.004822 |    0.2800 |    0.8958 |
+#> |.....................|     9.915 |     1.461 |   0.03159 |     1.148 |
+#> |.....................|   0.03063 |    0.7515 |    0.8109 |     1.355 |
+#> |.....................|     1.170 |     1.173 |...........|...........|
+#> |    F| Forward Diff. |     42.78 |     1.902 |    0.1464 |    0.2388 |
+#> |.....................|  -0.03417 |    -31.28 |    -5.931 |    0.8375 |
+#> |.....................|    -2.202 |    0.7305 |     5.128 |    -6.841 |
+#> |.....................|    -5.479 |    -5.554 |...........|...........|
+#> |   46|     473.76810 |    0.9929 |    -1.033 |   -0.9117 |   -0.8982 |
+#> |.....................|   -0.8424 |   -0.3358 |   -0.7928 |   -0.8897 |
+#> |.....................|   -0.8508 |   -0.8855 |   -0.9473 |   -0.7471 |
+#> |.....................|   -0.7734 |   -0.7737 |...........|...........|
+#> |    U|      473.7681 |     92.46 |    -5.337 |   -0.9444 |   -0.1104 |
+#> |.....................|     2.294 |     1.469 |   0.03154 |     1.147 |
+#> |.....................|   0.03067 |    0.7512 |    0.8101 |     1.360 |
+#> |.....................|     1.173 |     1.176 |...........|...........|
+#> |    X|      473.7681 |     92.46 |  0.004812 |    0.2800 |    0.8955 |
+#> |.....................|     9.917 |     1.469 |   0.03154 |     1.147 |
+#> |.....................|   0.03067 |    0.7512 |    0.8101 |     1.360 |
+#> |.....................|     1.173 |     1.176 |...........|...........|
+#> |    F| Forward Diff. |    -30.83 |     1.832 |   -0.1003 |    0.1743 |
+#> |.....................|   -0.2686 |    -30.77 |    -6.362 |     1.107 |
+#> |.....................|    -2.234 |    0.4249 |     4.678 |    -6.593 |
+#> |.....................|    -5.329 |    -5.340 |...........|...........|
+#> |   47|     473.32508 |    0.9983 |    -1.035 |   -0.9117 |   -0.8984 |
+#> |.....................|   -0.8422 |   -0.3229 |   -0.7959 |   -0.8909 |
+#> |.....................|   -0.8482 |   -0.8859 |   -0.9520 |   -0.7438 |
+#> |.....................|   -0.7708 |   -0.7715 |...........|...........|
+#> |    U|     473.32508 |     92.96 |    -5.339 |   -0.9445 |   -0.1106 |
+#> |.....................|     2.294 |     1.476 |   0.03149 |     1.147 |
+#> |.....................|   0.03071 |    0.7509 |    0.8061 |     1.364 |
+#> |.....................|     1.175 |     1.178 |...........|...........|
+#> |    X|     473.32508 |     92.96 |  0.004802 |    0.2800 |    0.8953 |
+#> |.....................|     9.918 |     1.476 |   0.03149 |     1.147 |
+#> |.....................|   0.03071 |    0.7509 |    0.8061 |     1.364 |
+#> |.....................|     1.175 |     1.178 |...........|...........|
+#> |    F| Forward Diff. |     38.19 |     1.865 |    0.1554 |    0.2504 |
+#> |.....................|  -0.02116 |    -30.15 |    -5.522 |    0.8218 |
+#> |.....................|    -2.215 |    0.6878 |     4.772 |    -6.537 |
+#> |.....................|    -5.232 |    -5.315 |...........|...........|
+#> |   48|     472.87290 |    0.9930 |    -1.038 |   -0.9119 |   -0.8988 |
+#> |.....................|   -0.8421 |   -0.3103 |   -0.8002 |   -0.8921 |
+#> |.....................|   -0.8451 |   -0.8864 |   -0.9564 |   -0.7407 |
+#> |.....................|   -0.7684 |   -0.7695 |...........|...........|
+#> |    U|      472.8729 |     92.47 |    -5.341 |   -0.9447 |   -0.1109 |
+#> |.....................|     2.294 |     1.483 |   0.03143 |     1.146 |
+#> |.....................|   0.03075 |    0.7506 |    0.8022 |     1.367 |
+#> |.....................|     1.178 |     1.180 |...........|...........|
+#> |    X|      472.8729 |     92.47 |  0.004791 |    0.2800 |    0.8950 |
+#> |.....................|     9.919 |     1.483 |   0.03143 |     1.146 |
+#> |.....................|   0.03075 |    0.7506 |    0.8022 |     1.367 |
+#> |.....................|     1.178 |     1.180 |...........|...........|
+#> |    F| Forward Diff. |    -31.43 |     1.786 |  -0.07853 |    0.1828 |
+#> |.....................|   -0.2451 |    -29.69 |    -5.937 |     1.129 |
+#> |.....................|    -2.237 |    0.5225 |     4.143 |    -6.356 |
+#> |.....................|    -5.097 |    -5.139 |...........|...........|
+#> |   49|     472.45068 |    0.9981 |    -1.040 |   -0.9121 |   -0.8991 |
+#> |.....................|   -0.8421 |   -0.2974 |   -0.8046 |   -0.8935 |
+#> |.....................|   -0.8420 |   -0.8871 |   -0.9597 |   -0.7375 |
+#> |.....................|   -0.7660 |   -0.7674 |...........|...........|
+#> |    U|     472.45068 |     92.94 |    -5.343 |   -0.9449 |   -0.1112 |
+#> |.....................|     2.294 |     1.491 |   0.03136 |     1.145 |
+#> |.....................|   0.03080 |    0.7500 |    0.7993 |     1.371 |
+#> |.....................|     1.180 |     1.183 |...........|...........|
+#> |    X|     472.45068 |     92.94 |  0.004780 |    0.2799 |    0.8947 |
+#> |.....................|     9.919 |     1.491 |   0.03136 |     1.145 |
+#> |.....................|   0.03080 |    0.7500 |    0.7993 |     1.371 |
+#> |.....................|     1.180 |     1.183 |...........|...........|
+#> |    F| Forward Diff. |     34.69 |     1.825 |    0.1712 |    0.2558 |
+#> |.....................| 0.0008262 |    -30.15 |    -5.461 |   0.02383 |
+#> |.....................|    -3.011 |    0.3236 |     4.609 |    -6.242 |
+#> |.....................|    -4.997 |    -5.107 |...........|...........|
+#> |   50|     472.02915 |    0.9936 |    -1.042 |   -0.9125 |   -0.8995 |
+#> |.....................|   -0.8422 |   -0.2847 |   -0.8092 |   -0.8923 |
+#> |.....................|   -0.8364 |   -0.8868 |   -0.9626 |   -0.7353 |
+#> |.....................|   -0.7644 |   -0.7660 |...........|...........|
+#> |    U|     472.02915 |     92.52 |    -5.345 |   -0.9452 |   -0.1116 |
+#> |.....................|     2.294 |     1.498 |   0.03129 |     1.146 |
+#> |.....................|   0.03088 |    0.7503 |    0.7968 |     1.374 |
+#> |.....................|     1.182 |     1.184 |...........|...........|
+#> |    X|     472.02915 |     92.52 |  0.004770 |    0.2799 |    0.8944 |
+#> |.....................|     9.918 |     1.498 |   0.03129 |     1.146 |
+#> |.....................|   0.03088 |    0.7503 |    0.7968 |     1.374 |
+#> |.....................|     1.182 |     1.184 |...........|...........|
+#> |    F| Forward Diff. |    -26.29 |     1.758 |  -0.04843 |    0.1910 |
+#> |.....................|   -0.1997 |    -28.69 |    -5.506 |     1.097 |
+#> |.....................|    -2.285 |    0.4947 |     2.297 |    -6.079 |
+#> |.....................|    -4.892 |    -4.970 |...........|...........|
+#> |   51|     471.69520 |    0.9992 |    -1.044 |   -0.9127 |   -0.8998 |
+#> |.....................|   -0.8423 |   -0.2715 |   -0.8127 |   -0.8918 |
+#> |.....................|   -0.8317 |   -0.8866 |   -0.9606 |   -0.7330 |
+#> |.....................|   -0.7627 |   -0.7642 |...........|...........|
+#> |    U|      471.6952 |     93.04 |    -5.347 |   -0.9454 |   -0.1120 |
+#> |.....................|     2.294 |     1.506 |   0.03124 |     1.146 |
+#> |.....................|   0.03096 |    0.7504 |    0.7985 |     1.377 |
+#> |.....................|     1.184 |     1.186 |...........|...........|
+#> |    X|      471.6952 |     93.04 |  0.004761 |    0.2798 |    0.8941 |
+#> |.....................|     9.917 |     1.506 |   0.03124 |     1.146 |
+#> |.....................|   0.03096 |    0.7504 |    0.7985 |     1.377 |
+#> |.....................|     1.184 |     1.186 |...........|...........|
+#> |    F| Forward Diff. |     46.70 |     1.815 |    0.2108 |    0.2607 |
+#> |.....................|   0.05766 |    -27.95 |    -4.639 |    0.9041 |
+#> |.....................|    -2.201 |    0.7590 |     4.326 |    -6.078 |
+#> |.....................|    -4.851 |    -4.972 |...........|...........|
+#> |   52|     471.30240 |    0.9939 |    -1.046 |   -0.9131 |   -0.9002 |
+#> |.....................|   -0.8425 |   -0.2596 |   -0.8187 |   -0.8939 |
+#> |.....................|   -0.8280 |   -0.8876 |   -0.9571 |   -0.7302 |
+#> |.....................|   -0.7606 |   -0.7622 |...........|...........|
+#> |    U|      471.3024 |     92.55 |    -5.350 |   -0.9458 |   -0.1124 |
+#> |.....................|     2.294 |     1.513 |   0.03115 |     1.145 |
+#> |.....................|   0.03101 |    0.7497 |    0.8016 |     1.380 |
+#> |.....................|     1.186 |     1.188 |...........|...........|
+#> |    X|      471.3024 |     92.55 |  0.004750 |    0.2797 |    0.8937 |
+#> |.....................|     9.915 |     1.513 |   0.03115 |     1.145 |
+#> |.....................|   0.03101 |    0.7497 |    0.8016 |     1.380 |
+#> |.....................|     1.186 |     1.188 |...........|...........|
+#> |    F| Forward Diff. |    -23.61 |     1.763 |  -0.06060 |    0.1836 |
+#> |.....................|   -0.1912 |    -28.31 |    -5.279 |    0.6597 |
+#> |.....................|    -2.739 |    0.2048 |     5.941 |    -5.864 |
+#> |.....................|    -4.747 |    -4.787 |...........|...........|
+#> |   53|     470.94339 |    0.9985 |    -1.048 |   -0.9133 |   -0.9006 |
+#> |.....................|   -0.8426 |   -0.2476 |   -0.8235 |   -0.8946 |
+#> |.....................|   -0.8237 |   -0.8877 |   -0.9629 |   -0.7278 |
+#> |.....................|   -0.7587 |   -0.7604 |...........|...........|
+#> |    U|     470.94339 |     92.98 |    -5.352 |   -0.9460 |   -0.1127 |
+#> |.....................|     2.294 |     1.520 |   0.03108 |     1.145 |
+#> |.....................|   0.03108 |    0.7496 |    0.7965 |     1.383 |
+#> |.....................|     1.188 |     1.190 |...........|...........|
+#> |    X|     470.94339 |     92.98 |  0.004740 |    0.2797 |    0.8934 |
+#> |.....................|     9.914 |     1.520 |   0.03108 |     1.145 |
+#> |.....................|   0.03108 |    0.7496 |    0.7965 |     1.383 |
+#> |.....................|     1.188 |     1.190 |...........|...........|
+#> |    F| Forward Diff. |     36.04 |     1.791 |    0.1836 |    0.2544 |
+#> |.....................|   0.04274 |    -27.03 |    -4.370 |    0.9159 |
+#> |.....................|    -2.217 |    0.6791 |     4.141 |    -5.840 |
+#> |.....................|    -4.667 |    -4.764 |...........|...........|
+#> |   54|     470.60274 |    0.9931 |    -1.051 |   -0.9136 |   -0.9010 |
+#> |.....................|   -0.8428 |   -0.2366 |   -0.8300 |   -0.8957 |
+#> |.....................|   -0.8190 |   -0.8879 |   -0.9681 |   -0.7257 |
+#> |.....................|   -0.7570 |   -0.7588 |...........|...........|
+#> |    U|     470.60274 |     92.48 |    -5.354 |   -0.9463 |   -0.1131 |
+#> |.....................|     2.294 |     1.526 |   0.03098 |     1.144 |
+#> |.....................|   0.03115 |    0.7494 |    0.7919 |     1.386 |
+#> |.....................|     1.190 |     1.192 |...........|...........|
+#> |    X|     470.60274 |     92.48 |  0.004728 |    0.2796 |    0.8930 |
+#> |.....................|     9.912 |     1.526 |   0.03098 |     1.144 |
+#> |.....................|   0.03115 |    0.7494 |    0.7919 |     1.386 |
+#> |.....................|     1.190 |     1.192 |...........|...........|
+#> |    F| Forward Diff. |    -35.91 |     1.718 |  -0.07847 |    0.1786 |
+#> |.....................|   -0.1996 |    -26.69 |    -4.843 |     1.231 |
+#> |.....................|    -2.229 |    0.5625 |     3.489 |    -5.662 |
+#> |.....................|    -4.557 |    -4.604 |...........|...........|
+#> |   55|     470.25392 |    0.9977 |    -1.054 |   -0.9140 |   -0.9015 |
+#> |.....................|   -0.8431 |   -0.2250 |   -0.8375 |   -0.8987 |
+#> |.....................|   -0.8153 |   -0.8894 |   -0.9673 |   -0.7229 |
+#> |.....................|   -0.7550 |   -0.7569 |...........|...........|
+#> |    U|     470.25392 |     92.90 |    -5.357 |   -0.9467 |   -0.1136 |
+#> |.....................|     2.293 |     1.533 |   0.03087 |     1.142 |
+#> |.....................|   0.03120 |    0.7483 |    0.7927 |     1.389 |
+#> |.....................|     1.192 |     1.194 |...........|...........|
+#> |    X|     470.25392 |     92.90 |  0.004715 |    0.2796 |    0.8926 |
+#> |.....................|     9.909 |     1.533 |   0.03087 |     1.142 |
+#> |.....................|   0.03120 |    0.7483 |    0.7927 |     1.389 |
+#> |.....................|     1.192 |     1.194 |...........|...........|
+#> |    F| Forward Diff. |     23.42 |     1.753 |    0.1414 |    0.2393 |
+#> |.....................|   0.01691 |    -26.51 |    -4.262 |    0.6993 |
+#> |.....................|    -2.408 |    0.5525 |     2.318 |    -5.573 |
+#> |.....................|    -4.475 |    -4.572 |...........|...........|
+#> |   56|     469.96066 |    0.9934 |    -1.056 |   -0.9144 |   -0.9019 |
+#> |.....................|   -0.8434 |   -0.2128 |   -0.8432 |   -0.9002 |
+#> |.....................|   -0.8113 |   -0.8903 |   -0.9627 |   -0.7205 |
+#> |.....................|   -0.7531 |   -0.7551 |...........|...........|
+#> |    U|     469.96066 |     92.50 |    -5.359 |   -0.9470 |   -0.1140 |
+#> |.....................|     2.293 |     1.540 |   0.03078 |     1.141 |
+#> |.....................|   0.03126 |    0.7476 |    0.7967 |     1.392 |
+#> |.....................|     1.194 |     1.196 |...........|...........|
+#> |    X|     469.96066 |     92.50 |  0.004704 |    0.2795 |    0.8922 |
+#> |.....................|     9.906 |     1.540 |   0.03078 |     1.141 |
+#> |.....................|   0.03126 |    0.7476 |    0.7967 |     1.392 |
+#> |.....................|     1.194 |     1.196 |...........|...........|
+#> |    F| Forward Diff. |    -33.10 |     1.713 |  -0.09549 |    0.1710 |
+#> |.....................|   -0.1943 |    -25.89 |    -4.557 |     1.045 |
+#> |.....................|    -2.243 |    0.5648 |     3.834 |    -5.392 |
+#> |.....................|    -4.399 |    -4.402 |...........|...........|
+#> |   57|     469.66426 |    0.9983 |    -1.059 |   -0.9147 |   -0.9023 |
+#> |.....................|   -0.8437 |   -0.2012 |   -0.8503 |   -0.9014 |
+#> |.....................|   -0.8068 |   -0.8914 |   -0.9589 |   -0.7186 |
+#> |.....................|   -0.7515 |   -0.7537 |...........|...........|
+#> |    U|     469.66426 |     92.95 |    -5.362 |   -0.9473 |   -0.1144 |
+#> |.....................|     2.293 |     1.547 |   0.03068 |     1.141 |
+#> |.....................|   0.03133 |    0.7468 |    0.8000 |     1.394 |
+#> |.....................|     1.196 |     1.197 |...........|...........|
+#> |    X|     469.66426 |     92.95 |  0.004691 |    0.2794 |    0.8919 |
+#> |.....................|     9.903 |     1.547 |   0.03068 |     1.141 |
+#> |.....................|   0.03133 |    0.7468 |    0.8000 |     1.394 |
+#> |.....................|     1.196 |     1.197 |...........|...........|
+#> |    F| Forward Diff. |     29.48 |     1.769 |    0.1441 |    0.2362 |
+#> |.....................|   0.03493 |    -25.40 |    -3.876 |    0.7581 |
+#> |.....................|    -2.246 |    0.6653 |     4.370 |    -5.362 |
+#> |.....................|    -4.340 |    -4.389 |...........|...........|
+#> |   58|     469.35361 |    0.9940 |    -1.062 |   -0.9149 |   -0.9027 |
+#> |.....................|   -0.8440 |   -0.1900 |   -0.8585 |   -0.9032 |
+#> |.....................|   -0.8026 |   -0.8931 |   -0.9615 |   -0.7168 |
+#> |.....................|   -0.7497 |   -0.7523 |...........|...........|
+#> |    U|     469.35361 |     92.56 |    -5.365 |   -0.9475 |   -0.1149 |
+#> |.....................|     2.293 |     1.553 |   0.03055 |     1.140 |
+#> |.....................|   0.03139 |    0.7454 |    0.7977 |     1.396 |
+#> |.....................|     1.198 |     1.199 |...........|...........|
+#> |    X|     469.35361 |     92.56 |  0.004677 |    0.2794 |    0.8915 |
+#> |.....................|     9.900 |     1.553 |   0.03055 |     1.140 |
+#> |.....................|   0.03139 |    0.7454 |    0.7977 |     1.396 |
+#> |.....................|     1.198 |     1.199 |...........|...........|
+#> |    F| Forward Diff. |    -26.71 |     1.702 |  -0.07338 |    0.1729 |
+#> |.....................|   -0.1601 |    -26.00 |    -4.465 |    0.4354 |
+#> |.....................|    -2.821 |    0.3110 |     5.728 |    -5.228 |
+#> |.....................|    -4.240 |    -4.266 |...........|...........|
+#> |   59|     469.04262 |    0.9978 |    -1.064 |   -0.9151 |   -0.9031 |
+#> |.....................|   -0.8443 |   -0.1798 |   -0.8657 |   -0.9030 |
+#> |.....................|   -0.7971 |   -0.8938 |   -0.9685 |   -0.7157 |
+#> |.....................|   -0.7487 |   -0.7515 |...........|...........|
+#> |    U|     469.04262 |     92.91 |    -5.368 |   -0.9477 |   -0.1152 |
+#> |.....................|     2.292 |     1.559 |   0.03044 |     1.140 |
+#> |.....................|   0.03147 |    0.7450 |    0.7916 |     1.398 |
+#> |.....................|     1.199 |     1.200 |...........|...........|
+#> |    X|     469.04262 |     92.91 |  0.004665 |    0.2794 |    0.8912 |
+#> |.....................|     9.897 |     1.559 |   0.03044 |     1.140 |
+#> |.....................|   0.03147 |    0.7450 |    0.7916 |     1.398 |
+#> |.....................|     1.199 |     1.200 |...........|...........|
+#> |   60|     468.78438 |    0.9975 |    -1.068 |   -0.9154 |   -0.9036 |
+#> |.....................|   -0.8447 |   -0.1709 |   -0.8764 |   -0.9025 |
+#> |.....................|   -0.7900 |   -0.8946 |   -0.9771 |   -0.7153 |
+#> |.....................|   -0.7482 |   -0.7514 |...........|...........|
+#> |    U|     468.78438 |     92.88 |    -5.371 |   -0.9479 |   -0.1157 |
+#> |.....................|     2.292 |     1.564 |   0.03028 |     1.140 |
+#> |.....................|   0.03158 |    0.7443 |    0.7841 |     1.398 |
+#> |.....................|     1.200 |     1.200 |...........|...........|
+#> |    X|     468.78438 |     92.88 |  0.004649 |    0.2793 |    0.8907 |
+#> |.....................|     9.893 |     1.564 |   0.03028 |     1.140 |
+#> |.....................|   0.03158 |    0.7443 |    0.7841 |     1.398 |
+#> |.....................|     1.200 |     1.200 |...........|...........|
+#> |   61|     467.65199 |    0.9960 |    -1.083 |   -0.9167 |   -0.9058 |
+#> |.....................|   -0.8469 |   -0.1283 |   -0.9284 |   -0.9002 |
+#> |.....................|   -0.7560 |   -0.8987 |    -1.018 |   -0.7133 |
+#> |.....................|   -0.7456 |   -0.7506 |...........|...........|
+#> |    U|     467.65199 |     92.74 |    -5.387 |   -0.9492 |   -0.1179 |
+#> |.....................|     2.290 |     1.589 |   0.02950 |     1.141 |
+#> |.....................|   0.03209 |    0.7413 |    0.7481 |     1.401 |
+#> |.....................|     1.202 |     1.201 |...........|...........|
+#> |    X|     467.65199 |     92.74 |  0.004577 |    0.2791 |    0.8887 |
+#> |.....................|     9.872 |     1.589 |   0.02950 |     1.141 |
+#> |.....................|   0.03209 |    0.7413 |    0.7481 |     1.401 |
+#> |.....................|     1.202 |     1.201 |...........|...........|
+#> |   62|     464.96560 |    0.9898 |    -1.148 |   -0.9222 |   -0.9151 |
+#> |.....................|   -0.8556 |   0.04847 |    -1.144 |   -0.8910 |
+#> |.....................|   -0.6148 |   -0.9154 |    -1.189 |   -0.7051 |
+#> |.....................|   -0.7350 |   -0.7474 |...........|...........|
+#> |    U|      464.9656 |     92.17 |    -5.451 |   -0.9543 |   -0.1273 |
+#> |.....................|     2.281 |     1.691 |   0.02626 |     1.147 |
+#> |.....................|   0.03421 |    0.7285 |    0.5986 |     1.411 |
+#> |.....................|     1.214 |     1.204 |...........|...........|
+#> |    X|      464.9656 |     92.17 |  0.004291 |    0.2780 |    0.8805 |
+#> |.....................|     9.786 |     1.691 |   0.02626 |     1.147 |
+#> |.....................|   0.03421 |    0.7285 |    0.5986 |     1.411 |
+#> |.....................|     1.214 |     1.204 |...........|...........|
+#> |    F| Forward Diff. |    -134.9 |    0.8693 |    0.2607 |    0.2086 |
+#> |.....................|    0.2111 |    -19.53 |    -3.427 |     3.399 |
+#> |.....................|    -2.172 |     1.526 |    -11.79 |    -4.993 |
+#> |.....................|    -3.321 |    -4.659 |...........|...........|
+#> |   63|     458.88877 |     1.003 |    -1.235 |   -0.9465 |   -0.9328 |
+#> |.....................|   -0.8841 |    0.3192 |    -1.460 |   -0.9475 |
+#> |.....................|   -0.4237 |   -0.9768 |    -1.134 |   -0.6574 |
+#> |.....................|   -0.7075 |   -0.6995 |...........|...........|
+#> |    U|     458.88877 |     93.40 |    -5.538 |   -0.9774 |   -0.1450 |
+#> |.....................|     2.252 |     1.848 |   0.02152 |     1.114 |
+#> |.....................|   0.03709 |    0.6820 |    0.6469 |     1.468 |
+#> |.....................|     1.243 |     1.255 |...........|...........|
+#> |    X|     458.88877 |     93.40 |  0.003933 |    0.2734 |    0.8651 |
+#> |.....................|     9.511 |     1.848 |   0.02152 |     1.114 |
+#> |.....................|   0.03709 |    0.6820 |    0.6469 |     1.468 |
+#> |.....................|     1.243 |     1.255 |...........|...........|
+#> |   64|     455.19412 |     1.006 |    -1.330 |   -0.9732 |   -0.9522 |
+#> |.....................|   -0.9154 |    0.6143 |    -1.806 |    -1.009 |
+#> |.....................|   -0.2144 |    -1.044 |    -1.075 |   -0.6056 |
+#> |.....................|   -0.6776 |   -0.6473 |...........|...........|
+#> |    U|     455.19412 |     93.67 |    -5.634 |    -1.003 |   -0.1644 |
+#> |.....................|     2.221 |     2.019 |   0.01631 |     1.078 |
+#> |.....................|   0.04023 |    0.6311 |    0.6989 |     1.531 |
+#> |.....................|     1.275 |     1.311 |...........|...........|
+#> |    X|     455.19412 |     93.67 |  0.003576 |    0.2684 |    0.8484 |
+#> |.....................|     9.218 |     2.019 |   0.01631 |     1.078 |
+#> |.....................|   0.04023 |    0.6311 |    0.6989 |     1.531 |
+#> |.....................|     1.275 |     1.311 |...........|...........|
+#> |    F| Forward Diff. |     18.82 |    0.9889 |    -1.032 |   -0.1489 |
+#> |.....................|    0.2009 |    -8.117 |   -0.5123 |    0.1656 |
+#> |.....................|    -2.314 |    -3.473 |   -0.8284 |    0.3432 |
+#> |.....................|   -0.8357 |   0.04588 |...........|...........|
+#> |   65|     458.62552 |     1.004 |    -1.494 |   -0.8145 |   -0.9319 |
+#> |.....................|   -0.9630 |     1.033 |    -2.192 |    -1.036 |
+#> |.....................|    0.2529 |   -0.5036 |   -0.8838 |   -0.8679 |
+#> |.....................|   -0.7178 |   -0.8209 |...........|...........|
+#> |    U|     458.62552 |     93.52 |    -5.797 |   -0.8527 |   -0.1440 |
+#> |.....................|     2.174 |     2.262 |   0.01051 |     1.062 |
+#> |.....................|   0.04725 |     1.041 |    0.8656 |     1.213 |
+#> |.....................|     1.232 |     1.125 |...........|...........|
+#> |    X|     458.62552 |     93.52 |  0.003036 |    0.2989 |    0.8659 |
+#> |.....................|     8.789 |     2.262 |   0.01051 |     1.062 |
+#> |.....................|   0.04725 |     1.041 |    0.8656 |     1.213 |
+#> |.....................|     1.232 |     1.125 |...........|...........|
+#> |   66|     454.48694 |     1.003 |    -1.384 |   -0.9206 |   -0.9455 |
+#> |.....................|   -0.9312 |    0.7538 |    -1.934 |    -1.018 |
+#> |.....................|  -0.05956 |   -0.8649 |    -1.011 |   -0.6924 |
+#> |.....................|   -0.6908 |   -0.7048 |...........|...........|
+#> |    U|     454.48694 |     93.41 |    -5.688 |   -0.9529 |   -0.1576 |
+#> |.....................|     2.205 |     2.100 |   0.01439 |     1.073 |
+#> |.....................|   0.04256 |    0.7669 |    0.7542 |     1.426 |
+#> |.....................|     1.261 |     1.250 |...........|...........|
+#> |    X|     454.48694 |     93.41 |  0.003387 |    0.2783 |    0.8542 |
+#> |.....................|     9.074 |     2.100 |   0.01439 |     1.073 |
+#> |.....................|   0.04256 |    0.7669 |    0.7542 |     1.426 |
+#> |.....................|     1.261 |     1.250 |...........|...........|
+#> |    F| Forward Diff. |    -11.88 |    0.8805 |     1.030 | 0.0001663 |
+#> |.....................|   -0.3119 |    -6.748 |    -1.151 |    0.2517 |
+#> |.....................|    -3.379 |     3.981 |     5.317 |    -4.395 |
+#> |.....................|    -1.890 |    -2.785 |...........|...........|
+#> |   67|     453.47854 |     1.004 |    -1.455 |   -0.9097 |   -0.9308 |
+#> |.....................|   -0.9364 |    0.8078 |    -2.047 |    -1.046 |
+#> |.....................|    0.2383 |   -0.8443 |   -0.9977 |   -0.6524 |
+#> |.....................|   -0.6789 |   -0.6970 |...........|...........|
+#> |    U|     453.47854 |     93.48 |    -5.759 |   -0.9426 |   -0.1429 |
+#> |.....................|     2.200 |     2.132 |   0.01270 |     1.056 |
+#> |.....................|   0.04703 |    0.7825 |    0.7661 |     1.474 |
+#> |.....................|     1.274 |     1.258 |...........|...........|
+#> |    X|     453.47854 |     93.48 |  0.003156 |    0.2804 |    0.8668 |
+#> |.....................|     9.026 |     2.132 |   0.01270 |     1.056 |
+#> |.....................|   0.04703 |    0.7825 |    0.7661 |     1.474 |
+#> |.....................|     1.274 |     1.258 |...........|...........|
+#> |    F| Forward Diff. |    -7.580 |    0.7096 |     1.748 |    0.4450 |
+#> |.....................|   -0.3063 |    -5.686 |    -1.090 |     2.089 |
+#> |.....................|    -1.806 |     4.661 |     3.477 |    -2.550 |
+#> |.....................|    -1.063 |    -2.646 |...........|...........|
+#> |   68|     452.65869 |     1.010 |    -1.604 |   -0.9910 |   -0.9601 |
+#> |.....................|   -0.9321 |    0.9548 |    -2.236 |    -1.333 |
+#> |.....................|    0.7427 |   -0.9083 |    -1.017 |   -0.7899 |
+#> |.....................|   -0.7453 |   -0.6781 |...........|...........|
+#> |    U|     452.65869 |     94.06 |    -5.907 |    -1.019 |   -0.1723 |
+#> |.....................|     2.204 |     2.217 |  0.009851 |    0.8906 |
+#> |.....................|   0.05461 |    0.7340 |    0.7490 |     1.308 |
+#> |.....................|     1.203 |     1.278 |...........|...........|
+#> |    X|     452.65869 |     94.06 |  0.002719 |    0.2652 |    0.8418 |
+#> |.....................|     9.065 |     2.217 |  0.009851 |    0.8906 |
+#> |.....................|   0.05461 |    0.7340 |    0.7490 |     1.308 |
+#> |.....................|     1.203 |     1.278 |...........|...........|
+#> |    F| Forward Diff. |     87.74 |    0.4343 |   -0.7887 |   -0.2527 |
+#> |.....................|   -0.1232 |    -3.287 |   -0.3715 |    -5.728 |
+#> |.....................|    -3.469 |     4.620 |     5.104 |    -8.863 |
+#> |.....................|    -5.024 |    -1.180 |...........|...........|
+#> |   69|     455.46876 |     1.000 |    -1.721 |   -0.9929 |    -1.109 |
+#> |.....................|   -0.8905 |     1.109 |    -2.343 |    -1.386 |
+#> |.....................|     1.193 |    -1.162 |   -0.9750 |   -0.9277 |
+#> |.....................|   -0.5804 |   -0.9245 |...........|...........|
+#> |    U|     455.46876 |     93.13 |    -6.025 |    -1.021 |   -0.3216 |
+#> |.....................|     2.246 |     2.306 |  0.008241 |    0.8595 |
+#> |.....................|   0.06138 |    0.5417 |    0.7859 |     1.140 |
+#> |.....................|     1.379 |     1.014 |...........|...........|
+#> |    X|     455.46876 |     93.13 |  0.002419 |    0.2648 |    0.7250 |
+#> |.....................|     9.450 |     2.306 |  0.008241 |    0.8595 |
+#> |.....................|   0.06138 |    0.5417 |    0.7859 |     1.140 |
+#> |.....................|     1.379 |     1.014 |...........|...........|
+#> |   70|     453.13548 |    0.9926 |    -1.633 |   -0.9913 |   -0.9976 |
+#> |.....................|   -0.9216 |    0.9941 |    -2.263 |    -1.345 |
+#> |.....................|    0.8563 |   -0.9728 |    -1.008 |   -0.8230 |
+#> |.....................|   -0.7030 |   -0.7398 |...........|...........|
+#> |    U|     453.13548 |     92.43 |    -5.937 |    -1.020 |   -0.2097 |
+#> |.....................|     2.215 |     2.240 |  0.009448 |    0.8833 |
+#> |.....................|   0.05632 |    0.6851 |    0.7575 |     1.268 |
+#> |.....................|     1.248 |     1.212 |...........|...........|
+#> |    X|     453.13548 |     92.43 |  0.002640 |    0.2651 |    0.8108 |
+#> |.....................|     9.161 |     2.240 |  0.009448 |    0.8833 |
+#> |.....................|   0.05632 |    0.6851 |    0.7575 |     1.268 |
+#> |.....................|     1.248 |     1.212 |...........|...........|
+#> |   71|     453.54485 |    0.9910 |    -1.615 |   -0.9910 |   -0.9747 |
+#> |.....................|   -0.9280 |    0.9706 |    -2.247 |    -1.337 |
+#> |.....................|    0.7875 |   -0.9341 |    -1.014 |   -0.8015 |
+#> |.....................|   -0.7281 |   -0.7020 |...........|...........|
+#> |    U|     453.54485 |     92.28 |    -5.919 |    -1.019 |   -0.1868 |
+#> |.....................|     2.209 |     2.226 |  0.009694 |    0.8882 |
+#> |.....................|   0.05529 |    0.7144 |    0.7517 |     1.294 |
+#> |.....................|     1.221 |     1.253 |...........|...........|
+#> |    X|     453.54485 |     92.28 |  0.002688 |    0.2651 |    0.8296 |
+#> |.....................|     9.103 |     2.226 |  0.009694 |    0.8882 |
+#> |.....................|   0.05529 |    0.7144 |    0.7517 |     1.294 |
+#> |.....................|     1.221 |     1.253 |...........|...........|
+#> |   72|     453.87696 |    0.9902 |    -1.606 |   -0.9909 |   -0.9627 |
+#> |.....................|   -0.9313 |    0.9582 |    -2.238 |    -1.332 |
+#> |.....................|    0.7513 |   -0.9138 |    -1.018 |   -0.7903 |
+#> |.....................|   -0.7413 |   -0.6822 |...........|...........|
+#> |    U|     453.87696 |     92.21 |    -5.909 |    -1.019 |   -0.1748 |
+#> |.....................|     2.205 |     2.219 |  0.009824 |    0.8908 |
+#> |.....................|   0.05474 |    0.7298 |    0.7487 |     1.307 |
+#> |.....................|     1.207 |     1.274 |...........|...........|
+#> |    X|     453.87696 |     92.21 |  0.002714 |    0.2652 |    0.8396 |
+#> |.....................|     9.072 |     2.219 |  0.009824 |    0.8908 |
+#> |.....................|   0.05474 |    0.7298 |    0.7487 |     1.307 |
+#> |.....................|     1.207 |     1.274 |...........|...........|
+#> |   73|     452.40810 |     1.003 |    -1.604 |   -0.9910 |   -0.9601 |
+#> |.....................|   -0.9321 |    0.9550 |    -2.236 |    -1.332 |
+#> |.....................|    0.7430 |   -0.9087 |    -1.018 |   -0.7892 |
+#> |.....................|   -0.7449 |   -0.6781 |...........|...........|
+#> |    U|      452.4081 |     93.41 |    -5.907 |    -1.019 |   -0.1722 |
+#> |.....................|     2.204 |     2.217 |  0.009851 |    0.8908 |
+#> |.....................|   0.05462 |    0.7337 |    0.7487 |     1.309 |
+#> |.....................|     1.203 |     1.278 |...........|...........|
+#> |    X|      452.4081 |     93.41 |  0.002719 |    0.2652 |    0.8418 |
+#> |.....................|     9.065 |     2.217 |  0.009851 |    0.8908 |
+#> |.....................|   0.05462 |    0.7337 |    0.7487 |     1.309 |
+#> |.....................|     1.203 |     1.278 |...........|...........|
+#> |    F| Forward Diff. |    -20.28 |    0.3985 |   -0.9900 |   -0.3302 |
+#> |.....................|   -0.4580 |    -3.509 |   -0.7634 |    -5.125 |
+#> |.....................|    -3.224 |     3.921 |     4.784 |    -8.607 |
+#> |.....................|    -4.910 |    -1.049 |...........|...........|
+#> |   74|     452.35774 |     1.005 |    -1.605 |   -0.9906 |   -0.9617 |
+#> |.....................|   -0.9314 |    0.9567 |    -2.238 |    -1.332 |
+#> |.....................|    0.7462 |   -0.9112 |    -1.018 |   -0.7890 |
+#> |.....................|   -0.7417 |   -0.6810 |...........|...........|
+#> |    U|     452.35774 |     93.58 |    -5.909 |    -1.019 |   -0.1738 |
+#> |.....................|     2.205 |     2.218 |  0.009828 |    0.8908 |
+#> |.....................|   0.05467 |    0.7317 |    0.7485 |     1.309 |
+#> |.....................|     1.207 |     1.275 |...........|...........|
+#> |    X|     452.35774 |     93.58 |  0.002715 |    0.2652 |    0.8405 |
+#> |.....................|     9.072 |     2.218 |  0.009828 |    0.8908 |
+#> |.....................|   0.05467 |    0.7317 |    0.7485 |     1.309 |
+#> |.....................|     1.207 |     1.275 |...........|...........|
+#> |    F| Forward Diff. |     9.319 |    0.4042 |   -0.9262 |   -0.3428 |
+#> |.....................|   -0.3413 |    -3.482 |   -0.6441 |    -5.151 |
+#> |.....................|    -3.223 |     3.864 |     4.863 |    -8.623 |
+#> |.....................|    -4.770 |    -1.217 |...........|...........|
+#> |   75|     452.31017 |     1.003 |    -1.607 |   -0.9902 |   -0.9631 |
+#> |.....................|   -0.9307 |    0.9586 |    -2.239 |    -1.332 |
+#> |.....................|    0.7493 |   -0.9137 |    -1.019 |   -0.7876 |
+#> |.....................|   -0.7383 |   -0.6834 |...........|...........|
+#> |    U|     452.31017 |     93.41 |    -5.910 |    -1.019 |   -0.1752 |
+#> |.....................|     2.206 |     2.219 |  0.009807 |    0.8910 |
+#> |.....................|   0.05471 |    0.7298 |    0.7478 |     1.310 |
+#> |.....................|     1.210 |     1.273 |...........|...........|
+#> |    X|     452.31017 |     93.41 |  0.002711 |    0.2653 |    0.8393 |
+#> |.....................|     9.078 |     2.219 |  0.009807 |    0.8910 |
+#> |.....................|   0.05471 |    0.7298 |    0.7478 |     1.310 |
+#> |.....................|     1.210 |     1.273 |...........|...........|
+#> |    F| Forward Diff. |    -20.20 |    0.3903 |   -0.9767 |   -0.3983 |
+#> |.....................|   -0.4106 |    -3.495 |   -0.7375 |    -5.052 |
+#> |.....................|    -3.297 |     3.718 |     4.704 |    -8.538 |
+#> |.....................|    -4.606 |    -1.295 |...........|...........|
+#> |   76|     452.25868 |     1.005 |    -1.609 |   -0.9898 |   -0.9648 |
+#> |.....................|   -0.9300 |    0.9604 |    -2.241 |    -1.332 |
+#> |.....................|    0.7529 |   -0.9160 |    -1.019 |   -0.7870 |
+#> |.....................|   -0.7354 |   -0.6858 |...........|...........|
+#> |    U|     452.25868 |     93.58 |    -5.912 |    -1.018 |   -0.1770 |
+#> |.....................|     2.207 |     2.220 |  0.009778 |    0.8908 |
+#> |.....................|   0.05477 |    0.7281 |    0.7476 |     1.311 |
+#> |.....................|     1.213 |     1.270 |...........|...........|
+#> |    X|     452.25868 |     93.58 |  0.002707 |    0.2654 |    0.8378 |
+#> |.....................|     9.084 |     2.220 |  0.009778 |    0.8908 |
+#> |.....................|   0.05477 |    0.7281 |    0.7476 |     1.311 |
+#> |.....................|     1.213 |     1.270 |...........|...........|
+#> |    F| Forward Diff. |     8.768 |    0.3959 |   -0.9108 |   -0.4152 |
+#> |.....................|   -0.2985 |    -3.789 |   -0.7277 |    -5.480 |
+#> |.....................|    -3.800 |     3.463 |     7.165 |    -8.525 |
+#> |.....................|    -4.480 |    -1.429 |...........|...........|
+#> |   77|     452.20380 |     1.003 |    -1.610 |   -0.9896 |   -0.9665 |
+#> |.....................|   -0.9299 |    0.9625 |    -2.243 |    -1.331 |
+#> |.....................|    0.7574 |   -0.9182 |    -1.020 |   -0.7855 |
+#> |.....................|   -0.7330 |   -0.6868 |...........|...........|
+#> |    U|      452.2038 |     93.42 |    -5.913 |    -1.018 |   -0.1787 |
+#> |.....................|     2.207 |     2.221 |  0.009753 |    0.8912 |
+#> |.....................|   0.05483 |    0.7265 |    0.7464 |     1.313 |
+#> |.....................|     1.216 |     1.269 |...........|...........|
+#> |    X|      452.2038 |     93.42 |  0.002704 |    0.2654 |    0.8364 |
+#> |.....................|     9.085 |     2.221 |  0.009753 |    0.8912 |
+#> |.....................|   0.05483 |    0.7265 |    0.7464 |     1.313 |
+#> |.....................|     1.216 |     1.269 |...........|...........|
+#> |    F| Forward Diff. |    -17.51 |    0.3875 |   -0.9566 |   -0.4713 |
+#> |.....................|   -0.3666 |    -3.384 |   -0.7134 |    -4.862 |
+#> |.....................|    -3.257 |     3.566 |     3.539 |    -8.382 |
+#> |.....................|    -4.308 |    -1.428 |...........|...........|
+#> |   78|     452.15674 |     1.006 |    -1.611 |   -0.9895 |   -0.9681 |
+#> |.....................|   -0.9296 |    0.9646 |    -2.244 |    -1.331 |
+#> |.....................|    0.7624 |   -0.9204 |    -1.020 |   -0.7847 |
+#> |.....................|   -0.7317 |   -0.6876 |...........|...........|
+#> |    U|     452.15674 |     93.63 |    -5.915 |    -1.018 |   -0.1803 |
+#> |.....................|     2.207 |     2.222 |  0.009729 |    0.8915 |
+#> |.....................|   0.05491 |    0.7248 |    0.7463 |     1.314 |
+#> |.....................|     1.217 |     1.268 |...........|...........|
+#> |    X|     452.15674 |     93.63 |  0.002700 |    0.2654 |    0.8350 |
+#> |.....................|     9.088 |     2.222 |  0.009729 |    0.8915 |
+#> |.....................|   0.05491 |    0.7248 |    0.7463 |     1.314 |
+#> |.....................|     1.217 |     1.268 |...........|...........|
+#> |    F| Forward Diff. |     16.34 |    0.3942 |   -0.8917 |   -0.4820 |
+#> |.....................|   -0.2498 |    -3.403 |   -0.6022 |    -5.023 |
+#> |.....................|    -3.383 |     3.482 |     3.627 |    -8.397 |
+#> |.....................|    -4.266 |    -1.517 |...........|...........|
+#> |   79|     452.11013 |     1.004 |    -1.613 |   -0.9892 |   -0.9692 |
+#> |.....................|   -0.9285 |    0.9667 |    -2.245 |    -1.330 |
+#> |.....................|    0.7674 |   -0.9230 |    -1.020 |   -0.7840 |
+#> |.....................|   -0.7312 |   -0.6887 |...........|...........|
+#> |    U|     452.11013 |     93.48 |    -5.917 |    -1.018 |   -0.1814 |
+#> |.....................|     2.208 |     2.224 |  0.009710 |    0.8921 |
+#> |.....................|   0.05499 |    0.7229 |    0.7466 |     1.315 |
+#> |.....................|     1.218 |     1.267 |...........|...........|
+#> |    X|     452.11013 |     93.48 |  0.002694 |    0.2655 |    0.8341 |
+#> |.....................|     9.098 |     2.224 |  0.009710 |    0.8921 |
+#> |.....................|   0.05499 |    0.7229 |    0.7466 |     1.315 |
+#> |.....................|     1.218 |     1.267 |...........|...........|
+#> |    F| Forward Diff. |    -8.858 |    0.3784 |   -0.9339 |   -0.5242 |
+#> |.....................|   -0.2958 |    -3.274 |   -0.6451 |    -4.716 |
+#> |.....................|    -3.235 |     3.524 |     3.578 |    -8.323 |
+#> |.....................|    -4.226 |    -1.527 |...........|...........|
+#> |   80|     452.06081 |     1.006 |    -1.615 |   -0.9885 |   -0.9698 |
+#> |.....................|   -0.9277 |    0.9688 |    -2.247 |    -1.329 |
+#> |.....................|    0.7723 |   -0.9255 |    -1.020 |   -0.7822 |
+#> |.....................|   -0.7302 |   -0.6891 |...........|...........|
+#> |    U|     452.06081 |     93.65 |    -5.919 |    -1.017 |   -0.1820 |
+#> |.....................|     2.209 |     2.225 |  0.009693 |    0.8927 |
+#> |.....................|   0.05506 |    0.7209 |    0.7465 |     1.317 |
+#> |.....................|     1.219 |     1.266 |...........|...........|
+#> |    X|     452.06081 |     93.65 |  0.002689 |    0.2656 |    0.8336 |
+#> |.....................|     9.105 |     2.225 |  0.009693 |    0.8927 |
+#> |.....................|   0.05506 |    0.7209 |    0.7465 |     1.317 |
+#> |.....................|     1.219 |     1.266 |...........|...........|
+#> |    F| Forward Diff. |     18.08 |    0.3814 |   -0.8701 |   -0.5179 |
+#> |.....................|   -0.1901 |    -3.027 |   -0.4828 |    -4.583 |
+#> |.....................|    -3.046 |     3.385 |     4.724 |    -8.292 |
+#> |.....................|    -4.215 |    -1.583 |...........|...........|
+#> |   81|     452.00089 |     1.004 |    -1.618 |   -0.9864 |   -0.9698 |
+#> |.....................|   -0.9276 |    0.9701 |    -2.249 |    -1.331 |
+#> |.....................|    0.7751 |   -0.9261 |    -1.021 |   -0.7787 |
+#> |.....................|   -0.7281 |   -0.6889 |...........|...........|
+#> |    U|     452.00089 |     93.48 |    -5.921 |    -1.015 |   -0.1820 |
+#> |.....................|     2.209 |     2.226 |  0.009656 |    0.8916 |
+#> |.....................|   0.05510 |    0.7205 |    0.7459 |     1.321 |
+#> |.....................|     1.221 |     1.267 |...........|...........|
+#> |    X|     452.00089 |     93.48 |  0.002683 |    0.2660 |    0.8336 |
+#> |.....................|     9.107 |     2.226 |  0.009656 |    0.8916 |
+#> |.....................|   0.05510 |    0.7205 |    0.7459 |     1.321 |
+#> |.....................|     1.221 |     1.267 |...........|...........|
+#> |    F| Forward Diff. |    -8.141 |    0.3688 |   -0.8752 |   -0.5418 |
+#> |.....................|   -0.2687 |    -3.191 |   -0.6153 |    -4.612 |
+#> |.....................|    -3.168 |     3.248 |     4.602 |    -8.159 |
+#> |.....................|    -4.118 |    -1.545 |...........|...........|
+#> |   82|     451.94404 |     1.006 |    -1.619 |   -0.9850 |   -0.9696 |
+#> |.....................|   -0.9279 |    0.9711 |    -2.251 |    -1.332 |
+#> |.....................|    0.7767 |   -0.9258 |    -1.022 |   -0.7739 |
+#> |.....................|   -0.7256 |   -0.6877 |...........|...........|
+#> |    U|     451.94404 |     93.65 |    -5.922 |    -1.014 |   -0.1817 |
+#> |.....................|     2.209 |     2.226 |  0.009627 |    0.8908 |
+#> |.....................|   0.05512 |    0.7207 |    0.7445 |     1.327 |
+#> |.....................|     1.224 |     1.268 |...........|...........|
+#> |    X|     451.94404 |     93.65 |  0.002679 |    0.2663 |    0.8338 |
+#> |.....................|     9.104 |     2.226 |  0.009627 |    0.8908 |
+#> |.....................|   0.05512 |    0.7207 |    0.7445 |     1.327 |
+#> |.....................|     1.224 |     1.268 |...........|...........|
+#> |   83|     451.90577 |     1.006 |    -1.621 |   -0.9832 |   -0.9693 |
+#> |.....................|   -0.9284 |    0.9716 |    -2.254 |    -1.336 |
+#> |.....................|    0.7778 |   -0.9242 |    -1.023 |   -0.7696 |
+#> |.....................|   -0.7233 |   -0.6864 |...........|...........|
+#> |    U|     451.90577 |     93.65 |    -5.925 |    -1.012 |   -0.1815 |
+#> |.....................|     2.208 |     2.227 |  0.009581 |    0.8887 |
+#> |.....................|   0.05514 |    0.7219 |    0.7437 |     1.332 |
+#> |.....................|     1.226 |     1.269 |...........|...........|
+#> |    X|     451.90577 |     93.65 |  0.002673 |    0.2666 |    0.8340 |
+#> |.....................|     9.099 |     2.227 |  0.009581 |    0.8887 |
+#> |.....................|   0.05514 |    0.7219 |    0.7437 |     1.332 |
+#> |.....................|     1.226 |     1.269 |...........|...........|
+#> |   84|     451.74017 |     1.006 |    -1.632 |   -0.9740 |   -0.9682 |
+#> |.....................|   -0.9311 |    0.9738 |    -2.270 |    -1.354 |
+#> |.....................|    0.7839 |   -0.9163 |    -1.028 |   -0.7474 |
+#> |.....................|   -0.7117 |   -0.6796 |...........|...........|
+#> |    U|     451.74017 |     93.64 |    -5.935 |    -1.003 |   -0.1804 |
+#> |.....................|     2.205 |     2.228 |  0.009348 |    0.8780 |
+#> |.....................|   0.05523 |    0.7279 |    0.7400 |     1.359 |
+#> |.....................|     1.239 |     1.277 |...........|...........|
+#> |    X|     451.74017 |     93.64 |  0.002645 |    0.2683 |    0.8350 |
+#> |.....................|     9.074 |     2.228 |  0.009348 |    0.8780 |
+#> |.....................|   0.05523 |    0.7279 |    0.7400 |     1.359 |
+#> |.....................|     1.239 |     1.277 |...........|...........|
+#> |   85|     451.58673 |     1.005 |    -1.675 |   -0.9364 |   -0.9637 |
+#> |.....................|   -0.9422 |    0.9828 |    -2.333 |    -1.429 |
+#> |.....................|    0.8084 |   -0.8841 |    -1.045 |   -0.6570 |
+#> |.....................|   -0.6645 |   -0.6522 |...........|...........|
+#> |    U|     451.58673 |     93.57 |    -5.978 |   -0.9678 |   -0.1758 |
+#> |.....................|     2.194 |     2.233 |  0.008399 |    0.8346 |
+#> |.....................|   0.05560 |    0.7523 |    0.7245 |     1.469 |
+#> |.....................|     1.289 |     1.306 |...........|...........|
+#> |    X|     451.58673 |     93.57 |  0.002533 |    0.2753 |    0.8388 |
+#> |.....................|     8.974 |     2.233 |  0.008399 |    0.8346 |
+#> |.....................|   0.05560 |    0.7523 |    0.7245 |     1.469 |
+#> |.....................|     1.289 |     1.306 |...........|...........|
+#> |    F| Forward Diff. |     7.829 |    0.3494 |    0.8366 |   -0.4922 |
+#> |.....................|   -0.7083 |    -3.782 |   -0.9020 |    -9.523 |
+#> |.....................|    -4.571 |     4.733 |     3.935 |    -3.194 |
+#> |.....................|    -1.280 |    0.5510 |...........|...........|
+#> |   86|     450.56328 |     1.003 |    -1.760 |   -0.9418 |   -0.9563 |
+#> |.....................|   -0.9480 |     1.050 |    -2.445 |    -1.421 |
+#> |.....................|    0.9402 |   -0.9310 |    -1.041 |   -0.6107 |
+#> |.....................|   -0.6547 |   -0.6413 |...........|...........|
+#> |    U|     450.56328 |     93.41 |    -6.064 |   -0.9728 |   -0.1684 |
+#> |.....................|     2.189 |     2.272 |  0.006706 |    0.8396 |
+#> |.....................|   0.05758 |    0.7168 |    0.7280 |     1.525 |
+#> |.....................|     1.300 |     1.318 |...........|...........|
+#> |    X|     450.56328 |     93.41 |  0.002326 |    0.2743 |    0.8450 |
+#> |.....................|     8.923 |     2.272 |  0.006706 |    0.8396 |
+#> |.....................|   0.05758 |    0.7168 |    0.7280 |     1.525 |
+#> |.....................|     1.300 |     1.318 |...........|...........|
+#> |   87|     449.70344 |     1.004 |    -1.916 |   -0.9511 |   -0.9429 |
+#> |.....................|   -0.9589 |     1.170 |    -2.653 |    -1.409 |
+#> |.....................|     1.180 |    -1.015 |    -1.032 |   -0.5274 |
+#> |.....................|   -0.6372 |   -0.6210 |...........|...........|
+#> |    U|     449.70344 |     93.47 |    -6.220 |   -0.9817 |   -0.1550 |
+#> |.....................|     2.178 |     2.342 |  0.003591 |    0.8462 |
+#> |.....................|   0.06119 |    0.6534 |    0.7360 |     1.626 |
+#> |.....................|     1.318 |     1.340 |...........|...........|
+#> |    X|     449.70344 |     93.47 |  0.001990 |    0.2726 |    0.8564 |
+#> |.....................|     8.826 |     2.342 |  0.003591 |    0.8462 |
+#> |.....................|   0.06119 |    0.6534 |    0.7360 |     1.626 |
+#> |.....................|     1.318 |     1.340 |...........|...........|
+#> |    F| Forward Diff. |    -19.90 |   -0.3168 |    0.4549 |    0.1875 |
+#> |.....................|    -1.116 |   -0.4934 |  -0.07687 |    -3.113 |
+#> |.....................|    -2.715 |    -1.586 |     5.430 |     3.365 |
+#> |.....................|    0.3009 |     1.974 |...........|...........|
+#> |   88|     451.98935 |     1.002 |    -1.890 |    -1.062 |    -1.052 |
+#> |.....................|   -0.7983 |     1.243 |    -2.828 |    -1.513 |
+#> |.....................|     1.600 |    -1.043 |    -1.029 |   -0.6268 |
+#> |.....................|   -0.3463 |   -0.6648 |...........|...........|
+#> |    U|     451.98935 |     93.35 |    -6.193 |    -1.087 |   -0.2643 |
+#> |.....................|     2.338 |     2.384 | 0.0009551 |    0.7857 |
+#> |.....................|   0.06749 |    0.6319 |    0.7389 |     1.506 |
+#> |.....................|     1.629 |     1.293 |...........|...........|
+#> |    X|     451.98935 |     93.35 |  0.002043 |    0.2523 |    0.7677 |
+#> |.....................|     10.36 |     2.384 | 0.0009551 |    0.7857 |
+#> |.....................|   0.06749 |    0.6319 |    0.7389 |     1.506 |
+#> |.....................|     1.629 |     1.293 |...........|...........|
+#> |   89|     449.56377 |     1.005 |    -1.911 |   -0.9716 |   -0.9631 |
+#> |.....................|   -0.9292 |     1.184 |    -2.685 |    -1.428 |
+#> |.....................|     1.258 |    -1.020 |    -1.032 |   -0.5459 |
+#> |.....................|   -0.5835 |   -0.6292 |...........|...........|
+#> |    U|     449.56377 |     93.56 |    -6.215 |    -1.001 |   -0.1752 |
+#> |.....................|     2.207 |     2.350 |  0.003105 |    0.8351 |
+#> |.....................|   0.06235 |    0.6495 |    0.7362 |     1.604 |
+#> |.....................|     1.376 |     1.331 |...........|...........|
+#> |    X|     449.56377 |     93.56 |  0.002000 |    0.2687 |    0.8393 |
+#> |.....................|     9.092 |     2.350 |  0.003105 |    0.8351 |
+#> |.....................|   0.06235 |    0.6495 |    0.7362 |     1.604 |
+#> |.....................|     1.376 |     1.331 |...........|...........|
+#> |    F| Forward Diff. |    -8.503 |   -0.3085 |   -0.7128 |   -0.4858 |
+#> |.....................|   -0.1462 |   -0.3349 |  -0.04630 |    -2.615 |
+#> |.....................|    -2.539 |    -1.761 |     5.421 |     2.664 |
+#> |.....................|     3.069 |     1.771 |...........|...........|
+#> |   90|     449.37295 |     1.008 |    -1.883 |   -0.9569 |   -0.9753 |
+#> |.....................|   -0.9112 |     1.201 |    -2.710 |    -1.458 |
+#> |.....................|     1.352 |    -1.030 |    -1.036 |   -0.5467 |
+#> |.....................|   -0.5933 |   -0.6460 |...........|...........|
+#> |    U|     449.37295 |     93.89 |    -6.186 |   -0.9871 |   -0.1875 |
+#> |.....................|     2.225 |     2.360 |  0.002726 |    0.8181 |
+#> |.....................|   0.06377 |    0.6417 |    0.7326 |     1.603 |
+#> |.....................|     1.365 |     1.313 |...........|...........|
+#> |    X|     449.37295 |     93.89 |  0.002058 |    0.2715 |    0.8291 |
+#> |.....................|     9.256 |     2.360 |  0.002726 |    0.8181 |
+#> |.....................|   0.06377 |    0.6417 |    0.7326 |     1.603 |
+#> |.....................|     1.365 |     1.313 |...........|...........|
+#> |    F| Forward Diff. |     31.95 |   -0.2055 |    0.2861 |   -0.8772 |
+#> |.....................|    0.4589 |  0.008909 |   0.01409 |    -2.994 |
+#> |.....................|    -2.511 |    -2.129 |     5.021 |     2.567 |
+#> |.....................|     2.446 |     1.004 |...........|...........|
+#> |   91|     449.07232 |     1.007 |    -1.848 |   -0.9883 |   -0.9607 |
+#> |.....................|   -0.9269 |     1.208 |    -2.721 |    -1.473 |
+#> |.....................|     1.446 |    -1.013 |    -1.041 |   -0.5472 |
+#> |.....................|   -0.6000 |   -0.6251 |...........|...........|
+#> |    U|     449.07232 |     93.73 |    -6.151 |    -1.017 |   -0.1729 |
+#> |.....................|     2.210 |     2.364 |  0.002568 |    0.8093 |
+#> |.....................|   0.06518 |    0.6543 |    0.7283 |     1.602 |
+#> |.....................|     1.358 |     1.335 |...........|...........|
+#> |    X|     449.07232 |     93.73 |  0.002130 |    0.2656 |    0.8412 |
+#> |.....................|     9.113 |     2.364 |  0.002568 |    0.8093 |
+#> |.....................|   0.06518 |    0.6543 |    0.7283 |     1.602 |
+#> |.....................|     1.358 |     1.335 |...........|...........|
+#> |   92|     449.34581 |     1.013 |    -1.744 |    -1.083 |   -0.9172 |
+#> |.....................|   -0.9739 |     1.229 |    -2.752 |    -1.520 |
+#> |.....................|     1.728 |   -0.9642 |    -1.054 |   -0.5478 |
+#> |.....................|   -0.6192 |   -0.5619 |...........|...........|
+#> |    U|     449.34581 |     94.33 |    -6.047 |    -1.106 |   -0.1294 |
+#> |.....................|     2.163 |     2.376 |  0.002092 |    0.7821 |
+#> |.....................|   0.06942 |    0.6916 |    0.7169 |     1.601 |
+#> |.....................|     1.337 |     1.403 |...........|...........|
+#> |    X|     449.34581 |     94.33 |  0.002364 |    0.2486 |    0.8787 |
+#> |.....................|     8.694 |     2.376 |  0.002092 |    0.7821 |
+#> |.....................|   0.06942 |    0.6916 |    0.7169 |     1.601 |
+#> |.....................|     1.337 |     1.403 |...........|...........|
+#> |    F| Forward Diff. |     11.36 |  -0.08356 |    -1.544 |   -0.3785 |
+#> |.....................|  -0.02879 |    0.1985 |   0.04898 |    -2.532 |
+#> |.....................|    -2.210 |    -1.428 |     5.624 |     2.440 |
+#> |.....................|     2.104 |     1.894 |...........|...........|
+#> |   93|     449.83746 |    0.9966 |    -1.806 |   -0.8436 |   -0.9213 |
+#> |.....................|    -1.016 |     1.236 |    -2.752 |    -1.567 |
+#> |.....................|     1.816 |    -1.085 |    -1.056 |   -0.6567 |
+#> |.....................|   -0.5363 |   -0.5852 |...........|...........|
+#> |    U|     449.83746 |     92.80 |    -6.109 |   -0.8802 |   -0.1335 |
+#> |.....................|     2.121 |     2.380 |  0.002093 |    0.7548 |
+#> |.....................|   0.07074 |    0.5997 |    0.7149 |     1.469 |
+#> |.....................|     1.426 |     1.378 |...........|...........|
+#> |    X|     449.83746 |     92.80 |  0.002222 |    0.2931 |    0.8750 |
+#> |.....................|     8.340 |     2.380 |  0.002093 |    0.7548 |
+#> |.....................|   0.07074 |    0.5997 |    0.7149 |     1.469 |
+#> |.....................|     1.426 |     1.378 |...........|...........|
+#> |   94|     449.05525 |     1.000 |    -1.836 |   -0.9477 |   -0.9497 |
+#> |.....................|   -0.9515 |     1.216 |    -2.730 |    -1.498 |
+#> |.....................|     1.549 |    -1.033 |    -1.047 |   -0.5784 |
+#> |.....................|   -0.5830 |   -0.6146 |...........|...........|
+#> |    U|     449.05525 |     93.13 |    -6.140 |   -0.9784 |   -0.1618 |
+#> |.....................|     2.185 |     2.368 |  0.002436 |    0.7946 |
+#> |.....................|   0.06673 |    0.6395 |    0.7230 |     1.564 |
+#> |.....................|     1.376 |     1.346 |...........|...........|
+#> |    X|     449.05525 |     93.13 |  0.002156 |    0.2732 |    0.8506 |
+#> |.....................|     8.891 |     2.368 |  0.002436 |    0.7946 |
+#> |.....................|   0.06673 |    0.6395 |    0.7230 |     1.564 |
+#> |.....................|     1.376 |     1.346 |...........|...........|
+#> |    F| Forward Diff. |    -56.82 |  -0.05113 |    0.4930 |  -0.04031 |
+#> |.....................|    -1.049 |   0.03445 |  -0.05944 |    -2.319 |
+#> |.....................|    -2.208 |    -2.328 |     3.545 |    0.3775 |
+#> |.....................|     2.643 |     2.387 |...........|...........|
+#> |   95|     448.75128 |     1.006 |    -1.837 |   -0.9543 |   -0.9497 |
+#> |.....................|   -0.9537 |     1.219 |    -2.732 |    -1.514 |
+#> |.....................|     1.608 |    -1.030 |    -1.050 |   -0.5750 |
+#> |.....................|   -0.5860 |   -0.6263 |...........|...........|
+#> |    U|     448.75128 |     93.69 |    -6.140 |   -0.9847 |   -0.1618 |
+#> |.....................|     2.183 |     2.370 |  0.002396 |    0.7854 |
+#> |.....................|   0.06761 |    0.6415 |    0.7208 |     1.568 |
+#> |.....................|     1.373 |     1.334 |...........|...........|
+#> |    X|     448.75128 |     93.69 |  0.002154 |    0.2720 |    0.8506 |
+#> |.....................|     8.872 |     2.370 |  0.002396 |    0.7854 |
+#> |.....................|   0.06761 |    0.6415 |    0.7208 |     1.568 |
+#> |.....................|     1.373 |     1.334 |...........|...........|
+#> |    F| Forward Diff. |     6.795 |  -0.02569 |    0.3964 |   0.03329 |
+#> |.....................|   -0.8574 |    0.1774 |   0.01390 |    -2.462 |
+#> |.....................|    -2.149 |    -2.476 |     3.910 |     1.045 |
+#> |.....................|     2.743 |     2.014 |...........|...........|
+#> |   96|     448.60805 |     1.005 |    -1.844 |   -0.9658 |   -0.9658 |
+#> |.....................|   -0.9330 |     1.222 |    -2.731 |    -1.528 |
+#> |.....................|     1.652 |    -1.023 |    -1.051 |   -0.5597 |
+#> |.....................|   -0.5993 |   -0.6478 |...........|...........|
+#> |    U|     448.60805 |     93.55 |    -6.147 |   -0.9955 |   -0.1780 |
+#> |.....................|     2.204 |     2.372 |  0.002406 |    0.7773 |
+#> |.....................|   0.06828 |    0.6470 |    0.7198 |     1.587 |
+#> |.....................|     1.359 |     1.311 |...........|...........|
+#> |    X|     448.60805 |     93.55 |  0.002140 |    0.2698 |    0.8370 |
+#> |.....................|     9.057 |     2.372 |  0.002406 |    0.7773 |
+#> |.....................|   0.06828 |    0.6470 |    0.7198 |     1.587 |
+#> |.....................|     1.359 |     1.311 |...........|...........|
+#> |   97|     448.54893 |     1.004 |    -1.854 |   -0.9831 |   -0.9905 |
+#> |.....................|   -0.9018 |     1.226 |    -2.730 |    -1.550 |
+#> |.....................|     1.719 |    -1.013 |    -1.051 |   -0.5361 |
+#> |.....................|   -0.6188 |   -0.6800 |...........|...........|
+#> |    U|     448.54893 |     93.53 |    -6.157 |    -1.012 |   -0.2026 |
+#> |.....................|     2.235 |     2.374 |  0.002422 |    0.7645 |
+#> |.....................|   0.06928 |    0.6548 |    0.7192 |     1.616 |
+#> |.....................|     1.338 |     1.276 |...........|...........|
+#> |    X|     448.54893 |     93.53 |  0.002118 |    0.2666 |    0.8166 |
+#> |.....................|     9.344 |     2.374 |  0.002422 |    0.7645 |
+#> |.....................|   0.06928 |    0.6548 |    0.7192 |     1.616 |
+#> |.....................|     1.338 |     1.276 |...........|...........|
+#> |    F| Forward Diff. |    -11.31 |  -0.05480 |    -1.344 |    -1.332 |
+#> |.....................|    0.5363 |    0.1616 |  -0.02955 |    -2.282 |
+#> |.....................|    -1.949 |    -1.541 |     5.051 |     2.875 |
+#> |.....................|     1.005 |   -0.6800 |...........|...........|
+#> |   98|     448.23423 |     1.005 |    -1.862 |   -0.9802 |   -0.9885 |
+#> |.....................|   -0.8649 |     1.225 |    -2.731 |    -1.570 |
+#> |.....................|     1.863 |   -0.9934 |    -1.058 |   -0.5422 |
+#> |.....................|   -0.6330 |   -0.6404 |...........|...........|
+#> |    U|     448.23423 |     93.60 |    -6.165 |    -1.009 |   -0.2007 |
+#> |.....................|     2.272 |     2.374 |  0.002415 |    0.7529 |
+#> |.....................|   0.07145 |    0.6695 |    0.7131 |     1.608 |
+#> |.....................|     1.323 |     1.319 |...........|...........|
+#> |    X|     448.23423 |     93.60 |  0.002101 |    0.2671 |    0.8182 |
+#> |.....................|     9.695 |     2.374 |  0.002415 |    0.7529 |
+#> |.....................|   0.07145 |    0.6695 |    0.7131 |     1.608 |
+#> |.....................|     1.323 |     1.319 |...........|...........|
+#> |   99|     448.52797 |     1.003 |    -1.887 |   -0.9721 |   -0.9832 |
+#> |.....................|   -0.7539 |     1.222 |    -2.732 |    -1.631 |
+#> |.....................|     2.296 |   -0.9358 |    -1.078 |   -0.5592 |
+#> |.....................|   -0.6753 |   -0.5215 |...........|...........|
+#> |    U|     448.52797 |     93.41 |    -6.190 |    -1.001 |   -0.1954 |
+#> |.....................|     2.383 |     2.371 |  0.002396 |    0.7173 |
+#> |.....................|   0.07796 |    0.7131 |    0.6963 |     1.588 |
+#> |.....................|     1.277 |     1.446 |...........|...........|
+#> |    X|     448.52797 |     93.41 |  0.002050 |    0.2687 |    0.8225 |
+#> |.....................|     10.83 |     2.371 |  0.002396 |    0.7173 |
+#> |.....................|   0.07796 |    0.7131 |    0.6963 |     1.588 |
+#> |.....................|     1.277 |     1.446 |...........|...........|
+#> |    F| Forward Diff. |    -1.417 |  -0.03842 |    -1.058 |    -1.257 |
+#> |.....................|     1.697 |    0.2446 |   0.02601 |    -1.725 |
+#> |.....................|    -1.728 |   -0.7541 |     3.822 |     2.423 |
+#> |.....................|    0.4552 |     1.132 |...........|...........|
+#> |  100|     447.48636 |     1.010 |    -1.889 |    -1.018 |   -0.9136 |
+#> |.....................|   -0.9465 |     1.241 |    -2.741 |    -1.706 |
+#> |.....................|     2.465 |   -0.9635 |    -1.095 |   -0.5705 |
+#> |.....................|   -0.6276 |   -0.6598 |...........|...........|
+#> |    U|     447.48636 |     94.00 |    -6.193 |    -1.045 |   -0.1257 |
+#> |.....................|     2.190 |     2.383 |  0.002265 |    0.6743 |
+#> |.....................|   0.08050 |    0.6921 |    0.6807 |     1.574 |
+#> |.....................|     1.328 |     1.298 |...........|...........|
+#> |    X|     447.48636 |     94.00 |  0.002044 |    0.2602 |    0.8818 |
+#> |.....................|     8.935 |     2.383 |  0.002265 |    0.6743 |
+#> |.....................|   0.08050 |    0.6921 |    0.6807 |     1.574 |
+#> |.....................|     1.328 |     1.298 |...........|...........|
+#> |    F| Forward Diff. |     49.18 |   0.06228 |    -2.520 |     1.219 |
+#> |.....................|   -0.3402 |    0.5332 |   0.01803 |    -1.013 |
+#> |.....................|   -0.7363 |    0.9697 |     2.720 |    0.6118 |
+#> |.....................|    0.4882 |   -0.1519 |...........|...........|
+#> |  101|     448.59314 |     1.009 |    -1.906 |   -0.9798 |    -1.202 |
+#> |.....................|    -1.107 |     1.243 |    -2.730 |    -1.791 |
+#> |.....................|     2.989 |   -0.9474 |    -1.110 |   -0.5914 |
+#> |.....................|   -0.6423 |   -0.5882 |...........|...........|
+#> |    U|     448.59314 |     93.96 |    -6.209 |    -1.009 |   -0.4139 |
+#> |.....................|     2.029 |     2.384 |  0.002422 |    0.6247 |
+#> |.....................|   0.08837 |    0.7043 |    0.6679 |     1.549 |
+#> |.....................|     1.313 |     1.375 |...........|...........|
+#> |    X|     448.59314 |     93.96 |  0.002010 |    0.2672 |    0.6611 |
+#> |.....................|     7.610 |     2.384 |  0.002422 |    0.6247 |
+#> |.....................|   0.08837 |    0.7043 |    0.6679 |     1.549 |
+#> |.....................|     1.313 |     1.375 |...........|...........|
+#> |  102|     447.34338 |     1.004 |    -1.893 |    -1.010 |   -0.9727 |
+#> |.....................|   -0.9794 |     1.241 |    -2.739 |    -1.723 |
+#> |.....................|     2.572 |   -0.9603 |    -1.099 |   -0.5748 |
+#> |.....................|   -0.6307 |   -0.6452 |...........|...........|
+#> |    U|     447.34338 |     93.48 |    -6.196 |    -1.037 |   -0.1848 |
+#> |.....................|     2.157 |     2.383 |  0.002297 |    0.6642 |
+#> |.....................|   0.08211 |    0.6946 |    0.6778 |     1.569 |
+#> |.....................|     1.325 |     1.314 |...........|...........|
+#> |    X|     447.34338 |     93.48 |  0.002037 |    0.2617 |    0.8313 |
+#> |.....................|     8.647 |     2.383 |  0.002297 |    0.6642 |
+#> |.....................|   0.08211 |    0.6946 |    0.6778 |     1.569 |
+#> |.....................|     1.325 |     1.314 |...........|...........|
+#> |    F| Forward Diff. |    -27.99 |   0.05620 |    -2.283 |   -0.5861 |
+#> |.....................|    -1.399 |    0.3409 |  -0.05316 |   -0.7185 |
+#> |.....................|   -0.6589 |    0.7167 |     1.472 |    0.2167 |
+#> |.....................|    0.2339 |    0.7351 |...........|...........|
+#> |  103|     447.24116 |     1.004 |    -1.898 |   -0.9880 |   -0.9438 |
+#> |.....................|   -0.9421 |     1.243 |    -2.723 |    -1.759 |
+#> |.....................|     2.683 |   -0.9737 |    -1.096 |   -0.5790 |
+#> |.....................|   -0.6284 |   -0.6557 |...........|...........|
+#> |    U|     447.24116 |     93.50 |    -6.201 |    -1.017 |   -0.1559 |
+#> |.....................|     2.195 |     2.384 |  0.002530 |    0.6435 |
+#> |.....................|   0.08377 |    0.6844 |    0.6802 |     1.564 |
+#> |.....................|     1.328 |     1.302 |...........|...........|
+#> |    X|     447.24116 |     93.50 |  0.002027 |    0.2657 |    0.8556 |
+#> |.....................|     8.976 |     2.384 |  0.002530 |    0.6435 |
+#> |.....................|   0.08377 |    0.6844 |    0.6802 |     1.564 |
+#> |.....................|     1.328 |     1.302 |...........|...........|
+#> |    F| Forward Diff. |    -22.25 |   0.02611 |    -1.124 |    0.2366 |
+#> |.....................|   -0.4078 |    0.2597 |  -0.06938 |   -0.8187 |
+#> |.....................|   -0.5375 |  0.002218 |     1.533 |   -0.1306 |
+#> |.....................|    0.2372 |    0.1318 |...........|...........|
+#> |  104|     447.36545 |     1.010 |    -1.910 |   -0.9563 |    -1.018 |
+#> |.....................|   -0.9640 |     1.238 |    -2.696 |    -1.806 |
+#> |.....................|     2.921 |   -0.9760 |    -1.100 |   -0.5866 |
+#> |.....................|   -0.6320 |   -0.6434 |...........|...........|
+#> |    U|     447.36545 |     94.05 |    -6.214 |   -0.9866 |   -0.2304 |
+#> |.....................|     2.173 |     2.381 |  0.002941 |    0.6159 |
+#> |.....................|   0.08734 |    0.6827 |    0.6770 |     1.554 |
+#> |.....................|     1.324 |     1.315 |...........|...........|
+#> |    X|     447.36545 |     94.05 |  0.002002 |    0.2716 |    0.7942 |
+#> |.....................|     8.780 |     2.381 |  0.002941 |    0.6159 |
+#> |.....................|   0.08734 |    0.6827 |    0.6770 |     1.554 |
+#> |.....................|     1.324 |     1.315 |...........|...........|
+#> |  105|     447.25244 |     1.009 |    -1.902 |   -0.9770 |   -0.9694 |
+#> |.....................|   -0.9495 |     1.241 |    -2.714 |    -1.775 |
+#> |.....................|     2.765 |   -0.9745 |    -1.097 |   -0.5816 |
+#> |.....................|   -0.6297 |   -0.6515 |...........|...........|
+#> |    U|     447.25244 |     93.94 |    -6.205 |    -1.006 |   -0.1815 |
+#> |.....................|     2.187 |     2.383 |  0.002671 |    0.6341 |
+#> |.....................|   0.08500 |    0.6838 |    0.6790 |     1.560 |
+#> |.....................|     1.326 |     1.307 |...........|...........|
+#> |    X|     447.25244 |     93.94 |  0.002018 |    0.2677 |    0.8340 |
+#> |.....................|     8.909 |     2.383 |  0.002671 |    0.6341 |
+#> |.....................|   0.08500 |    0.6838 |    0.6790 |     1.560 |
+#> |.....................|     1.326 |     1.307 |...........|...........|
+#> |  106|     447.24908 |     1.008 |    -1.900 |   -0.9828 |   -0.9557 |
+#> |.....................|   -0.9455 |     1.242 |    -2.719 |    -1.766 |
+#> |.....................|     2.721 |   -0.9741 |    -1.097 |   -0.5802 |
+#> |.....................|   -0.6290 |   -0.6537 |...........|...........|
+#> |    U|     447.24908 |     93.91 |    -6.203 |    -1.012 |   -0.1678 |
+#> |.....................|     2.191 |     2.383 |  0.002596 |    0.6392 |
+#> |.....................|   0.08434 |    0.6841 |    0.6795 |     1.562 |
+#> |.....................|     1.327 |     1.304 |...........|...........|
+#> |    X|     447.24908 |     93.91 |  0.002023 |    0.2667 |    0.8455 |
+#> |.....................|     8.945 |     2.383 |  0.002596 |    0.6392 |
+#> |.....................|   0.08434 |    0.6841 |    0.6795 |     1.562 |
+#> |.....................|     1.327 |     1.304 |...........|...........|
+#> |  107|     447.25180 |     1.008 |    -1.899 |   -0.9855 |   -0.9493 |
+#> |.....................|   -0.9436 |     1.242 |    -2.721 |    -1.762 |
+#> |.....................|     2.700 |   -0.9739 |    -1.097 |   -0.5796 |
+#> |.....................|   -0.6287 |   -0.6548 |...........|...........|
+#> |    U|      447.2518 |     93.89 |    -6.202 |    -1.014 |   -0.1614 |
+#> |.....................|     2.193 |     2.383 |  0.002560 |    0.6416 |
+#> |.....................|   0.08403 |    0.6843 |    0.6798 |     1.563 |
+#> |.....................|     1.327 |     1.303 |...........|...........|
+#> |    X|      447.2518 |     93.89 |  0.002025 |    0.2662 |    0.8509 |
+#> |.....................|     8.962 |     2.383 |  0.002560 |    0.6416 |
+#> |.....................|   0.08403 |    0.6843 |    0.6798 |     1.563 |
+#> |.....................|     1.327 |     1.303 |...........|...........|
+#> |  108|     447.25421 |     1.008 |    -1.898 |   -0.9869 |   -0.9460 |
+#> |.....................|   -0.9426 |     1.242 |    -2.722 |    -1.760 |
+#> |.....................|     2.690 |   -0.9738 |    -1.096 |   -0.5792 |
+#> |.....................|   -0.6286 |   -0.6553 |...........|...........|
+#> |    U|     447.25421 |     93.88 |    -6.202 |    -1.015 |   -0.1582 |
+#> |.....................|     2.194 |     2.384 |  0.002542 |    0.6428 |
+#> |.....................|   0.08388 |    0.6843 |    0.6799 |     1.563 |
+#> |.....................|     1.327 |     1.303 |...........|...........|
+#> |    X|     447.25421 |     93.88 |  0.002026 |    0.2659 |    0.8537 |
+#> |.....................|     8.970 |     2.384 |  0.002542 |    0.6428 |
+#> |.....................|   0.08388 |    0.6843 |    0.6799 |     1.563 |
+#> |.....................|     1.327 |     1.303 |...........|...........|
+#> |  109|     447.24978 |     1.008 |    -1.898 |   -0.9878 |   -0.9438 |
+#> |.....................|   -0.9420 |     1.242 |    -2.723 |    -1.759 |
+#> |.....................|     2.683 |   -0.9737 |    -1.096 |   -0.5790 |
+#> |.....................|   -0.6285 |   -0.6557 |...........|...........|
+#> |    U|     447.24978 |     93.86 |    -6.201 |    -1.016 |   -0.1560 |
+#> |.....................|     2.195 |     2.384 |  0.002530 |    0.6436 |
+#> |.....................|   0.08377 |    0.6844 |    0.6800 |     1.564 |
+#> |.....................|     1.328 |     1.302 |...........|...........|
+#> |    X|     447.24978 |     93.86 |  0.002027 |    0.2657 |    0.8556 |
+#> |.....................|     8.976 |     2.384 |  0.002530 |    0.6436 |
+#> |.....................|   0.08377 |    0.6844 |    0.6800 |     1.564 |
+#> |.....................|     1.328 |     1.302 |...........|...........|
+#> |  110|     447.22094 |     1.006 |    -1.898 |   -0.9879 |   -0.9438 |
+#> |.....................|   -0.9420 |     1.243 |    -2.723 |    -1.759 |
+#> |.....................|     2.683 |   -0.9737 |    -1.096 |   -0.5790 |
+#> |.....................|   -0.6284 |   -0.6557 |...........|...........|
+#> |    U|     447.22094 |     93.66 |    -6.201 |    -1.016 |   -0.1560 |
+#> |.....................|     2.195 |     2.384 |  0.002530 |    0.6435 |
+#> |.....................|   0.08377 |    0.6844 |    0.6801 |     1.564 |
+#> |.....................|     1.328 |     1.302 |...........|...........|
+#> |    X|     447.22094 |     93.66 |  0.002027 |    0.2657 |    0.8556 |
+#> |.....................|     8.976 |     2.384 |  0.002530 |    0.6435 |
+#> |.....................|   0.08377 |    0.6844 |    0.6801 |     1.564 |
+#> |.....................|     1.328 |     1.302 |...........|...........|
+#> |    F| Forward Diff. |    0.7136 |   0.03206 |    -1.028 |    0.2620 |
+#> |.....................|   -0.3312 |    0.3050 |  -0.05505 |   -0.8960 |
+#> |.....................|   -0.4549 |   0.03409 |     2.494 |   -0.1555 |
+#> |.....................|    0.2265 |    0.1085 |...........|...........|
+#> |  111|     447.21344 |     1.005 |    -1.898 |   -0.9873 |   -0.9440 |
+#> |.....................|   -0.9418 |     1.242 |    -2.723 |    -1.758 |
+#> |.....................|     2.683 |   -0.9737 |    -1.098 |   -0.5789 |
+#> |.....................|   -0.6286 |   -0.6557 |...........|...........|
+#> |    U|     447.21344 |     93.62 |    -6.201 |    -1.016 |   -0.1561 |
+#> |.....................|     2.195 |     2.384 |  0.002531 |    0.6439 |
+#> |.....................|   0.08377 |    0.6844 |    0.6789 |     1.564 |
+#> |.....................|     1.327 |     1.302 |...........|...........|
+#> |    X|     447.21344 |     93.62 |  0.002027 |    0.2658 |    0.8555 |
+#> |.....................|     8.978 |     2.384 |  0.002531 |    0.6439 |
+#> |.....................|   0.08377 |    0.6844 |    0.6789 |     1.564 |
+#> |.....................|     1.327 |     1.302 |...........|...........|
+#> |    F| Forward Diff. |    -4.689 |   0.03686 |    -1.013 |    0.2539 |
+#> |.....................|   -0.3408 |    0.6592 |   0.03740 |   -0.5502 |
+#> |.....................|   -0.2201 |    0.3219 |     2.382 |   -0.1778 |
+#> |.....................|    0.2028 |   0.08770 |...........|...........|
+#> |  112|     447.19216 |     1.006 |    -1.899 |   -0.9854 |   -0.9463 |
+#> |.....................|   -0.9420 |     1.239 |    -2.724 |    -1.756 |
+#> |.....................|     2.680 |   -0.9744 |    -1.101 |   -0.5784 |
+#> |.....................|   -0.6293 |   -0.6560 |...........|...........|
+#> |    U|     447.19216 |     93.64 |    -6.203 |    -1.014 |   -0.1585 |
+#> |.....................|     2.195 |     2.382 |  0.002523 |    0.6453 |
+#> |.....................|   0.08373 |    0.6839 |    0.6759 |     1.564 |
+#> |.....................|     1.327 |     1.302 |...........|...........|
+#> |    X|     447.19216 |     93.64 |  0.002024 |    0.2662 |    0.8535 |
+#> |.....................|     8.976 |     2.382 |  0.002523 |    0.6453 |
+#> |.....................|   0.08373 |    0.6839 |    0.6759 |     1.564 |
+#> |.....................|     1.327 |     1.302 |...........|...........|
+#> |  113|     447.14896 |     1.005 |    -1.904 |   -0.9796 |   -0.9535 |
+#> |.....................|   -0.9426 |     1.230 |    -2.725 |    -1.748 |
+#> |.....................|     2.670 |   -0.9764 |    -1.111 |   -0.5767 |
+#> |.....................|   -0.6315 |   -0.6570 |...........|...........|
+#> |    U|     447.14896 |     93.56 |    -6.208 |    -1.009 |   -0.1657 |
+#> |.....................|     2.194 |     2.376 |  0.002500 |    0.6498 |
+#> |.....................|   0.08358 |    0.6823 |    0.6675 |     1.566 |
+#> |.....................|     1.324 |     1.301 |...........|...........|
+#> |    X|     447.14896 |     93.56 |  0.002014 |    0.2673 |    0.8473 |
+#> |.....................|     8.971 |     2.376 |  0.002500 |    0.6498 |
+#> |.....................|   0.08358 |    0.6823 |    0.6675 |     1.566 |
+#> |.....................|     1.324 |     1.301 |...........|...........|
+#> |  114|     447.12523 |     1.003 |    -1.923 |   -0.9566 |   -0.9821 |
+#> |.....................|   -0.9448 |     1.194 |    -2.731 |    -1.717 |
+#> |.....................|     2.632 |   -0.9846 |    -1.149 |   -0.5701 |
+#> |.....................|   -0.6401 |   -0.6607 |...........|...........|
+#> |    U|     447.12523 |     93.36 |    -6.227 |   -0.9868 |   -0.1943 |
+#> |.....................|     2.192 |     2.355 |  0.002410 |    0.6677 |
+#> |.....................|   0.08300 |    0.6762 |    0.6336 |     1.574 |
+#> |.....................|     1.315 |     1.297 |...........|...........|
+#> |    X|     447.12523 |     93.36 |  0.001976 |    0.2715 |    0.8234 |
+#> |.....................|     8.951 |     2.355 |  0.002410 |    0.6677 |
+#> |.....................|   0.08300 |    0.6762 |    0.6336 |     1.574 |
+#> |.....................|     1.315 |     1.297 |...........|...........|
+#> |    F| Forward Diff. |    -42.78 |    0.1470 |    0.5793 |   -0.8455 |
+#> |.....................|   -0.3546 |   -0.4331 |   -0.1071 |  -0.02049 |
+#> |.....................|   -0.3358 |   -0.3904 |    -2.177 |    0.2043 |
+#> |.....................|   -0.1377 |   -0.3207 |...........|...........|
+#> |  115|     447.09924 |     1.007 |    -1.940 |   -0.9416 |    -1.018 |
+#> |.....................|   -0.9550 |     1.181 |    -2.719 |    -1.734 |
+#> |.....................|     2.734 |   -0.9861 |    -1.153 |   -0.5706 |
+#> |.....................|   -0.6433 |   -0.6564 |...........|...........|
+#> |    U|     447.09924 |     93.80 |    -6.243 |   -0.9727 |   -0.2297 |
+#> |.....................|     2.182 |     2.348 |  0.002591 |    0.6578 |
+#> |.....................|   0.08453 |    0.6750 |    0.6303 |     1.574 |
+#> |.....................|     1.312 |     1.301 |...........|...........|
+#> |    X|     447.09924 |     93.80 |  0.001943 |    0.2743 |    0.7947 |
+#> |.....................|     8.860 |     2.348 |  0.002591 |    0.6578 |
+#> |.....................|   0.08453 |    0.6750 |    0.6303 |     1.574 |
+#> |.....................|     1.312 |     1.301 |...........|...........|
+#> |    F| Forward Diff. |     15.04 |    0.1387 |     1.646 |    -1.777 |
+#> |.....................|   -0.3749 |   -0.5049 |  -0.07528 |    0.1505 |
+#> |.....................|   -0.2071 |   -0.6675 |    -2.129 |    0.2735 |
+#> |.....................|  -0.05533 |   -0.2849 |...........|...........|
+#> |  116|     447.06926 |     1.008 |    -1.968 |   -0.9759 |   -0.9363 |
+#> |.....................|   -0.9300 |     1.192 |    -2.714 |    -1.733 |
+#> |.....................|     2.676 |   -0.9757 |    -1.142 |   -0.5672 |
+#> |.....................|   -0.6383 |   -0.6598 |...........|...........|
+#> |    U|     447.06926 |     93.90 |    -6.272 |    -1.005 |   -0.1484 |
+#> |.....................|     2.207 |     2.354 |  0.002664 |    0.6586 |
+#> |.....................|   0.08367 |    0.6829 |    0.6398 |     1.578 |
+#> |.....................|     1.317 |     1.298 |...........|...........|
+#> |    X|     447.06926 |     93.90 |  0.001889 |    0.2679 |    0.8621 |
+#> |.....................|     9.084 |     2.354 |  0.002664 |    0.6586 |
+#> |.....................|   0.08367 |    0.6829 |    0.6398 |     1.578 |
+#> |.....................|     1.317 |     1.298 |...........|...........|
+#> |    F| Forward Diff. |     31.57 |   0.06960 |   -0.1881 |    0.5445 |
+#> |.....................|    0.2088 |   -0.3879 |  -0.06801 |   -0.3419 |
+#> |.....................|   -0.4021 |   0.02711 |    -1.273 |    0.2199 |
+#> |.....................|   -0.1004 |   -0.4182 |...........|...........|
+#> |  117|     447.12806 |     1.006 |    -2.047 |   -0.9734 |   -0.9587 |
+#> |.....................|   -0.9336 |     1.189 |    -2.704 |    -1.764 |
+#> |.....................|     2.737 |   -0.9879 |    -1.112 |   -0.5826 |
+#> |.....................|   -0.6349 |   -0.6438 |...........|...........|
+#> |    U|     447.12806 |     93.67 |    -6.350 |    -1.003 |   -0.1708 |
+#> |.....................|     2.203 |     2.352 |  0.002825 |    0.6405 |
+#> |.....................|   0.08458 |    0.6737 |    0.6664 |     1.559 |
+#> |.....................|     1.321 |     1.315 |...........|...........|
+#> |    X|     447.12806 |     93.67 |  0.001747 |    0.2684 |    0.8430 |
+#> |.....................|     9.052 |     2.352 |  0.002825 |    0.6405 |
+#> |.....................|   0.08458 |    0.6737 |    0.6664 |     1.559 |
+#> |.....................|     1.321 |     1.315 |...........|...........|
+#> |  118|     447.05003 |     1.006 |    -1.997 |   -0.9750 |   -0.9445 |
+#> |.....................|   -0.9313 |     1.191 |    -2.710 |    -1.744 |
+#> |.....................|     2.698 |   -0.9801 |    -1.131 |   -0.5728 |
+#> |.....................|   -0.6370 |   -0.6539 |...........|...........|
+#> |    U|     447.05003 |     93.71 |    -6.300 |    -1.004 |   -0.1566 |
+#> |.....................|     2.205 |     2.354 |  0.002723 |    0.6520 |
+#> |.....................|   0.08400 |    0.6796 |    0.6495 |     1.571 |
+#> |.....................|     1.318 |     1.304 |...........|...........|
+#> |    X|     447.05003 |     93.71 |  0.001836 |    0.2681 |    0.8551 |
+#> |.....................|     9.073 |     2.354 |  0.002723 |    0.6520 |
+#> |.....................|   0.08400 |    0.6796 |    0.6495 |     1.571 |
+#> |.....................|     1.318 |     1.304 |...........|...........|
+#> |    F| Forward Diff. |     4.860 |  -0.01375 |   -0.2473 |    0.2780 |
+#> |.....................|   0.08862 |   -0.4372 |  -0.08802 |   -0.3404 |
+#> |.....................|   -0.3654 |   -0.2345 |   -0.3468 |   0.08396 |
+#> |.....................|  -0.01035 |  -0.06837 |...........|...........|
+#> |  119|     447.04716 |     1.006 |    -1.989 |   -0.9725 |   -0.9518 |
+#> |.....................|   -0.9334 |     1.193 |    -2.718 |    -1.756 |
+#> |.....................|     2.735 |   -0.9825 |    -1.129 |   -0.5738 |
+#> |.....................|   -0.6372 |   -0.6523 |...........|...........|
+#> |    U|     447.04716 |     93.69 |    -6.292 |    -1.002 |   -0.1639 |
+#> |.....................|     2.203 |     2.355 |  0.002610 |    0.6452 |
+#> |.....................|   0.08456 |    0.6777 |    0.6509 |     1.570 |
+#> |.....................|     1.318 |     1.306 |...........|...........|
+#> |    X|     447.04716 |     93.69 |  0.001850 |    0.2686 |    0.8488 |
+#> |.....................|     9.053 |     2.355 |  0.002610 |    0.6452 |
+#> |.....................|   0.08456 |    0.6777 |    0.6509 |     1.570 |
+#> |.....................|     1.318 |     1.306 |...........|...........|
+#> |    F| Forward Diff. |     2.456 | -0.007589 |   -0.1181 |   0.06051 |
+#> |.....................|   0.03158 |   -0.4028 |  -0.08358 |   -0.4018 |
+#> |.....................|   -0.3358 |   -0.3459 |   -0.2609 |   0.03632 |
+#> |.....................|  -0.03277 |   0.02331 |...........|...........|
+#> |  120|     447.04716 |     1.006 |    -1.989 |   -0.9725 |   -0.9518 |
+#> |.....................|   -0.9334 |     1.193 |    -2.718 |    -1.756 |
+#> |.....................|     2.735 |   -0.9825 |    -1.129 |   -0.5738 |
+#> |.....................|   -0.6372 |   -0.6523 |...........|...........|
+#> |    U|     447.04716 |     93.69 |    -6.292 |    -1.002 |   -0.1639 |
+#> |.....................|     2.203 |     2.355 |  0.002610 |    0.6452 |
+#> |.....................|   0.08456 |    0.6777 |    0.6509 |     1.570 |
+#> |.....................|     1.318 |     1.306 |...........|...........|
+#> |    X|     447.04716 |     93.69 |  0.001850 |    0.2686 |    0.8488 |
+#> |.....................|     9.053 |     2.355 |  0.002610 |    0.6452 |
+#> |.....................|   0.08456 |    0.6777 |    0.6509 |     1.570 |
+#> |.....................|     1.318 |     1.306 |...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem",
+  error_model = "obs_tc")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> 1:    9.1294e+01  -5.0486e+00  -1.7441e+00  -3.5640e+00  -2.1387e+00   4.8639e-01   5.5948e+00   1.4680e+00   1.1057e+00   2.3810e+00   4.8150e-01   4.3452e-01   1.0359e+01   2.3790e-05   7.8082e+00   5.1813e-01
+#> 2:    9.1224e+01  -5.2308e+00  -1.9743e+00  -4.0115e+00  -1.8311e+00   9.8058e-02   5.3151e+00   1.3946e+00   1.0504e+00   2.8908e+00   4.5742e-01   5.2252e-01   5.9132e+00   5.7000e-04   6.5362e+00   1.8571e-07
+#> 3:    9.1371e+01  -5.5075e+00  -2.1136e+00  -4.0542e+00  -1.4871e+00  -4.1222e-02   5.0493e+00   1.3249e+00   9.9785e-01   3.4546e+00   4.3455e-01   6.3380e-01   4.0626e+00   1.0302e-05   4.6845e+00   5.0378e-04
+#> 4:    91.3391   -5.7912   -2.1450   -3.9623   -1.3302   -0.1356    4.7969    1.2586    0.9480    3.2819    0.4128    0.6021    3.3624    0.0249    3.6770    0.0248
+#> 5:    91.5018   -6.0214   -2.1492   -3.9323   -1.2118   -0.0647    4.5570    1.1957    0.9006    3.1178    0.3922    0.5720    2.9393    0.0349    3.1610    0.0371
+#> 6:    91.4496   -5.8734   -2.0974   -3.9977   -1.0936   -0.0608    4.3292    1.3347    0.8695    3.1231    0.3726    0.5434    2.5921    0.0366    2.7534    0.0396
+#> 7:    91.6540   -5.8545   -2.1019   -3.9268   -0.9717   -0.1622    4.1127    1.8221    0.8771    2.9670    0.3539    0.5162    2.3468    0.0466    2.4323    0.0474
+#> 8:    91.7226   -5.8139   -2.0764   -4.0030   -0.9804   -0.1283    3.9071    2.3972    0.9978    2.9945    0.3362    0.4904    2.0001    0.0405    2.0620    0.0557
+#> 9:    91.9975   -5.6339   -2.0812   -3.9379   -0.9156   -0.0654    4.4265    2.2773    0.9479    3.0945    0.3194    0.4659    1.8817    0.0397    1.4473    0.0845
+#> 10:    91.9477   -5.6101   -2.0459   -3.8821   -0.9368   -0.0428    4.9868    2.2787    0.9297    3.2162    0.3035    0.4426    1.6910    0.0397    1.3759    0.0892
+#> 11:    92.1798   -5.5425   -2.0676   -3.9349   -0.9248   -0.0339    5.0312    2.1648    0.8832    3.0554    0.2883    0.4205    1.6613    0.0375    1.3387    0.0823
+#> 12:    92.1456   -5.6294   -2.1011   -3.8899   -0.9195   -0.0410    4.7796    2.0565    0.9077    3.1066    0.3013    0.3995    1.6018    0.0393    1.5496    0.0691
+#> 13:    91.6764   -5.5607   -2.0911   -3.8832   -0.9268   -0.0367    4.5407    1.9537    0.9246    3.0425    0.2862    0.3795    1.6900    0.0350    1.4050    0.0712
+#> 14:    91.4832   -5.5007   -2.1133   -3.8869   -0.9208   -0.0202    4.3136    1.8560    0.8795    3.0389    0.2719    0.3605    1.5526    0.0389    1.7056    0.0502
+#> 15:    91.7854   -5.4454   -2.1124   -3.8750   -0.8842   -0.0608    4.0979    1.7632    0.9004    3.0463    0.2583    0.3425    1.6201    0.0384    1.2463    0.0747
+#> 16:    91.7608   -5.4097   -2.1449   -3.8750   -0.8797   -0.0532    3.8930    1.6751    0.9666    3.0463    0.2454    0.3254    1.6086    0.0384    1.0840    0.0850
+#> 17:    91.6692   -5.5401   -2.1688   -3.8762   -0.9022   -0.0101    3.6984    1.8405    1.0323    2.9672    0.2331    0.3091    1.4625    0.0371    1.1135    0.0841
+#> 18:    91.3169   -5.5720   -2.1777   -3.8851   -0.9396    0.0040    3.5135    1.8186    1.0419    3.0783    0.2215    0.2936    1.4778    0.0396    1.3403    0.0732
+#> 19:    91.4384   -5.6696   -2.1469   -3.8892   -0.9318   -0.0103    3.3378    2.2700    1.0489    3.0592    0.2128    0.2790    1.3854    0.0379    1.1760    0.0858
+#> 20:    91.3273   -5.7800   -2.1388   -3.9004   -0.9536   -0.0159    3.1709    2.7506    1.0297    3.0477    0.2021    0.2650    1.4542    0.0419    1.1576    0.0856
+#> 21:    91.7477   -5.7952   -2.1436   -3.9164   -0.9263   -0.0184    3.0124    3.0737    1.0414    3.0435    0.1948    0.2518    1.5026    0.0398    1.1833    0.0791
+#> 22:    91.6492   -6.0575   -2.1196   -3.9168   -0.9471   -0.0153    2.8617    4.1317    1.0322    3.0494    0.1850    0.2392    1.4351    0.0409    1.0739    0.0873
+#> 23:    91.8536   -6.2824   -2.1596   -3.9174   -0.9405    0.0031    2.7187    5.3935    1.0143    3.1085    0.1758    0.2272    1.4534    0.0404    1.0651    0.0805
+#> 24:    92.1616   -6.2246   -2.0912   -3.9224   -0.9338    0.0118    2.5827    5.7533    0.9636    3.0780    0.1741    0.2158    1.5863    0.0336    1.0915    0.0804
+#> 25:    92.2576   -6.2746   -2.1058   -3.9587   -0.9355    0.0189    2.4536    5.4656    0.9706    3.3477    0.1780    0.2051    1.4555    0.0365    1.0838    0.0782
+#> 26:    92.3314   -6.1739   -2.1211   -3.9676   -0.9474    0.0525    2.4934    5.5785    0.9981    3.3705    0.1835    0.1948    1.4433    0.0379    1.1300    0.0783
+#> 27:    92.8206   -6.1111   -2.0900   -3.9787   -0.9472    0.0058    2.5201    5.4329    1.0145    3.5013    0.1856    0.1851    1.4484    0.0391    1.1809    0.0723
+#> 28:    92.8685   -6.0934   -2.0963   -3.9872   -0.9693    0.0053    2.9812    5.1612    0.9925    3.5416    0.1816    0.1758    1.4713    0.0389    1.1766    0.0704
+#> 29:    92.6774   -5.8779   -2.0833   -3.9954   -0.9546   -0.0099    4.3751    4.9032    1.0762    3.5483    0.1755    0.1670    1.4844    0.0378    1.3435    0.0599
+#> 30:    92.6704   -5.9657   -2.0746   -3.9920   -0.9342   -0.0329    4.1563    4.6580    1.0571    3.5382    0.1667    0.1587    1.4510    0.0427    1.2218    0.0678
+#> 31:    92.4139   -5.7428   -2.0922   -3.9765   -0.9178   -0.0302    3.9485    4.4251    1.0210    3.5601    0.1596    0.1507    1.5981    0.0349    1.3086    0.0619
+#> 32:    92.8243   -5.8072   -2.1154   -3.9699   -0.9130    0.0065    3.7511    4.2039    1.0622    3.4768    0.1667    0.1432    1.5321    0.0333    1.3779    0.0611
+#> 33:    92.8737   -5.6655   -2.1132   -3.9763   -0.9155    0.0183    3.5635    3.9937    1.1068    3.5075    0.1583    0.1360    1.5351    0.0341    1.2700    0.0673
+#> 34:    93.0233   -5.7429   -2.1022   -3.9648   -0.9057    0.0202    3.3853    3.7940    1.0830    3.4532    0.1504    0.1292    1.5128    0.0368    1.1942    0.0702
+#> 35:    93.1333   -5.7707   -2.1003   -4.0004   -0.9031    0.0201    3.2161    3.6043    1.1161    3.4701    0.1429    0.1228    1.6003    0.0307    1.1387    0.0734
+#> 36:    93.1398   -5.7700   -2.1168   -3.9678   -0.9038    0.0107    3.0553    3.4241    1.1209    3.4126    0.1358    0.1166    1.4919    0.0331    1.0642    0.0755
+#> 37:    92.8847   -5.6651   -2.1538   -3.9634   -0.9176    0.0364    2.9995    3.2529    1.1108    3.3776    0.1402    0.1173    1.5093    0.0396    1.1550    0.0693
+#> 38:    93.2326   -5.5244   -2.1571   -3.9909   -0.9231    0.0179    2.8832    3.0902    1.0763    3.5170    0.1332    0.1205    1.4962    0.0472    1.1657    0.0679
+#> 39:    92.9946   -5.4516   -2.1475   -3.9365   -0.9067    0.0309    3.0986    2.9357    1.0562    3.4194    0.1265    0.1251    1.4786    0.0464    1.1183    0.0721
+#> 40:    93.2028   -5.6148   -2.1367   -3.9235   -0.9048    0.0099    2.9436    2.7889    1.1256    3.3460    0.1241    0.1288    1.4515    0.0459    1.0449    0.0753
+#> 41:    93.1297   -5.4665   -2.0545   -4.0108   -0.9136   -0.0216    2.7964    2.6495    1.1471    3.4754    0.1281    0.1223    1.7359    0.0321    1.0876    0.0780
+#> 42:    93.0469   -5.3767   -2.0820   -4.0213   -0.9361   -0.0264    2.6566    2.5170    1.0897    3.5120    0.1411    0.1162    1.7070    0.0276    1.2377    0.0691
+#> 43:    93.3305   -5.4943   -2.0910   -4.0226   -0.9414   -0.0201    2.5238    2.3912    1.0896    3.4589    0.1621    0.1126    1.5584    0.0393    1.1485    0.0705
+#> 44:    93.2566   -5.4919   -2.1016   -4.0718   -0.9373    0.0024    2.3976    2.2716    1.0451    3.8959    0.1612    0.1162    1.5769    0.0286    1.2778    0.0693
+#> 45:    93.0284   -5.4885   -2.1012   -4.0740   -0.9202   -0.0197    2.2777    2.1580    1.0268    3.9297    0.1553    0.1104    1.5589    0.0289    1.1388    0.0778
+#> 46:    92.7188   -5.5807   -2.1102   -4.0875   -0.9465    0.0076    2.1638    2.2084    0.9840    4.0322    0.1475    0.1048    1.6729    0.0295    1.2763    0.0735
+#> 47:    92.6718   -5.5108   -2.1268   -4.0638   -0.9220    0.0131    2.0556    2.0980    1.0064    3.8306    0.1475    0.0996    1.6527    0.0271    1.3190    0.0659
+#> 48:    92.6727   -5.5268   -2.1326   -4.0693   -0.8999    0.0259    1.9529    2.2445    1.0387    3.8064    0.1459    0.0946    1.6587    0.0283    1.3555    0.0604
+#> 49:    92.5230   -5.5592   -2.1701   -4.0595   -0.9087    0.0350    1.8552    2.5181    1.0238    3.7514    0.1552    0.0899    1.5473    0.0307    1.2437    0.0662
+#> 50:    92.4920   -5.5778   -2.1309   -4.0711   -0.9317    0.0383    1.7625    2.6771    1.0203    3.7435    0.1587    0.0854    1.5727    0.0330    1.2555    0.0611
+#> 51:    92.4606   -5.5485   -2.1346   -4.0687   -0.9148    0.0638    1.6743    2.8079    1.0402    3.6978    0.1513    0.0811    1.5476    0.0335    1.2744    0.0658
+#> 52:    92.6305   -5.6829   -2.1658   -4.0697   -0.9298    0.0848    1.5906    2.8530    1.0565    3.6998    0.1644    0.0798    1.4751    0.0296    1.1351    0.0747
+#> 53:    92.6412   -5.5519   -2.1984   -4.1605   -0.9472    0.0803    1.8328    2.7103    1.0501    4.4111    0.1626    0.0758    1.5735    0.0343    1.2247    0.0643
+#> 54:    92.7616   -5.5718   -2.1826   -4.2028   -0.9382    0.0939    1.9108    2.5748    1.0708    4.7287    0.1775    0.0720    1.4860    0.0299    1.2190    0.0638
+#> 55:    92.8466   -5.6434   -2.1590   -4.0501   -0.9219    0.0660    2.3709    2.4461    1.0399    4.4922    0.1686    0.0684    1.5899    0.0297    1.2586    0.0598
+#> 56:    92.8839   -5.6503   -2.1758   -4.0467   -0.9265    0.0765    2.2523    2.3238    1.0755    4.2676    0.1698    0.0666    1.5357    0.0319    1.1854    0.0633
+#> 57:    92.8882   -5.3950   -2.1926   -4.0282   -0.9455    0.0600    2.4994    2.2076    1.0411    4.0542    0.1684    0.0633    1.5839    0.0342    1.2789    0.0612
+#> 58:    92.9510   -5.4362   -2.1993   -4.0402   -0.9349    0.0576    2.3744    2.0972    1.0184    3.8515    0.1757    0.0604    1.5796    0.0328    1.3027    0.0570
+#> 59:    92.8806   -5.4605   -2.2176   -4.2201   -0.9360    0.0998    2.2557    1.9923    1.0248    5.1421    0.1904    0.0573    1.6469    0.0325    1.4177    0.0534
+#> 60:    92.8606   -5.4697   -2.2016   -4.1707   -0.9218    0.0747    2.1429    1.8927    1.0489    4.8850    0.1809    0.0545    1.5984    0.0318    1.2879    0.0589
+#> 61:    92.8939   -5.5167   -2.2169   -4.1567   -0.9434    0.0680    2.1067    1.9160    1.0677    4.6408    0.1775    0.0517    1.5223    0.0404    1.2033    0.0623
+#> 62:    93.1569   -5.6121   -2.2073   -4.1427   -0.9431    0.0717    2.5977    2.0627    1.0518    4.5133    0.1758    0.0494    1.4644    0.0364    1.1857    0.0621
+#> 63:    93.2362   -5.5056   -2.1832   -4.0832   -0.9433    0.0754    3.4639    1.9596    1.0905    4.2877    0.1851    0.0536    1.5500    0.0320    1.2533    0.0610
+#> 64:    93.3935   -5.4320   -2.1735   -4.0754   -0.9601    0.0719    5.0337    1.8616    1.0723    4.0733    0.1907    0.0649    1.5436    0.0270    1.4154    0.0546
+#> 65:    93.1102   -5.5419   -2.1870   -4.0496   -0.9481    0.0753    5.0250    1.9760    1.1263    3.8696    0.1902    0.0617    1.4779    0.0262    1.1326    0.0712
+#> 66:    92.9832   -5.7640   -2.1941   -4.0532   -0.9444    0.0635    5.2049    2.6553    1.1258    3.7699    0.1915    0.0586    1.4926    0.0307    1.0960    0.0645
+#> 67:    92.6674   -5.6976   -2.1858   -4.0855   -0.9209    0.0562    4.9447    2.5225    1.1285    4.0204    0.1948    0.0556    1.4667    0.0315    1.1023    0.0650
+#> 68:    92.7718   -5.7724   -2.1760   -4.0242   -0.9354    0.0441    4.6975    2.8536    1.1471    3.8194    0.1922    0.0529    1.4283    0.0329    1.1174    0.0664
+#> 69:    92.8377   -5.7554   -2.1833   -4.0670   -0.9412    0.0834    4.4626    2.7404    1.1565    3.7904    0.1826    0.0502    1.4628    0.0318    1.0793    0.0747
+#> 70:    92.6830   -5.9071   -2.2266   -4.0604   -0.9399    0.0730    4.2394    3.5629    1.1459    3.7282    0.1734    0.0477    1.4892    0.0331    1.1526    0.0683
+#> 71:    92.5729   -5.8185   -2.2009   -4.0623   -0.9401    0.0878    4.0275    3.3847    1.0886    3.7348    0.1648    0.0453    1.4739    0.0373    1.0902    0.0678
+#> 72:    92.1755   -6.0270   -2.2108   -4.1507   -0.9564    0.0665    3.8261    3.9851    1.1200    4.1726    0.1617    0.0431    1.4478    0.0348    1.1400    0.0673
+#> 73:    91.8986   -6.0175   -2.1916   -4.1416   -0.9347    0.0243    3.6348    4.0607    1.1553    4.0576    0.1802    0.0409    1.4330    0.0406    1.0914    0.0712
+#> 74:    91.7729   -5.8767   -2.1898   -4.0934   -0.9122    0.0184    3.4531    3.8577    1.1254    3.8547    0.1827    0.0389    1.3372    0.0524    1.0717    0.0687
+#> 75:    91.3098   -5.9950   -2.1572   -4.1349   -0.9427    0.0190    3.4756    3.8000    1.1626    3.8402    0.1969    0.0369    1.3378    0.0501    1.1602    0.0685
+#> 76:    91.3766   -5.8701   -2.2042   -4.1128   -0.9081    0.0539    3.9350    3.6100    1.2348    3.7994    0.1891    0.0369    1.3400    0.0495    1.0656    0.0738
+#> 77:    91.6057   -5.7437   -2.1988   -4.1241   -0.8890    0.0500    5.0868    3.4295    1.1971    3.8470    0.1950    0.0469    1.4928    0.0397    1.1129    0.0700
+#> 78:    91.7868   -5.7832   -2.1844   -4.1102   -0.9104    0.0698    4.8325    3.2580    1.1670    3.6547    0.1993    0.0502    1.4336    0.0340    0.9512    0.0805
+#> 79:    91.7221   -5.7881   -2.2166   -4.1137   -0.9160    0.0672    4.5909    3.0951    1.1582    3.5765    0.1928    0.0486    1.4632    0.0352    1.0210    0.0728
+#> 80:    91.8608   -5.8064   -2.2006   -4.0971   -0.9209    0.0642    4.3613    3.2163    1.1481    3.4758    0.1832    0.0462    1.4368    0.0356    1.0605    0.0710
+#> 81:    91.6423   -5.8749   -2.2037   -4.0893   -0.9187    0.0503    4.1432    3.5329    1.0907    3.5148    0.2011    0.0451    1.4719    0.0346    1.1684    0.0646
+#> 82:    91.8319   -6.0898   -2.2251   -4.0826   -0.9368    0.0842    4.1509    4.4964    1.0606    3.4836    0.1910    0.0428    1.4468    0.0387    1.1605    0.0637
+#> 83:    91.9794   -6.0417   -2.1947   -4.1042   -0.9114    0.0741    6.5949    4.5668    1.1113    3.6409    0.1815    0.0407    1.4780    0.0346    1.1277    0.0634
+#> 84:    91.8669   -6.1877   -2.1979   -4.1052   -0.9300    0.0807    6.2651    5.1958    1.1750    3.6752    0.1724    0.0386    1.4931    0.0278    1.0401    0.0685
+#> 85:    91.6789   -6.0634   -2.1896   -4.1357   -0.9371    0.0933    5.9519    4.9360    1.1259    3.8493    0.1732    0.0367    1.5058    0.0275    1.1356    0.0670
+#> 86:    91.6989   -6.2114   -2.2056   -4.1542   -0.9646    0.0882    5.6543    5.0411    1.1091    3.9411    0.1988    0.0349    1.4099    0.0338    1.1811    0.0636
+#> 87:    92.3758   -6.3779   -2.2062   -4.1739   -0.9385    0.0916    5.3716    6.2290    1.1213    4.0290    0.1889    0.0331    1.4809    0.0306    1.1443    0.0626
+#> 88:    92.2757   -6.2016   -2.2215   -4.1389   -0.9582    0.0942    5.1030    5.9176    1.0797    4.0768    0.1990    0.0315    1.4282    0.0386    1.2235    0.0629
+#> 89:    92.1970   -6.3356   -2.2081   -4.1412   -0.9555    0.1057    4.8478    5.9597    1.1474    4.0677    0.1890    0.0299    1.3856    0.0377    1.1807    0.0640
+#> 90:    92.0813   -6.4550   -2.2045   -4.1524   -0.9553    0.0885    4.6054    6.9999    1.1542    3.9901    0.1880    0.0284    1.3416    0.0416    1.1379    0.0653
+#> 91:    91.7111   -6.5289   -2.2203   -4.1763   -0.9288    0.0823    5.4933    6.9237    1.1601    4.0435    0.1839    0.0360    1.3387    0.0401    1.1768    0.0591
+#> 92:    92.1217   -6.5567   -2.2232   -4.2082   -0.9411    0.0815    8.0692    6.7286    1.1684    3.9422    0.1763    0.0411    1.3740    0.0463    1.1538    0.0613
+#> 93:    92.7497   -6.3512   -2.2463   -4.1806   -0.9633    0.0724    7.6657    6.3922    1.1870    3.8858    0.1796    0.0391    1.4232    0.0454    1.3749    0.0497
+#> 94:    92.2679   -6.3542   -2.2473   -4.1873   -0.9382    0.0711    7.2824    6.0726    1.1940    3.8847    0.1956    0.0371    1.3812    0.0465    1.2897    0.0521
+#> 95:    92.0257   -6.2448   -2.2624   -4.1681   -0.9624    0.0810    6.9183    5.7690    1.1345    3.8091    0.1858    0.0359    1.3026    0.0509    1.3000    0.0530
+#> 96:    91.5166   -5.9442   -2.2924   -4.2449   -0.9238    0.1058    7.1159    5.4805    1.1231    4.2529    0.1953    0.0343    1.4063    0.0445    1.3479    0.0482
+#> 97:    91.1606   -5.8541   -2.2912   -4.2398   -0.8875    0.1101    9.4515    5.2065    1.1256    4.3194    0.2081    0.0337    1.3436    0.0498    1.3317    0.0496
+#> 98:    91.2787   -6.0967   -2.2703   -4.2641   -0.9260    0.0869    8.9789    4.9462    1.2070    4.2238    0.1977    0.0373    1.3124    0.0495    1.1362    0.0653
+#> 99:    91.6449   -5.9441   -2.2562   -4.2355   -0.9312    0.1237    8.5300    4.6988    1.2343    4.0468    0.1878    0.0369    1.3508    0.0462    1.0542    0.0704
+#> 100:    91.7795   -5.8857   -2.2516   -4.3381   -0.9344    0.1291    8.1035    4.4639    1.2355    4.6941    0.1968    0.0393    1.4327    0.0358    1.1170    0.0668
+#> 101:    92.2537   -5.7930   -2.2345   -4.3477   -0.9272    0.1340    8.3402    4.2407    1.1961    4.7638    0.1933    0.0402    1.4683    0.0375    1.1216    0.0626
+#> 102:    92.3920   -6.0193   -2.2332   -4.3487   -0.9155    0.1565   11.1006    4.2977    1.1700    4.8048    0.2260    0.0444    1.4443    0.0342    1.0888    0.0674
+#> 103:    92.0043   -5.7825   -2.2376   -4.2616   -0.9043    0.1686   10.5455    4.0829    1.1587    4.5646    0.2147    0.0422    1.4198    0.0338    1.1639    0.0625
+#> 104:    92.1575   -5.8497   -2.2470   -4.2456   -0.9128    0.1762   10.0183    3.8787    1.1405    4.3364    0.2040    0.0440    1.3919    0.0379    1.2040    0.0582
+#> 105:    92.2784   -5.7971   -2.2582   -4.2100   -0.9128    0.1731    9.5173    3.6848    1.1351    4.1196    0.1938    0.0418    1.3982    0.0404    1.1069    0.0656
+#> 106:    92.4336   -5.7752   -2.2690   -4.3771   -0.8925    0.1644    9.0415    3.5005    1.1547    5.0970    0.1841    0.0476    1.3670    0.0423    1.1716    0.0625
+#> 107:    92.5128   -5.8328   -2.2549   -4.4193   -0.9403    0.2268    8.5894    3.3255    1.1160    5.2711    0.1749    0.0453    1.4023    0.0347    1.0279    0.0757
+#> 108:    92.8926   -5.7266   -2.2606   -4.5037   -0.9392    0.2394    8.1599    3.1592    1.1293    5.9652    0.1661    0.0447    1.3837    0.0346    0.9545    0.0747
+#> 109:    92.4657   -5.8687   -2.2884   -4.4108   -0.9043    0.2611    7.7519    4.0001    1.0729    5.6669    0.1578    0.0424    1.3441    0.0351    0.9758    0.0708
+#> 110:    92.6620   -5.6900   -2.2825   -4.4337   -0.9003    0.2602    7.3643    3.8001    1.0843    5.3836    0.1499    0.0433    1.4652    0.0302    0.9950    0.0722
+#> 111:    92.8949   -5.6946   -2.2661   -4.5240   -0.9233    0.2372    6.9961    3.6101    1.0845    5.8133    0.1551    0.0411    1.5005    0.0327    0.9284    0.0753
+#> 112:    93.4237   -5.6562   -2.2474   -4.4809   -0.9441    0.2322    6.6463    3.4296    1.1498    5.5227    0.1474    0.0409    1.4612    0.0317    0.9336    0.0762
+#> 113:    93.1883   -5.6891   -2.2846   -4.3984   -0.9416    0.2317    6.3140    3.2581    1.1062    5.2465    0.1596    0.0463    1.3924    0.0380    1.0268    0.0698
+#> 114:    93.4464   -5.7087   -2.2902   -4.4274   -0.9401    0.2638    5.9983    3.0952    1.1170    5.0203    0.1516    0.0495    1.4108    0.0361    1.0355    0.0682
+#> 115:    93.1873   -5.8732   -2.2668   -4.5086   -0.9636    0.2516    5.6984    3.3427    1.1141    5.7549    0.1440    0.0490    1.5010    0.0309    1.0443    0.0679
+#> 116:    92.6878   -5.8520   -2.2903   -4.5349   -0.9663    0.2612    5.4135    3.2444    1.1048    5.8809    0.1471    0.0511    1.3910    0.0360    1.0423    0.0702
+#> 117:    92.7775   -5.7892   -2.2897   -4.4572   -0.9544    0.2380    5.1428    3.0822    1.0731    5.5869    0.1397    0.0703    1.3493    0.0360    0.9831    0.0713
+#> 118:    93.1533   -5.8045   -2.2859   -4.4787   -0.9667    0.2150    4.8857    3.0277    1.0872    5.6786    0.1439    0.0812    1.3838    0.0373    1.0547    0.0696
+#> 119:    92.8370   -5.7208   -2.2738   -4.4627   -0.9462    0.2095    4.6414    2.8764    1.1172    5.6197    0.1643    0.0772    1.3394    0.0348    0.9180    0.0803
+#> 120:    92.5430   -5.7795   -2.3004   -4.4203   -0.9479    0.2313    4.4093    2.8377    1.1312    5.3387    0.1655    0.0803    1.2967    0.0360    1.0699    0.0761
+#> 121:    92.5318   -5.6550   -2.2866   -4.5065   -0.9166    0.2321    4.1888    2.6959    1.0994    6.0180    0.1686    0.0763    1.3882    0.0322    0.9895    0.0733
+#> 122:    92.7380   -5.6688   -2.2968   -4.4523   -0.9279    0.2529    3.9794    2.5611    1.0642    5.7171    0.1601    0.0851    1.3786    0.0316    0.9358    0.0742
+#> 123:    93.0753   -5.7451   -2.2896   -4.5423   -0.9371    0.2724    3.7804    2.9938    1.0758    5.9349    0.1521    0.0808    1.4275    0.0339    0.9652    0.0727
+#> 124:    93.2708   -5.8004   -2.2782   -4.4951   -0.9451    0.2590    3.5914    3.0594    1.0875    5.6382    0.1607    0.0768    1.3628    0.0340    1.0577    0.0693
+#> 125:    93.4025   -5.7710   -2.2990   -4.4498   -0.9661    0.2633    3.4118    2.9276    1.0809    5.3563    0.1527    0.0730    1.3816    0.0406    1.0295    0.0671
+#> 126:    93.4928   -5.7054   -2.3002   -4.4087   -0.9394    0.2965    3.4732    2.7812    1.1275    5.0884    0.1481    0.0693    1.2949    0.0423    0.9084    0.0726
+#> 127:    93.6449   -5.6593   -2.2683   -4.3418   -0.9194    0.2560    4.2986    2.6422    1.1070    4.8340    0.1449    0.0707    1.4258    0.0341    0.8802    0.0777
+#> 128:    93.7430   -5.6359   -2.2686   -4.4174   -0.9500    0.2279    5.2477    2.5101    1.1046    5.5376    0.1512    0.0859    1.4523    0.0327    0.8659    0.0826
+#> 129:    93.7432   -5.6851   -2.2849   -4.2019   -0.9660    0.1995    7.2497    2.8789    1.1315    5.2607    0.1762    0.0972    1.3901    0.0357    1.1264    0.0743
+#> 130:    93.2409   -5.8965   -2.2946   -4.1880   -0.9774    0.1719    7.4467    3.2276    1.1464    4.9977    0.1720    0.0924    1.3517    0.0446    1.0461    0.0705
+#> 131:    92.7780   -6.0551   -2.2647   -4.1894   -0.9579    0.1391    7.0744    3.7584    1.1291    4.7478    0.1714    0.0995    1.2542    0.0438    0.9139    0.0777
+#> 132:    92.7157   -6.1161   -2.2501   -4.1784   -0.9651    0.1146    6.7207    3.9259    1.1674    4.5104    0.1712    0.0957    1.2549    0.0473    0.8964    0.0803
+#> 133:    92.2696   -5.8545   -2.2717   -4.1907   -0.9782    0.0985    6.3846    3.7296    1.1652    4.2849    0.1626    0.1198    1.2208    0.0498    0.9730    0.0822
+#> 134:    92.2067   -5.8603   -2.2743   -4.2095   -0.9754    0.1398    6.0654    3.5431    1.1551    4.0706    0.1695    0.1138    1.3022    0.0432    0.9960    0.0795
+#> 135:    92.3979   -5.9500   -2.3053   -4.1938   -0.9425    0.1134    5.7621    3.3660    1.1771    3.8671    0.1610    0.1081    1.3373    0.0462    1.1323    0.0665
+#> 136:    92.3749   -5.8701   -2.2979   -4.2493   -0.9386    0.1504    5.4740    3.3090    1.1638    3.9609    0.1724    0.1027    1.3578    0.0389    1.1943    0.0650
+#> 137:    92.6942   -5.9020   -2.2755   -4.2318   -0.9464    0.1541    5.2003    3.5521    1.1704    3.8948    0.1685    0.0976    1.4170    0.0399    1.1472    0.0626
+#> 138:    92.7234   -5.8085   -2.2653   -4.2164   -0.9662    0.1808    4.9403    3.3745    1.1977    3.8348    0.1694    0.0927    1.4229    0.0387    1.0934    0.0708
+#> 139:    92.7341   -5.7737   -2.2685   -4.1759   -0.9334    0.1554    4.6933    3.2057    1.1971    3.6962    0.1917    0.0881    1.4324    0.0363    1.1669    0.0652
+#> 140:    92.1593   -5.6287   -2.2576   -4.1977   -0.9232    0.1345    4.6967    3.0455    1.1676    3.8133    0.2060    0.0837    1.5032    0.0349    1.1418    0.0678
+#> 141:    92.3199   -5.8323   -2.2451   -4.1948   -0.9447    0.1295    4.9624    3.3893    1.1408    3.8423    0.1957    0.0795    1.4470    0.0325    1.0892    0.0739
+#> 142:    92.7246   -6.1252   -2.2304   -4.1984   -0.9160    0.0816    4.7143    4.6501    1.1420    3.8554    0.1901    0.0755    1.4847    0.0386    1.2815    0.0576
+#> 143:    92.4130   -6.0231   -2.2261   -4.2205   -0.9495    0.1020    4.4786    4.4176    1.1454    4.0301    0.1929    0.0717    1.4103    0.0410    1.0418    0.0739
+#> 144:    92.4006   -5.9898   -2.2232   -4.2429   -0.9553    0.1131    4.2547    4.1967    1.1579    4.2583    0.1904    0.0681    1.4272    0.0339    1.0591    0.0737
+#> 145:    92.5011   -6.2340   -2.2232   -4.1872   -0.9560    0.1322    6.1775    4.8941    1.1594    4.0453    0.1811    0.0647    1.4059    0.0298    1.0219    0.0752
+#> 146:    92.7460   -6.2989   -2.2417   -4.2501   -0.9650    0.1527    5.8686    5.6454    1.1154    4.0076    0.1720    0.0758    1.4027    0.0348    1.1220    0.0689
+#> 147:    93.0630   -6.0839   -2.2217   -4.1822   -0.9661    0.1634    5.5752    5.3631    1.0596    3.8072    0.1743    0.0733    1.3682    0.0393    1.0992    0.0700
+#> 148:    92.7639   -5.8682   -2.2550   -4.1926   -0.9440    0.1599    5.8048    5.0950    1.0858    3.6230    0.1749    0.0696    1.3364    0.0436    1.0967    0.0721
+#> 149:    92.6183   -6.1270   -2.2379   -4.1103   -0.9643    0.1202    5.8027    4.8402    1.1089    3.4860    0.1661    0.0661    1.3061    0.0457    1.0014    0.0724
+#> 150:    92.7472   -6.1515   -2.2199   -4.1027   -0.9611    0.1014    5.6767    4.5982    1.1061    3.6113    0.1578    0.0654    1.3543    0.0405    1.0847    0.0707
+#> 151:    92.9566   -5.8911   -2.2174   -4.0722   -0.9516    0.0992    5.9638    4.3683    1.1057    3.5122    0.1767    0.0621    1.3619    0.0396    1.0158    0.0734
+#> 152:    93.0035   -5.8395   -2.2559   -4.0650   -0.9389    0.0928    4.4799    3.2331    1.0387    3.4826    0.1713    0.0604    1.3425    0.0428    1.1101    0.0635
+#> 153:    92.7242   -5.7832   -2.2538   -4.1288   -0.9159    0.1047    4.6102    3.0838    1.0527    3.8052    0.1718    0.0597    1.3905    0.0398    1.1371    0.0635
+#> 154:    92.2125   -5.9077   -2.2400   -4.0922   -0.9106    0.1033    4.4732    3.8350    1.0261    3.6148    0.1955    0.0643    1.3176    0.0419    1.1130    0.0635
+#> 155:    92.6226   -5.6271   -2.2239   -4.0122   -0.8948    0.0647    4.5553    2.5675    1.0412    3.0513    0.1845    0.0866    1.3266    0.0459    1.0244    0.0680
+#> 156:    92.6532   -5.5576   -2.2251   -4.0066   -0.9006    0.0922    3.8517    2.3273    1.0455    3.0971    0.1928    0.0863    1.4000    0.0394    0.9203    0.0754
+#> 157:    92.5192   -5.4834   -2.2356   -4.0069   -0.9321    0.0904    3.0410    1.8841    0.9867    3.1990    0.1905    0.0816    1.3927    0.0407    1.1517    0.0614
+#> 158:    92.5628   -5.5318   -2.2044   -4.0269   -0.9319    0.0742    3.5124    1.9585    1.0692    3.1835    0.1958    0.0934    1.4038    0.0324    0.9680    0.0758
+#> 159:    92.9690   -5.6416   -2.2134   -4.0156   -0.9556    0.0560    4.3830    2.2442    1.0543    3.2358    0.1873    0.0951    1.3624    0.0375    1.1207    0.0696
+#> 160:    92.9861   -5.5872   -2.2207   -3.9908   -0.9190    0.0417    4.1202    2.1685    1.0711    3.1521    0.1766    0.0913    1.3760    0.0371    1.0970    0.0713
+#> 161:    93.3139   -5.5349   -2.1972   -3.9860   -0.9365    0.0011    4.2865    1.8741    1.0759    3.0304    0.2007    0.0750    1.3650    0.0411    1.1220    0.0662
+#> 162:    93.3324   -5.6135   -2.1579   -4.0151   -0.9507   -0.0091    4.6402    2.0208    1.0535    3.0349    0.1935    0.0764    1.4069    0.0383    1.2550    0.0598
+#> 163:    93.0110   -5.5253   -2.1419   -4.0151   -0.9197   -0.0072    5.8946    1.9087    1.0965    3.0349    0.1833    0.0814    1.5095    0.0290    1.1314    0.0665
+#> 164:    93.0848   -5.4980   -2.1670   -4.0213   -0.9345    0.0150    4.9128    1.8293    1.0379    3.0653    0.1728    0.0835    1.4913    0.0343    1.0589    0.0687
+#> 165:    92.9407   -5.3978   -2.1707   -4.0090   -0.9480    0.0126    3.4620    1.3870    1.0594    3.0115    0.1702    0.0982    1.5550    0.0296    1.0978    0.0694
+#> 166:    93.1504   -5.4880   -2.1890   -3.9958   -0.9511    0.0316    2.7859    1.8457    1.0294    3.0739    0.1738    0.1031    1.5109    0.0308    1.1800    0.0651
+#> 167:    92.8442   -5.4673   -2.1984   -4.0259   -0.9262    0.0243    2.0497    1.6348    1.0469    3.1258    0.1650    0.0981    1.6185    0.0291    1.1733    0.0655
+#> 168:    92.9484   -5.6255   -2.2012   -4.0136   -0.9309    0.0199    1.8121    2.0784    1.0415    3.1795    0.1816    0.0929    1.5727    0.0268    1.4222    0.0543
+#> 169:    93.0266   -5.6135   -2.1677   -4.0179   -0.9279    0.0375    1.7553    2.1663    1.0298    3.1675    0.2013    0.0926    1.5356    0.0274    1.2960    0.0596
+#> 170:    92.9844   -5.6286   -2.1839   -4.0509   -0.9471    0.0414    1.9485    2.4078    1.0656    3.2787    0.2112    0.0950    1.5210    0.0265    1.3069    0.0616
+#> 171:    92.6832   -5.6238   -2.2059   -4.0710   -0.9175    0.0383    1.5941    2.2918    1.1095    3.3435    0.1921    0.0895    1.4678    0.0345    1.2189    0.0618
+#> 172:    92.5302   -5.5653   -2.2086   -4.0429   -0.9412    0.0773    1.5302    2.2565    1.1293    3.2157    0.1924    0.0680    1.4438    0.0367    1.2084    0.0661
+#> 173:    92.3877   -5.5357   -2.2141   -4.0246   -0.9268    0.0866    1.2153    2.0588    1.0844    3.2941    0.2060    0.0726    1.4686    0.0359    1.3683    0.0596
+#> 174:    92.4410   -5.4921   -2.1955   -4.0398   -0.9269    0.0645    1.6903    2.0042    1.1236    3.3646    0.1847    0.0804    1.5533    0.0310    1.2320    0.0675
+#> 175:    92.4192   -5.4726   -2.1945   -4.0271   -0.9222    0.0728    1.1344    1.9292    1.1085    3.3173    0.1875    0.0912    1.5350    0.0302    1.2461    0.0679
+#> 176:    92.3581   -5.5256   -2.2055   -3.9958   -0.9211    0.0720    1.1140    1.8097    1.0898    3.1459    0.2018    0.1104    1.4391    0.0323    1.2240    0.0677
+#> 177:    92.2144   -5.6699   -2.2357   -4.0017   -0.9402    0.0785    1.1932    2.6190    1.0355    3.1852    0.2266    0.1125    1.4705    0.0327    1.2866    0.0621
+#> 178:    92.3608   -5.7040   -2.2245   -4.0242   -0.9642    0.0596    0.7932    2.6061    0.9408    3.1080    0.1958    0.1180    1.5158    0.0365    1.3571    0.0600
+#> 179:    92.4358   -5.6877   -2.2243   -4.0166   -0.9486    0.0595    0.7591    2.3791    0.9241    3.0638    0.1900    0.1257    1.4317    0.0363    1.2359    0.0686
+#> 180:    92.5146   -5.7856   -2.2343   -4.0098   -0.9522    0.0522    0.4573    2.6882    0.9636    3.0406    0.1835    0.1270    1.4631    0.0361    1.2192    0.0701
+#> 181:    92.5469   -5.7684   -2.2220   -4.0549   -0.9488    0.0901    0.4189    2.4963    0.9873    3.1470    0.1744    0.1268    1.5165    0.0336    1.1359    0.0760
+#> 182:    92.5829   -5.7658   -2.2385   -4.0362   -0.9723    0.0572    0.3720    2.5387    0.9203    3.0397    0.1769    0.1636    1.4781    0.0375    1.2697    0.0677
+#> 183:    92.5737   -5.9187   -2.2130   -4.0638   -0.9876    0.0797    0.3084    3.3137    0.9467    3.0532    0.1737    0.1599    1.4288    0.0309    1.3024    0.0617
+#> 184:    92.4989   -5.9837   -2.1994   -4.0476   -0.9737    0.0594    0.2533    3.6658    0.9248    3.1230    0.1776    0.1552    1.3829    0.0316    1.2818    0.0621
+#> 185:    92.5677   -6.0227   -2.2084   -4.0403   -0.9584    0.0609    0.2215    3.8810    0.9134    3.0961    0.1739    0.1473    1.4202    0.0319    1.2731    0.0579
+#> 186:    92.7090   -5.9641   -2.2218   -4.0319   -0.9573    0.0575    0.2917    3.9574    0.9373    3.0666    0.1691    0.1703    1.4378    0.0296    1.2775    0.0601
+#> 187:    92.7358   -6.2503   -2.2003   -4.0534   -0.9742    0.0691    0.3037    5.2011    0.9333    3.0796    0.1647    0.1553    1.4254    0.0293    1.1987    0.0629
+#> 188:    92.6733   -6.1434   -2.1988   -4.0792   -0.9878    0.0860    0.3122    4.9451    0.9080    3.1891    0.1628    0.1558    1.4099    0.0317    1.3162    0.0593
+#> 189:    92.7256   -6.0886   -2.1766   -4.0419   -0.9672    0.0550    0.3758    4.3461    0.9140    3.0795    0.1697    0.1649    1.5310    0.0301    1.3258    0.0566
+#> 190:    92.5144   -6.1827   -2.2159   -4.0525   -0.9677    0.0728    0.3855    4.3370    0.9706    3.0518    0.1486    0.1841    1.4390    0.0295    1.1259    0.0740
+#> 191:    92.6209   -6.1257   -2.2287   -4.1095   -0.9670    0.1034    0.3340    4.3051    0.9486    3.1970    0.1549    0.1776    1.4397    0.0296    1.2004    0.0684
+#> 192:    92.6156   -6.1289   -2.2067   -4.1191   -0.9900    0.1090    0.3069    4.1314    0.9134    3.1476    0.1596    0.1912    1.4380    0.0301    1.1238    0.0720
+#> 193:    92.5434   -5.9782   -2.1800   -4.0845   -0.9547    0.1173    0.2694    3.6834    0.9005    2.9479    0.1582    0.1733    1.4538    0.0294    0.8798    0.0866
+#> 194:    92.5884   -5.7815   -2.2110   -4.0714   -0.9510    0.0928    0.2493    2.8236    0.9615    2.9852    0.1488    0.1730    1.4409    0.0297    1.1446    0.0677
+#> 195:    92.6180   -5.9277   -2.2213   -4.0714   -0.9379    0.1177    0.1993    3.5172    0.8976    2.9852    0.1449    0.1735    1.5012    0.0299    1.2131    0.0618
+#> 196:    92.5920   -5.7723   -2.2496   -4.0669   -0.9184    0.1262    0.2595    3.2454    0.9419    2.9697    0.1600    0.1881    1.4017    0.0338    0.9594    0.0790
+#> 197:    92.6292   -5.8658   -2.2434   -4.0640   -0.9365    0.1216    0.2491    3.3540    0.9267    2.9523    0.1598    0.1749    1.3953    0.0383    1.0788    0.0702
+#> 198:    92.6911   -5.8407   -2.2605   -4.0640   -0.9319    0.1264    0.1930    3.2321    0.8884    2.9523    0.1320    0.1940    1.4026    0.0358    1.0613    0.0704
+#> 199:    92.6480   -5.6988   -2.2599   -4.0668   -0.9395    0.1328    0.1412    2.6535    0.8915    2.9610    0.1573    0.2052    1.4353    0.0360    0.9900    0.0742
+#> 200:    92.7139   -5.6152   -2.2522   -4.0684   -0.9192    0.1589    0.1686    2.4362    0.9098    3.0185    0.1702    0.1705    1.4153    0.0338    1.1747    0.0705
+#> 201:    92.7134   -5.7029   -2.2504   -4.0502   -0.9270    0.1453    0.1499    2.6851    0.8909    2.9484    0.1749    0.1772    1.3851    0.0363    1.1255    0.0714
+#> 202:    92.7087   -5.7236   -2.2421   -4.0499   -0.9364    0.1238    0.1324    2.7215    0.8810    2.9507    0.1694    0.1913    1.3864    0.0365    1.1192    0.0705
+#> 203:    92.7013   -5.7563   -2.2293   -4.0494   -0.9394    0.1134    0.1269    2.8279    0.8866    2.9501    0.1618    0.1915    1.3981    0.0356    1.0942    0.0710
+#> 204:    92.6964   -5.8134   -2.2208   -4.0646   -0.9373    0.1144    0.1192    3.1058    0.8973    3.0279    0.1523    0.1983    1.4126    0.0345    1.0629    0.0723
+#> 205:    92.6936   -5.8441   -2.2195   -4.0787   -0.9373    0.1144    0.1068    3.2553    0.9029    3.0962    0.1473    0.2001    1.4217    0.0344    1.0532    0.0719
+#> 206:    92.6881   -5.8805   -2.2209   -4.0887   -0.9432    0.1187    0.1016    3.4269    0.9126    3.1477    0.1479    0.1957    1.4251    0.0348    1.0697    0.0712
+#> 207:    92.6929   -5.9304   -2.2259   -4.0987   -0.9473    0.1234    0.1028    3.6444    0.9261    3.1982    0.1469    0.1910    1.4170    0.0348    1.0586    0.0717
+#> 208:    92.6907   -5.9413   -2.2275   -4.1043   -0.9482    0.1267    0.1038    3.6864    0.9313    3.2244    0.1467    0.1889    1.4121    0.0343    1.0499    0.0718
+#> 209:    92.6917   -5.9265   -2.2304   -4.1109   -0.9498    0.1289    0.1022    3.5975    0.9363    3.2487    0.1478    0.1863    1.4053    0.0344    1.0521    0.0716
+#> 210:    92.6966   -5.9218   -2.2322   -4.1164   -0.9516    0.1337    0.0984    3.5650    0.9413    3.2688    0.1493    0.1874    1.3949    0.0342    1.0499    0.0719
+#> 211:    92.7020   -5.9160   -2.2351   -4.1209   -0.9542    0.1385    0.0958    3.5091    0.9390    3.2968    0.1503    0.1873    1.3925    0.0345    1.0547    0.0718
+#> 212:    92.7065   -5.9119   -2.2376   -4.1247   -0.9564    0.1432    0.0933    3.4520    0.9373    3.3205    0.1531    0.1901    1.3869    0.0346    1.0625    0.0717
+#> 213:    92.7107   -5.9047   -2.2402   -4.1286   -0.9575    0.1455    0.0930    3.3990    0.9361    3.3369    0.1536    0.1932    1.3814    0.0349    1.0698    0.0712
+#> 214:    92.7110   -5.9061   -2.2415   -4.1321   -0.9585    0.1483    0.0921    3.3864    0.9364    3.3517    0.1542    0.1963    1.3794    0.0348    1.0721    0.0712
+#> 215:    92.7116   -5.9128   -2.2417   -4.1360   -0.9581    0.1510    0.0941    3.4201    0.9347    3.3646    0.1545    0.1988    1.3764    0.0350    1.0731    0.0712
+#> 216:    92.7135   -5.9184   -2.2432   -4.1383   -0.9589    0.1540    0.0957    3.4623    0.9337    3.3698    0.1541    0.2016    1.3761    0.0353    1.0737    0.0714
+#> 217:    92.7143   -5.9262   -2.2453   -4.1428   -0.9604    0.1568    0.0981    3.5202    0.9323    3.3854    0.1542    0.2053    1.3770    0.0352    1.0779    0.0716
+#> 218:    92.7102   -5.9169   -2.2463   -4.1446   -0.9606    0.1604    0.1000    3.4823    0.9305    3.3851    0.1530    0.2083    1.3802    0.0353    1.0819    0.0716
+#> 219:    92.7062   -5.9089   -2.2470   -4.1481   -0.9597    0.1636    0.1000    3.4465    0.9295    3.3874    0.1529    0.2125    1.3779    0.0352    1.0836    0.0716
+#> 220:    92.7027   -5.9052   -2.2480   -4.1509   -0.9594    0.1668    0.1020    3.4302    0.9264    3.3877    0.1531    0.2168    1.3780    0.0352    1.0893    0.0713
+#> 221:    92.7029   -5.8990   -2.2497   -4.1541   -0.9586    0.1696    0.1017    3.4007    0.9227    3.3916    0.1535    0.2208    1.3781    0.0354    1.0925    0.0709
+#> 222:    92.7063   -5.8993   -2.2519   -4.1604   -0.9582    0.1732    0.1025    3.4099    0.9190    3.4135    0.1537    0.2268    1.3791    0.0355    1.1031    0.0702
+#> 223:    92.7090   -5.8932   -2.2537   -4.1669   -0.9573    0.1757    0.1022    3.3946    0.9157    3.4424    0.1543    0.2319    1.3802    0.0356    1.1040    0.0701
+#> 224:    92.7116   -5.8930   -2.2545   -4.1712   -0.9561    0.1774    0.1017    3.3964    0.9133    3.4673    0.1550    0.2355    1.3795    0.0356    1.1018    0.0701
+#> 225:    92.7136   -5.8911   -2.2564   -4.1715   -0.9551    0.1788    0.1016    3.4013    0.9125    3.4628    0.1548    0.2380    1.3756    0.0359    1.1003    0.0700
+#> 226:    92.7153   -5.8883   -2.2569   -4.1711   -0.9536    0.1793    0.1016    3.4046    0.9134    3.4575    0.1549    0.2398    1.3737    0.0360    1.1016    0.0699
+#> 227:    92.7163   -5.8830   -2.2575   -4.1720   -0.9526    0.1796    0.1019    3.3952    0.9129    3.4575    0.1545    0.2407    1.3718    0.0363    1.1015    0.0698
+#> 228:    92.7182   -5.8865   -2.2578   -4.1728   -0.9528    0.1804    0.1017    3.4198    0.9113    3.4576    0.1538    0.2433    1.3722    0.0363    1.1068    0.0695
+#> 229:    92.7199   -5.8965   -2.2578   -4.1718   -0.9523    0.1812    0.1023    3.5030    0.9097    3.4503    0.1529    0.2463    1.3749    0.0363    1.1093    0.0694
+#> 230:    92.7205   -5.8997   -2.2578   -4.1712   -0.9514    0.1825    0.1025    3.5337    0.9071    3.4446    0.1519    0.2497    1.3802    0.0362    1.1115    0.0693
+#> 231:    92.7208   -5.9001   -2.2581   -4.1711   -0.9511    0.1838    0.1044    3.5537    0.9037    3.4423    0.1510    0.2533    1.3834    0.0361    1.1125    0.0693
+#> 232:    92.7183   -5.9041   -2.2588   -4.1715   -0.9504    0.1855    0.1061    3.5958    0.9001    3.4391    0.1503    0.2572    1.3871    0.0362    1.1161    0.0690
+#> 233:    92.7169   -5.9106   -2.2593   -4.1725   -0.9490    0.1866    0.1073    3.6433    0.8968    3.4367    0.1496    0.2609    1.3900    0.0362    1.1179    0.0688
+#> 234:    92.7125   -5.9165   -2.2594   -4.1728   -0.9479    0.1873    0.1098    3.6870    0.8932    3.4321    0.1498    0.2641    1.3907    0.0363    1.1177    0.0687
+#> 235:    92.7072   -5.9203   -2.2592   -4.1729   -0.9472    0.1876    0.1128    3.7229    0.8899    3.4269    0.1506    0.2676    1.3913    0.0364    1.1212    0.0686
+#> 236:    92.7048   -5.9319   -2.2603   -4.1724   -0.9467    0.1879    0.1147    3.7863    0.8879    3.4175    0.1510    0.2705    1.3898    0.0365    1.1181    0.0688
+#> 237:    92.7037   -5.9349   -2.2609   -4.1720   -0.9461    0.1881    0.1152    3.8047    0.8862    3.4096    0.1512    0.2731    1.3891    0.0367    1.1164    0.0688
+#> 238:    92.7027   -5.9359   -2.2605   -4.1715   -0.9459    0.1884    0.1151    3.7997    0.8842    3.4023    0.1516    0.2755    1.3905    0.0366    1.1171    0.0688
+#> 239:    92.7027   -5.9375   -2.2599   -4.1712   -0.9463    0.1881    0.1143    3.8187    0.8835    3.3954    0.1521    0.2780    1.3923    0.0366    1.1193    0.0688
+#> 240:    92.7025   -5.9409   -2.2593   -4.1710   -0.9467    0.1884    0.1135    3.8437    0.8830    3.3888    0.1530    0.2797    1.3939    0.0366    1.1266    0.0685
+#> 241:    92.7006   -5.9429   -2.2589   -4.1703   -0.9469    0.1887    0.1130    3.8580    0.8825    3.3820    0.1529    0.2815    1.3967    0.0364    1.1299    0.0685
+#> 242:    92.6977   -5.9366   -2.2594   -4.1693   -0.9471    0.1887    0.1130    3.8245    0.8810    3.3742    0.1534    0.2833    1.3967    0.0364    1.1323    0.0685
+#> 243:    92.6951   -5.9310   -2.2605   -4.1683   -0.9473    0.1891    0.1131    3.7904    0.8807    3.3666    0.1541    0.2853    1.3953    0.0364    1.1380    0.0683
+#> 244:    92.6928   -5.9289   -2.2610   -4.1680   -0.9471    0.1899    0.1130    3.7709    0.8797    3.3604    0.1545    0.2880    1.3947    0.0364    1.1399    0.0683
+#> 245:    92.6902   -5.9291   -2.2615   -4.1677   -0.9472    0.1914    0.1129    3.7637    0.8787    3.3538    0.1549    0.2898    1.3942    0.0364    1.1440    0.0681
+#> 246:    92.6880   -5.9271   -2.2617   -4.1677   -0.9472    0.1926    0.1131    3.7457    0.8785    3.3500    0.1549    0.2916    1.3938    0.0364    1.1468    0.0681
+#> 247:    92.6865   -5.9264   -2.2613   -4.1676   -0.9471    0.1930    0.1127    3.7331    0.8793    3.3487    0.1551    0.2918    1.3931    0.0364    1.1464    0.0683
+#> 248:    92.6855   -5.9212   -2.2604   -4.1671   -0.9476    0.1935    0.1116    3.7055    0.8795    3.3451    0.1549    0.2923    1.3942    0.0363    1.1453    0.0684
+#> 249:    92.6848   -5.9190   -2.2600   -4.1667   -0.9482    0.1939    0.1110    3.6857    0.8801    3.3428    0.1548    0.2923    1.3942    0.0363    1.1440    0.0685
+#> 250:    92.6858   -5.9194   -2.2605   -4.1663   -0.9489    0.1945    0.1109    3.6821    0.8806    3.3397    0.1547    0.2920    1.3932    0.0363    1.1430    0.0686
+#> 251:    92.6849   -5.9179   -2.2610   -4.1665   -0.9492    0.1950    0.1111    3.6795    0.8814    3.3392    0.1550    0.2919    1.3922    0.0364    1.1434    0.0685
+#> 252:    92.6848   -5.9141   -2.2615   -4.1660   -0.9493    0.1957    0.1110    3.6611    0.8818    3.3363    0.1548    0.2918    1.3919    0.0364    1.1423    0.0686
+#> 253:    92.6837   -5.9110   -2.2637   -4.1634   -0.9493    0.1952    0.1114    3.6462    0.8788    3.3481    0.1550    0.2920    1.3941    0.0363    1.1417    0.0688
+#> 254:    92.6827   -5.9082   -2.2650   -4.1608   -0.9492    0.1944    0.1117    3.6309    0.8753    3.3595    0.1548    0.2921    1.3964    0.0361    1.1415    0.0688
+#> 255:    92.6829   -5.9076   -2.2662   -4.1585   -0.9495    0.1934    0.1118    3.6221    0.8723    3.3737    0.1547    0.2923    1.3977    0.0359    1.1397    0.0689
+#> 256:    92.6821   -5.9079   -2.2672   -4.1559   -0.9495    0.1923    0.1118    3.6279    0.8697    3.3865    0.1547    0.2925    1.3990    0.0357    1.1387    0.0691
+#> 257:    92.6822   -5.9054   -2.2686   -4.1534   -0.9499    0.1914    0.1119    3.6202    0.8673    3.3988    0.1548    0.2923    1.4010    0.0356    1.1438    0.0690
+#> 258:    92.6828   -5.9054   -2.2700   -4.1509   -0.9498    0.1900    0.1121    3.6166    0.8651    3.4085    0.1547    0.2926    1.4028    0.0356    1.1473    0.0688
+#> 259:    92.6842   -5.9087   -2.2710   -4.1474   -0.9496    0.1890    0.1128    3.6314    0.8629    3.4154    0.1548    0.2923    1.4040    0.0355    1.1482    0.0689
+#> 260:    92.6852   -5.9118   -2.2717   -4.1444   -0.9493    0.1885    0.1124    3.6485    0.8606    3.4227    0.1544    0.2919    1.4073    0.0354    1.1518    0.0688
+#> 261:    92.6858   -5.9137   -2.2721   -4.1419   -0.9493    0.1882    0.1122    3.6641    0.8581    3.4314    0.1543    0.2913    1.4106    0.0353    1.1577    0.0684
+#> 262:    92.6861   -5.9117   -2.2726   -4.1394   -0.9493    0.1881    0.1116    3.6572    0.8558    3.4391    0.1541    0.2908    1.4137    0.0352    1.1613    0.0682
+#> 263:    92.6855   -5.9124   -2.2730   -4.1372   -0.9494    0.1875    0.1113    3.6626    0.8533    3.4465    0.1541    0.2905    1.4152    0.0351    1.1636    0.0681
+#> 264:    92.6841   -5.9137   -2.2734   -4.1351   -0.9496    0.1871    0.1109    3.6703    0.8505    3.4529    0.1538    0.2903    1.4156    0.0350    1.1632    0.0681
+#> 265:    92.6833   -5.9153   -2.2741   -4.1327   -0.9498    0.1867    0.1108    3.6816    0.8472    3.4581    0.1535    0.2899    1.4168    0.0350    1.1647    0.0679
+#> 266:    92.6835   -5.9147   -2.2752   -4.1307   -0.9497    0.1865    0.1107    3.6768    0.8450    3.4641    0.1531    0.2896    1.4176    0.0349    1.1640    0.0679
+#> 267:    92.6835   -5.9167   -2.2761   -4.1283   -0.9499    0.1862    0.1105    3.6851    0.8430    3.4700    0.1530    0.2892    1.4178    0.0348    1.1639    0.0679
+#> 268:    92.6841   -5.9141   -2.2767   -4.1269   -0.9503    0.1860    0.1107    3.6718    0.8407    3.4775    0.1533    0.2891    1.4187    0.0348    1.1673    0.0677
+#> 269:    92.6845   -5.9094   -2.2774   -4.1253   -0.9503    0.1855    0.1112    3.6520    0.8384    3.4840    0.1535    0.2890    1.4192    0.0348    1.1686    0.0675
+#> 270:    92.6847   -5.9042   -2.2779   -4.1239   -0.9505    0.1853    0.1107    3.6288    0.8365    3.4895    0.1536    0.2889    1.4192    0.0347    1.1698    0.0675
+#> 271:    92.6849   -5.9000   -2.2785   -4.1228   -0.9506    0.1853    0.1102    3.6083    0.8348    3.4956    0.1536    0.2889    1.4191    0.0346    1.1692    0.0676
+#> 272:    92.6850   -5.8965   -2.2794   -4.1223   -0.9507    0.1853    0.1092    3.5892    0.8331    3.5071    0.1538    0.2889    1.4194    0.0345    1.1700    0.0676
+#> 273:    92.6851   -5.8916   -2.2805   -4.1222   -0.9508    0.1850    0.1089    3.5697    0.8315    3.5211    0.1538    0.2889    1.4209    0.0345    1.1720    0.0675
+#> 274:    92.6849   -5.8898   -2.2815   -4.1218   -0.9506    0.1852    0.1084    3.5607    0.8301    3.5339    0.1542    0.2886    1.4221    0.0344    1.1728    0.0675
+#> 275:    92.6844   -5.8885   -2.2830   -4.1215   -0.9504    0.1855    0.1080    3.5514    0.8284    3.5491    0.1545    0.2883    1.4238    0.0343    1.1756    0.0673
+#> 276:    92.6834   -5.8885   -2.2843   -4.1210   -0.9501    0.1859    0.1077    3.5477    0.8272    3.5648    0.1547    0.2878    1.4243    0.0343    1.1749    0.0674
+#> 277:    92.6829   -5.8892   -2.2858   -4.1208   -0.9500    0.1862    0.1071    3.5505    0.8257    3.5807    0.1552    0.2872    1.4244    0.0343    1.1747    0.0674
+#> 278:    92.6825   -5.8885   -2.2871   -4.1205   -0.9499    0.1862    0.1072    3.5463    0.8245    3.5960    0.1555    0.2866    1.4247    0.0343    1.1742    0.0675
+#> 279:    92.6815   -5.8887   -2.2883   -4.1201   -0.9501    0.1864    0.1072    3.5433    0.8239    3.6088    0.1556    0.2860    1.4247    0.0343    1.1737    0.0676
+#> 280:    92.6800   -5.8901   -2.2896   -4.1211   -0.9503    0.1865    0.1078    3.5481    0.8238    3.6285    0.1556    0.2848    1.4252    0.0344    1.1742    0.0676
+#> 281:    92.6779   -5.8914   -2.2907   -4.1218   -0.9502    0.1865    0.1084    3.5491    0.8240    3.6471    0.1558    0.2838    1.4251    0.0343    1.1732    0.0677
+#> 282:    92.6767   -5.8906   -2.2919   -4.1236   -0.9501    0.1862    0.1091    3.5462    0.8248    3.6747    0.1558    0.2825    1.4250    0.0344    1.1732    0.0677
+#> 283:    92.6750   -5.8895   -2.2928   -4.1253   -0.9499    0.1857    0.1097    3.5418    0.8260    3.7025    0.1555    0.2814    1.4253    0.0344    1.1712    0.0678
+#> 284:    92.6736   -5.8903   -2.2934   -4.1271   -0.9497    0.1854    0.1107    3.5438    0.8269    3.7297    0.1553    0.2800    1.4257    0.0343    1.1698    0.0678
+#> 285:    92.6730   -5.8917   -2.2942   -4.1284   -0.9497    0.1852    0.1116    3.5481    0.8274    3.7528    0.1551    0.2787    1.4260    0.0343    1.1689    0.0678
+#> 286:    92.6715   -5.8913   -2.2947   -4.1285   -0.9492    0.1849    0.1122    3.5473    0.8274    3.7660    0.1550    0.2775    1.4265    0.0342    1.1678    0.0679
+#> 287:    92.6702   -5.8925   -2.2952   -4.1290   -0.9489    0.1846    0.1125    3.5531    0.8268    3.7818    0.1549    0.2764    1.4269    0.0342    1.1673    0.0678
+#> 288:    92.6688   -5.8918   -2.2959   -4.1290   -0.9490    0.1843    0.1126    3.5495    0.8262    3.7946    0.1546    0.2756    1.4275    0.0341    1.1673    0.0678
+#> 289:    92.6673   -5.8907   -2.2966   -4.1295   -0.9490    0.1841    0.1124    3.5445    0.8260    3.8067    0.1543    0.2750    1.4280    0.0342    1.1690    0.0677
+#> 290:    92.6657   -5.8909   -2.2973   -4.1302   -0.9490    0.1838    0.1123    3.5433    0.8260    3.8201    0.1540    0.2744    1.4279    0.0342    1.1687    0.0676
+#> 291:    92.6642   -5.8902   -2.2978   -4.1312   -0.9493    0.1835    0.1124    3.5399    0.8262    3.8365    0.1538    0.2738    1.4279    0.0342    1.1695    0.0676
+#> 292:    92.6635   -5.8917   -2.2983   -4.1316   -0.9495    0.1831    0.1121    3.5453    0.8263    3.8517    0.1535    0.2733    1.4275    0.0342    1.1695    0.0675
+#> 293:    92.6622   -5.8936   -2.2991   -4.1323   -0.9497    0.1830    0.1121    3.5526    0.8265    3.8692    0.1533    0.2728    1.4274    0.0342    1.1701    0.0675
+#> 294:    92.6604   -5.8936   -2.2999   -4.1328   -0.9499    0.1826    0.1126    3.5505    0.8263    3.8838    0.1533    0.2723    1.4273    0.0342    1.1712    0.0675
+#> 295:    92.6593   -5.8924   -2.3007   -4.1329   -0.9498    0.1823    0.1131    3.5443    0.8262    3.9004    0.1531    0.2717    1.4276    0.0342    1.1718    0.0674
+#> 296:    92.6586   -5.8906   -2.3016   -4.1323   -0.9496    0.1822    0.1133    3.5374    0.8266    3.9103    0.1530    0.2707    1.4272    0.0343    1.1714    0.0674
+#> 297:    92.6578   -5.8889   -2.3026   -4.1329   -0.9494    0.1819    0.1139    3.5315    0.8271    3.9280    0.1528    0.2697    1.4267    0.0343    1.1697    0.0675
+#> 298:    92.6575   -5.8885   -2.3036   -4.1330   -0.9490    0.1814    0.1143    3.5303    0.8275    3.9410    0.1527    0.2689    1.4263    0.0344    1.1688    0.0675
+#> 299:    92.6566   -5.8879   -2.3047   -4.1329   -0.9488    0.1807    0.1147    3.5286    0.8282    3.9507    0.1526    0.2679    1.4263    0.0345    1.1680    0.0674
+#> 300:    92.6555   -5.8862   -2.3057   -4.1325   -0.9483    0.1802    0.1151    3.5225    0.8293    3.9582    0.1527    0.2671    1.4261    0.0345    1.1677    0.0674
+#> 301:    92.6545   -5.8854   -2.3067   -4.1326   -0.9480    0.1795    0.1156    3.5191    0.8300    3.9691    0.1530    0.2665    1.4257    0.0346    1.1672    0.0674
+#> 302:    92.6539   -5.8839   -2.3078   -4.1322   -0.9477    0.1788    0.1161    3.5154    0.8309    3.9769    0.1532    0.2657    1.4252    0.0346    1.1664    0.0675
+#> 303:    92.6541   -5.8799   -2.3089   -4.1327   -0.9474    0.1782    0.1161    3.5012    0.8319    3.9913    0.1534    0.2649    1.4242    0.0347    1.1653    0.0675
+#> 304:    92.6554   -5.8766   -2.3096   -4.1326   -0.9472    0.1774    0.1164    3.4879    0.8328    3.9978    0.1536    0.2641    1.4234    0.0348    1.1644    0.0675
+#> 305:    92.6559   -5.8732   -2.3104   -4.1325   -0.9470    0.1764    0.1161    3.4740    0.8334    4.0037    0.1535    0.2633    1.4231    0.0348    1.1634    0.0676
+#> 306:    92.6564   -5.8717   -2.3113   -4.1322   -0.9470    0.1758    0.1161    3.4705    0.8341    4.0097    0.1537    0.2622    1.4236    0.0348    1.1628    0.0676
+#> 307:    92.6573   -5.8703   -2.3121   -4.1320   -0.9469    0.1748    0.1158    3.4630    0.8349    4.0154    0.1538    0.2614    1.4231    0.0348    1.1617    0.0677
+#> 308:    92.6578   -5.8695   -2.3129   -4.1318   -0.9465    0.1738    0.1154    3.4585    0.8356    4.0210    0.1540    0.2607    1.4229    0.0348    1.1604    0.0677
+#> 309:    92.6577   -5.8691   -2.3132   -4.1317   -0.9465    0.1732    0.1151    3.4548    0.8369    4.0270    0.1540    0.2596    1.4233    0.0348    1.1589    0.0678
+#> 310:    92.6580   -5.8680   -2.3135   -4.1309   -0.9466    0.1727    0.1147    3.4472    0.8377    4.0280    0.1540    0.2587    1.4231    0.0348    1.1569    0.0679
+#> 311:    92.6575   -5.8681   -2.3141   -4.1303   -0.9466    0.1722    0.1144    3.4477    0.8384    4.0303    0.1539    0.2577    1.4236    0.0348    1.1557    0.0679
+#> 312:    92.6571   -5.8685   -2.3145   -4.1299   -0.9467    0.1720    0.1143    3.4498    0.8393    4.0328    0.1538    0.2566    1.4237    0.0348    1.1545    0.0680
+#> 313:    92.6559   -5.8685   -2.3150   -4.1296   -0.9469    0.1718    0.1142    3.4483    0.8403    4.0358    0.1537    0.2555    1.4234    0.0348    1.1532    0.0681
+#> 314:    92.6543   -5.8699   -2.3155   -4.1294   -0.9471    0.1715    0.1142    3.4526    0.8404    4.0401    0.1537    0.2546    1.4236    0.0347    1.1522    0.0681
+#> 315:    92.6528   -5.8713   -2.3161   -4.1289   -0.9472    0.1712    0.1144    3.4584    0.8402    4.0427    0.1537    0.2538    1.4234    0.0347    1.1520    0.0682
+#> 316:    92.6510   -5.8726   -2.3166   -4.1283   -0.9472    0.1705    0.1146    3.4647    0.8404    4.0443    0.1537    0.2528    1.4236    0.0347    1.1511    0.0682
+#> 317:    92.6496   -5.8736   -2.3170   -4.1281   -0.9474    0.1699    0.1147    3.4701    0.8406    4.0497    0.1536    0.2520    1.4238    0.0347    1.1504    0.0683
+#> 318:    92.6479   -5.8745   -2.3174   -4.1276   -0.9475    0.1695    0.1153    3.4729    0.8410    4.0511    0.1535    0.2510    1.4238    0.0347    1.1503    0.0683
+#> 319:    92.6463   -5.8773   -2.3175   -4.1272   -0.9476    0.1690    0.1155    3.4868    0.8409    4.0527    0.1535    0.2502    1.4234    0.0347    1.1484    0.0685
+#> 320:    92.6447   -5.8770   -2.3179   -4.1263   -0.9478    0.1684    0.1158    3.4849    0.8407    4.0516    0.1534    0.2493    1.4238    0.0347    1.1483    0.0685
+#> 321:    92.6433   -5.8768   -2.3181   -4.1255   -0.9479    0.1679    0.1161    3.4850    0.8405    4.0511    0.1533    0.2485    1.4238    0.0346    1.1474    0.0686
+#> 322:    92.6425   -5.8766   -2.3182   -4.1246   -0.9480    0.1673    0.1161    3.4839    0.8403    4.0505    0.1530    0.2474    1.4243    0.0346    1.1458    0.0687
+#> 323:    92.6414   -5.8778   -2.3183   -4.1241   -0.9481    0.1669    0.1162    3.4888    0.8402    4.0517    0.1530    0.2466    1.4244    0.0346    1.1454    0.0687
+#> 324:    92.6404   -5.8771   -2.3186   -4.1236   -0.9482    0.1666    0.1161    3.4855    0.8401    4.0525    0.1529    0.2459    1.4247    0.0345    1.1446    0.0687
+#> 325:    92.6396   -5.8753   -2.3188   -4.1231   -0.9483    0.1664    0.1156    3.4767    0.8396    4.0533    0.1529    0.2454    1.4253    0.0345    1.1438    0.0689
+#> 326:    92.6397   -5.8766   -2.3192   -4.1226   -0.9484    0.1663    0.1152    3.4798    0.8389    4.0542    0.1527    0.2449    1.4253    0.0345    1.1431    0.0690
+#> 327:    92.6395   -5.8785   -2.3197   -4.1224   -0.9483    0.1660    0.1151    3.4880    0.8382    4.0557    0.1528    0.2445    1.4250    0.0345    1.1430    0.0690
+#> 328:    92.6397   -5.8805   -2.3202   -4.1221   -0.9483    0.1657    0.1153    3.5011    0.8373    4.0568    0.1528    0.2442    1.4246    0.0345    1.1427    0.0690
+#> 329:    92.6390   -5.8838   -2.3208   -4.1219   -0.9482    0.1655    0.1161    3.5176    0.8365    4.0580    0.1530    0.2439    1.4241    0.0345    1.1429    0.0690
+#> 330:    92.6380   -5.8862   -2.3215   -4.1216   -0.9484    0.1653    0.1166    3.5286    0.8355    4.0584    0.1529    0.2437    1.4234    0.0346    1.1428    0.0690
+#> 331:    92.6367   -5.8867   -2.3223   -4.1206   -0.9484    0.1651    0.1165    3.5288    0.8348    4.0577    0.1528    0.2435    1.4233    0.0346    1.1429    0.0690
+#> 332:    92.6360   -5.8859   -2.3230   -4.1199   -0.9485    0.1650    0.1165    3.5235    0.8343    4.0572    0.1527    0.2433    1.4227    0.0346    1.1429    0.0689
+#> 333:    92.6361   -5.8839   -2.3237   -4.1194   -0.9485    0.1649    0.1162    3.5142    0.8340    4.0564    0.1527    0.2430    1.4224    0.0347    1.1429    0.0689
+#> 334:    92.6359   -5.8824   -2.3244   -4.1190   -0.9486    0.1649    0.1158    3.5070    0.8337    4.0567    0.1527    0.2424    1.4218    0.0347    1.1442    0.0689
+#> 335:    92.6366   -5.8826   -2.3250   -4.1186   -0.9485    0.1645    0.1157    3.5069    0.8334    4.0574    0.1527    0.2419    1.4214    0.0347    1.1448    0.0688
+#> 336:    92.6374   -5.8816   -2.3253   -4.1182   -0.9486    0.1644    0.1158    3.5034    0.8330    4.0580    0.1528    0.2415    1.4212    0.0347    1.1471    0.0687
+#> 337:    92.6378   -5.8810   -2.3258   -4.1176   -0.9487    0.1642    0.1159    3.5023    0.8325    4.0582    0.1528    0.2410    1.4212    0.0347    1.1467    0.0688
+#> 338:    92.6383   -5.8814   -2.3262   -4.1168   -0.9488    0.1637    0.1160    3.5028    0.8322    4.0571    0.1526    0.2409    1.4216    0.0346    1.1456    0.0689
+#> 339:    92.6392   -5.8808   -2.3266   -4.1160   -0.9490    0.1631    0.1161    3.4989    0.8318    4.0566    0.1524    0.2408    1.4220    0.0346    1.1441    0.0690
+#> 340:    92.6393   -5.8810   -2.3269   -4.1152   -0.9491    0.1626    0.1157    3.4997    0.8316    4.0564    0.1524    0.2407    1.4216    0.0346    1.1419    0.0692
+#> 341:    92.6394   -5.8807   -2.3272   -4.1148   -0.9492    0.1619    0.1153    3.4966    0.8308    4.0552    0.1523    0.2405    1.4218    0.0346    1.1415    0.0692
+#> 342:    92.6394   -5.8806   -2.3274   -4.1141   -0.9493    0.1612    0.1146    3.4936    0.8303    4.0537    0.1522    0.2405    1.4221    0.0346    1.1406    0.0692
+#> 343:    92.6398   -5.8819   -2.3277   -4.1134   -0.9494    0.1606    0.1141    3.4961    0.8297    4.0519    0.1522    0.2402    1.4219    0.0347    1.1404    0.0692
+#> 344:    92.6401   -5.8823   -2.3280   -4.1128   -0.9497    0.1599    0.1137    3.4963    0.8293    4.0504    0.1523    0.2400    1.4214    0.0346    1.1404    0.0692
+#> 345:    92.6404   -5.8829   -2.3283   -4.1124   -0.9498    0.1593    0.1136    3.4958    0.8289    4.0494    0.1523    0.2396    1.4214    0.0346    1.1398    0.0692
+#> 346:    92.6405   -5.8829   -2.3283   -4.1119   -0.9499    0.1587    0.1135    3.4953    0.8287    4.0484    0.1522    0.2394    1.4216    0.0346    1.1397    0.0692
+#> 347:    92.6404   -5.8833   -2.3288   -4.1117   -0.9500    0.1582    0.1133    3.4965    0.8289    4.0480    0.1521    0.2391    1.4211    0.0346    1.1388    0.0692
+#> 348:    92.6407   -5.8838   -2.3293   -4.1113   -0.9502    0.1578    0.1132    3.4978    0.8290    4.0471    0.1520    0.2388    1.4209    0.0346    1.1385    0.0692
+#> 349:    92.6409   -5.8847   -2.3299   -4.1110   -0.9503    0.1571    0.1128    3.5024    0.8290    4.0474    0.1519    0.2386    1.4207    0.0347    1.1379    0.0692
+#> 350:    92.6413   -5.8853   -2.3304   -4.1107   -0.9504    0.1567    0.1125    3.5037    0.8287    4.0478    0.1519    0.2383    1.4207    0.0347    1.1366    0.0693
+#> 351:    92.6415   -5.8868   -2.3310   -4.1104   -0.9504    0.1562    0.1122    3.5109    0.8287    4.0490    0.1518    0.2378    1.4208    0.0347    1.1364    0.0693
+#> 352:    92.6413   -5.8882   -2.3316   -4.1103   -0.9504    0.1557    0.1120    3.5196    0.8287    4.0517    0.1517    0.2375    1.4207    0.0346    1.1361    0.0693
+#> 353:    92.6414   -5.8890   -2.3322   -4.1101   -0.9503    0.1553    0.1117    3.5237    0.8290    4.0533    0.1517    0.2371    1.4202    0.0346    1.1345    0.0693
+#> 354:    92.6417   -5.8879   -2.3327   -4.1099   -0.9502    0.1548    0.1115    3.5206    0.8294    4.0546    0.1515    0.2368    1.4200    0.0346    1.1336    0.0694
+#> 355:    92.6417   -5.8882   -2.3333   -4.1096   -0.9500    0.1541    0.1115    3.5265    0.8296    4.0548    0.1514    0.2364    1.4203    0.0346    1.1325    0.0694
+#> 356:    92.6414   -5.8881   -2.3338   -4.1093   -0.9497    0.1535    0.1115    3.5339    0.8299    4.0553    0.1513    0.2362    1.4204    0.0346    1.1318    0.0694
+#> 357:    92.6414   -5.8874   -2.3343   -4.1087   -0.9497    0.1529    0.1117    3.5320    0.8302    4.0548    0.1512    0.2358    1.4205    0.0346    1.1315    0.0694
+#> 358:    92.6415   -5.8865   -2.3349   -4.1087   -0.9497    0.1523    0.1118    3.5274    0.8308    4.0583    0.1510    0.2354    1.4206    0.0346    1.1308    0.0695
+#> 359:    92.6415   -5.8855   -2.3352   -4.1085   -0.9497    0.1518    0.1123    3.5208    0.8308    4.0597    0.1509    0.2349    1.4205    0.0346    1.1298    0.0695
+#> 360:    92.6413   -5.8851   -2.3356   -4.1080   -0.9496    0.1513    0.1125    3.5176    0.8308    4.0606    0.1508    0.2344    1.4207    0.0346    1.1289    0.0695
+#> 361:    92.6412   -5.8854   -2.3359   -4.1076   -0.9498    0.1508    0.1126    3.5187    0.8308    4.0618    0.1508    0.2338    1.4214    0.0345    1.1279    0.0695
+#> 362:    92.6415   -5.8861   -2.3362   -4.1072   -0.9499    0.1503    0.1126    3.5210    0.8306    4.0636    0.1507    0.2333    1.4218    0.0345    1.1273    0.0695
+#> 363:    92.6412   -5.8884   -2.3364   -4.1066   -0.9499    0.1498    0.1126    3.5327    0.8305    4.0646    0.1507    0.2328    1.4221    0.0345    1.1273    0.0695
+#> 364:    92.6411   -5.8895   -2.3367   -4.1062   -0.9501    0.1494    0.1126    3.5366    0.8306    4.0659    0.1507    0.2322    1.4227    0.0345    1.1280    0.0695
+#> 365:    92.6411   -5.8908   -2.3367   -4.1060   -0.9502    0.1489    0.1125    3.5405    0.8307    4.0690    0.1507    0.2317    1.4228    0.0344    1.1280    0.0695
+#> 366:    92.6412   -5.8926   -2.3366   -4.1062   -0.9502    0.1484    0.1125    3.5483    0.8307    4.0724    0.1507    0.2311    1.4228    0.0344    1.1280    0.0695
+#> 367:    92.6406   -5.8940   -2.3366   -4.1059   -0.9503    0.1483    0.1124    3.5557    0.8308    4.0738    0.1507    0.2305    1.4228    0.0344    1.1273    0.0695
+#> 368:    92.6402   -5.8940   -2.3365   -4.1059   -0.9504    0.1483    0.1122    3.5538    0.8306    4.0773    0.1507    0.2299    1.4228    0.0344    1.1266    0.0696
+#> 369:    92.6398   -5.8933   -2.3366   -4.1058   -0.9504    0.1482    0.1122    3.5489    0.8303    4.0796    0.1507    0.2295    1.4228    0.0343    1.1261    0.0696
+#> 370:    92.6394   -5.8928   -2.3366   -4.1059   -0.9504    0.1481    0.1123    3.5445    0.8302    4.0819    0.1506    0.2291    1.4229    0.0343    1.1258    0.0696
+#> 371:    92.6390   -5.8930   -2.3369   -4.1062   -0.9503    0.1481    0.1125    3.5446    0.8299    4.0854    0.1506    0.2285    1.4230    0.0343    1.1257    0.0696
+#> 372:    92.6387   -5.8926   -2.3372   -4.1064   -0.9503    0.1482    0.1125    3.5424    0.8298    4.0887    0.1505    0.2281    1.4234    0.0343    1.1262    0.0696
+#> 373:    92.6385   -5.8927   -2.3376   -4.1067   -0.9502    0.1483    0.1126    3.5447    0.8297    4.0919    0.1504    0.2275    1.4236    0.0343    1.1268    0.0696
+#> 374:    92.6382   -5.8932   -2.3380   -4.1064   -0.9502    0.1481    0.1131    3.5490    0.8295    4.0929    0.1503    0.2272    1.4238    0.0343    1.1267    0.0696
+#> 375:    92.6385   -5.8944   -2.3383   -4.1062   -0.9502    0.1481    0.1136    3.5562    0.8292    4.0936    0.1503    0.2269    1.4240    0.0343    1.1274    0.0695
+#> 376:    92.6388   -5.8942   -2.3387   -4.1061   -0.9502    0.1481    0.1141    3.5575    0.8295    4.0942    0.1502    0.2267    1.4236    0.0343    1.1272    0.0695
+#> 377:    92.6389   -5.8942   -2.3392   -4.1060   -0.9502    0.1482    0.1145    3.5579    0.8298    4.0950    0.1501    0.2264    1.4233    0.0344    1.1272    0.0695
+#> 378:    92.6388   -5.8939   -2.3397   -4.1060   -0.9502    0.1481    0.1150    3.5558    0.8298    4.0959    0.1500    0.2261    1.4232    0.0344    1.1271    0.0695
+#> 379:    92.6388   -5.8934   -2.3399   -4.1062   -0.9500    0.1483    0.1153    3.5521    0.8294    4.0980    0.1500    0.2257    1.4236    0.0344    1.1279    0.0694
+#> 380:    92.6390   -5.8920   -2.3402   -4.1065   -0.9499    0.1484    0.1155    3.5446    0.8292    4.1007    0.1500    0.2254    1.4241    0.0344    1.1285    0.0694
+#> 381:    92.6394   -5.8906   -2.3404   -4.1069   -0.9498    0.1485    0.1157    3.5378    0.8290    4.1040    0.1500    0.2250    1.4249    0.0343    1.1296    0.0694
+#> 382:    92.6403   -5.8893   -2.3406   -4.1085   -0.9498    0.1487    0.1157    3.5319    0.8289    4.1195    0.1500    0.2246    1.4250    0.0343    1.1301    0.0694
+#> 383:    92.6402   -5.8882   -2.3408   -4.1096   -0.9499    0.1488    0.1155    3.5269    0.8287    4.1290    0.1500    0.2243    1.4253    0.0343    1.1300    0.0694
+#> 384:    92.6401   -5.8871   -2.3412   -4.1102   -0.9498    0.1490    0.1155    3.5219    0.8285    4.1340    0.1499    0.2241    1.4254    0.0343    1.1297    0.0694
+#> 385:    92.6396   -5.8867   -2.3417   -4.1105   -0.9497    0.1493    0.1155    3.5195    0.8281    4.1364    0.1498    0.2238    1.4252    0.0343    1.1297    0.0695
+#> 386:    92.6393   -5.8863   -2.3423   -4.1116   -0.9496    0.1497    0.1153    3.5190    0.8280    4.1452    0.1497    0.2235    1.4251    0.0343    1.1307    0.0694
+#> 387:    92.6391   -5.8865   -2.3429   -4.1124   -0.9495    0.1498    0.1155    3.5219    0.8280    4.1502    0.1497    0.2234    1.4247    0.0343    1.1301    0.0695
+#> 388:    92.6389   -5.8861   -2.3436   -4.1129   -0.9494    0.1501    0.1158    3.5228    0.8278    4.1540    0.1496    0.2233    1.4243    0.0343    1.1293    0.0695
+#> 389:    92.6384   -5.8849   -2.3442   -4.1132   -0.9491    0.1504    0.1159    3.5195    0.8276    4.1571    0.1496    0.2231    1.4242    0.0343    1.1284    0.0696
+#> 390:    92.6382   -5.8838   -2.3447   -4.1134   -0.9489    0.1506    0.1159    3.5172    0.8276    4.1603    0.1497    0.2230    1.4242    0.0343    1.1273    0.0697
+#> 391:    92.6380   -5.8821   -2.3454   -4.1140   -0.9486    0.1509    0.1159    3.5134    0.8274    4.1661    0.1498    0.2228    1.4238    0.0343    1.1266    0.0697
+#> 392:    92.6374   -5.8800   -2.3460   -4.1140   -0.9485    0.1513    0.1158    3.5069    0.8274    4.1673    0.1499    0.2226    1.4235    0.0343    1.1258    0.0698
+#> 393:    92.6372   -5.8785   -2.3467   -4.1140   -0.9485    0.1514    0.1159    3.5019    0.8275    4.1684    0.1499    0.2223    1.4232    0.0343    1.1258    0.0698
+#> 394:    92.6372   -5.8765   -2.3473   -4.1142   -0.9485    0.1515    0.1161    3.4955    0.8275    4.1710    0.1499    0.2221    1.4228    0.0344    1.1260    0.0697
+#> 395:    92.6371   -5.8761   -2.3476   -4.1145   -0.9485    0.1515    0.1164    3.4940    0.8273    4.1739    0.1498    0.2220    1.4227    0.0344    1.1254    0.0698
+#> 396:    92.6370   -5.8759   -2.3480   -4.1147   -0.9485    0.1516    0.1166    3.4942    0.8269    4.1764    0.1498    0.2217    1.4222    0.0344    1.1252    0.0698
+#> 397:    92.6371   -5.8756   -2.3483   -4.1149   -0.9486    0.1516    0.1167    3.4914    0.8267    4.1796    0.1498    0.2214    1.4219    0.0344    1.1253    0.0697
+#> 398:    92.6371   -5.8756   -2.3486   -4.1155   -0.9486    0.1518    0.1167    3.4909    0.8268    4.1840    0.1498    0.2210    1.4216    0.0344    1.1250    0.0697
+#> 399:    92.6368   -5.8765   -2.3489   -4.1157   -0.9485    0.1519    0.1170    3.4958    0.8266    4.1866    0.1498    0.2205    1.4213    0.0344    1.1245    0.0698
+#> 400:    92.6368   -5.8769   -2.3491   -4.1158   -0.9485    0.1522    0.1174    3.4972    0.8266    4.1888    0.1499    0.2200    1.4209    0.0344    1.1242    0.0698
+#> 401:    92.6366   -5.8768   -2.3493   -4.1161   -0.9484    0.1524    0.1175    3.4964    0.8267    4.1913    0.1499    0.2196    1.4204    0.0344    1.1240    0.0698
+#> 402:    92.6362   -5.8767   -2.3495   -4.1164   -0.9483    0.1525    0.1176    3.4961    0.8267    4.1937    0.1499    0.2192    1.4201    0.0344    1.1240    0.0698
+#> 403:    92.6362   -5.8769   -2.3497   -4.1166   -0.9483    0.1526    0.1178    3.4981    0.8270    4.1960    0.1499    0.2187    1.4197    0.0345    1.1236    0.0698
+#> 404:    92.6359   -5.8772   -2.3499   -4.1166   -0.9483    0.1527    0.1179    3.4997    0.8272    4.1968    0.1499    0.2183    1.4193    0.0345    1.1232    0.0698
+#> 405:    92.6355   -5.8763   -2.3501   -4.1165   -0.9483    0.1527    0.1180    3.4946    0.8273    4.1976    0.1500    0.2180    1.4189    0.0345    1.1230    0.0698
+#> 406:    92.6351   -5.8768   -2.3503   -4.1164   -0.9482    0.1528    0.1184    3.4953    0.8274    4.1979    0.1500    0.2176    1.4184    0.0345    1.1227    0.0698
+#> 407:    92.6346   -5.8772   -2.3505   -4.1165   -0.9481    0.1527    0.1187    3.4965    0.8275    4.1999    0.1500    0.2173    1.4182    0.0344    1.1222    0.0698
+#> 408:    92.6344   -5.8786   -2.3508   -4.1167   -0.9482    0.1528    0.1190    3.5025    0.8276    4.2020    0.1500    0.2171    1.4178    0.0344    1.1215    0.0699
+#> 409:    92.6342   -5.8806   -2.3511   -4.1168   -0.9484    0.1529    0.1193    3.5134    0.8277    4.2037    0.1500    0.2167    1.4176    0.0344    1.1212    0.0699
+#> 410:    92.6341   -5.8826   -2.3514   -4.1170   -0.9486    0.1531    0.1193    3.5229    0.8279    4.2061    0.1500    0.2163    1.4175    0.0344    1.1212    0.0699
+#> 411:    92.6339   -5.8840   -2.3517   -4.1172   -0.9488    0.1532    0.1192    3.5280    0.8280    4.2087    0.1499    0.2159    1.4175    0.0345    1.1208    0.0699
+#> 412:    92.6338   -5.8850   -2.3520   -4.1175   -0.9489    0.1534    0.1193    3.5311    0.8280    4.2121    0.1497    0.2155    1.4177    0.0345    1.1204    0.0699
+#> 413:    92.6343   -5.8859   -2.3523   -4.1177   -0.9491    0.1536    0.1191    3.5337    0.8282    4.2156    0.1497    0.2151    1.4176    0.0345    1.1198    0.0699
+#> 414:    92.6350   -5.8861   -2.3526   -4.1184   -0.9491    0.1540    0.1191    3.5350    0.8283    4.2209    0.1496    0.2147    1.4177    0.0345    1.1196    0.0699
+#> 415:    92.6354   -5.8866   -2.3528   -4.1191   -0.9492    0.1543    0.1191    3.5373    0.8284    4.2258    0.1496    0.2142    1.4179    0.0345    1.1191    0.0699
+#> 416:    92.6360   -5.8873   -2.3531   -4.1201   -0.9493    0.1548    0.1193    3.5431    0.8286    4.2328    0.1495    0.2137    1.4178    0.0345    1.1187    0.0699
+#> 417:    92.6361   -5.8878   -2.3533   -4.1213   -0.9494    0.1551    0.1192    3.5465    0.8288    4.2415    0.1494    0.2131    1.4182    0.0345    1.1189    0.0699
+#> 418:    92.6366   -5.8883   -2.3535   -4.1221   -0.9495    0.1555    0.1194    3.5499    0.8291    4.2477    0.1493    0.2127    1.4180    0.0345    1.1184    0.0699
+#> 419:    92.6367   -5.8885   -2.3536   -4.1236   -0.9495    0.1560    0.1195    3.5517    0.8292    4.2588    0.1492    0.2123    1.4179    0.0345    1.1180    0.0700
+#> 420:    92.6371   -5.8874   -2.3536   -4.1249   -0.9495    0.1564    0.1197    3.5474    0.8293    4.2666    0.1491    0.2118    1.4181    0.0345    1.1182    0.0700
+#> 421:    92.6374   -5.8860   -2.3537   -4.1263   -0.9494    0.1569    0.1197    3.5416    0.8292    4.2759    0.1492    0.2114    1.4184    0.0345    1.1188    0.0699
+#> 422:    92.6377   -5.8850   -2.3538   -4.1279   -0.9493    0.1572    0.1197    3.5365    0.8292    4.2865    0.1491    0.2110    1.4185    0.0345    1.1188    0.0700
+#> 423:    92.6380   -5.8844   -2.3540   -4.1299   -0.9494    0.1576    0.1196    3.5323    0.8290    4.2999    0.1491    0.2106    1.4186    0.0345    1.1192    0.0699
+#> 424:    92.6382   -5.8842   -2.3541   -4.1312   -0.9495    0.1581    0.1198    3.5309    0.8290    4.3092    0.1491    0.2103    1.4184    0.0345    1.1197    0.0699
+#> 425:    92.6382   -5.8838   -2.3543   -4.1320   -0.9495    0.1584    0.1197    3.5281    0.8289    4.3140    0.1491    0.2099    1.4185    0.0346    1.1196    0.0699
+#> 426:    92.6380   -5.8829   -2.3545   -4.1327   -0.9494    0.1587    0.1196    3.5234    0.8293    4.3183    0.1491    0.2096    1.4182    0.0346    1.1194    0.0699
+#> 427:    92.6375   -5.8823   -2.3548   -4.1335   -0.9494    0.1589    0.1197    3.5189    0.8295    4.3233    0.1492    0.2092    1.4180    0.0346    1.1196    0.0699
+#> 428:    92.6370   -5.8813   -2.3552   -4.1343   -0.9494    0.1592    0.1199    3.5140    0.8295    4.3286    0.1491    0.2088    1.4182    0.0346    1.1198    0.0699
+#> 429:    92.6368   -5.8802   -2.3556   -4.1356   -0.9495    0.1597    0.1202    3.5093    0.8296    4.3372    0.1491    0.2086    1.4182    0.0346    1.1208    0.0699
+#> 430:    92.6370   -5.8794   -2.3560   -4.1366   -0.9496    0.1602    0.1201    3.5058    0.8297    4.3439    0.1492    0.2084    1.4183    0.0346    1.1216    0.0698
+#> 431:    92.6371   -5.8792   -2.3564   -4.1372   -0.9497    0.1606    0.1201    3.5029    0.8298    4.3473    0.1493    0.2082    1.4182    0.0346    1.1215    0.0698
+#> 432:    92.6371   -5.8793   -2.3567   -4.1377   -0.9499    0.1609    0.1201    3.5008    0.8297    4.3499    0.1494    0.2080    1.4180    0.0346    1.1218    0.0698
+#> 433:    92.6370   -5.8799   -2.3570   -4.1387   -0.9501    0.1612    0.1201    3.5014    0.8298    4.3560    0.1495    0.2078    1.4180    0.0346    1.1218    0.0699
+#> 434:    92.6371   -5.8790   -2.3573   -4.1398   -0.9501    0.1615    0.1200    3.4982    0.8300    4.3624    0.1496    0.2076    1.4179    0.0346    1.1213    0.0699
+#> 435:    92.6368   -5.8789   -2.3576   -4.1409   -0.9501    0.1619    0.1199    3.4979    0.8302    4.3697    0.1496    0.2074    1.4176    0.0346    1.1205    0.0699
+#> 436:    92.6365   -5.8792   -2.3579   -4.1424   -0.9500    0.1623    0.1197    3.4987    0.8304    4.3798    0.1497    0.2073    1.4173    0.0346    1.1198    0.0699
+#> 437:    92.6364   -5.8798   -2.3582   -4.1439   -0.9500    0.1627    0.1195    3.5017    0.8307    4.3905    0.1497    0.2071    1.4172    0.0346    1.1191    0.0700
+#> 438:    92.6362   -5.8803   -2.3585   -4.1450   -0.9499    0.1631    0.1193    3.5053    0.8309    4.3973    0.1497    0.2070    1.4172    0.0346    1.1186    0.0700
+#> 439:    92.6361   -5.8811   -2.3588   -4.1463   -0.9498    0.1634    0.1190    3.5101    0.8312    4.4052    0.1496    0.2069    1.4172    0.0346    1.1188    0.0700
+#> 440:    92.6360   -5.8816   -2.3591   -4.1477   -0.9498    0.1637    0.1187    3.5127    0.8315    4.4145    0.1495    0.2068    1.4172    0.0346    1.1189    0.0700
+#> 441:    92.6357   -5.8816   -2.3594   -4.1492   -0.9499    0.1640    0.1185    3.5136    0.8319    4.4252    0.1494    0.2069    1.4175    0.0346    1.1191    0.0700
+#> 442:    92.6356   -5.8819   -2.3596   -4.1501   -0.9500    0.1642    0.1181    3.5151    0.8323    4.4310    0.1494    0.2070    1.4176    0.0346    1.1193    0.0700
+#> 443:    92.6356   -5.8825   -2.3598   -4.1512   -0.9501    0.1643    0.1180    3.5178    0.8324    4.4379    0.1493    0.2071    1.4179    0.0346    1.1196    0.0700
+#> 444:    92.6352   -5.8827   -2.3602   -4.1525   -0.9502    0.1644    0.1180    3.5169    0.8327    4.4458    0.1493    0.2073    1.4178    0.0346    1.1198    0.0700
+#> 445:    92.6348   -5.8828   -2.3605   -4.1534   -0.9502    0.1643    0.1180    3.5178    0.8329    4.4505    0.1493    0.2074    1.4178    0.0346    1.1202    0.0700
+#> 446:    92.6342   -5.8830   -2.3609   -4.1541   -0.9503    0.1643    0.1183    3.5182    0.8331    4.4539    0.1494    0.2077    1.4176    0.0346    1.1199    0.0700
+#> 447:    92.6334   -5.8832   -2.3613   -4.1548   -0.9503    0.1643    0.1188    3.5188    0.8333    4.4571    0.1494    0.2079    1.4172    0.0346    1.1198    0.0700
+#> 448:    92.6331   -5.8833   -2.3616   -4.1557   -0.9503    0.1643    0.1190    3.5190    0.8335    4.4613    0.1494    0.2080    1.4170    0.0346    1.1198    0.0700
+#> 449:    92.6327   -5.8835   -2.3619   -4.1563   -0.9504    0.1641    0.1192    3.5191    0.8335    4.4636    0.1493    0.2081    1.4172    0.0346    1.1196    0.0700
+#> 450:    92.6322   -5.8831   -2.3620   -4.1566   -0.9505    0.1639    0.1194    3.5152    0.8340    4.4647    0.1492    0.2083    1.4172    0.0346    1.1189    0.0700
+#> 451:    92.6315   -5.8835   -2.3622   -4.1569   -0.9505    0.1635    0.1194    3.5192    0.8343    4.4648    0.1492    0.2084    1.4169    0.0346    1.1187    0.0700
+#> 452:    92.6312   -5.8834   -2.3625   -4.1572   -0.9506    0.1632    0.1193    3.5173    0.8345    4.4654    0.1492    0.2086    1.4166    0.0346    1.1183    0.0700
+#> 453:    92.6309   -5.8838   -2.3628   -4.1574   -0.9506    0.1629    0.1193    3.5175    0.8348    4.4660    0.1493    0.2087    1.4166    0.0346    1.1180    0.0700
+#> 454:    92.6307   -5.8832   -2.3629   -4.1574   -0.9507    0.1625    0.1193    3.5128    0.8354    4.4658    0.1493    0.2087    1.4164    0.0346    1.1176    0.0700
+#> 455:    92.6305   -5.8821   -2.3632   -4.1579   -0.9508    0.1624    0.1192    3.5071    0.8360    4.4678    0.1494    0.2089    1.4164    0.0346    1.1171    0.0701
+#> 456:    92.6307   -5.8811   -2.3634   -4.1589   -0.9509    0.1623    0.1190    3.5014    0.8364    4.4730    0.1494    0.2088    1.4168    0.0346    1.1168    0.0701
+#> 457:    92.6307   -5.8808   -2.3636   -4.1597   -0.9509    0.1621    0.1188    3.4980    0.8368    4.4772    0.1494    0.2089    1.4168    0.0347    1.1166    0.0701
+#> 458:    92.6308   -5.8813   -2.3638   -4.1607   -0.9510    0.1621    0.1185    3.4994    0.8369    4.4823    0.1494    0.2088    1.4168    0.0347    1.1161    0.0701
+#> 459:    92.6308   -5.8819   -2.3639   -4.1615   -0.9511    0.1620    0.1184    3.5008    0.8371    4.4861    0.1494    0.2086    1.4167    0.0347    1.1155    0.0701
+#> 460:    92.6309   -5.8824   -2.3642   -4.1621   -0.9511    0.1621    0.1182    3.5024    0.8374    4.4886    0.1493    0.2085    1.4164    0.0347    1.1148    0.0702
+#> 461:    92.6309   -5.8821   -2.3647   -4.1631   -0.9511    0.1621    0.1181    3.5000    0.8378    4.4937    0.1493    0.2084    1.4160    0.0347    1.1141    0.0702
+#> 462:    92.6309   -5.8825   -2.3651   -4.1638   -0.9511    0.1623    0.1180    3.5006    0.8381    4.4975    0.1492    0.2082    1.4156    0.0348    1.1133    0.0702
+#> 463:    92.6307   -5.8824   -2.3656   -4.1654   -0.9510    0.1624    0.1179    3.5000    0.8382    4.5074    0.1491    0.2081    1.4154    0.0348    1.1124    0.0702
+#> 464:    92.6305   -5.8825   -2.3660   -4.1668   -0.9510    0.1625    0.1178    3.5001    0.8384    4.5171    0.1491    0.2080    1.4149    0.0348    1.1115    0.0703
+#> 465:    92.6302   -5.8828   -2.3664   -4.1681   -0.9511    0.1626    0.1179    3.5012    0.8386    4.5247    0.1490    0.2079    1.4151    0.0348    1.1107    0.0703
+#> 466:    92.6300   -5.8827   -2.3668   -4.1697   -0.9511    0.1626    0.1179    3.5005    0.8390    4.5370    0.1490    0.2079    1.4148    0.0349    1.1098    0.0704
+#> 467:    92.6301   -5.8828   -2.3671   -4.1721   -0.9512    0.1628    0.1180    3.4991    0.8393    4.5562    0.1490    0.2078    1.4148    0.0349    1.1092    0.0704
+#> 468:    92.6303   -5.8833   -2.3675   -4.1745   -0.9513    0.1630    0.1181    3.4996    0.8397    4.5756    0.1489    0.2078    1.4148    0.0349    1.1086    0.0704
+#> 469:    92.6304   -5.8835   -2.3680   -4.1759   -0.9513    0.1630    0.1181    3.4991    0.8401    4.5829    0.1490    0.2080    1.4145    0.0349    1.1082    0.0704
+#> 470:    92.6304   -5.8839   -2.3685   -4.1772   -0.9512    0.1630    0.1183    3.4993    0.8405    4.5904    0.1490    0.2081    1.4142    0.0349    1.1079    0.0704
+#> 471:    92.6304   -5.8838   -2.3690   -4.1786   -0.9511    0.1631    0.1182    3.4992    0.8408    4.5981    0.1489    0.2082    1.4143    0.0350    1.1075    0.0704
+#> 472:    92.6301   -5.8839   -2.3695   -4.1800   -0.9511    0.1631    0.1182    3.5005    0.8413    4.6063    0.1488    0.2083    1.4143    0.0350    1.1072    0.0704
+#> 473:    92.6296   -5.8841   -2.3699   -4.1811   -0.9510    0.1630    0.1182    3.5019    0.8417    4.6119    0.1487    0.2085    1.4142    0.0350    1.1065    0.0704
+#> 474:    92.6293   -5.8843   -2.3704   -4.1823   -0.9510    0.1629    0.1184    3.5038    0.8422    4.6182    0.1487    0.2087    1.4145    0.0350    1.1060    0.0704
+#> 475:    92.6293   -5.8851   -2.3709   -4.1839   -0.9509    0.1628    0.1185    3.5084    0.8426    4.6277    0.1487    0.2089    1.4142    0.0351    1.1057    0.0704
+#> 476:    92.6293   -5.8854   -2.3713   -4.1847   -0.9509    0.1627    0.1185    3.5137    0.8430    4.6318    0.1486    0.2092    1.4139    0.0351    1.1057    0.0704
+#> 477:    92.6292   -5.8858   -2.3718   -4.1859   -0.9508    0.1627    0.1183    3.5201    0.8430    4.6397    0.1485    0.2095    1.4139    0.0351    1.1060    0.0704
+#> 478:    92.6291   -5.8871   -2.3722   -4.1867   -0.9508    0.1625    0.1181    3.5291    0.8432    4.6449    0.1483    0.2098    1.4140    0.0351    1.1058    0.0704
+#> 479:    92.6293   -5.8891   -2.3726   -4.1873   -0.9509    0.1623    0.1178    3.5422    0.8435    4.6486    0.1482    0.2100    1.4139    0.0352    1.1056    0.0704
+#> 480:    92.6294   -5.8910   -2.3730   -4.1881   -0.9509    0.1622    0.1175    3.5568    0.8437    4.6535    0.1482    0.2102    1.4140    0.0352    1.1053    0.0705
+#> 481:    92.6297   -5.8919   -2.3734   -4.1888   -0.9509    0.1621    0.1174    3.5650    0.8440    4.6572    0.1482    0.2104    1.4138    0.0353    1.1051    0.0705
+#> 482:    92.6293   -5.8929   -2.3737   -4.1894   -0.9509    0.1619    0.1173    3.5745    0.8444    4.6620    0.1482    0.2107    1.4134    0.0353    1.1047    0.0705
+#> 483:    92.6284   -5.8939   -2.3741   -4.1901   -0.9508    0.1616    0.1176    3.5832    0.8446    4.6672    0.1482    0.2109    1.4131    0.0353    1.1044    0.0705
+#> 484:    92.6276   -5.8943   -2.3744   -4.1904   -0.9507    0.1615    0.1179    3.5877    0.8447    4.6692    0.1483    0.2113    1.4128    0.0353    1.1041    0.0705
+#> 485:    92.6266   -5.8947   -2.3746   -4.1912   -0.9507    0.1616    0.1182    3.5903    0.8448    4.6751    0.1483    0.2115    1.4126    0.0354    1.1042    0.0705
+#> 486:    92.6258   -5.8952   -2.3749   -4.1918   -0.9508    0.1615    0.1185    3.5929    0.8450    4.6799    0.1485    0.2115    1.4125    0.0354    1.1045    0.0704
+#> 487:    92.6250   -5.8956   -2.3750   -4.1923   -0.9509    0.1614    0.1189    3.5922    0.8452    4.6835    0.1486    0.2115    1.4122    0.0354    1.1050    0.0704
+#> 488:    92.6242   -5.8956   -2.3752   -4.1927   -0.9510    0.1613    0.1191    3.5898    0.8453    4.6866    0.1487    0.2115    1.4119    0.0354    1.1051    0.0704
+#> 489:    92.6238   -5.8954   -2.3753   -4.1932   -0.9511    0.1611    0.1190    3.5871    0.8454    4.6905    0.1487    0.2115    1.4118    0.0354    1.1057    0.0704
+#> 490:    92.6237   -5.8951   -2.3754   -4.1936   -0.9511    0.1611    0.1188    3.5839    0.8454    4.6945    0.1487    0.2114    1.4117    0.0354    1.1064    0.0703
+#> 491:    92.6235   -5.8942   -2.3755   -4.1941   -0.9511    0.1610    0.1187    3.5790    0.8455    4.6981    0.1488    0.2115    1.4118    0.0354    1.1068    0.0703
+#> 492:    92.6234   -5.8938   -2.3755   -4.1952   -0.9512    0.1609    0.1186    3.5760    0.8454    4.7074    0.1488    0.2115    1.4119    0.0354    1.1074    0.0703
+#> 493:    92.6236   -5.8938   -2.3755   -4.1958   -0.9512    0.1608    0.1186    3.5747    0.8454    4.7121    0.1488    0.2114    1.4120    0.0354    1.1078    0.0702
+#> 494:    92.6239   -5.8945   -2.3756   -4.1964   -0.9513    0.1607    0.1186    3.5772    0.8455    4.7167    0.1488    0.2115    1.4120    0.0354    1.1082    0.0702
+#> 495:    92.6242   -5.8950   -2.3756   -4.1971   -0.9514    0.1605    0.1187    3.5798    0.8454    4.7227    0.1489    0.2117    1.4122    0.0354    1.1084    0.0702
+#> 496:    92.6242   -5.8962   -2.3757   -4.1978   -0.9514    0.1603    0.1189    3.5870    0.8455    4.7283    0.1489    0.2119    1.4121    0.0354    1.1090    0.0702
+#> 497:    92.6241   -5.8972   -2.3757   -4.1981   -0.9514    0.1602    0.1191    3.5934    0.8454    4.7298    0.1488    0.2120    1.4123    0.0354    1.1096    0.0701
+#> 498:    92.6244   -5.8973   -2.3758   -4.1981   -0.9514    0.1601    0.1190    3.5947    0.8454    4.7296    0.1488    0.2121    1.4123    0.0354    1.1101    0.0701
+#> 499:    92.6244   -5.8968   -2.3759   -4.1980   -0.9514    0.1600    0.1188    3.5935    0.8453    4.7290    0.1488    0.2124    1.4123    0.0354    1.1108    0.0701
+#> 500:    92.6245   -5.8959   -2.3759   -4.1978   -0.9513    0.1597    0.1188    3.5912    0.8452    4.7282    0.1488    0.2126    1.4123    0.0354    1.1111    0.0701
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei",
+  error_model = "obs_tc")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_A1 |f_parent_qlogis |    log_k1 |
+#> |.....................|    log_k2 |  g_qlogis |sigma_low_parent |rsd_high_parent |
+#> |.....................|sigma_low_A1 |rsd_high_A1 |        o1 |        o2 |
+#> |.....................|        o3 |        o4 |        o5 |        o6 |
+#> |    1|     495.80376 |     1.000 |    -1.000 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9885 |   -0.8832 |   -0.8755 |   -0.8915 |
+#> |.....................|   -0.8755 |   -0.8915 |   -0.8776 |   -0.8741 |
+#> |.....................|   -0.8681 |   -0.8727 |   -0.8749 |   -0.8675 |
+#> |    U|     495.80376 |     91.48 |    -5.189 |   -0.8875 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8280 |   0.05769 |
+#> |.....................|    0.8280 |   0.05769 |    0.7296 |    0.8969 |
+#> |.....................|     1.185 |    0.9628 |    0.8582 |     1.216 |
+#> |    X|     495.80376 |     91.48 |  0.005580 |    0.2916 |    0.1119 |
+#> |.....................|  0.009750 |    0.6128 |    0.8280 |   0.05769 |
+#> |.....................|    0.8280 |   0.05769 |    0.7296 |    0.8969 |
+#> |.....................|     1.185 |    0.9628 |    0.8582 |     1.216 |
+#> |    G|    Gill Diff. |     40.10 |     2.344 |  -0.09792 |   0.01304 |
+#> |.....................|   -0.4854 |    0.6353 |    -23.92 |    -17.76 |
+#> |.....................|    -5.723 |    -2.232 |     1.261 |     9.993 |
+#> |.....................|    -12.68 |   -0.7774 |     8.106 |    -12.55 |
+#> |    2|     3318.3701 |    0.2710 |    -1.043 |   -0.9092 |   -0.9382 |
+#> |.....................|   -0.9796 |   -0.8947 |   -0.4406 |   -0.5686 |
+#> |.....................|   -0.7715 |   -0.8509 |   -0.9005 |    -1.056 |
+#> |.....................|   -0.6376 |   -0.8586 |    -1.022 |   -0.6393 |
+#> |    U|     3318.3701 |     24.79 |    -5.231 |   -0.8859 |    -2.190 |
+#> |.....................|    -4.622 |    0.4536 |     1.008 |   0.06701 |
+#> |.....................|    0.8711 |   0.05887 |    0.7129 |    0.7340 |
+#> |.....................|     1.458 |    0.9764 |    0.7317 |     1.493 |
+#> |    X|     3318.3701 |     24.79 |  0.005347 |    0.2920 |    0.1119 |
+#> |.....................|  0.009837 |    0.6115 |     1.008 |   0.06701 |
+#> |.....................|    0.8711 |   0.05887 |    0.7129 |    0.7340 |
+#> |.....................|     1.458 |    0.9764 |    0.7317 |     1.493 |
+#> |    3|     512.37365 |    0.9271 |    -1.004 |   -0.9108 |   -0.9380 |
+#> |.....................|   -0.9876 |   -0.8843 |   -0.8320 |   -0.8592 |
+#> |.....................|   -0.8651 |   -0.8874 |   -0.8799 |   -0.8923 |
+#> |.....................|   -0.8451 |   -0.8713 |   -0.8896 |   -0.8447 |
+#> |    U|     512.37365 |     84.82 |    -5.193 |   -0.8873 |    -2.190 |
+#> |.....................|    -4.630 |    0.4584 |    0.8460 |   0.05863 |
+#> |.....................|    0.8323 |   0.05781 |    0.7279 |    0.8806 |
+#> |.....................|     1.212 |    0.9641 |    0.8455 |     1.244 |
+#> |    X|     512.37365 |     84.82 |  0.005556 |    0.2917 |    0.1119 |
+#> |.....................|  0.009759 |    0.6126 |    0.8460 |   0.05863 |
+#> |.....................|    0.8323 |   0.05781 |    0.7279 |    0.8806 |
+#> |.....................|     1.212 |    0.9641 |    0.8455 |     1.244 |
+#> |    4|     495.44913 |    0.9909 |    -1.001 |   -0.9110 |   -0.9380 |
+#> |.....................|   -0.9883 |   -0.8833 |   -0.8701 |   -0.8874 |
+#> |.....................|   -0.8742 |   -0.8910 |   -0.8778 |   -0.8764 |
+#> |.....................|   -0.8653 |   -0.8726 |   -0.8767 |   -0.8647 |
+#> |    U|     495.44913 |     90.65 |    -5.189 |   -0.8874 |    -2.190 |
+#> |.....................|    -4.630 |    0.4589 |    0.8303 |   0.05781 |
+#> |.....................|    0.8286 |   0.05771 |    0.7294 |    0.8949 |
+#> |.....................|     1.189 |    0.9629 |    0.8566 |     1.219 |
+#> |    X|     495.44913 |     90.65 |  0.005577 |    0.2916 |    0.1119 |
+#> |.....................|  0.009751 |    0.6127 |    0.8303 |   0.05781 |
+#> |.....................|    0.8286 |   0.05771 |    0.7294 |    0.8949 |
+#> |.....................|     1.189 |    0.9629 |    0.8566 |     1.219 |
+#> |    F| Forward Diff. |    -32.24 |     2.221 |   -0.3999 |    0.1183 |
+#> |.....................|   -0.4367 |    0.6696 |    -24.35 |    -18.50 |
+#> |.....................|    -5.733 |    -2.007 |     1.154 |     9.098 |
+#> |.....................|    -12.48 |   -0.2426 |     8.051 |    -12.28 |
+#> |    5|     495.09570 |    0.9990 |    -1.001 |   -0.9109 |   -0.9380 |
+#> |.....................|   -0.9882 |   -0.8835 |   -0.8640 |   -0.8828 |
+#> |.....................|   -0.8728 |   -0.8905 |   -0.8781 |   -0.8786 |
+#> |.....................|   -0.8621 |   -0.8725 |   -0.8788 |   -0.8616 |
+#> |    U|      495.0957 |     91.39 |    -5.190 |   -0.8874 |    -2.190 |
+#> |.....................|    -4.630 |    0.4588 |    0.8328 |   0.05794 |
+#> |.....................|    0.8291 |   0.05772 |    0.7292 |    0.8928 |
+#> |.....................|     1.192 |    0.9630 |    0.8549 |     1.223 |
+#> |    X|      495.0957 |     91.39 |  0.005574 |    0.2917 |    0.1119 |
+#> |.....................|  0.009752 |    0.6127 |    0.8328 |   0.05794 |
+#> |.....................|    0.8291 |   0.05772 |    0.7292 |    0.8928 |
+#> |.....................|     1.192 |    0.9630 |    0.8549 |     1.223 |
+#> |    F| Forward Diff. |     32.16 |     2.311 |   -0.1335 |   0.03619 |
+#> |.....................|   -0.4432 |    0.6445 |    -23.23 |    -17.46 |
+#> |.....................|    -5.567 |    -2.162 |     1.281 |     9.656 |
+#> |.....................|    -12.09 |   -0.7018 |     7.779 |    -12.29 |
+#> |    6|     494.75975 |    0.9908 |    -1.002 |   -0.9109 |   -0.9380 |
+#> |.....................|   -0.9881 |   -0.8836 |   -0.8581 |   -0.8783 |
+#> |.....................|   -0.8714 |   -0.8899 |   -0.8785 |   -0.8811 |
+#> |.....................|   -0.8590 |   -0.8723 |   -0.8807 |   -0.8584 |
+#> |    U|     494.75975 |     90.64 |    -5.190 |   -0.8873 |    -2.190 |
+#> |.....................|    -4.630 |    0.4587 |    0.8352 |   0.05807 |
+#> |.....................|    0.8297 |   0.05774 |    0.7290 |    0.8906 |
+#> |.....................|     1.196 |    0.9632 |    0.8532 |     1.227 |
+#> |    X|     494.75975 |     90.64 |  0.005570 |    0.2917 |    0.1119 |
+#> |.....................|  0.009754 |    0.6127 |    0.8352 |   0.05807 |
+#> |.....................|    0.8297 |   0.05774 |    0.7290 |    0.8906 |
+#> |.....................|     1.196 |    0.9632 |    0.8532 |     1.227 |
+#> |    F| Forward Diff. |    -33.18 |     2.192 |   -0.4095 |    0.1210 |
+#> |.....................|   -0.4089 |    0.6743 |    -23.19 |    -17.83 |
+#> |.....................|    -5.624 |    -1.860 |     1.146 |     8.868 |
+#> |.....................|    -11.42 |  -0.05808 |     7.519 |    -12.11 |
+#> |    7|     494.42957 |    0.9992 |    -1.002 |   -0.9108 |   -0.9380 |
+#> |.....................|   -0.9880 |   -0.8838 |   -0.8522 |   -0.8738 |
+#> |.....................|   -0.8699 |   -0.8894 |   -0.8788 |   -0.8834 |
+#> |.....................|   -0.8561 |   -0.8723 |   -0.8827 |   -0.8554 |
+#> |    U|     494.42957 |     91.41 |    -5.191 |   -0.8872 |    -2.190 |
+#> |.....................|    -4.630 |    0.4586 |    0.8377 |   0.05820 |
+#> |.....................|    0.8303 |   0.05775 |    0.7287 |    0.8886 |
+#> |.....................|     1.199 |    0.9632 |    0.8515 |     1.231 |
+#> |    X|     494.42957 |     91.41 |  0.005567 |    0.2917 |    0.1119 |
+#> |.....................|  0.009755 |    0.6127 |    0.8377 |   0.05820 |
+#> |.....................|    0.8303 |   0.05775 |    0.7287 |    0.8886 |
+#> |.....................|     1.199 |    0.9632 |    0.8515 |     1.231 |
+#> |    F| Forward Diff. |     33.60 |     2.291 |   -0.1177 |   0.03548 |
+#> |.....................|   -0.4327 |    0.6500 |    -23.13 |    -16.67 |
+#> |.....................|    -5.444 |    -2.054 |     1.165 |     9.367 |
+#> |.....................|    -12.23 |    0.1305 |     7.522 |    -12.12 |
+#> |    8|     494.10805 |    0.9907 |    -1.003 |   -0.9107 |   -0.9380 |
+#> |.....................|   -0.9879 |   -0.8840 |   -0.8463 |   -0.8696 |
+#> |.....................|   -0.8686 |   -0.8889 |   -0.8791 |   -0.8857 |
+#> |.....................|   -0.8530 |   -0.8723 |   -0.8846 |   -0.8523 |
+#> |    U|     494.10805 |     90.63 |    -5.191 |   -0.8872 |    -2.190 |
+#> |.....................|    -4.630 |    0.4586 |    0.8401 |   0.05833 |
+#> |.....................|    0.8309 |   0.05777 |    0.7285 |    0.8865 |
+#> |.....................|     1.203 |    0.9632 |    0.8499 |     1.234 |
+#> |    X|     494.10805 |     90.63 |  0.005564 |    0.2917 |    0.1119 |
+#> |.....................|  0.009756 |    0.6127 |    0.8401 |   0.05833 |
+#> |.....................|    0.8309 |   0.05777 |    0.7285 |    0.8865 |
+#> |.....................|     1.203 |    0.9632 |    0.8499 |     1.234 |
+#> |    F| Forward Diff. |    -33.55 |     2.169 |   -0.4095 |    0.1317 |
+#> |.....................|   -0.3875 |    0.6809 |    -22.57 |    -17.16 |
+#> |.....................|    -5.560 |    -1.906 |     1.113 |     8.554 |
+#> |.....................|    -12.00 |   -0.1191 |     7.606 |    -11.94 |
+#> |    9|     493.79074 |    0.9992 |    -1.003 |   -0.9106 |   -0.9381 |
+#> |.....................|   -0.9878 |   -0.8841 |   -0.8406 |   -0.8652 |
+#> |.....................|   -0.8671 |   -0.8884 |   -0.8793 |   -0.8879 |
+#> |.....................|   -0.8500 |   -0.8723 |   -0.8865 |   -0.8493 |
+#> |    U|     493.79074 |     91.41 |    -5.192 |   -0.8871 |    -2.190 |
+#> |.....................|    -4.630 |    0.4585 |    0.8425 |   0.05845 |
+#> |.....................|    0.8315 |   0.05778 |    0.7283 |    0.8845 |
+#> |.....................|     1.207 |    0.9632 |    0.8482 |     1.238 |
+#> |    X|     493.79074 |     91.41 |  0.005561 |    0.2917 |    0.1119 |
+#> |.....................|  0.009757 |    0.6127 |    0.8425 |   0.05845 |
+#> |.....................|    0.8315 |   0.05778 |    0.7283 |    0.8845 |
+#> |.....................|     1.207 |    0.9632 |    0.8482 |     1.238 |
+#> |    F| Forward Diff. |     33.91 |     2.267 |   -0.1078 |   0.03893 |
+#> |.....................|   -0.4090 |    0.6560 |    -22.34 |    -15.94 |
+#> |.....................|    -5.274 |    -2.001 |     1.140 |     9.131 |
+#> |.....................|    -12.00 |   -0.1724 |     7.294 |    -11.95 |
+#> |   10|     493.48645 |    0.9905 |    -1.004 |   -0.9106 |   -0.9381 |
+#> |.....................|   -0.9877 |   -0.8843 |   -0.8348 |   -0.8611 |
+#> |.....................|   -0.8658 |   -0.8879 |   -0.8796 |   -0.8903 |
+#> |.....................|   -0.8469 |   -0.8723 |   -0.8884 |   -0.8462 |
+#> |    U|     493.48645 |     90.62 |    -5.193 |   -0.8871 |    -2.190 |
+#> |.....................|    -4.630 |    0.4584 |    0.8449 |   0.05857 |
+#> |.....................|    0.8320 |   0.05780 |    0.7281 |    0.8824 |
+#> |.....................|     1.210 |    0.9632 |    0.8466 |     1.242 |
+#> |    X|     493.48645 |     90.62 |  0.005558 |    0.2917 |    0.1119 |
+#> |.....................|  0.009758 |    0.6126 |    0.8449 |   0.05857 |
+#> |.....................|    0.8320 |   0.05780 |    0.7281 |    0.8824 |
+#> |.....................|     1.210 |    0.9632 |    0.8466 |     1.242 |
+#> |    F| Forward Diff. |    -34.40 |     2.145 |   -0.4154 |    0.1312 |
+#> |.....................|   -0.3648 |    0.6865 |    -22.08 |    -16.36 |
+#> |.....................|    -5.345 |    -1.756 |     1.231 |     8.303 |
+#> |.....................|    -11.76 |  -0.07864 |     7.355 |    -11.77 |
+#> |   11|     493.18511 |    0.9993 |    -1.004 |   -0.9105 |   -0.9381 |
+#> |.....................|   -0.9876 |   -0.8845 |   -0.8292 |   -0.8570 |
+#> |.....................|   -0.8644 |   -0.8875 |   -0.8799 |   -0.8924 |
+#> |.....................|   -0.8439 |   -0.8722 |   -0.8902 |   -0.8432 |
+#> |    U|     493.18511 |     91.42 |    -5.193 |   -0.8870 |    -2.190 |
+#> |.....................|    -4.630 |    0.4583 |    0.8472 |   0.05869 |
+#> |.....................|    0.8326 |   0.05781 |    0.7279 |    0.8805 |
+#> |.....................|     1.214 |    0.9633 |    0.8450 |     1.246 |
+#> |    X|     493.18511 |     91.42 |  0.005555 |    0.2917 |    0.1119 |
+#> |.....................|  0.009759 |    0.6126 |    0.8472 |   0.05869 |
+#> |.....................|    0.8326 |   0.05781 |    0.7279 |    0.8805 |
+#> |.....................|     1.214 |    0.9633 |    0.8450 |     1.246 |
+#> |    F| Forward Diff. |     34.43 |     2.240 |   -0.1040 |   0.04282 |
+#> |.....................|   -0.3912 |    0.6547 |    -21.84 |    -15.27 |
+#> |.....................|    -5.158 |    -1.914 |     1.030 |     8.876 |
+#> |.....................|    -11.77 |   -0.1415 |     7.047 |    -11.78 |
+#> |   12|     492.89407 |    0.9905 |    -1.005 |   -0.9105 |   -0.9381 |
+#> |.....................|   -0.9875 |   -0.8847 |   -0.8236 |   -0.8530 |
+#> |.....................|   -0.8631 |   -0.8870 |   -0.8802 |   -0.8947 |
+#> |.....................|   -0.8409 |   -0.8722 |   -0.8921 |   -0.8401 |
+#> |    U|     492.89407 |     90.61 |    -5.194 |   -0.8870 |    -2.190 |
+#> |.....................|    -4.630 |    0.4582 |    0.8495 |   0.05880 |
+#> |.....................|    0.8332 |   0.05782 |    0.7277 |    0.8785 |
+#> |.....................|     1.217 |    0.9633 |    0.8434 |     1.249 |
+#> |    X|     492.89407 |     90.61 |  0.005551 |    0.2917 |    0.1119 |
+#> |.....................|  0.009760 |    0.6126 |    0.8495 |   0.05880 |
+#> |.....................|    0.8332 |   0.05782 |    0.7277 |    0.8785 |
+#> |.....................|     1.217 |    0.9633 |    0.8434 |     1.249 |
+#> |    F| Forward Diff. |    -34.81 |     2.117 |   -0.4182 |    0.1353 |
+#> |.....................|   -0.3428 |    0.6933 |    -21.54 |    -15.66 |
+#> |.....................|    -5.188 |    -1.708 |     1.147 |     8.020 |
+#> |.....................|    -11.52 |  -0.06705 |     7.151 |    -11.60 |
+#> |   13|     492.59250 |    0.9992 |    -1.006 |   -0.9104 |   -0.9382 |
+#> |.....................|   -0.9874 |   -0.8848 |   -0.8179 |   -0.8489 |
+#> |.....................|   -0.8617 |   -0.8865 |   -0.8805 |   -0.8968 |
+#> |.....................|   -0.8378 |   -0.8722 |   -0.8940 |   -0.8371 |
+#> |    U|      492.5925 |     91.41 |    -5.194 |   -0.8869 |    -2.190 |
+#> |.....................|    -4.629 |    0.4582 |    0.8519 |   0.05892 |
+#> |.....................|    0.8337 |   0.05784 |    0.7275 |    0.8766 |
+#> |.....................|     1.221 |    0.9633 |    0.8418 |     1.253 |
+#> |    X|      492.5925 |     91.41 |  0.005548 |    0.2918 |    0.1119 |
+#> |.....................|  0.009760 |    0.6126 |    0.8519 |   0.05892 |
+#> |.....................|    0.8337 |   0.05784 |    0.7275 |    0.8766 |
+#> |.....................|     1.221 |    0.9633 |    0.8418 |     1.253 |
+#> |    F| Forward Diff. |     33.40 |     2.217 |  -0.09736 |   0.04377 |
+#> |.....................|   -0.3664 |    0.6618 |    -21.29 |    -14.62 |
+#> |.....................|    -5.018 |    -1.838 |    0.9818 |     8.628 |
+#> |.....................|    -11.52 |   -0.1307 |     6.857 |    -11.62 |
+#> |   14|     492.30478 |    0.9905 |    -1.006 |   -0.9103 |   -0.9382 |
+#> |.....................|   -0.9873 |   -0.8850 |   -0.8121 |   -0.8449 |
+#> |.....................|   -0.8604 |   -0.8860 |   -0.8808 |   -0.8991 |
+#> |.....................|   -0.8347 |   -0.8722 |   -0.8958 |   -0.8339 |
+#> |    U|     492.30478 |     90.62 |    -5.195 |   -0.8868 |    -2.190 |
+#> |.....................|    -4.629 |    0.4581 |    0.8543 |   0.05904 |
+#> |.....................|    0.8343 |   0.05785 |    0.7273 |    0.8745 |
+#> |.....................|     1.225 |    0.9633 |    0.8402 |     1.257 |
+#> |    X|     492.30478 |     90.62 |  0.005545 |    0.2918 |    0.1119 |
+#> |.....................|  0.009761 |    0.6126 |    0.8543 |   0.05904 |
+#> |.....................|    0.8343 |   0.05785 |    0.7273 |    0.8745 |
+#> |.....................|     1.225 |    0.9633 |    0.8402 |     1.257 |
+#> |    F| Forward Diff. |    -34.08 |     2.096 |   -0.4157 |    0.1370 |
+#> |.....................|   -0.3212 |    0.6979 |    -20.95 |    -14.99 |
+#> |.....................|    -5.046 |    -1.607 |     1.055 |     8.026 |
+#> |.....................|    -11.31 |    0.3535 |     6.819 |    -11.49 |
+#> |   15|     492.00325 |    0.9991 |    -1.007 |   -0.9102 |   -0.9382 |
+#> |.....................|   -0.9872 |   -0.8852 |   -0.8063 |   -0.8408 |
+#> |.....................|   -0.8590 |   -0.8856 |   -0.8811 |   -0.9014 |
+#> |.....................|   -0.8316 |   -0.8723 |   -0.8977 |   -0.8307 |
+#> |    U|     492.00325 |     91.40 |    -5.195 |   -0.8867 |    -2.190 |
+#> |.....................|    -4.629 |    0.4580 |    0.8567 |   0.05916 |
+#> |.....................|    0.8349 |   0.05786 |    0.7271 |    0.8725 |
+#> |.....................|     1.229 |    0.9632 |    0.8386 |     1.261 |
+#> |    X|     492.00325 |     91.40 |  0.005542 |    0.2918 |    0.1119 |
+#> |.....................|  0.009762 |    0.6125 |    0.8567 |   0.05916 |
+#> |.....................|    0.8349 |   0.05786 |    0.7271 |    0.8725 |
+#> |.....................|     1.229 |    0.9632 |    0.8386 |     1.261 |
+#> |    F| Forward Diff. |     32.19 |     2.189 |  -0.09620 |   0.04245 |
+#> |.....................|   -0.3450 |    0.6659 |    -21.28 |    -14.00 |
+#> |.....................|    -4.881 |    -1.759 |     1.243 |     8.359 |
+#> |.....................|    -10.62 |  -0.07477 |     6.614 |    -11.44 |
+#> |   16|     491.72015 |    0.9906 |    -1.007 |   -0.9102 |   -0.9382 |
+#> |.....................|   -0.9871 |   -0.8854 |   -0.8003 |   -0.8368 |
+#> |.....................|   -0.8576 |   -0.8851 |   -0.8814 |   -0.9037 |
+#> |.....................|   -0.8285 |   -0.8722 |   -0.8996 |   -0.8275 |
+#> |    U|     491.72015 |     90.62 |    -5.196 |   -0.8867 |    -2.190 |
+#> |.....................|    -4.629 |    0.4579 |    0.8592 |   0.05927 |
+#> |.....................|    0.8354 |   0.05788 |    0.7268 |    0.8703 |
+#> |.....................|     1.232 |    0.9633 |    0.8370 |     1.265 |
+#> |    X|     491.72015 |     90.62 |  0.005538 |    0.2918 |    0.1119 |
+#> |.....................|  0.009763 |    0.6125 |    0.8592 |   0.05927 |
+#> |.....................|    0.8354 |   0.05788 |    0.7268 |    0.8703 |
+#> |.....................|     1.232 |    0.9633 |    0.8370 |     1.265 |
+#> |    F| Forward Diff. |    -33.41 |     2.074 |   -0.4123 |    0.1389 |
+#> |.....................|   -0.2981 |    0.7039 |    -20.39 |    -14.31 |
+#> |.....................|    -4.887 |    -1.550 |    0.9656 |     7.818 |
+#> |.....................|    -11.05 |   -0.4282 |     6.582 |    -11.31 |
+#> |   17|     491.42294 |    0.9990 |    -1.008 |   -0.9101 |   -0.9383 |
+#> |.....................|   -0.9870 |   -0.8856 |   -0.7943 |   -0.8327 |
+#> |.....................|   -0.8562 |   -0.8846 |   -0.8817 |   -0.9060 |
+#> |.....................|   -0.8254 |   -0.8721 |   -0.9015 |   -0.8242 |
+#> |    U|     491.42294 |     91.39 |    -5.197 |   -0.8866 |    -2.190 |
+#> |.....................|    -4.629 |    0.4578 |    0.8616 |   0.05939 |
+#> |.....................|    0.8360 |   0.05789 |    0.7266 |    0.8683 |
+#> |.....................|     1.236 |    0.9634 |    0.8354 |     1.269 |
+#> |    X|     491.42294 |     91.39 |  0.005535 |    0.2918 |    0.1119 |
+#> |.....................|  0.009764 |    0.6125 |    0.8616 |   0.05939 |
+#> |.....................|    0.8360 |   0.05789 |    0.7266 |    0.8683 |
+#> |.....................|     1.236 |    0.9634 |    0.8354 |     1.269 |
+#> |    F| Forward Diff. |     31.50 |     2.165 |  -0.08876 |   0.04676 |
+#> |.....................|   -0.3226 |    0.6753 |    -20.70 |    -13.34 |
+#> |.....................|    -4.747 |    -1.707 |    0.9017 |     8.141 |
+#> |.....................|    -10.29 |  -0.02981 |     6.402 |    -11.28 |
+#> |   18|     491.14065 |    0.9907 |    -1.009 |   -0.9100 |   -0.9383 |
+#> |.....................|   -0.9870 |   -0.8858 |   -0.7882 |   -0.8287 |
+#> |.....................|   -0.8548 |   -0.8841 |   -0.8820 |   -0.9084 |
+#> |.....................|   -0.8223 |   -0.8721 |   -0.9034 |   -0.8208 |
+#> |    U|     491.14065 |     90.64 |    -5.197 |   -0.8866 |    -2.190 |
+#> |.....................|    -4.629 |    0.4577 |    0.8642 |   0.05950 |
+#> |.....................|    0.8366 |   0.05791 |    0.7264 |    0.8661 |
+#> |.....................|     1.240 |    0.9634 |    0.8337 |     1.273 |
+#> |    X|     491.14065 |     90.64 |  0.005531 |    0.2918 |    0.1119 |
+#> |.....................|  0.009765 |    0.6125 |    0.8642 |   0.05950 |
+#> |.....................|    0.8366 |   0.05791 |    0.7264 |    0.8661 |
+#> |.....................|     1.240 |    0.9634 |    0.8337 |     1.273 |
+#> |    F| Forward Diff. |    -32.29 |     2.052 |   -0.4043 |    0.1403 |
+#> |.....................|   -0.2785 |    0.7107 |    -20.12 |    -13.83 |
+#> |.....................|    -4.879 |    -1.515 |    0.4622 |     7.293 |
+#> |.....................|    -10.82 |   -0.3681 |     6.384 |    -11.14 |
+#> |   19|     490.84537 |    0.9989 |    -1.009 |   -0.9099 |   -0.9383 |
+#> |.....................|   -0.9869 |   -0.8860 |   -0.7821 |   -0.8246 |
+#> |.....................|   -0.8533 |   -0.8837 |   -0.8821 |   -0.9106 |
+#> |.....................|   -0.8190 |   -0.8720 |   -0.9053 |   -0.8174 |
+#> |    U|     490.84537 |     91.38 |    -5.198 |   -0.8865 |    -2.190 |
+#> |.....................|    -4.629 |    0.4576 |    0.8667 |   0.05962 |
+#> |.....................|    0.8372 |   0.05792 |    0.7263 |    0.8641 |
+#> |.....................|     1.243 |    0.9635 |    0.8321 |     1.277 |
+#> |    X|     490.84537 |     91.38 |  0.005528 |    0.2918 |    0.1119 |
+#> |.....................|  0.009766 |    0.6124 |    0.8667 |   0.05962 |
+#> |.....................|    0.8372 |   0.05792 |    0.7263 |    0.8641 |
+#> |.....................|     1.243 |    0.9635 |    0.8321 |     1.277 |
+#> |    F| Forward Diff. |     30.35 |     2.134 |  -0.08371 |   0.04933 |
+#> |.....................|   -0.3000 |    0.6785 |    -20.24 |    -12.73 |
+#> |.....................|    -4.623 |    -1.604 |     1.054 |     8.092 |
+#> |.....................|    -10.77 |   -0.4405 |     6.181 |    -11.10 |
+#> |   20|     490.56963 |    0.9908 |    -1.010 |   -0.9099 |   -0.9383 |
+#> |.....................|   -0.9868 |   -0.8862 |   -0.7758 |   -0.8207 |
+#> |.....................|   -0.8519 |   -0.8832 |   -0.8824 |   -0.9131 |
+#> |.....................|   -0.8157 |   -0.8719 |   -0.9072 |   -0.8140 |
+#> |    U|     490.56963 |     90.64 |    -5.199 |   -0.8865 |    -2.190 |
+#> |.....................|    -4.629 |    0.4575 |    0.8693 |   0.05974 |
+#> |.....................|    0.8378 |   0.05793 |    0.7261 |    0.8619 |
+#> |.....................|     1.247 |    0.9636 |    0.8305 |     1.281 |
+#> |    X|     490.56963 |     90.64 |  0.005524 |    0.2918 |    0.1119 |
+#> |.....................|  0.009767 |    0.6124 |    0.8693 |   0.05974 |
+#> |.....................|    0.8378 |   0.05793 |    0.7261 |    0.8619 |
+#> |.....................|     1.247 |    0.9636 |    0.8305 |     1.281 |
+#> |    F| Forward Diff. |    -31.85 |     2.030 |   -0.4014 |    0.1424 |
+#> |.....................|   -0.2574 |    0.7152 |    -19.39 |    -13.12 |
+#> |.....................|    -4.602 |    -1.387 |    0.5883 |     7.042 |
+#> |.....................|    -10.56 |   -0.3115 |     6.249 |    -10.92 |
+#> |   21|     490.28521 |    0.9989 |    -1.011 |   -0.9098 |   -0.9384 |
+#> |.....................|   -0.9867 |   -0.8865 |   -0.7697 |   -0.8166 |
+#> |.....................|   -0.8504 |   -0.8827 |   -0.8826 |   -0.9153 |
+#> |.....................|   -0.8124 |   -0.8718 |   -0.9092 |   -0.8105 |
+#> |    U|     490.28521 |     91.39 |    -5.199 |   -0.8864 |    -2.190 |
+#> |.....................|    -4.629 |    0.4574 |    0.8718 |   0.05985 |
+#> |.....................|    0.8384 |   0.05795 |    0.7259 |    0.8599 |
+#> |.....................|     1.251 |    0.9637 |    0.8288 |     1.285 |
+#> |    X|     490.28521 |     91.39 |  0.005521 |    0.2919 |    0.1119 |
+#> |.....................|  0.009767 |    0.6124 |    0.8718 |   0.05985 |
+#> |.....................|    0.8384 |   0.05795 |    0.7259 |    0.8599 |
+#> |.....................|     1.251 |    0.9637 |    0.8288 |     1.285 |
+#> |    F| Forward Diff. |     30.53 |     2.112 |  -0.07114 |   0.05276 |
+#> |.....................|   -0.2779 |    0.6845 |    -19.81 |    -12.13 |
+#> |.....................|    -4.498 |    -1.539 |    0.6449 |     7.769 |
+#> |.....................|    -10.55 |   -0.3696 |     5.980 |    -10.93 |
+#> |   22|     489.99923 |    0.9911 |    -1.011 |   -0.9097 |   -0.9384 |
+#> |.....................|   -0.9866 |   -0.8867 |   -0.7633 |   -0.8127 |
+#> |.....................|   -0.8489 |   -0.8823 |   -0.8828 |   -0.9178 |
+#> |.....................|   -0.8089 |   -0.8716 |   -0.9111 |   -0.8070 |
+#> |    U|     489.99923 |     90.67 |    -5.200 |   -0.8863 |    -2.190 |
+#> |.....................|    -4.629 |    0.4573 |    0.8745 |   0.05997 |
+#> |.....................|    0.8390 |   0.05796 |    0.7258 |    0.8577 |
+#> |.....................|     1.255 |    0.9638 |    0.8271 |     1.290 |
+#> |    X|     489.99923 |     90.67 |  0.005517 |    0.2919 |    0.1119 |
+#> |.....................|  0.009768 |    0.6124 |    0.8745 |   0.05997 |
+#> |.....................|    0.8390 |   0.05796 |    0.7258 |    0.8577 |
+#> |.....................|     1.255 |    0.9638 |    0.8271 |     1.290 |
+#> |    F| Forward Diff. |    -29.14 |     2.012 |   -0.3844 |    0.1417 |
+#> |.....................|   -0.2358 |    0.7218 |    -18.90 |    -12.37 |
+#> |.....................|    -4.517 |    -1.329 |    0.4904 |     6.799 |
+#> |.....................|    -10.31 |   -0.2514 |     6.013 |    -10.75 |
+#> |   23|     489.73483 |    0.9991 |    -1.012 |   -0.9096 |   -0.9384 |
+#> |.....................|   -0.9865 |   -0.8869 |   -0.7571 |   -0.8087 |
+#> |.....................|   -0.8475 |   -0.8818 |   -0.8829 |   -0.9201 |
+#> |.....................|   -0.8055 |   -0.8715 |   -0.9131 |   -0.8034 |
+#> |    U|     489.73483 |     91.40 |    -5.201 |   -0.8862 |    -2.190 |
+#> |.....................|    -4.629 |    0.4572 |    0.8771 |   0.06008 |
+#> |.....................|    0.8396 |   0.05797 |    0.7257 |    0.8557 |
+#> |.....................|     1.259 |    0.9639 |    0.8254 |     1.294 |
+#> |    X|     489.73483 |     91.40 |  0.005513 |    0.2919 |    0.1119 |
+#> |.....................|  0.009769 |    0.6123 |    0.8771 |   0.06008 |
+#> |.....................|    0.8396 |   0.05797 |    0.7257 |    0.8557 |
+#> |.....................|     1.259 |    0.9639 |    0.8254 |     1.294 |
+#> |    F| Forward Diff. |     31.68 |     2.089 |  -0.05219 |   0.05312 |
+#> |.....................|   -0.2568 |    0.6912 |    -19.25 |    -11.50 |
+#> |.....................|    -4.291 |    -1.478 |    0.6044 |     7.316 |
+#> |.....................|    -10.30 |   -0.3159 |     5.756 |    -10.75 |
+#> |   24|     489.43925 |    0.9914 |    -1.013 |   -0.9096 |   -0.9385 |
+#> |.....................|   -0.9865 |   -0.8872 |   -0.7505 |   -0.8049 |
+#> |.....................|   -0.8460 |   -0.8813 |   -0.8831 |   -0.9225 |
+#> |.....................|   -0.8020 |   -0.8714 |   -0.9150 |   -0.7997 |
+#> |    U|     489.43925 |     90.70 |    -5.201 |   -0.8862 |    -2.190 |
+#> |.....................|    -4.628 |    0.4571 |    0.8798 |   0.06019 |
+#> |.....................|    0.8402 |   0.05799 |    0.7256 |    0.8535 |
+#> |.....................|     1.264 |    0.9640 |    0.8238 |     1.298 |
+#> |    X|     489.43925 |     90.70 |  0.005509 |    0.2919 |    0.1119 |
+#> |.....................|  0.009770 |    0.6123 |    0.8798 |   0.06019 |
+#> |.....................|    0.8402 |   0.05799 |    0.7256 |    0.8535 |
+#> |.....................|     1.264 |    0.9640 |    0.8238 |     1.298 |
+#> |    F| Forward Diff. |    -26.48 |     1.993 |   -0.3684 |    0.1403 |
+#> |.....................|   -0.2166 |    0.7270 |    -18.36 |    -11.77 |
+#> |.....................|    -4.393 |    -1.275 |    0.4390 |     6.578 |
+#> |.....................|    -10.04 |   -0.2187 |     5.799 |    -10.58 |
+#> |   25|     489.19181 |    0.9992 |    -1.013 |   -0.9095 |   -0.9385 |
+#> |.....................|   -0.9864 |   -0.8874 |   -0.7441 |   -0.8009 |
+#> |.....................|   -0.8445 |   -0.8809 |   -0.8833 |   -0.9248 |
+#> |.....................|   -0.7985 |   -0.8714 |   -0.9170 |   -0.7960 |
+#> |    U|     489.19181 |     91.41 |    -5.202 |   -0.8861 |    -2.190 |
+#> |.....................|    -4.628 |    0.4570 |    0.8824 |   0.06031 |
+#> |.....................|    0.8409 |   0.05800 |    0.7255 |    0.8514 |
+#> |.....................|     1.268 |    0.9641 |    0.8221 |     1.303 |
+#> |    X|     489.19181 |     91.41 |  0.005505 |    0.2919 |    0.1119 |
+#> |.....................|  0.009770 |    0.6123 |    0.8824 |   0.06031 |
+#> |.....................|    0.8409 |   0.05800 |    0.7255 |    0.8514 |
+#> |.....................|     1.268 |    0.9641 |    0.8221 |     1.303 |
+#> |    F| Forward Diff. |     32.48 |     2.067 |  -0.03453 |   0.05414 |
+#> |.....................|   -0.2360 |    0.6938 |    -18.67 |    -10.89 |
+#> |.....................|    -4.178 |    -1.425 |    0.5548 |     7.078 |
+#> |.....................|    -10.01 |   -0.2144 |     5.548 |    -10.57 |
+#> |   26|     488.89118 |    0.9917 |    -1.014 |   -0.9094 |   -0.9385 |
+#> |.....................|   -0.9863 |   -0.8877 |   -0.7375 |   -0.7972 |
+#> |.....................|   -0.8430 |   -0.8804 |   -0.8834 |   -0.9272 |
+#> |.....................|   -0.7949 |   -0.8713 |   -0.9189 |   -0.7921 |
+#> |    U|     488.89118 |     90.73 |    -5.203 |   -0.8860 |    -2.190 |
+#> |.....................|    -4.628 |    0.4568 |    0.8852 |   0.06041 |
+#> |.....................|    0.8415 |   0.05801 |    0.7253 |    0.8493 |
+#> |.....................|     1.272 |    0.9642 |    0.8204 |     1.308 |
+#> |    X|     488.89118 |     90.73 |  0.005501 |    0.2919 |    0.1119 |
+#> |.....................|  0.009771 |    0.6123 |    0.8852 |   0.06041 |
+#> |.....................|    0.8415 |   0.05801 |    0.7253 |    0.8493 |
+#> |.....................|     1.272 |    0.9642 |    0.8204 |     1.308 |
+#> |    F| Forward Diff. |    -24.34 |     1.974 |   -0.3522 |    0.1400 |
+#> |.....................|   -0.1957 |    0.7323 |    -17.88 |    -11.06 |
+#> |.....................|    -4.245 |    -1.195 |    0.3418 |     6.336 |
+#> |.....................|    -9.795 |   -0.1748 |     5.588 |    -10.40 |
+#> |   27|     488.65823 |    0.9993 |    -1.015 |   -0.9093 |   -0.9386 |
+#> |.....................|   -0.9862 |   -0.8880 |   -0.7310 |   -0.7933 |
+#> |.....................|   -0.8415 |   -0.8800 |   -0.8835 |   -0.9295 |
+#> |.....................|   -0.7913 |   -0.8712 |   -0.9210 |   -0.7883 |
+#> |    U|     488.65823 |     91.42 |    -5.204 |   -0.8859 |    -2.190 |
+#> |.....................|    -4.628 |    0.4567 |    0.8878 |   0.06053 |
+#> |.....................|    0.8421 |   0.05803 |    0.7253 |    0.8472 |
+#> |.....................|     1.276 |    0.9642 |    0.8187 |     1.312 |
+#> |    X|     488.65823 |     91.42 |  0.005497 |    0.2919 |    0.1119 |
+#> |.....................|  0.009772 |    0.6122 |    0.8878 |   0.06053 |
+#> |.....................|    0.8421 |   0.05803 |    0.7253 |    0.8472 |
+#> |.....................|     1.276 |    0.9642 |    0.8187 |     1.312 |
+#> |    F| Forward Diff. |     33.05 |     2.045 |  -0.01570 |   0.05526 |
+#> |.....................|   -0.2154 |    0.6997 |    -18.21 |    -10.28 |
+#> |.....................|    -4.052 |    -1.334 |    0.4619 |     6.811 |
+#> |.....................|    -9.752 |   -0.1974 |     5.317 |    -10.39 |
+#> |   28|     488.35451 |    0.9920 |    -1.016 |   -0.9093 |   -0.9386 |
+#> |.....................|   -0.9862 |   -0.8883 |   -0.7243 |   -0.7897 |
+#> |.....................|   -0.8399 |   -0.8795 |   -0.8836 |   -0.9319 |
+#> |.....................|   -0.7876 |   -0.8712 |   -0.9229 |   -0.7844 |
+#> |    U|     488.35451 |     90.75 |    -5.204 |   -0.8859 |    -2.190 |
+#> |.....................|    -4.628 |    0.4566 |    0.8906 |   0.06063 |
+#> |.....................|    0.8427 |   0.05804 |    0.7252 |    0.8450 |
+#> |.....................|     1.281 |    0.9643 |    0.8170 |     1.317 |
+#> |    X|     488.35451 |     90.75 |  0.005493 |    0.2920 |    0.1119 |
+#> |.....................|  0.009772 |    0.6122 |    0.8906 |   0.06063 |
+#> |.....................|    0.8427 |   0.05804 |    0.7252 |    0.8450 |
+#> |.....................|     1.281 |    0.9643 |    0.8170 |     1.317 |
+#> |    F| Forward Diff. |    -22.42 |     1.954 |   -0.3353 |    0.1391 |
+#> |.....................|   -0.1757 |    0.7405 |    -17.32 |    -10.46 |
+#> |.....................|    -4.053 |    -1.161 |    0.2825 |     6.114 |
+#> |.....................|    -9.506 |   -0.1281 |     5.370 |    -10.21 |
+#> |   29|     488.13711 |    0.9995 |    -1.016 |   -0.9092 |   -0.9387 |
+#> |.....................|   -0.9861 |   -0.8886 |   -0.7177 |   -0.7858 |
+#> |.....................|   -0.8384 |   -0.8791 |   -0.8837 |   -0.9342 |
+#> |.....................|   -0.7840 |   -0.8711 |   -0.9249 |   -0.7804 |
+#> |    U|     488.13711 |     91.44 |    -5.205 |   -0.8858 |    -2.190 |
+#> |.....................|    -4.628 |    0.4565 |    0.8934 |   0.06074 |
+#> |.....................|    0.8434 |   0.05805 |    0.7251 |    0.8430 |
+#> |.....................|     1.285 |    0.9643 |    0.8153 |     1.322 |
+#> |    X|     488.13711 |     91.44 |  0.005489 |    0.2920 |    0.1119 |
+#> |.....................|  0.009773 |    0.6122 |    0.8934 |   0.06074 |
+#> |.....................|    0.8434 |   0.05805 |    0.7251 |    0.8430 |
+#> |.....................|     1.285 |    0.9643 |    0.8153 |     1.322 |
+#> |    F| Forward Diff. |     33.81 |     2.022 |  0.006720 |   0.05587 |
+#> |.....................|   -0.1935 |    0.7042 |    -17.76 |    -9.667 |
+#> |.....................|    -3.890 |    -1.276 |    0.4404 |     6.589 |
+#> |.....................|    -9.459 |   -0.1517 |     5.102 |    -10.20 |
+#> |   30|     487.82953 |    0.9922 |    -1.017 |   -0.9091 |   -0.9387 |
+#> |.....................|   -0.9861 |   -0.8889 |   -0.7108 |   -0.7824 |
+#> |.....................|   -0.8369 |   -0.8787 |   -0.8838 |   -0.9367 |
+#> |.....................|   -0.7803 |   -0.8711 |   -0.9268 |   -0.7763 |
+#> |    U|     487.82953 |     90.77 |    -5.206 |   -0.8858 |    -2.190 |
+#> |.....................|    -4.628 |    0.4563 |    0.8962 |   0.06084 |
+#> |.....................|    0.8440 |   0.05806 |    0.7251 |    0.8408 |
+#> |.....................|     1.289 |    0.9644 |    0.8136 |     1.327 |
+#> |    X|     487.82953 |     90.77 |  0.005484 |    0.2920 |    0.1119 |
+#> |.....................|  0.009774 |    0.6121 |    0.8962 |   0.06084 |
+#> |.....................|    0.8440 |   0.05806 |    0.7251 |    0.8408 |
+#> |.....................|     1.289 |    0.9644 |    0.8136 |     1.327 |
+#> |    F| Forward Diff. |    -20.31 |     1.935 |   -0.3119 |    0.1382 |
+#> |.....................|   -0.1555 |    0.7438 |    -16.49 |    -9.852 |
+#> |.....................|    -3.955 |    -1.103 |    0.2044 |     5.876 |
+#> |.....................|    -9.237 |   -0.1098 |     5.167 |    -10.02 |
+#> |   31|     487.63293 |    0.9997 |    -1.018 |   -0.9090 |   -0.9388 |
+#> |.....................|   -0.9860 |   -0.8892 |   -0.7043 |   -0.7786 |
+#> |.....................|   -0.8354 |   -0.8782 |   -0.8838 |   -0.9390 |
+#> |.....................|   -0.7766 |   -0.8711 |   -0.9289 |   -0.7723 |
+#> |    U|     487.63293 |     91.46 |    -5.207 |   -0.8857 |    -2.191 |
+#> |.....................|    -4.628 |    0.4562 |    0.8989 |   0.06095 |
+#> |.....................|    0.8446 |   0.05808 |    0.7250 |    0.8387 |
+#> |.....................|     1.294 |    0.9644 |    0.8119 |     1.332 |
+#> |    X|     487.63293 |     91.46 |  0.005480 |    0.2920 |    0.1119 |
+#> |.....................|  0.009774 |    0.6121 |    0.8989 |   0.06095 |
+#> |.....................|    0.8446 |   0.05808 |    0.7250 |    0.8387 |
+#> |.....................|     1.294 |    0.9644 |    0.8119 |     1.332 |
+#> |    F| Forward Diff. |     35.34 |     2.001 |   0.03668 |   0.05608 |
+#> |.....................|   -0.1731 |    0.7098 |    -16.98 |    -9.135 |
+#> |.....................|    -3.742 |    -1.209 |    0.3780 |     6.351 |
+#> |.....................|    -9.183 |    0.6525 |     4.885 |    -10.01 |
+#> |   32|     487.31820 |    0.9926 |    -1.019 |   -0.9090 |   -0.9388 |
+#> |.....................|   -0.9860 |   -0.8895 |   -0.6975 |   -0.7753 |
+#> |.....................|   -0.8338 |   -0.8778 |   -0.8838 |   -0.9414 |
+#> |.....................|   -0.7728 |   -0.8714 |   -0.9308 |   -0.7679 |
+#> |    U|      487.3182 |     90.81 |    -5.208 |   -0.8856 |    -2.191 |
+#> |.....................|    -4.628 |    0.4560 |    0.9017 |   0.06104 |
+#> |.....................|    0.8453 |   0.05809 |    0.7250 |    0.8366 |
+#> |.....................|     1.298 |    0.9641 |    0.8102 |     1.337 |
+#> |    X|      487.3182 |     90.81 |  0.005475 |    0.2920 |    0.1119 |
+#> |.....................|  0.009775 |    0.6121 |    0.9017 |   0.06104 |
+#> |.....................|    0.8453 |   0.05809 |    0.7250 |    0.8366 |
+#> |.....................|     1.298 |    0.9641 |    0.8102 |     1.337 |
+#> |    F| Forward Diff. |    -17.75 |     1.917 |   -0.2852 |    0.1361 |
+#> |.....................|   -0.1360 |    0.7493 |    -16.63 |    -9.386 |
+#> |.....................|    -3.766 |    -1.006 |    0.1674 |     5.665 |
+#> |.....................|    -8.945 |    0.7251 |     4.960 |    -9.828 |
+#> |   33|     487.13531 |    0.9998 |    -1.020 |   -0.9089 |   -0.9389 |
+#> |.....................|   -0.9859 |   -0.8898 |   -0.6907 |   -0.7715 |
+#> |.....................|   -0.8323 |   -0.8774 |   -0.8839 |   -0.9437 |
+#> |.....................|   -0.7691 |   -0.8717 |   -0.9328 |   -0.7639 |
+#> |    U|     487.13531 |     91.47 |    -5.208 |   -0.8855 |    -2.191 |
+#> |.....................|    -4.628 |    0.4559 |    0.9045 |   0.06116 |
+#> |.....................|    0.8459 |   0.05810 |    0.7250 |    0.8345 |
+#> |.....................|     1.303 |    0.9638 |    0.8084 |     1.342 |
+#> |    X|     487.13531 |     91.47 |  0.005471 |    0.2920 |    0.1118 |
+#> |.....................|  0.009775 |    0.6120 |    0.9045 |   0.06116 |
+#> |.....................|    0.8459 |   0.05810 |    0.7250 |    0.8345 |
+#> |.....................|     1.303 |    0.9638 |    0.8084 |     1.342 |
+#> |    F| Forward Diff. |     35.92 |     1.979 |   0.06301 |   0.05698 |
+#> |.....................|   -0.1526 |    0.7131 |    -16.77 |    -8.520 |
+#> |.....................|    -3.634 |    -1.163 |    0.3177 |     6.099 |
+#> |.....................|    -8.917 |    0.6421 |     4.685 |    -9.820 |
+#> |   34|     486.82694 |    0.9926 |    -1.021 |   -0.9088 |   -0.9389 |
+#> |.....................|   -0.9859 |   -0.8902 |   -0.6837 |   -0.7686 |
+#> |.....................|   -0.8308 |   -0.8770 |   -0.8839 |   -0.9460 |
+#> |.....................|   -0.7654 |   -0.8723 |   -0.9347 |   -0.7596 |
+#> |    U|     486.82694 |     90.81 |    -5.209 |   -0.8855 |    -2.191 |
+#> |.....................|    -4.628 |    0.4557 |    0.9074 |   0.06124 |
+#> |.....................|    0.8465 |   0.05811 |    0.7250 |    0.8324 |
+#> |.....................|     1.307 |    0.9632 |    0.8069 |     1.347 |
+#> |    X|     486.82694 |     90.81 |  0.005466 |    0.2920 |    0.1118 |
+#> |.....................|  0.009775 |    0.6120 |    0.9074 |   0.06124 |
+#> |.....................|    0.8465 |   0.05811 |    0.7250 |    0.8324 |
+#> |.....................|     1.307 |    0.9632 |    0.8069 |     1.347 |
+#> |    F| Forward Diff. |    -17.49 |     1.895 |   -0.2726 |    0.1382 |
+#> |.....................|   -0.1159 |    0.7566 |    -16.14 |    -8.833 |
+#> |.....................|    -3.638 |   -0.9303 |    0.1285 |     5.442 |
+#> |.....................|    -8.630 |    0.7091 |     4.774 |    -9.639 |
+#> |   35|     486.64804 |    0.9998 |    -1.021 |   -0.9087 |   -0.9390 |
+#> |.....................|   -0.9858 |   -0.8905 |   -0.6768 |   -0.7649 |
+#> |.....................|   -0.8293 |   -0.8767 |   -0.8839 |   -0.9483 |
+#> |.....................|   -0.7617 |   -0.8727 |   -0.9367 |   -0.7554 |
+#> |    U|     486.64804 |     91.46 |    -5.210 |   -0.8854 |    -2.191 |
+#> |.....................|    -4.628 |    0.4556 |    0.9103 |   0.06135 |
+#> |.....................|    0.8472 |   0.05812 |    0.7250 |    0.8304 |
+#> |.....................|     1.311 |    0.9629 |    0.8051 |     1.352 |
+#> |    X|     486.64804 |     91.46 |  0.005462 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6120 |    0.9103 |   0.06135 |
+#> |.....................|    0.8472 |   0.05812 |    0.7250 |    0.8304 |
+#> |.....................|     1.311 |    0.9629 |    0.8051 |     1.352 |
+#> |    F| Forward Diff. |     35.26 |     1.955 |   0.07649 |   0.05940 |
+#> |.....................|   -0.1319 |    0.7217 |    -16.38 |    -8.030 |
+#> |.....................|    -3.491 |    -1.078 |    0.2504 |     5.851 |
+#> |.....................|    -8.624 |    0.5993 |     4.494 |    -9.625 |
+#> |   36|     486.34524 |    0.9928 |    -1.022 |   -0.9087 |   -0.9390 |
+#> |.....................|   -0.9858 |   -0.8909 |   -0.6696 |   -0.7621 |
+#> |.....................|   -0.8278 |   -0.8763 |   -0.8838 |   -0.9506 |
+#> |.....................|   -0.7579 |   -0.8733 |   -0.9385 |   -0.7509 |
+#> |    U|     486.34524 |     90.82 |    -5.211 |   -0.8854 |    -2.191 |
+#> |.....................|    -4.628 |    0.4554 |    0.9133 |   0.06143 |
+#> |.....................|    0.8478 |   0.05813 |    0.7251 |    0.8283 |
+#> |.....................|     1.316 |    0.9622 |    0.8036 |     1.358 |
+#> |    X|     486.34524 |     90.82 |  0.005456 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6119 |    0.9133 |   0.06143 |
+#> |.....................|    0.8478 |   0.05813 |    0.7251 |    0.8283 |
+#> |.....................|     1.316 |    0.9622 |    0.8036 |     1.358 |
+#> |    F| Forward Diff. |    -16.53 |     1.875 |   -0.2661 |    0.1390 |
+#> |.....................|  -0.09763 |    0.7654 |    -15.70 |    -8.237 |
+#> |.....................|    -3.491 |   -0.9040 |   0.06392 |     5.213 |
+#> |.....................|    -8.361 |    0.6621 |     4.584 |    -9.445 |
+#> |   37|     486.17476 |    0.9998 |    -1.023 |   -0.9086 |   -0.9391 |
+#> |.....................|   -0.9858 |   -0.8913 |   -0.6626 |   -0.7586 |
+#> |.....................|   -0.8262 |   -0.8759 |   -0.8838 |   -0.9529 |
+#> |.....................|   -0.7542 |   -0.8736 |   -0.9406 |   -0.7467 |
+#> |    U|     486.17476 |     91.47 |    -5.212 |   -0.8853 |    -2.191 |
+#> |.....................|    -4.628 |    0.4552 |    0.9162 |   0.06153 |
+#> |.....................|    0.8484 |   0.05814 |    0.7250 |    0.8263 |
+#> |.....................|     1.320 |    0.9619 |    0.8018 |     1.363 |
+#> |    X|     486.17476 |     91.47 |  0.005452 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6119 |    0.9162 |   0.06153 |
+#> |.....................|    0.8484 |   0.05814 |    0.7250 |    0.8263 |
+#> |.....................|     1.320 |    0.9619 |    0.8018 |     1.363 |
+#> |    F| Forward Diff. |     35.23 |     1.932 |   0.08715 |   0.05955 |
+#> |.....................|   -0.1122 |    0.7274 |    -16.01 |    -7.627 |
+#> |.....................|    -3.363 |    -1.024 |    0.1942 |     5.616 |
+#> |.....................|    -8.345 |    0.5641 |     4.322 |    -9.424 |
+#> |   38|     485.87468 |    0.9930 |    -1.024 |   -0.9086 |   -0.9392 |
+#> |.....................|   -0.9858 |   -0.8917 |   -0.6553 |   -0.7561 |
+#> |.....................|   -0.8248 |   -0.8756 |   -0.8837 |   -0.9551 |
+#> |.....................|   -0.7504 |   -0.8743 |   -0.9424 |   -0.7420 |
+#> |    U|     485.87468 |     90.84 |    -5.213 |   -0.8853 |    -2.191 |
+#> |.....................|    -4.628 |    0.4550 |    0.9192 |   0.06160 |
+#> |.....................|    0.8490 |   0.05815 |    0.7252 |    0.8243 |
+#> |.....................|     1.325 |    0.9613 |    0.8003 |     1.369 |
+#> |    X|     485.87468 |     90.84 |  0.005446 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6118 |    0.9192 |   0.06160 |
+#> |.....................|    0.8490 |   0.05815 |    0.7252 |    0.8243 |
+#> |.....................|     1.325 |    0.9613 |    0.8003 |     1.369 |
+#> |    F| Forward Diff. |    -15.16 |     1.855 |   -0.2494 |    0.1393 |
+#> |.....................|  -0.07811 |    0.7704 |    -15.31 |    -7.716 |
+#> |.....................|    -3.357 |   -0.8175 |  -0.03012 |     4.971 |
+#> |.....................|    -8.100 |    0.5955 |     4.407 |    -9.242 |
+#> |   39|     485.71812 |     1.000 |    -1.025 |   -0.9085 |   -0.9392 |
+#> |.....................|   -0.9858 |   -0.8921 |   -0.6482 |   -0.7526 |
+#> |.....................|   -0.8232 |   -0.8752 |   -0.8836 |   -0.9573 |
+#> |.....................|   -0.7467 |   -0.8746 |   -0.9444 |   -0.7377 |
+#> |    U|     485.71812 |     91.48 |    -5.214 |   -0.8852 |    -2.191 |
+#> |.....................|    -4.628 |    0.4548 |    0.9221 |   0.06170 |
+#> |.....................|    0.8497 |   0.05816 |    0.7252 |    0.8222 |
+#> |.....................|     1.329 |    0.9610 |    0.7985 |     1.374 |
+#> |    X|     485.71812 |     91.48 |  0.005442 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6118 |    0.9221 |   0.06170 |
+#> |.....................|    0.8497 |   0.05816 |    0.7252 |    0.8222 |
+#> |.....................|     1.329 |    0.9610 |    0.7985 |     1.374 |
+#> |    F| Forward Diff. |     36.02 |     1.911 |    0.1144 |   0.05926 |
+#> |.....................|  -0.09370 |    0.7314 |    -15.47 |    -7.071 |
+#> |.....................|    -3.248 |   -0.9743 |    0.1265 |     5.377 |
+#> |.....................|    -7.775 |    0.5175 |     4.130 |    -9.229 |
+#> |   40|     485.42108 |    0.9931 |    -1.026 |   -0.9085 |   -0.9393 |
+#> |.....................|   -0.9858 |   -0.8926 |   -0.6408 |   -0.7505 |
+#> |.....................|   -0.8218 |   -0.8750 |   -0.8834 |   -0.9594 |
+#> |.....................|   -0.7430 |   -0.8752 |   -0.9461 |   -0.7328 |
+#> |    U|     485.42108 |     90.85 |    -5.215 |   -0.8852 |    -2.191 |
+#> |.....................|    -4.628 |    0.4546 |    0.9252 |   0.06176 |
+#> |.....................|    0.8503 |   0.05817 |    0.7254 |    0.8204 |
+#> |.....................|     1.333 |    0.9604 |    0.7970 |     1.380 |
+#> |    X|     485.42108 |     90.85 |  0.005436 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6117 |    0.9252 |   0.06176 |
+#> |.....................|    0.8503 |   0.05817 |    0.7254 |    0.8204 |
+#> |.....................|     1.333 |    0.9604 |    0.7970 |     1.380 |
+#> |    F| Forward Diff. |    -14.37 |     1.836 |   -0.2333 |    0.1389 |
+#> |.....................|  -0.05951 |    0.7785 |    -14.33 |    -7.292 |
+#> |.....................|    -3.229 |   -0.7699 |  -0.05471 |     4.764 |
+#> |.....................|    -7.801 |    0.5597 |     4.229 |    -9.048 |
+#> |   41|     485.26815 |    0.9999 |    -1.027 |   -0.9084 |   -0.9394 |
+#> |.....................|   -0.9858 |   -0.8930 |   -0.6338 |   -0.7470 |
+#> |.....................|   -0.8202 |   -0.8746 |   -0.8833 |   -0.9618 |
+#> |.....................|   -0.7392 |   -0.8755 |   -0.9482 |   -0.7284 |
+#> |    U|     485.26815 |     91.48 |    -5.216 |   -0.8851 |    -2.191 |
+#> |.....................|    -4.628 |    0.4544 |    0.9281 |   0.06186 |
+#> |.....................|    0.8509 |   0.05818 |    0.7254 |    0.8183 |
+#> |.....................|     1.338 |    0.9601 |    0.7953 |     1.385 |
+#> |    X|     485.26815 |     91.48 |  0.005431 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6117 |    0.9281 |   0.06186 |
+#> |.....................|    0.8509 |   0.05818 |    0.7254 |    0.8183 |
+#> |.....................|     1.338 |    0.9601 |    0.7953 |     1.385 |
+#> |    F| Forward Diff. |     35.37 |     1.889 |    0.1323 |   0.06297 |
+#> |.....................|  -0.07437 |    0.7390 |    -14.80 |    -6.641 |
+#> |.....................|    -3.116 |   -0.8690 |   0.09880 |     5.162 |
+#> |.....................|    -7.761 |    0.4865 |     3.967 |    -9.019 |
+#> |   42|     484.97448 |    0.9934 |    -1.028 |   -0.9084 |   -0.9395 |
+#> |.....................|   -0.9859 |   -0.8935 |   -0.6264 |   -0.7452 |
+#> |.....................|   -0.8188 |   -0.8744 |   -0.8830 |   -0.9639 |
+#> |.....................|   -0.7352 |   -0.8762 |   -0.9500 |   -0.7231 |
+#> |    U|     484.97448 |     90.88 |    -5.217 |   -0.8851 |    -2.191 |
+#> |.....................|    -4.628 |    0.4542 |    0.9311 |   0.06191 |
+#> |.....................|    0.8515 |   0.05819 |    0.7257 |    0.8164 |
+#> |.....................|     1.343 |    0.9594 |    0.7937 |     1.392 |
+#> |    X|     484.97448 |     90.88 |  0.005424 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6116 |    0.9311 |   0.06191 |
+#> |.....................|    0.8515 |   0.05819 |    0.7257 |    0.8164 |
+#> |.....................|     1.343 |    0.9594 |    0.7937 |     1.392 |
+#> |    F| Forward Diff. |    -12.51 |     1.817 |   -0.2072 |    0.1320 |
+#> |.....................|  -0.04147 |    0.7868 |    -13.90 |    -6.839 |
+#> |.....................|    -3.097 |   -0.6966 |  -0.09701 |     4.567 |
+#> |.....................|    -7.500 |    0.5336 |     4.059 |    -8.839 |
+#> |   43|     484.82513 |    0.9998 |    -1.029 |   -0.9083 |   -0.9395 |
+#> |.....................|   -0.9858 |   -0.8939 |   -0.6193 |   -0.7417 |
+#> |.....................|   -0.8172 |   -0.8741 |   -0.8829 |   -0.9662 |
+#> |.....................|   -0.7313 |   -0.8765 |   -0.9521 |   -0.7185 |
+#> |    U|     484.82513 |     91.47 |    -5.218 |   -0.8851 |    -2.191 |
+#> |.....................|    -4.628 |    0.4540 |    0.9341 |   0.06202 |
+#> |.....................|    0.8522 |   0.05820 |    0.7257 |    0.8143 |
+#> |.....................|     1.347 |    0.9592 |    0.7919 |     1.397 |
+#> |    X|     484.82513 |     91.47 |  0.005419 |    0.2921 |    0.1118 |
+#> |.....................|  0.009776 |    0.6116 |    0.9341 |   0.06202 |
+#> |.....................|    0.8522 |   0.05820 |    0.7257 |    0.8143 |
+#> |.....................|     1.347 |    0.9592 |    0.7919 |     1.397 |
+#> |    F| Forward Diff. |     34.86 |     1.871 |    0.1566 |   0.07097 |
+#> |.....................|  -0.05046 |    0.7508 |    -14.35 |    -6.106 |
+#> |.....................|    -2.960 |   -0.8322 |   0.03576 |     4.926 |
+#> |.....................|    -7.463 |    0.4624 |     3.813 |    -8.806 |
+#> |   44|     484.54032 |    0.9935 |    -1.030 |   -0.9084 |   -0.9396 |
+#> |.....................|   -0.9859 |   -0.8946 |   -0.6118 |   -0.7403 |
+#> |.....................|   -0.8157 |   -0.8739 |   -0.8825 |   -0.9682 |
+#> |.....................|   -0.7274 |   -0.8772 |   -0.9538 |   -0.7130 |
+#> |    U|     484.54032 |     90.89 |    -5.219 |   -0.8851 |    -2.191 |
+#> |.....................|    -4.628 |    0.4537 |    0.9372 |   0.06206 |
+#> |.....................|    0.8528 |   0.05820 |    0.7260 |    0.8125 |
+#> |.....................|     1.352 |    0.9585 |    0.7904 |     1.404 |
+#> |    X|     484.54032 |     90.89 |  0.005412 |    0.2921 |    0.1118 |
+#> |.....................|  0.009775 |    0.6115 |    0.9372 |   0.06206 |
+#> |.....................|    0.8528 |   0.05820 |    0.7260 |    0.8125 |
+#> |.....................|     1.352 |    0.9585 |    0.7904 |     1.404 |
+#> |    F| Forward Diff. |    -11.88 |     1.798 |   -0.1931 |    0.1288 |
+#> |.....................|  -0.02100 |    0.7941 |    -13.56 |    -6.327 |
+#> |.....................|    -2.985 |   -0.6346 |   -0.1369 |     4.355 |
+#> |.....................|    -7.207 |    0.4876 |     3.910 |    -8.603 |
+#> |   45|     484.39828 |    0.9999 |    -1.031 |   -0.9082 |   -0.9397 |
+#> |.....................|   -0.9859 |   -0.8950 |   -0.6045 |   -0.7369 |
+#> |.....................|   -0.8141 |   -0.8736 |   -0.8824 |   -0.9706 |
+#> |.....................|   -0.7235 |   -0.8774 |   -0.9559 |   -0.7084 |
+#> |    U|     484.39828 |     91.47 |    -5.220 |   -0.8850 |    -2.191 |
+#> |.....................|    -4.628 |    0.4535 |    0.9402 |   0.06215 |
+#> |.....................|    0.8534 |   0.05821 |    0.7261 |    0.8104 |
+#> |.....................|     1.357 |    0.9582 |    0.7886 |     1.409 |
+#> |    X|     484.39828 |     91.47 |  0.005407 |    0.2921 |    0.1118 |
+#> |.....................|  0.009775 |    0.6115 |    0.9402 |   0.06215 |
+#> |.....................|    0.8534 |   0.05821 |    0.7261 |    0.8104 |
+#> |.....................|     1.357 |    0.9582 |    0.7886 |     1.409 |
+#> |    F| Forward Diff. |     34.75 |     1.847 |    0.1787 |   0.06647 |
+#> |.....................|  -0.03069 |    0.7556 |    -13.39 |    -5.638 |
+#> |.....................|    -2.842 |   -0.7351 |  -0.07352 |     4.648 |
+#> |.....................|    -7.153 |    0.4383 |     3.662 |    -8.575 |
+#> |   46|     484.12389 |    0.9935 |    -1.033 |   -0.9083 |   -0.9398 |
+#> |.....................|   -0.9861 |   -0.8957 |   -0.5972 |   -0.7360 |
+#> |.....................|   -0.8127 |   -0.8736 |   -0.8818 |   -0.9724 |
+#> |.....................|   -0.7196 |   -0.8781 |   -0.9577 |   -0.7026 |
+#> |    U|     484.12389 |     90.89 |    -5.221 |   -0.8851 |    -2.192 |
+#> |.....................|    -4.628 |    0.4532 |    0.9432 |   0.06218 |
+#> |.....................|    0.8540 |   0.05821 |    0.7265 |    0.8087 |
+#> |.....................|     1.361 |    0.9576 |    0.7871 |     1.416 |
+#> |    X|     484.12389 |     90.89 |  0.005400 |    0.2921 |    0.1117 |
+#> |.....................|  0.009773 |    0.6114 |    0.9432 |   0.06218 |
+#> |.....................|    0.8540 |   0.05821 |    0.7265 |    0.8087 |
+#> |.....................|     1.361 |    0.9576 |    0.7871 |     1.416 |
+#> |    F| Forward Diff. |    -12.23 |     1.776 |   -0.1772 |    0.1286 |
+#> |.....................| -0.003904 |    0.8005 |    -13.23 |    -5.967 |
+#> |.....................|    -2.801 |   -0.5825 |   -0.1993 |     4.126 |
+#> |.....................|    -6.930 |    0.4309 |     3.746 |    -8.373 |
+#> |   47|     483.96910 |    0.9995 |    -1.034 |   -0.9082 |   -0.9399 |
+#> |.....................|   -0.9861 |   -0.8963 |   -0.5897 |   -0.7331 |
+#> |.....................|   -0.8111 |   -0.8733 |   -0.8815 |   -0.9747 |
+#> |.....................|   -0.7157 |   -0.8785 |   -0.9598 |   -0.6976 |
+#> |    U|      483.9691 |     91.44 |    -5.222 |   -0.8850 |    -2.192 |
+#> |.....................|    -4.628 |    0.4529 |    0.9464 |   0.06226 |
+#> |.....................|    0.8547 |   0.05822 |    0.7267 |    0.8067 |
+#> |.....................|     1.366 |    0.9573 |    0.7854 |     1.423 |
+#> |    X|      483.9691 |     91.44 |  0.005394 |    0.2921 |    0.1117 |
+#> |.....................|  0.009773 |    0.6113 |    0.9464 |   0.06226 |
+#> |.....................|    0.8547 |   0.05822 |    0.7267 |    0.8067 |
+#> |.....................|     1.366 |    0.9573 |    0.7854 |     1.423 |
+#> |    F| Forward Diff. |     31.42 |     1.822 |    0.1778 |   0.07033 |
+#> |.....................|  -0.01094 |    0.7681 |    -13.66 |    -5.343 |
+#> |.....................|    -2.704 |   -0.6601 |  -0.05834 |     4.483 |
+#> |.....................|    -6.846 |    0.3977 |     3.514 |    -8.343 |
+#> |   48|     483.71026 |    0.9937 |    -1.035 |   -0.9084 |   -0.9400 |
+#> |.....................|   -0.9863 |   -0.8970 |   -0.5817 |   -0.7327 |
+#> |.....................|   -0.8099 |   -0.8734 |   -0.8808 |   -0.9764 |
+#> |.....................|   -0.7120 |   -0.8790 |   -0.9614 |   -0.6918 |
+#> |    U|     483.71026 |     90.90 |    -5.224 |   -0.8851 |    -2.192 |
+#> |.....................|    -4.628 |    0.4526 |    0.9497 |   0.06228 |
+#> |.....................|    0.8552 |   0.05822 |    0.7272 |    0.8052 |
+#> |.....................|     1.370 |    0.9567 |    0.7840 |     1.430 |
+#> |    X|     483.71026 |     90.90 |  0.005386 |    0.2921 |    0.1117 |
+#> |.....................|  0.009771 |    0.6112 |    0.9497 |   0.06228 |
+#> |.....................|    0.8552 |   0.05822 |    0.7272 |    0.8052 |
+#> |.....................|     1.370 |    0.9567 |    0.7840 |     1.430 |
+#> |    F| Forward Diff. |    -11.41 |     1.753 |   -0.1608 |    0.1222 |
+#> |.....................|   0.01159 |    0.8050 |    -10.44 |    -3.810 |
+#> |.....................|    -1.727 |    0.1311 |     2.133 |     3.863 |
+#> |.....................|    -5.017 |     1.937 |     3.587 |    -8.159 |
+#> |   49|     483.59835 |     1.000 |    -1.037 |   -0.9083 |   -0.9401 |
+#> |.....................|   -0.9863 |   -0.8977 |   -0.5748 |   -0.7309 |
+#> |.....................|   -0.8089 |   -0.8737 |   -0.8826 |   -0.9789 |
+#> |.....................|   -0.7088 |   -0.8807 |   -0.9637 |   -0.6861 |
+#> |    U|     483.59835 |     91.50 |    -5.225 |   -0.8850 |    -2.192 |
+#> |.....................|    -4.628 |    0.4523 |    0.9525 |   0.06233 |
+#> |.....................|    0.8556 |   0.05821 |    0.7260 |    0.8029 |
+#> |.....................|     1.374 |    0.9551 |    0.7819 |     1.437 |
+#> |    X|     483.59835 |     91.50 |  0.005379 |    0.2921 |    0.1117 |
+#> |.....................|  0.009771 |    0.6112 |    0.9525 |   0.06233 |
+#> |.....................|    0.8556 |   0.05821 |    0.7260 |    0.8029 |
+#> |.....................|     1.374 |    0.9551 |    0.7819 |     1.437 |
+#> |    F| Forward Diff. |     35.70 |     1.806 |    0.2381 |   0.06477 |
+#> |.....................|  0.008951 |    0.7715 |    -12.71 |    -4.946 |
+#> |.....................|    -2.552 |   -0.6506 |  -0.07612 |     4.309 |
+#> |.....................|    -6.609 |    0.2622 |     3.318 |    -8.104 |
+#> |   50|     483.34903 |    0.9946 |    -1.038 |   -0.9084 |   -0.9402 |
+#> |.....................|   -0.9865 |   -0.8986 |   -0.5687 |   -0.7321 |
+#> |.....................|   -0.8087 |   -0.8746 |   -0.8853 |   -0.9811 |
+#> |.....................|   -0.7064 |   -0.8834 |   -0.9659 |   -0.6790 |
+#> |    U|     483.34903 |     90.99 |    -5.227 |   -0.8851 |    -2.192 |
+#> |.....................|    -4.629 |    0.4518 |    0.9551 |   0.06229 |
+#> |.....................|    0.8557 |   0.05818 |    0.7240 |    0.8009 |
+#> |.....................|     1.377 |    0.9526 |    0.7800 |     1.445 |
+#> |    X|     483.34903 |     90.99 |  0.005370 |    0.2921 |    0.1117 |
+#> |.....................|  0.009769 |    0.6111 |    0.9551 |   0.06229 |
+#> |.....................|    0.8557 |   0.05818 |    0.7240 |    0.8009 |
+#> |.....................|     1.377 |    0.9526 |    0.7800 |     1.445 |
+#> |    F| Forward Diff. |    -5.120 |     1.736 |  -0.09503 |    0.1090 |
+#> |.....................|   0.03046 |    0.8092 |    -12.63 |    -5.226 |
+#> |.....................|    -2.620 |   -0.5304 |   -0.3057 |     3.753 |
+#> |.....................|    -6.427 |   0.07650 |     3.398 |    -7.915 |
+#> |   51|     483.15597 |    0.9980 |    -1.040 |   -0.9083 |   -0.9402 |
+#> |.....................|   -0.9866 |   -0.8991 |   -0.5603 |   -0.7286 |
+#> |.....................|   -0.8069 |   -0.8742 |   -0.8851 |   -0.9836 |
+#> |.....................|   -0.7022 |   -0.8834 |   -0.9682 |   -0.6737 |
+#> |    U|     483.15597 |     91.30 |    -5.228 |   -0.8851 |    -2.192 |
+#> |.....................|    -4.629 |    0.4516 |    0.9585 |   0.06239 |
+#> |.....................|    0.8564 |   0.05819 |    0.7241 |    0.7987 |
+#> |.....................|     1.382 |    0.9525 |    0.7781 |     1.452 |
+#> |    X|     483.15597 |     91.30 |  0.005364 |    0.2921 |    0.1117 |
+#> |.....................|  0.009769 |    0.6110 |    0.9585 |   0.06239 |
+#> |.....................|    0.8564 |   0.05819 |    0.7241 |    0.7987 |
+#> |.....................|     1.382 |    0.9525 |    0.7781 |     1.452 |
+#> |   52|     483.02721 |     1.004 |    -1.042 |   -0.9082 |   -0.9404 |
+#> |.....................|   -0.9866 |   -0.9001 |   -0.5449 |   -0.7222 |
+#> |.....................|   -0.8037 |   -0.8736 |   -0.8847 |   -0.9882 |
+#> |.....................|   -0.6943 |   -0.8835 |   -0.9723 |   -0.6641 |
+#> |    U|     483.02721 |     91.87 |    -5.230 |   -0.8850 |    -2.192 |
+#> |.....................|    -4.629 |    0.4511 |    0.9649 |   0.06258 |
+#> |.....................|    0.8577 |   0.05821 |    0.7244 |    0.7946 |
+#> |.....................|     1.391 |    0.9524 |    0.7746 |     1.463 |
+#> |    X|     483.02721 |     91.87 |  0.005352 |    0.2921 |    0.1117 |
+#> |.....................|  0.009768 |    0.6109 |    0.9649 |   0.06258 |
+#> |.....................|    0.8577 |   0.05821 |    0.7244 |    0.7946 |
+#> |.....................|     1.391 |    0.9524 |    0.7746 |     1.463 |
+#> |    F| Forward Diff. |     64.04 |     1.793 |    0.5284 |   0.01389 |
+#> |.....................|   0.02898 |    0.7509 |    -12.63 |    -3.976 |
+#> |.....................|    -2.339 |   -0.6213 |    0.1061 |     4.124 |
+#> |.....................|    -6.092 |   0.06517 |     2.880 |    -7.726 |
+#> |   53|     482.23689 |    0.9946 |    -1.047 |   -0.9090 |   -0.9407 |
+#> |.....................|   -0.9878 |   -0.9036 |   -0.5201 |   -0.7284 |
+#> |.....................|   -0.8010 |   -0.8752 |   -0.8830 |   -0.9901 |
+#> |.....................|   -0.6858 |   -0.8831 |   -0.9756 |   -0.6451 |
+#> |    U|     482.23689 |     90.99 |    -5.236 |   -0.8857 |    -2.192 |
+#> |.....................|    -4.630 |    0.4496 |    0.9752 |   0.06240 |
+#> |.....................|    0.8589 |   0.05816 |    0.7257 |    0.7929 |
+#> |.....................|     1.401 |    0.9528 |    0.7717 |     1.486 |
+#> |    X|     482.23689 |     90.99 |  0.005323 |    0.2920 |    0.1116 |
+#> |.....................|  0.009757 |    0.6105 |    0.9752 |   0.06240 |
+#> |.....................|    0.8589 |   0.05816 |    0.7257 |    0.7929 |
+#> |.....................|     1.401 |    0.9528 |    0.7717 |     1.486 |
+#> |    F| Forward Diff. |    -6.401 |     1.688 |  -0.06693 |    0.1101 |
+#> |.....................|   0.07752 |    0.8485 |    -12.38 |    -4.258 |
+#> |.....................|    -2.381 |   -0.3971 |   -0.4532 |     3.327 |
+#> |.....................|    -5.692 |   0.09795 |     3.049 |    -7.221 |
+#> |   54|     481.84664 |     1.002 |    -1.052 |   -0.9094 |   -0.9410 |
+#> |.....................|   -0.9885 |   -0.9064 |   -0.4925 |   -0.7287 |
+#> |.....................|   -0.7974 |   -0.8758 |   -0.8811 |   -0.9941 |
+#> |.....................|   -0.6765 |   -0.8831 |   -0.9802 |   -0.6288 |
+#> |    U|     481.84664 |     91.67 |    -5.240 |   -0.8860 |    -2.193 |
+#> |.....................|    -4.631 |    0.4482 |    0.9866 |   0.06239 |
+#> |.....................|    0.8604 |   0.05815 |    0.7270 |    0.7893 |
+#> |.....................|     1.412 |    0.9528 |    0.7678 |     1.506 |
+#> |    X|     481.84664 |     91.67 |  0.005298 |    0.2919 |    0.1116 |
+#> |.....................|  0.009749 |    0.6102 |    0.9866 |   0.06239 |
+#> |.....................|    0.8604 |   0.05815 |    0.7270 |    0.7893 |
+#> |.....................|     1.412 |    0.9528 |    0.7678 |     1.506 |
+#> |    F| Forward Diff. |     47.13 |     1.726 |    0.4206 |   0.02536 |
+#> |.....................|   0.06828 |    0.8062 |    -11.83 |    -3.346 |
+#> |.....................|    -2.102 |   -0.4847 |  -0.09759 |     3.731 |
+#> |.....................|    -5.096 |   -0.5769 |     2.736 |    -6.997 |
+#> |   55|     481.27209 |    0.9943 |    -1.058 |   -0.9105 |   -0.9413 |
+#> |.....................|   -0.9900 |   -0.9106 |   -0.4653 |   -0.7394 |
+#> |.....................|   -0.7957 |   -0.8780 |   -0.8782 |   -0.9956 |
+#> |.....................|   -0.6736 |   -0.8789 |   -0.9829 |   -0.6135 |
+#> |    U|     481.27209 |     90.96 |    -5.246 |   -0.8870 |    -2.193 |
+#> |.....................|    -4.632 |    0.4464 |    0.9978 |   0.06208 |
+#> |.....................|    0.8611 |   0.05808 |    0.7292 |    0.7879 |
+#> |.....................|     1.416 |    0.9569 |    0.7655 |     1.525 |
+#> |    X|     481.27209 |     90.96 |  0.005268 |    0.2917 |    0.1116 |
+#> |.....................|  0.009735 |    0.6098 |    0.9978 |   0.06208 |
+#> |.....................|    0.8611 |   0.05808 |    0.7292 |    0.7879 |
+#> |.....................|     1.416 |    0.9569 |    0.7655 |     1.525 |
+#> |    F| Forward Diff. |    -10.35 |     1.643 |   -0.1028 |    0.1091 |
+#> |.....................|    0.1039 |    0.8949 |    -11.59 |    -3.607 |
+#> |.....................|    -2.172 |   -0.3207 |   -0.4703 |     3.042 |
+#> |.....................|    -5.188 |    0.5388 |     2.890 |    -6.602 |
+#> |   56|     480.86800 |    0.9992 |    -1.064 |   -0.9113 |   -0.9415 |
+#> |.....................|   -0.9915 |   -0.9152 |   -0.4371 |   -0.7498 |
+#> |.....................|   -0.7937 |   -0.8800 |   -0.8752 |   -0.9980 |
+#> |.....................|   -0.6700 |   -0.8785 |   -0.9867 |   -0.5989 |
+#> |    U|       480.868 |     91.41 |    -5.252 |   -0.8877 |    -2.193 |
+#> |.....................|    -4.634 |    0.4442 |     1.010 |   0.06178 |
+#> |.....................|    0.8619 |   0.05803 |    0.7313 |    0.7858 |
+#> |.....................|     1.420 |    0.9572 |    0.7622 |     1.543 |
+#> |    X|       480.868 |     91.41 |  0.005236 |    0.2916 |    0.1115 |
+#> |.....................|  0.009720 |    0.6093 |     1.010 |   0.06178 |
+#> |.....................|    0.8619 |   0.05803 |    0.7313 |    0.7858 |
+#> |.....................|     1.420 |    0.9572 |    0.7622 |     1.543 |
+#> |   57|     480.18757 |    0.9994 |    -1.075 |   -0.9131 |   -0.9420 |
+#> |.....................|   -0.9946 |   -0.9242 |   -0.3882 |   -0.7756 |
+#> |.....................|   -0.7917 |   -0.8845 |   -0.8694 |    -1.000 |
+#> |.....................|   -0.6674 |   -0.8772 |   -0.9919 |   -0.5742 |
+#> |    U|     480.18757 |     91.43 |    -5.264 |   -0.8893 |    -2.194 |
+#> |.....................|    -4.637 |    0.4401 |     1.030 |   0.06104 |
+#> |.....................|    0.8627 |   0.05790 |    0.7356 |    0.7839 |
+#> |.....................|     1.423 |    0.9585 |    0.7577 |     1.573 |
+#> |    X|     480.18757 |     91.43 |  0.005177 |    0.2913 |    0.1115 |
+#> |.....................|  0.009690 |    0.6083 |     1.030 |   0.06104 |
+#> |.....................|    0.8627 |   0.05790 |    0.7356 |    0.7839 |
+#> |.....................|     1.423 |    0.9585 |    0.7577 |     1.573 |
+#> |   58|     477.33677 |     1.000 |    -1.128 |   -0.9215 |   -0.9444 |
+#> |.....................|    -1.009 |   -0.9662 |   -0.1601 |   -0.8958 |
+#> |.....................|   -0.7824 |   -0.9055 |   -0.8420 |    -1.010 |
+#> |.....................|   -0.6551 |   -0.8713 |    -1.016 |   -0.4591 |
+#> |    U|     477.33677 |     91.51 |    -5.317 |   -0.8967 |    -2.196 |
+#> |.....................|    -4.651 |    0.4208 |     1.124 |   0.05757 |
+#> |.....................|    0.8666 |   0.05729 |    0.7556 |    0.7749 |
+#> |.....................|     1.438 |    0.9642 |    0.7367 |     1.713 |
+#> |    X|     477.33677 |     91.51 |  0.004910 |    0.2897 |    0.1112 |
+#> |.....................|  0.009550 |    0.6037 |     1.124 |   0.05757 |
+#> |.....................|    0.8666 |   0.05729 |    0.7556 |    0.7749 |
+#> |.....................|     1.438 |    0.9642 |    0.7367 |     1.713 |
+#> |   59|     470.34077 |     1.005 |    -1.340 |   -0.9551 |   -0.9536 |
+#> |.....................|    -1.067 |    -1.134 |    0.7520 |    -1.376 |
+#> |.....................|   -0.7448 |   -0.9894 |   -0.7326 |    -1.050 |
+#> |.....................|   -0.6055 |   -0.8475 |    -1.115 |  0.001078 |
+#> |    U|     470.34077 |     91.93 |    -5.528 |   -0.9265 |    -2.205 |
+#> |.....................|    -4.709 |    0.3439 |     1.502 |   0.04372 |
+#> |.....................|    0.8821 |   0.05487 |    0.8354 |    0.7391 |
+#> |.....................|     1.496 |    0.9871 |    0.6524 |     2.272 |
+#> |    X|     470.34077 |     91.93 |  0.003973 |    0.2836 |    0.1102 |
+#> |.....................|  0.009011 |    0.5851 |     1.502 |   0.04372 |
+#> |.....................|    0.8821 |   0.05487 |    0.8354 |    0.7391 |
+#> |.....................|     1.496 |    0.9871 |    0.6524 |     2.272 |
+#> |    F| Forward Diff. |     26.15 |    0.9841 |   -0.2917 |   -0.5557 |
+#> |.....................|    0.1743 |   0.07961 |    -5.483 |    -2.977 |
+#> |.....................|    -1.594 |    -1.883 |     1.921 |     2.622 |
+#> |.....................|    -2.684 |     3.199 |    -3.516 |   -0.2713 |
+#> |   60|     503.34963 |     1.001 |    -1.624 |   -0.8890 |   -0.8555 |
+#> |.....................|    -1.160 |    -1.269 |     1.871 |    -1.579 |
+#> |.....................|   -0.5570 |   -0.7566 |   -0.9888 |    -1.205 |
+#> |.....................|   -0.4219 |    -1.204 |   -0.3205 |  0.003684 |
+#> |    U|     503.34963 |     91.54 |    -5.813 |   -0.8679 |    -2.107 |
+#> |.....................|    -4.802 |    0.2817 |     1.965 |   0.03787 |
+#> |.....................|    0.9599 |   0.06159 |    0.6484 |    0.5998 |
+#> |.....................|     1.714 |    0.6438 |     1.334 |     2.275 |
+#> |    X|     503.34963 |     91.54 |  0.002989 |    0.2957 |    0.1216 |
+#> |.....................|  0.008214 |    0.5700 |     1.965 |   0.03787 |
+#> |.....................|    0.9599 |   0.06159 |    0.6484 |    0.5998 |
+#> |.....................|     1.714 |    0.6438 |     1.334 |     2.275 |
+#> |   61|     469.52776 |     1.001 |    -1.377 |   -0.9480 |   -0.9425 |
+#> |.....................|    -1.079 |    -1.153 |    0.9014 |    -1.405 |
+#> |.....................|   -0.7213 |   -0.9635 |   -0.7590 |    -1.066 |
+#> |.....................|   -0.5863 |   -0.9260 |    -1.020 |  0.002305 |
+#> |    U|     469.52776 |     91.55 |    -5.565 |   -0.9203 |    -2.194 |
+#> |.....................|    -4.721 |    0.3353 |     1.564 |   0.04288 |
+#> |.....................|    0.8919 |   0.05562 |    0.8161 |    0.7248 |
+#> |.....................|     1.519 |    0.9115 |    0.7335 |     2.274 |
+#> |    X|     469.52776 |     91.55 |  0.003829 |    0.2849 |    0.1114 |
+#> |.....................|  0.008907 |    0.5831 |     1.564 |   0.04288 |
+#> |.....................|    0.8919 |   0.05562 |    0.8161 |    0.7248 |
+#> |.....................|     1.519 |    0.9115 |    0.7335 |     2.274 |
+#> |    F| Forward Diff. |    -33.46 |    0.8466 |   -0.2714 |   -0.3437 |
+#> |.....................| -0.005169 |    0.9674 |    -4.363 |    -2.175 |
+#> |.....................|   -0.4723 |    -1.194 |     1.668 |     1.180 |
+#> |.....................|    -1.975 |    -3.231 |     4.715 |    0.6860 |
+#> |   62|     468.69396 |     1.009 |    -1.417 |   -0.9407 |   -0.9181 |
+#> |.....................|    -1.088 |    -1.184 |     1.029 |    -1.410 |
+#> |.....................|   -0.7106 |   -0.9016 |   -0.8502 |    -1.110 |
+#> |.....................|   -0.5641 |   -0.8957 |    -1.025 |  -0.08379 |
+#> |    U|     468.69396 |     92.28 |    -5.606 |   -0.9138 |    -2.170 |
+#> |.....................|    -4.730 |    0.3207 |     1.617 |   0.04273 |
+#> |.....................|    0.8963 |   0.05740 |    0.7496 |    0.6857 |
+#> |.....................|     1.546 |    0.9407 |    0.7298 |     2.169 |
+#> |    X|     468.69396 |     92.28 |  0.003677 |    0.2862 |    0.1142 |
+#> |.....................|  0.008826 |    0.5795 |     1.617 |   0.04273 |
+#> |.....................|    0.8963 |   0.05740 |    0.7496 |    0.6857 |
+#> |.....................|     1.546 |    0.9407 |    0.7298 |     2.169 |
+#> |    F| Forward Diff. |     44.64 |    0.7919 |    0.8591 |   -0.3536 |
+#> |.....................|   -0.1337 |    0.2061 |    -3.251 |     1.076 |
+#> |.....................|    0.6486 |   -0.6734 | -0.006662 |    -4.031 |
+#> |.....................|   -0.9510 |    -1.369 |     2.636 |    0.2207 |
+#> |   63|     468.25975 |     1.001 |    -1.457 |   -0.9435 |   -0.8944 |
+#> |.....................|    -1.092 |    -1.207 |     1.162 |    -1.430 |
+#> |.....................|   -0.7163 |   -0.8453 |   -0.9089 |    -1.031 |
+#> |.....................|   -0.5350 |   -0.9084 |    -1.055 |   -0.1705 |
+#> |    U|     468.25975 |     91.62 |    -5.645 |   -0.9163 |    -2.146 |
+#> |.....................|    -4.734 |    0.3104 |     1.671 |   0.04217 |
+#> |.....................|    0.8939 |   0.05903 |    0.7067 |    0.7562 |
+#> |.....................|     1.580 |    0.9284 |    0.7040 |     2.064 |
+#> |    X|     468.25975 |     91.62 |  0.003534 |    0.2857 |    0.1169 |
+#> |.....................|  0.008791 |    0.5770 |     1.671 |   0.04217 |
+#> |.....................|    0.8939 |   0.05903 |    0.7067 |    0.7562 |
+#> |.....................|     1.580 |    0.9284 |    0.7040 |     2.064 |
+#> |    F| Forward Diff. |    -27.10 |    0.6132 |  -0.09159 |  -0.08800 |
+#> |.....................|   -0.1078 |   -0.3202 |    -2.388 |     1.638 |
+#> |.....................|     1.140 |    0.1171 |    0.1600 |     3.377 |
+#> |.....................|     1.163 |    -2.226 |   -0.6898 |   -0.6683 |
+#> |   64|     467.71969 |     1.007 |    -1.501 |   -0.9546 |   -0.8725 |
+#> |.....................|    -1.088 |    -1.196 |     1.309 |    -1.518 |
+#> |.....................|   -0.7729 |   -0.8084 |   -0.9408 |    -1.028 |
+#> |.....................|   -0.5596 |   -0.8715 |    -1.022 |   -0.2167 |
+#> |    U|     467.71969 |     92.14 |    -5.690 |   -0.9262 |    -2.124 |
+#> |.....................|    -4.730 |    0.3152 |     1.732 |   0.03962 |
+#> |.....................|    0.8705 |   0.06009 |    0.6835 |    0.7588 |
+#> |.....................|     1.551 |    0.9640 |    0.7321 |     2.007 |
+#> |    X|     467.71969 |     92.14 |  0.003381 |    0.2837 |    0.1195 |
+#> |.....................|  0.008831 |    0.5781 |     1.732 |   0.03962 |
+#> |.....................|    0.8705 |   0.06009 |    0.6835 |    0.7588 |
+#> |.....................|     1.551 |    0.9640 |    0.7321 |     2.007 |
+#> |    F| Forward Diff. |     13.64 |    0.5263 |  -0.09449 |  -0.03300 |
+#> |.....................|   -0.2497 |    0.5177 |    -1.944 |     1.719 |
+#> |.....................|   0.02781 |   -0.4546 |    0.1053 |     4.139 |
+#> |.....................|    0.2369 |    0.8861 |     1.752 |   -0.4404 |
+#> |   65|     467.30536 |     1.004 |    -1.542 |   -0.9574 |   -0.8551 |
+#> |.....................|    -1.078 |    -1.202 |     1.437 |    -1.633 |
+#> |.....................|   -0.8162 |   -0.7674 |   -0.9588 |    -1.081 |
+#> |.....................|   -0.5907 |   -0.8860 |    -1.037 |   -0.2723 |
+#> |    U|     467.30536 |     91.87 |    -5.731 |   -0.9286 |    -2.107 |
+#> |.....................|    -4.720 |    0.3124 |     1.785 |   0.03631 |
+#> |.....................|    0.8526 |   0.06127 |    0.6704 |    0.7116 |
+#> |.....................|     1.514 |    0.9500 |    0.7187 |     1.940 |
+#> |    X|     467.30536 |     91.87 |  0.003244 |    0.2832 |    0.1216 |
+#> |.....................|  0.008917 |    0.5775 |     1.785 |   0.03631 |
+#> |.....................|    0.8526 |   0.06127 |    0.6704 |    0.7116 |
+#> |.....................|     1.514 |    0.9500 |    0.7187 |     1.940 |
+#> |    F| Forward Diff. |    -28.84 |    0.5077 |   -0.1377 |   0.05990 |
+#> |.....................|   -0.2272 |    0.7424 |    -2.070 |   -0.4026 |
+#> |.....................|   -0.6342 |   -0.6074 |   -0.7367 |    -1.927 |
+#> |.....................|    -1.174 |   -0.4282 |   -0.2913 |   -0.8226 |
+#> |   66|     467.70919 |     1.018 |    -1.590 |   -0.9528 |   -0.8478 |
+#> |.....................|    -1.050 |    -1.273 |     1.541 |    -1.746 |
+#> |.....................|   -0.7981 |   -0.6862 |   -0.9431 |    -1.082 |
+#> |.....................|   -0.5846 |   -0.9179 |    -1.062 |   -0.3171 |
+#> |    U|     467.70919 |     93.14 |    -5.778 |   -0.9245 |    -2.100 |
+#> |.....................|    -4.692 |    0.2799 |     1.829 |   0.03305 |
+#> |.....................|    0.8601 |   0.06362 |    0.6818 |    0.7103 |
+#> |.....................|     1.521 |    0.9193 |    0.6977 |     1.885 |
+#> |    X|     467.70919 |     93.14 |  0.003094 |    0.2840 |    0.1225 |
+#> |.....................|  0.009168 |    0.5695 |     1.829 |   0.03305 |
+#> |.....................|    0.8601 |   0.06362 |    0.6818 |    0.7103 |
+#> |.....................|     1.521 |    0.9193 |    0.6977 |     1.885 |
+#> |   67|     467.47896 |     1.015 |    -1.557 |   -0.9559 |   -0.8529 |
+#> |.....................|    -1.069 |    -1.224 |     1.469 |    -1.667 |
+#> |.....................|   -0.8105 |   -0.7423 |   -0.9538 |    -1.081 |
+#> |.....................|   -0.5885 |   -0.8957 |    -1.045 |   -0.2858 |
+#> |    U|     467.47896 |     92.90 |    -5.746 |   -0.9273 |    -2.105 |
+#> |.....................|    -4.711 |    0.3023 |     1.799 |   0.03531 |
+#> |.....................|    0.8550 |   0.06200 |    0.6740 |    0.7117 |
+#> |.....................|     1.517 |    0.9407 |    0.7123 |     1.923 |
+#> |    X|     467.47896 |     92.90 |  0.003197 |    0.2835 |    0.1219 |
+#> |.....................|  0.008994 |    0.5750 |     1.799 |   0.03531 |
+#> |.....................|    0.8550 |   0.06200 |    0.6740 |    0.7117 |
+#> |.....................|     1.517 |    0.9407 |    0.7123 |     1.923 |
+#> |   68|     467.47242 |     1.015 |    -1.547 |   -0.9569 |   -0.8545 |
+#> |.....................|    -1.075 |    -1.209 |     1.447 |    -1.644 |
+#> |.....................|   -0.8142 |   -0.7594 |   -0.9570 |    -1.080 |
+#> |.....................|   -0.5898 |   -0.8890 |    -1.040 |   -0.2763 |
+#> |    U|     467.47242 |     92.83 |    -5.736 |   -0.9282 |    -2.106 |
+#> |.....................|    -4.717 |    0.3092 |     1.790 |   0.03600 |
+#> |.....................|    0.8534 |   0.06150 |    0.6716 |    0.7121 |
+#> |.....................|     1.515 |    0.9472 |    0.7168 |     1.935 |
+#> |    X|     467.47242 |     92.83 |  0.003229 |    0.2833 |    0.1217 |
+#> |.....................|  0.008942 |    0.5767 |     1.790 |   0.03600 |
+#> |.....................|    0.8534 |   0.06150 |    0.6716 |    0.7121 |
+#> |.....................|     1.515 |    0.9472 |    0.7168 |     1.935 |
+#> |   69|     467.34503 |     1.012 |    -1.542 |   -0.9574 |   -0.8552 |
+#> |.....................|    -1.078 |    -1.203 |     1.437 |    -1.633 |
+#> |.....................|   -0.8160 |   -0.7673 |   -0.9586 |    -1.080 |
+#> |.....................|   -0.5904 |   -0.8859 |    -1.037 |   -0.2720 |
+#> |    U|     467.34503 |     92.56 |    -5.731 |   -0.9286 |    -2.107 |
+#> |.....................|    -4.720 |    0.3123 |     1.786 |   0.03631 |
+#> |.....................|    0.8527 |   0.06128 |    0.6705 |    0.7121 |
+#> |.....................|     1.514 |    0.9501 |    0.7188 |     1.940 |
+#> |    X|     467.34503 |     92.56 |  0.003244 |    0.2832 |    0.1216 |
+#> |.....................|  0.008918 |    0.5775 |     1.786 |   0.03631 |
+#> |.....................|    0.8527 |   0.06128 |    0.6705 |    0.7121 |
+#> |.....................|     1.514 |    0.9501 |    0.7188 |     1.940 |
+#> |   70|     467.25859 |     1.007 |    -1.542 |   -0.9574 |   -0.8552 |
+#> |.....................|    -1.078 |    -1.202 |     1.437 |    -1.633 |
+#> |.....................|   -0.8161 |   -0.7674 |   -0.9587 |    -1.080 |
+#> |.....................|   -0.5906 |   -0.8860 |    -1.037 |   -0.2722 |
+#> |    U|     467.25859 |     92.16 |    -5.731 |   -0.9286 |    -2.107 |
+#> |.....................|    -4.720 |    0.3124 |     1.785 |   0.03631 |
+#> |.....................|    0.8526 |   0.06127 |    0.6704 |    0.7118 |
+#> |.....................|     1.514 |    0.9500 |    0.7187 |     1.940 |
+#> |    X|     467.25859 |     92.16 |  0.003244 |    0.2832 |    0.1216 |
+#> |.....................|  0.008918 |    0.5775 |     1.785 |   0.03631 |
+#> |.....................|    0.8526 |   0.06127 |    0.6704 |    0.7118 |
+#> |.....................|     1.514 |    0.9500 |    0.7187 |     1.940 |
+#> |    F| Forward Diff. |    0.4422 |    0.5213 |   0.04284 |   0.02840 |
+#> |.....................|   -0.2383 |    0.7531 |    -2.043 |  -0.07081 |
+#> |.....................|   -0.6548 |   -0.6872 |   -0.7073 |    -1.773 |
+#> |.....................|    -1.488 |   -0.4400 |   -0.3907 |   -0.8156 |
+#> |   71|     467.25330 |     1.007 |    -1.543 |   -0.9574 |   -0.8552 |
+#> |.....................|    -1.078 |    -1.203 |     1.439 |    -1.633 |
+#> |.....................|   -0.8155 |   -0.7668 |   -0.9581 |    -1.079 |
+#> |.....................|   -0.5893 |   -0.8856 |    -1.037 |   -0.2714 |
+#> |    U|      467.2533 |     92.12 |    -5.731 |   -0.9287 |    -2.107 |
+#> |.....................|    -4.720 |    0.3121 |     1.786 |   0.03631 |
+#> |.....................|    0.8529 |   0.06129 |    0.6709 |    0.7133 |
+#> |.....................|     1.516 |    0.9504 |    0.7190 |     1.941 |
+#> |    X|      467.2533 |     92.12 |  0.003243 |    0.2832 |    0.1216 |
+#> |.....................|  0.008919 |    0.5774 |     1.786 |   0.03631 |
+#> |.....................|    0.8529 |   0.06129 |    0.6709 |    0.7133 |
+#> |.....................|     1.516 |    0.9504 |    0.7190 |     1.941 |
+#> |    F| Forward Diff. |    -3.065 |    0.5175 |   0.01752 |   0.03302 |
+#> |.....................|   -0.2370 |    0.7457 |    -1.985 |  -0.01476 |
+#> |.....................|   -0.5869 |   -0.6438 |   -0.7222 |    -1.672 |
+#> |.....................|    -1.086 |   -0.3942 |   -0.3461 |   -0.8075 |
+#> |   72|     467.24583 |     1.008 |    -1.544 |   -0.9571 |   -0.8551 |
+#> |.....................|    -1.077 |    -1.206 |     1.442 |    -1.635 |
+#> |.....................|   -0.8142 |   -0.7642 |   -0.9569 |    -1.078 |
+#> |.....................|   -0.5901 |   -0.8857 |    -1.037 |   -0.2715 |
+#> |    U|     467.24583 |     92.22 |    -5.733 |   -0.9284 |    -2.107 |
+#> |.....................|    -4.719 |    0.3108 |     1.788 |   0.03626 |
+#> |.....................|    0.8534 |   0.06137 |    0.6718 |    0.7144 |
+#> |.....................|     1.515 |    0.9503 |    0.7191 |     1.941 |
+#> |    X|     467.24583 |     92.22 |  0.003238 |    0.2832 |    0.1216 |
+#> |.....................|  0.008927 |    0.5771 |     1.788 |   0.03626 |
+#> |.....................|    0.8534 |   0.06137 |    0.6718 |    0.7144 |
+#> |.....................|     1.515 |    0.9503 |    0.7191 |     1.941 |
+#> |    F| Forward Diff. |     6.834 |    0.5162 |   0.08982 |   0.01752 |
+#> |.....................|   -0.2436 |    0.7158 |    -2.020 |  -0.04939 |
+#> |.....................|   -0.5459 |   -0.6263 |   -0.5712 |    -1.499 |
+#> |.....................|    -1.429 |   -0.4150 |   -0.4098 |   -0.8001 |
+#> |   73|     467.23713 |     1.007 |    -1.546 |   -0.9569 |   -0.8551 |
+#> |.....................|    -1.076 |    -1.209 |     1.446 |    -1.636 |
+#> |.....................|   -0.8132 |   -0.7618 |   -0.9559 |    -1.076 |
+#> |.....................|   -0.5919 |   -0.8860 |    -1.037 |   -0.2716 |
+#> |    U|     467.23713 |     92.12 |    -5.734 |   -0.9282 |    -2.107 |
+#> |.....................|    -4.718 |    0.3095 |     1.789 |   0.03621 |
+#> |.....................|    0.8538 |   0.06143 |    0.6724 |    0.7154 |
+#> |.....................|     1.513 |    0.9500 |    0.7191 |     1.941 |
+#> |    X|     467.23713 |     92.12 |  0.003233 |    0.2833 |    0.1216 |
+#> |.....................|  0.008936 |    0.5768 |     1.789 |   0.03621 |
+#> |.....................|    0.8538 |   0.06143 |    0.6724 |    0.7154 |
+#> |.....................|     1.513 |    0.9500 |    0.7191 |     1.941 |
+#> |    F| Forward Diff. |    -3.249 |    0.5067 |   0.04417 |   0.02698 |
+#> |.....................|   -0.2393 |    0.6753 |    -1.942 |   -0.1419 |
+#> |.....................|   -0.5001 |   -0.5983 |   -0.6679 |    -1.518 |
+#> |.....................|    -1.576 |   -0.4506 |   -0.4091 |   -0.8075 |
+#> |   74|     467.22826 |     1.008 |    -1.548 |   -0.9568 |   -0.8550 |
+#> |.....................|    -1.074 |    -1.212 |     1.450 |    -1.638 |
+#> |.....................|   -0.8127 |   -0.7593 |   -0.9548 |    -1.076 |
+#> |.....................|   -0.5925 |   -0.8862 |    -1.037 |   -0.2718 |
+#> |    U|     467.22826 |     92.20 |    -5.736 |   -0.9281 |    -2.107 |
+#> |.....................|    -4.716 |    0.3080 |     1.791 |   0.03615 |
+#> |.....................|    0.8540 |   0.06151 |    0.6733 |    0.7160 |
+#> |.....................|     1.512 |    0.9499 |    0.7192 |     1.940 |
+#> |    X|     467.22826 |     92.20 |  0.003227 |    0.2833 |    0.1216 |
+#> |.....................|  0.008947 |    0.5764 |     1.791 |   0.03615 |
+#> |.....................|    0.8540 |   0.06151 |    0.6733 |    0.7160 |
+#> |.....................|     1.512 |    0.9499 |    0.7192 |     1.940 |
+#> |    F| Forward Diff. |     4.158 |    0.5052 |   0.09162 |   0.01474 |
+#> |.....................|   -0.2441 |    0.6411 |    -1.927 |  0.008374 |
+#> |.....................|   -0.4204 |   -0.5681 |   -0.5325 |    -1.398 |
+#> |.....................|    -1.545 |   -0.4616 |   -0.4623 |   -0.8062 |
+#> |   75|     467.21798 |     1.007 |    -1.549 |   -0.9567 |   -0.8549 |
+#> |.....................|    -1.073 |    -1.215 |     1.453 |    -1.641 |
+#> |.....................|   -0.8130 |   -0.7568 |   -0.9541 |    -1.075 |
+#> |.....................|   -0.5920 |   -0.8862 |    -1.036 |   -0.2722 |
+#> |    U|     467.21798 |     92.13 |    -5.738 |   -0.9280 |    -2.107 |
+#> |.....................|    -4.715 |    0.3065 |     1.792 |   0.03607 |
+#> |.....................|    0.8539 |   0.06158 |    0.6738 |    0.7163 |
+#> |.....................|     1.512 |    0.9498 |    0.7195 |     1.940 |
+#> |    X|     467.21798 |     92.13 |  0.003221 |    0.2833 |    0.1216 |
+#> |.....................|  0.008959 |    0.5760 |     1.792 |   0.03607 |
+#> |.....................|    0.8539 |   0.06158 |    0.6738 |    0.7163 |
+#> |.....................|     1.512 |    0.9498 |    0.7195 |     1.940 |
+#> |    F| Forward Diff. |    -2.820 |    0.4989 |   0.05960 |   0.01935 |
+#> |.....................|   -0.2421 |    0.6061 |    -1.914 |   -0.2151 |
+#> |.....................|   -0.5103 |   -0.6093 |   -0.7625 |    -1.437 |
+#> |.....................|    -1.510 |   -0.4672 |   -0.4489 |   -0.8043 |
+#> |   76|     467.20848 |     1.008 |    -1.551 |   -0.9569 |   -0.8547 |
+#> |.....................|    -1.072 |    -1.218 |     1.456 |    -1.643 |
+#> |.....................|   -0.8130 |   -0.7539 |   -0.9520 |    -1.075 |
+#> |.....................|   -0.5920 |   -0.8859 |    -1.036 |   -0.2725 |
+#> |    U|     467.20848 |     92.20 |    -5.740 |   -0.9282 |    -2.106 |
+#> |.....................|    -4.714 |    0.3053 |     1.793 |   0.03601 |
+#> |.....................|    0.8539 |   0.06166 |    0.6753 |    0.7165 |
+#> |.....................|     1.512 |    0.9501 |    0.7200 |     1.939 |
+#> |    X|     467.20848 |     92.20 |  0.003215 |    0.2833 |    0.1217 |
+#> |.....................|  0.008973 |    0.5757 |     1.793 |   0.03601 |
+#> |.....................|    0.8539 |   0.06166 |    0.6753 |    0.7165 |
+#> |.....................|     1.512 |    0.9501 |    0.7200 |     1.939 |
+#> |    F| Forward Diff. |     3.706 |    0.4993 |    0.1020 |   0.01046 |
+#> |.....................|   -0.2448 |    0.5847 |    -1.899 |   -0.1702 |
+#> |.....................|   -0.3837 |   -0.5516 |   -0.5275 |    -1.370 |
+#> |.....................|    -1.509 |   -0.4527 |   -0.4630 |   -0.7991 |
+#> |   77|     467.20140 |     1.007 |    -1.554 |   -0.9572 |   -0.8545 |
+#> |.....................|    -1.070 |    -1.221 |     1.459 |    -1.644 |
+#> |.....................|   -0.8137 |   -0.7511 |   -0.9495 |    -1.075 |
+#> |.....................|   -0.5926 |   -0.8856 |    -1.035 |   -0.2726 |
+#> |    U|      467.2014 |     92.12 |    -5.742 |   -0.9285 |    -2.106 |
+#> |.....................|    -4.712 |    0.3041 |     1.795 |   0.03600 |
+#> |.....................|    0.8536 |   0.06174 |    0.6772 |    0.7169 |
+#> |.....................|     1.512 |    0.9504 |    0.7205 |     1.939 |
+#> |    X|      467.2014 |     92.12 |  0.003207 |    0.2832 |    0.1217 |
+#> |.....................|  0.008990 |    0.5754 |     1.795 |   0.03600 |
+#> |.....................|    0.8536 |   0.06174 |    0.6772 |    0.7169 |
+#> |.....................|     1.512 |    0.9504 |    0.7205 |     1.939 |
+#> |    F| Forward Diff. |    -4.697 |    0.4875 |   0.03394 |   0.01314 |
+#> |.....................|   -0.2450 |    0.5527 |    -1.903 |   -0.2230 |
+#> |.....................|   -0.3367 |   -0.5055 |   -0.4386 |    -1.334 |
+#> |.....................|    -1.570 |   -0.4518 |   -0.4312 |   -0.7987 |
+#> |   78|     467.19155 |     1.008 |    -1.556 |   -0.9574 |   -0.8545 |
+#> |.....................|    -1.067 |    -1.224 |     1.462 |    -1.645 |
+#> |.....................|   -0.8159 |   -0.7492 |   -0.9499 |    -1.074 |
+#> |.....................|   -0.5924 |   -0.8858 |    -1.035 |   -0.2722 |
+#> |    U|     467.19155 |     92.18 |    -5.745 |   -0.9286 |    -2.106 |
+#> |.....................|    -4.709 |    0.3027 |     1.796 |   0.03596 |
+#> |.....................|    0.8527 |   0.06180 |    0.6768 |    0.7173 |
+#> |.....................|     1.512 |    0.9502 |    0.7208 |     1.940 |
+#> |    X|     467.19155 |     92.18 |  0.003200 |    0.2832 |    0.1217 |
+#> |.....................|  0.009010 |    0.5751 |     1.796 |   0.03596 |
+#> |.....................|    0.8527 |   0.06180 |    0.6768 |    0.7173 |
+#> |.....................|     1.512 |    0.9502 |    0.7208 |     1.940 |
+#> |    F| Forward Diff. |     2.102 |    0.4867 |   0.07498 |  0.004893 |
+#> |.....................|   -0.2442 |    0.5250 |    -1.879 |   -0.1740 |
+#> |.....................|   -0.3775 |   -0.5383 |   -0.4109 |    -1.255 |
+#> |.....................|    -1.562 |   -0.4584 |   -0.4426 |   -0.7882 |
+#> |   79|     467.18237 |     1.007 |    -1.558 |   -0.9574 |   -0.8544 |
+#> |.....................|    -1.065 |    -1.226 |     1.465 |    -1.647 |
+#> |.....................|   -0.8177 |   -0.7470 |   -0.9510 |    -1.074 |
+#> |.....................|   -0.5912 |   -0.8859 |    -1.035 |   -0.2717 |
+#> |    U|     467.18237 |     92.12 |    -5.747 |   -0.9286 |    -2.106 |
+#> |.....................|    -4.707 |    0.3016 |     1.797 |   0.03591 |
+#> |.....................|    0.8519 |   0.06186 |    0.6761 |    0.7177 |
+#> |.....................|     1.513 |    0.9501 |    0.7212 |     1.940 |
+#> |    X|     467.18237 |     92.12 |  0.003193 |    0.2832 |    0.1217 |
+#> |.....................|  0.009031 |    0.5748 |     1.797 |   0.03591 |
+#> |.....................|    0.8519 |   0.06186 |    0.6761 |    0.7177 |
+#> |.....................|     1.513 |    0.9501 |    0.7212 |     1.940 |
+#> |    F| Forward Diff. |    -4.940 |    0.4761 |   0.03110 |  0.006161 |
+#> |.....................|   -0.2415 |    0.4988 |    -1.880 |   -0.2651 |
+#> |.....................|   -0.3787 |   -0.5263 |   -0.4799 |    -1.241 |
+#> |.....................|    -1.481 |   -0.4641 |   -0.4124 |   -0.7761 |
+#> |   80|     467.17113 |     1.008 |    -1.561 |   -0.9574 |   -0.8542 |
+#> |.....................|    -1.062 |    -1.228 |     1.469 |    -1.648 |
+#> |.....................|   -0.8192 |   -0.7442 |   -0.9515 |    -1.074 |
+#> |.....................|   -0.5909 |   -0.8858 |    -1.034 |   -0.2714 |
+#> |    U|     467.17113 |     92.19 |    -5.749 |   -0.9286 |    -2.106 |
+#> |.....................|    -4.704 |    0.3008 |     1.799 |   0.03586 |
+#> |.....................|    0.8513 |   0.06194 |    0.6757 |    0.7179 |
+#> |.....................|     1.514 |    0.9502 |    0.7215 |     1.941 |
+#> |    X|     467.17113 |     92.19 |  0.003185 |    0.2832 |    0.1217 |
+#> |.....................|  0.009056 |    0.5746 |     1.799 |   0.03586 |
+#> |.....................|    0.8513 |   0.06194 |    0.6757 |    0.7179 |
+#> |.....................|     1.514 |    0.9502 |    0.7215 |     1.941 |
+#> |   81|     467.15723 |     1.008 |    -1.564 |   -0.9575 |   -0.8538 |
+#> |.....................|    -1.058 |    -1.230 |     1.473 |    -1.651 |
+#> |.....................|   -0.8215 |   -0.7400 |   -0.9524 |    -1.074 |
+#> |.....................|   -0.5906 |   -0.8857 |    -1.034 |   -0.2712 |
+#> |    U|     467.15723 |     92.19 |    -5.753 |   -0.9287 |    -2.106 |
+#> |.....................|    -4.700 |    0.2996 |     1.800 |   0.03578 |
+#> |.....................|    0.8504 |   0.06206 |    0.6750 |    0.7179 |
+#> |.....................|     1.514 |    0.9503 |    0.7219 |     1.941 |
+#> |    X|     467.15723 |     92.19 |  0.003173 |    0.2832 |    0.1218 |
+#> |.....................|  0.009093 |    0.5743 |     1.800 |   0.03578 |
+#> |.....................|    0.8504 |   0.06206 |    0.6750 |    0.7179 |
+#> |.....................|     1.514 |    0.9503 |    0.7219 |     1.941 |
+#> |   82|     467.09153 |     1.008 |    -1.583 |   -0.9578 |   -0.8521 |
+#> |.....................|    -1.038 |    -1.244 |     1.497 |    -1.664 |
+#> |.....................|   -0.8331 |   -0.7187 |   -0.9572 |    -1.074 |
+#> |.....................|   -0.5894 |   -0.8854 |    -1.031 |   -0.2699 |
+#> |    U|     467.09153 |     92.20 |    -5.772 |   -0.9290 |    -2.104 |
+#> |.....................|    -4.680 |    0.2934 |     1.810 |   0.03540 |
+#> |.....................|    0.8456 |   0.06268 |    0.6715 |    0.7181 |
+#> |.....................|     1.516 |    0.9506 |    0.7239 |     1.943 |
+#> |    X|     467.09153 |     92.20 |  0.003114 |    0.2831 |    0.1220 |
+#> |.....................|  0.009282 |    0.5728 |     1.810 |   0.03540 |
+#> |.....................|    0.8456 |   0.06268 |    0.6715 |    0.7181 |
+#> |.....................|     1.516 |    0.9506 |    0.7239 |     1.943 |
+#> |   83|     466.89701 |     1.009 |    -1.658 |   -0.9591 |   -0.8451 |
+#> |.....................|   -0.9556 |    -1.297 |     1.590 |    -1.717 |
+#> |.....................|   -0.8794 |   -0.6338 |   -0.9760 |    -1.073 |
+#> |.....................|   -0.5844 |   -0.8840 |    -1.022 |   -0.2647 |
+#> |    U|     466.89701 |     92.27 |    -5.846 |   -0.9301 |    -2.097 |
+#> |.....................|    -4.598 |    0.2688 |     1.849 |   0.03388 |
+#> |.....................|    0.8264 |   0.06513 |    0.6578 |    0.7186 |
+#> |.....................|     1.521 |    0.9519 |    0.7320 |     1.949 |
+#> |    X|     466.89701 |     92.27 |  0.002890 |    0.2829 |    0.1228 |
+#> |.....................|   0.01008 |    0.5668 |     1.849 |   0.03388 |
+#> |.....................|    0.8264 |   0.06513 |    0.6578 |    0.7186 |
+#> |.....................|     1.521 |    0.9519 |    0.7320 |     1.949 |
+#> |   84|     466.81525 |     1.010 |    -1.758 |   -0.9608 |   -0.8357 |
+#> |.....................|   -0.8455 |    -1.369 |     1.715 |    -1.787 |
+#> |.....................|   -0.9414 |   -0.5201 |    -1.001 |    -1.072 |
+#> |.....................|   -0.5775 |   -0.8822 |    -1.009 |   -0.2576 |
+#> |    U|     466.81525 |     92.41 |    -5.946 |   -0.9316 |    -2.087 |
+#> |.....................|    -4.488 |    0.2358 |     1.901 |   0.03185 |
+#> |.....................|    0.8007 |   0.06841 |    0.6394 |    0.7195 |
+#> |.....................|     1.530 |    0.9537 |    0.7428 |     1.958 |
+#> |    X|     466.81525 |     92.41 |  0.002615 |    0.2826 |    0.1240 |
+#> |.....................|   0.01125 |    0.5587 |     1.901 |   0.03185 |
+#> |.....................|    0.8007 |   0.06841 |    0.6394 |    0.7195 |
+#> |.....................|     1.530 |    0.9537 |    0.7428 |     1.958 |
+#> |    F| Forward Diff. |     1.005 |   0.03859 |    0.3281 |   -0.1495 |
+#> |.....................|    0.1126 |   -0.4190 |   -0.9638 |    -1.159 |
+#> |.....................|   -0.4187 |   -0.1084 |    -1.236 |     1.865 |
+#> |.....................|   -0.3960 |   -0.4043 |   -0.1671 |    0.1635 |
+#> |   85|     467.22945 |     1.009 |    -1.931 |    -1.059 |   -0.7851 |
+#> |.....................|   -0.6667 |    -1.418 |     1.962 |    -1.804 |
+#> |.....................|    -1.038 |   -0.3298 |   -0.7816 |    -1.157 |
+#> |.....................|   -0.5368 |   -0.8226 |   -0.9633 |   -0.3812 |
+#> |    U|     467.22945 |     92.33 |    -6.120 |    -1.019 |    -2.037 |
+#> |.....................|    -4.309 |    0.2137 |     2.003 |   0.03136 |
+#> |.....................|    0.7606 |   0.07390 |    0.7997 |    0.6429 |
+#> |.....................|     1.578 |     1.011 |    0.7823 |     1.807 |
+#> |    X|     467.22945 |     92.33 |  0.002199 |    0.2652 |    0.1304 |
+#> |.....................|   0.01345 |    0.5532 |     2.003 |   0.03136 |
+#> |.....................|    0.7606 |   0.07390 |    0.7997 |    0.6429 |
+#> |.....................|     1.578 |     1.011 |    0.7823 |     1.807 |
+#> |   86|     466.68655 |     1.009 |    -1.812 |   -0.9919 |   -0.8198 |
+#> |.....................|   -0.7896 |    -1.384 |     1.793 |    -1.792 |
+#> |.....................|   -0.9716 |   -0.4604 |   -0.9317 |    -1.100 |
+#> |.....................|   -0.5645 |   -0.8633 |   -0.9948 |   -0.2964 |
+#> |    U|     466.68655 |     92.33 |    -6.001 |   -0.9592 |    -2.072 |
+#> |.....................|    -4.432 |    0.2290 |     1.933 |   0.03172 |
+#> |.....................|    0.7883 |   0.07013 |    0.6901 |    0.6945 |
+#> |.....................|     1.545 |    0.9719 |    0.7553 |     1.910 |
+#> |    X|     466.68655 |     92.33 |  0.002477 |    0.2770 |    0.1260 |
+#> |.....................|   0.01190 |    0.5570 |     1.933 |   0.03172 |
+#> |.....................|    0.7883 |   0.07013 |    0.6901 |    0.6945 |
+#> |.....................|     1.545 |    0.9719 |    0.7553 |     1.910 |
+#> |    F| Forward Diff. |    -11.18 |   0.05254 |   -0.8763 |  -0.07569 |
+#> |.....................|    0.1998 |   -0.2059 |   -0.4605 |   -0.7124 |
+#> |.....................|   -0.3271 |   0.07217 |    0.9692 |     1.710 |
+#> |.....................|   -0.7229 |    0.7265 |    0.2517 |  -0.09129 |
+#> |   87|     466.82655 |     1.009 |    -1.865 |   -0.9192 |   -0.7946 |
+#> |.....................|   -0.7769 |    -1.362 |     1.859 |    -1.827 |
+#> |.....................|   -0.9838 |   -0.4392 |   -0.9155 |    -1.146 |
+#> |.....................|   -0.4995 |   -0.8511 |    -1.000 |   -0.3560 |
+#> |    U|     466.82655 |     92.34 |    -6.054 |   -0.8947 |    -2.046 |
+#> |.....................|    -4.419 |    0.2394 |     1.960 |   0.03072 |
+#> |.....................|    0.7832 |   0.07074 |    0.7019 |    0.6527 |
+#> |.....................|     1.622 |    0.9836 |    0.7506 |     1.838 |
+#> |    X|     466.82655 |     92.34 |  0.002349 |    0.2901 |    0.1292 |
+#> |.....................|   0.01205 |    0.5596 |     1.960 |   0.03072 |
+#> |.....................|    0.7832 |   0.07074 |    0.7019 |    0.6527 |
+#> |.....................|     1.622 |    0.9836 |    0.7506 |     1.838 |
+#> |   88|     466.65072 |     1.010 |    -1.827 |   -0.9719 |   -0.8129 |
+#> |.....................|   -0.7861 |    -1.378 |     1.811 |    -1.801 |
+#> |.....................|   -0.9749 |   -0.4546 |   -0.9274 |    -1.113 |
+#> |.....................|   -0.5467 |   -0.8600 |   -0.9963 |   -0.3127 |
+#> |    U|     466.65072 |     92.43 |    -6.015 |   -0.9415 |    -2.065 |
+#> |.....................|    -4.428 |    0.2318 |     1.940 |   0.03144 |
+#> |.....................|    0.7869 |   0.07030 |    0.6933 |    0.6830 |
+#> |.....................|     1.566 |    0.9750 |    0.7540 |     1.891 |
+#> |    X|     466.65072 |     92.43 |  0.002441 |    0.2806 |    0.1269 |
+#> |.....................|   0.01194 |    0.5577 |     1.940 |   0.03144 |
+#> |.....................|    0.7869 |   0.07030 |    0.6933 |    0.6830 |
+#> |.....................|     1.566 |    0.9750 |    0.7540 |     1.891 |
+#> |    F| Forward Diff. |    -1.340 |   0.07863 |    0.1180 |  -0.03302 |
+#> |.....................|    0.1973 |  -0.03638 |   -0.4314 |   -0.7320 |
+#> |.....................|   -0.3719 |   0.04356 |    0.7597 |     1.009 |
+#> |.....................|    0.3079 |    0.4883 |   -0.4019 |   -0.3069 |
+#> |   89|     466.64054 |     1.012 |    -1.843 |   -0.9769 |   -0.8069 |
+#> |.....................|   -0.7968 |    -1.376 |     1.833 |    -1.786 |
+#> |.....................|   -0.9571 |   -0.4600 |   -0.9463 |    -1.118 |
+#> |.....................|   -0.5553 |   -0.8554 |   -0.9954 |   -0.3119 |
+#> |    U|     466.64054 |     92.56 |    -6.031 |   -0.9459 |    -2.059 |
+#> |.....................|    -4.439 |    0.2329 |     1.949 |   0.03189 |
+#> |.....................|    0.7943 |   0.07014 |    0.6795 |    0.6783 |
+#> |.....................|     1.556 |    0.9795 |    0.7548 |     1.892 |
+#> |    X|     466.64054 |     92.56 |  0.002403 |    0.2797 |    0.1276 |
+#> |.....................|   0.01181 |    0.5580 |     1.949 |   0.03189 |
+#> |.....................|    0.7943 |   0.07014 |    0.6795 |    0.6783 |
+#> |.....................|     1.556 |    0.9795 |    0.7548 |     1.892 |
+#> |    F| Forward Diff. |     13.35 |   0.06546 |  -0.02976 |   0.01632 |
+#> |.....................|    0.1680 |  -0.06031 |   -0.2101 |    0.2297 |
+#> |.....................|  -0.01975 |    0.1913 |    0.1108 |    0.6100 |
+#> |.....................| -0.008263 |     1.320 |   0.06198 |   -0.2490 |
+#> |   90|     466.63994 |     1.010 |    -1.856 |   -0.9836 |   -0.8023 |
+#> |.....................|   -0.8121 |    -1.369 |     1.859 |    -1.781 |
+#> |.....................|   -0.9548 |   -0.4699 |   -0.9506 |    -1.117 |
+#> |.....................|   -0.5644 |   -0.8726 |    -1.009 |   -0.3176 |
+#> |    U|     466.63994 |     92.43 |    -6.045 |   -0.9518 |    -2.054 |
+#> |.....................|    -4.454 |    0.2360 |     1.960 |   0.03203 |
+#> |.....................|    0.7952 |   0.06986 |    0.6763 |    0.6795 |
+#> |.....................|     1.545 |    0.9629 |    0.7430 |     1.885 |
+#> |    X|     466.63994 |     92.43 |  0.002371 |    0.2785 |    0.1282 |
+#> |.....................|   0.01163 |    0.5587 |     1.960 |   0.03203 |
+#> |.....................|    0.7952 |   0.06986 |    0.6763 |    0.6795 |
+#> |.....................|     1.545 |    0.9629 |    0.7430 |     1.885 |
+#> |    F| Forward Diff. |    0.1431 |   0.02593 |   -0.4247 |   0.08835 |
+#> |.....................|    0.1490 |  -0.08497 |   0.03702 |    0.4153 |
+#> |.....................|  -0.04754 |    0.2015 |   0.06787 |   -0.3581 |
+#> |.....................|   -0.4069 |   0.09362 |   -0.9227 |   -0.5264 |
+#> |   91|     466.65402 |     1.008 |    -1.856 |   -0.9767 |   -0.8037 |
+#> |.....................|   -0.8145 |    -1.367 |     1.858 |    -1.788 |
+#> |.....................|   -0.9540 |   -0.4731 |   -0.9517 |    -1.111 |
+#> |.....................|   -0.5579 |   -0.8741 |   -0.9943 |   -0.3092 |
+#> |    U|     466.65402 |     92.22 |    -6.045 |   -0.9458 |    -2.055 |
+#> |.....................|    -4.457 |    0.2367 |     1.960 |   0.03184 |
+#> |.....................|    0.7955 |   0.06976 |    0.6755 |    0.6846 |
+#> |.....................|     1.553 |    0.9615 |    0.7557 |     1.895 |
+#> |    X|     466.65402 |     92.22 |  0.002370 |    0.2797 |    0.1280 |
+#> |.....................|   0.01160 |    0.5589 |     1.960 |   0.03184 |
+#> |.....................|    0.7955 |   0.06976 |    0.6755 |    0.6846 |
+#> |.....................|     1.553 |    0.9615 |    0.7557 |     1.895 |
+#> |   92|     466.63541 |     1.010 |    -1.856 |   -0.9812 |   -0.8028 |
+#> |.....................|   -0.8129 |    -1.368 |     1.858 |    -1.783 |
+#> |.....................|   -0.9545 |   -0.4710 |   -0.9509 |    -1.115 |
+#> |.....................|   -0.5622 |   -0.8731 |    -1.004 |   -0.3147 |
+#> |    U|     466.63541 |     92.36 |    -6.045 |   -0.9498 |    -2.055 |
+#> |.....................|    -4.455 |    0.2363 |     1.960 |   0.03197 |
+#> |.....................|    0.7953 |   0.06982 |    0.6761 |    0.6812 |
+#> |.....................|     1.548 |    0.9624 |    0.7474 |     1.888 |
+#> |    X|     466.63541 |     92.36 |  0.002371 |    0.2789 |    0.1281 |
+#> |.....................|   0.01162 |    0.5588 |     1.960 |   0.03197 |
+#> |.....................|    0.7953 |   0.06982 |    0.6761 |    0.6812 |
+#> |.....................|     1.548 |    0.9624 |    0.7474 |     1.888 |
+#> |    F| Forward Diff. |    -7.597 |   0.01585 |   -0.3721 |   0.09081 |
+#> |.....................|    0.1473 |  -0.05128 |   0.01723 |    0.2650 |
+#> |.....................|  -0.04930 |    0.2121 |    0.3911 |   -0.1952 |
+#> |.....................|   -0.2951 |   0.01195 |   -0.4116 |   -0.4404 |
+#> |   93|     466.62967 |     1.010 |    -1.857 |   -0.9822 |   -0.8038 |
+#> |.....................|   -0.8179 |    -1.367 |     1.859 |    -1.785 |
+#> |.....................|   -0.9524 |   -0.4748 |   -0.9515 |    -1.114 |
+#> |.....................|   -0.5617 |   -0.8740 |    -1.004 |   -0.3130 |
+#> |    U|     466.62967 |     92.43 |    -6.045 |   -0.9507 |    -2.056 |
+#> |.....................|    -4.460 |    0.2370 |     1.960 |   0.03192 |
+#> |.....................|    0.7962 |   0.06971 |    0.6756 |    0.6815 |
+#> |.....................|     1.548 |    0.9616 |    0.7476 |     1.890 |
+#> |    X|     466.62967 |     92.43 |  0.002369 |    0.2787 |    0.1280 |
+#> |.....................|   0.01156 |    0.5590 |     1.960 |   0.03192 |
+#> |.....................|    0.7962 |   0.06971 |    0.6756 |    0.6815 |
+#> |.....................|     1.548 |    0.9616 |    0.7476 |     1.890 |
+#> |    F| Forward Diff. |    0.1737 |   0.01712 |   -0.3712 |   0.07555 |
+#> |.....................|    0.1320 |  -0.03330 |   -0.1756 |    0.3015 |
+#> |.....................|  -0.06297 |    0.1717 |   0.09645 |   -0.1674 |
+#> |.....................|   -0.2756 |  -0.01624 |   -0.3459 |   -0.4307 |
+#> |   94|     466.62779 |     1.010 |    -1.856 |   -0.9797 |   -0.8047 |
+#> |.....................|   -0.8221 |    -1.366 |     1.862 |    -1.786 |
+#> |.....................|   -0.9500 |   -0.4779 |   -0.9517 |    -1.113 |
+#> |.....................|   -0.5623 |   -0.8742 |    -1.003 |   -0.3111 |
+#> |    U|     466.62779 |     92.40 |    -6.045 |   -0.9484 |    -2.056 |
+#> |.....................|    -4.464 |    0.2375 |     1.961 |   0.03188 |
+#> |.....................|    0.7972 |   0.06963 |    0.6755 |    0.6823 |
+#> |.....................|     1.548 |    0.9614 |    0.7480 |     1.893 |
+#> |    X|     466.62779 |     92.40 |  0.002370 |    0.2792 |    0.1279 |
+#> |.....................|   0.01152 |    0.5591 |     1.961 |   0.03188 |
+#> |.....................|    0.7972 |   0.06963 |    0.6755 |    0.6823 |
+#> |.....................|     1.548 |    0.9614 |    0.7480 |     1.893 |
+#> |    F| Forward Diff. |    -2.926 |   0.01199 |   -0.2808 |   0.07297 |
+#> |.....................|    0.1250 |  -0.02504 |   0.02207 |    0.2419 |
+#> |.....................|  -0.03068 |    0.1983 |    0.3271 |  -0.08125 |
+#> |.....................|   -0.2841 |  -0.05347 |   -0.2873 |   -0.3919 |
+#> |   95|     466.62386 |     1.010 |    -1.856 |   -0.9811 |   -0.8057 |
+#> |.....................|   -0.8267 |    -1.365 |     1.862 |    -1.788 |
+#> |.....................|   -0.9479 |   -0.4822 |   -0.9526 |    -1.114 |
+#> |.....................|   -0.5610 |   -0.8741 |    -1.003 |   -0.3093 |
+#> |    U|     466.62386 |     92.43 |    -6.045 |   -0.9497 |    -2.057 |
+#> |.....................|    -4.469 |    0.2377 |     1.961 |   0.03183 |
+#> |.....................|    0.7980 |   0.06950 |    0.6749 |    0.6820 |
+#> |.....................|     1.549 |    0.9614 |    0.7483 |     1.895 |
+#> |    X|     466.62386 |     92.43 |  0.002370 |    0.2789 |    0.1278 |
+#> |.....................|   0.01146 |    0.5592 |     1.961 |   0.03183 |
+#> |.....................|    0.7980 |   0.06950 |    0.6749 |    0.6820 |
+#> |.....................|     1.549 |    0.9614 |    0.7483 |     1.895 |
+#> |    F| Forward Diff. |    0.1137 |   0.01564 |   -0.3265 |   0.06191 |
+#> |.....................|    0.1094 |  -0.02529 |   0.01125 |    0.2123 |
+#> |.....................|  -0.07598 |    0.1365 |    0.2003 |   -0.1363 |
+#> |.....................|   -0.2276 |  -0.05501 |   -0.2526 |   -0.4116 |
+#> |   96|     466.62386 |     1.010 |    -1.856 |   -0.9811 |   -0.8057 |
+#> |.....................|   -0.8267 |    -1.365 |     1.862 |    -1.788 |
+#> |.....................|   -0.9479 |   -0.4822 |   -0.9526 |    -1.114 |
+#> |.....................|   -0.5610 |   -0.8741 |    -1.003 |   -0.3093 |
+#> |    U|     466.62386 |     92.43 |    -6.045 |   -0.9497 |    -2.057 |
+#> |.....................|    -4.469 |    0.2377 |     1.961 |   0.03183 |
+#> |.....................|    0.7980 |   0.06950 |    0.6749 |    0.6820 |
+#> |.....................|     1.549 |    0.9614 |    0.7483 |     1.895 |
+#> |    X|     466.62386 |     92.43 |  0.002370 |    0.2789 |    0.1278 |
+#> |.....................|   0.01146 |    0.5592 |     1.961 |   0.03183 |
+#> |.....................|    0.7980 |   0.06950 |    0.6749 |    0.6820 |
+#> |.....................|     1.549 |    0.9614 |    0.7483 |     1.895 |
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                
+AIC(
+  f_nlmixr_sfo_sfo_focei_const$nm,
+  f_nlmixr_fomc_sfo_focei_const$nm,
+  f_nlmixr_dfop_sfo_focei_const$nm,
+  f_nlmixr_fomc_sfo_saem_obs$nm,
+  f_nlmixr_fomc_sfo_focei_obs$nm,
+  f_nlmixr_dfop_sfo_saem_obs$nm,
+  f_nlmixr_dfop_sfo_focei_obs$nm,
+  f_nlmixr_fomc_sfo_focei_tc$nm,
+  f_nlmixr_dfop_sfo_focei_tc$nm,
+  f_nlmixr_fomc_sfo_saem_obs_tc$nm,
+  f_nlmixr_fomc_sfo_focei_obs_tc$nm,
+  f_nlmixr_dfop_sfo_saem_obs_tc$nm,
+  f_nlmixr_dfop_sfo_focei_obs_tc$nm
+)
+
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #>                                   df       AIC
+#> f_nlmixr_sfo_sfo_focei_const$nm    9 1082.4868
+#> f_nlmixr_fomc_sfo_focei_const$nm  11  814.4317
+#> f_nlmixr_dfop_sfo_focei_const$nm  13  866.0485
+#> f_nlmixr_fomc_sfo_saem_obs$nm     12  791.7256
+#> f_nlmixr_fomc_sfo_focei_obs$nm    12  794.5998
+#> f_nlmixr_dfop_sfo_saem_obs$nm     14  812.0463
+#> f_nlmixr_dfop_sfo_focei_obs$nm    14  846.9228
+#> f_nlmixr_fomc_sfo_focei_tc$nm     12  812.3585
+#> f_nlmixr_dfop_sfo_focei_tc$nm     14  842.3479
+#> f_nlmixr_fomc_sfo_saem_obs_tc$nm  14  817.1261
+#> f_nlmixr_fomc_sfo_focei_obs_tc$nm 14  787.4863
+#> f_nlmixr_dfop_sfo_saem_obs_tc$nm  16  858.3213
+#> f_nlmixr_dfop_sfo_focei_obs_tc$nm 16  811.0630
                                                                # Currently, FOMC-SFO with two-component error by variable fitted by focei gives the
+# lowest AIC
+plot(f_nlmixr_fomc_sfo_focei_obs_tc)
+
                                                                summary(f_nlmixr_fomc_sfo_focei_obs_tc)
+
                                                                #> nlmixr version used for fitting:    2.0.4 
+#> mkin version used for pre-fitting:  1.0.5 
+#> R version used for fitting:         4.1.0 
+#> Date of fit:     Fri Jun 11 10:54:54 2021 
+#> Date of summary: Fri Jun 11 10:56:12 2021 
+#> 
+#> Equations:
+#> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
+#> d_A1/dt = + f_parent_to_A1 * (alpha/beta) * 1/((time/beta) + 1) *
+#>            parent - k_A1 * A1
+#> 
+#> Data:
+#> 170 observations of 2 variable(s) grouped in 5 datasets
+#> 
+#> Degradation model predictions using RxODE
+#> 
+#> Fitted in 23.28 s
+#> 
+#> Variance model: Two-component variance unique to each observed variable 
+#> 
+#> Mean of starting values for individual parameters:
+#>        parent_0        log_k_A1 f_parent_qlogis       log_alpha        log_beta 
+#>         93.1168         -5.3034         -0.9442         -0.1065          2.2909 
+#> 
+#> Mean of starting values for error model parameters:
+#> sigma_low_parent  rsd_high_parent     sigma_low_A1      rsd_high_A1 
+#>          1.15958          0.03005          1.15958          0.03005 
+#> 
+#> Fixed degradation parameter values:
+#> None
+#> 
+#> Results:
+#> 
+#> Likelihood calculated by focei  
+#>     AIC   BIC logLik
+#>   787.5 831.4 -379.7
+#> 
+#> Optimised parameters:
+#>                    est.   lower   upper
+#> parent_0        93.6898 91.2681 96.1114
+#> log_k_A1        -6.2923 -8.3662 -4.2185
+#> f_parent_qlogis -1.0019 -1.3760 -0.6278
+#> log_alpha       -0.1639 -0.6641  0.3363
+#> log_beta         2.2031  1.6723  2.7340
+#> 
+#> Correlation: 
+#>                 prnt_0 lg__A1 f_prn_ lg_lph
+#> log_k_A1         0.368                     
+#> f_parent_qlogis -0.788 -0.401              
+#> log_alpha        0.338  0.942 -0.307       
+#> log_beta        -0.401 -0.761  0.253 -0.555
+#> 
+#> Random effects (omega):
+#>                     eta.parent_0 eta.log_k_A1 eta.f_parent_qlogis eta.log_alpha
+#> eta.parent_0                4.74         0.00              0.0000        0.0000
+#> eta.log_k_A1                0.00         5.57              0.0000        0.0000
+#> eta.f_parent_qlogis         0.00         0.00              0.1646        0.0000
+#> eta.log_alpha               0.00         0.00              0.0000        0.3312
+#> eta.log_beta                0.00         0.00              0.0000        0.0000
+#>                     eta.log_beta
+#> eta.parent_0              0.0000
+#> eta.log_k_A1              0.0000
+#> eta.f_parent_qlogis       0.0000
+#> eta.log_alpha             0.0000
+#> eta.log_beta              0.3438
+#> 
+#> Variance model:
+#> sigma_low_parent  rsd_high_parent     sigma_low_A1      rsd_high_A1 
+#>          2.35467          0.00261          0.64525          0.08456 
+#> 
+#> Backtransformed parameters:
+#>                    est.     lower    upper
+#> parent_0       93.68976 9.127e+01 96.11140
+#> k_A1            0.00185 2.326e-04  0.01472
+#> f_parent_to_A1  0.26857 2.017e-01  0.34801
+#> alpha           0.84879 5.147e-01  1.39971
+#> beta            9.05342 5.325e+00 15.39359
+#> 
+#> Resulting formation fractions:
+#>                 ff
+#> parent_A1   0.2686
+#> parent_sink 0.7314
+#> 
+#> Estimated disappearance times:
+#>          DT50   DT90 DT50back
+#> parent  11.43  127.4    38.35
+#> A1     374.59 1244.4       NA
                                                                # }
+
                                                                +
                                                                + +
                                                                + + +
                                                                +
                                                                +

                                                                Developed by Johannes Ranke.

                                                                +
                                                                + +
                                                                +

                                                                Site built with pkgdown 1.6.1.

                                                                +
                                                                + +
                                                                +
                                                                + + + + + + + + diff --git a/docs/dev/reference/plot.mixed.mmkin-1.png b/docs/dev/reference/plot.mixed.mmkin-1.png index 2224d96e..be3c664a 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-1.png and b/docs/dev/reference/plot.mixed.mmkin-1.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-2.png b/docs/dev/reference/plot.mixed.mmkin-2.png index 28168495..b0e43b11 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-2.png and b/docs/dev/reference/plot.mixed.mmkin-2.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-3.png b/docs/dev/reference/plot.mixed.mmkin-3.png index d18275dd..a9b96726 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-3.png and b/docs/dev/reference/plot.mixed.mmkin-3.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin-4.png b/docs/dev/reference/plot.mixed.mmkin-4.png index 2fd52425..22219e5e 100644 Binary files a/docs/dev/reference/plot.mixed.mmkin-4.png and b/docs/dev/reference/plot.mixed.mmkin-4.png differ diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index 36796580..a4222991 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ mkin - 1.0.4.9000 + 1.0.5
                                                                @@ -157,6 +157,8 @@ xlim = range(x$data$time), resplot = c("predicted", "time"), pred_over = NULL, + test_log_parms = FALSE, + conf.level = 0.6, ymax = "auto", maxabs = "auto", ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), @@ -210,6 +212,16 @@ predicted values?

                                                                pred_over

                                                                Named list of alternative predictions as obtained from mkinpredict with a compatible mkinmod.

                                                                + + + test_log_parms +

                                                                Passed to mean_degparms in the case of an +mixed.mmkin object

                                                                + + + conf.level +

                                                                Passed to mean_degparms in the case of an +mixed.mmkin object

                                                                ymax @@ -278,16 +290,21 @@ corresponding model prediction lines for the different datasets.

                                                                # For this fit we need to increase pnlsMaxiter, and we increase the # tolerance in order to speed up the fit for this example evaluation +# It still takes 20 seconds to run f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) plot(f_nlme)
                                                                f_saem <- saem(f, transformations = "saemix")
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:34:35 2021" +#> [1] "Fri Jun 11 10:56:37 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:42 2021"
                                                                plot(f_saem) +#> [1] "Fri Jun 11 10:56:44 2021"
                                                                plot(f_saem)
                                                                +f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs") +f_nlmix <- nlmix(f_obs) +
                                                                #> Error in nlmix(f_obs): could not find function "nlmix"
                                                                plot(f_nlmix) +
                                                                #> Error in plot(f_nlmix): object 'f_nlmix' not found
                                                                # We can overlay the two variants if we generate predictions pred_nlme <- mkinpredict(dfop_sfo, f_nlme$bparms.optim[-1], diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index 371567d8..f5ace044 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -47,6 +47,8 @@ below to see their documentation. nlmenlme + nlmixrnlmixr + " /> @@ -79,7 +81,7 @@ below to see their documentation. mkin - 1.0.3.9000 + 1.0.5
                                                                @@ -146,7 +148,7 @@ below to see their documentation.

                                                                Objects exported from other packages

                                                                - Source: R/lrtest.mkinfit.R, R/nlme.mmkin.R + Source: R/lrtest.mkinfit.R, R/nlme.mmkin.R, R/nlmixr.R
                                                                @@ -158,6 +160,8 @@ below to see their documentation.

                                                                nlme

                                                                nlme

                                                                +
                                                                nlmixr

                                                                nlmixr

                                                                +
                                                                diff --git a/docs/dev/reference/saem-1.png b/docs/dev/reference/saem-1.png index 0da31388..0e87d741 100644 Binary files a/docs/dev/reference/saem-1.png and b/docs/dev/reference/saem-1.png differ diff --git a/docs/dev/reference/saem-2.png b/docs/dev/reference/saem-2.png index 010950ba..456a4c58 100644 Binary files a/docs/dev/reference/saem-2.png and b/docs/dev/reference/saem-2.png differ diff --git a/docs/dev/reference/saem-3.png b/docs/dev/reference/saem-3.png index 829f22bf..27d43e53 100644 Binary files a/docs/dev/reference/saem-3.png and b/docs/dev/reference/saem-3.png differ diff --git a/docs/dev/reference/saem-4.png b/docs/dev/reference/saem-4.png index 4e976fa2..5c089bbc 100644 Binary files a/docs/dev/reference/saem-4.png and b/docs/dev/reference/saem-4.png differ diff --git a/docs/dev/reference/saem-5.png b/docs/dev/reference/saem-5.png index f50969b4..8212ec67 100644 Binary files a/docs/dev/reference/saem-5.png and b/docs/dev/reference/saem-5.png differ diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 23102df3..98faad6f 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.0.4.9000 + 1.0.5
                                                                @@ -161,8 +161,9 @@ Expectation Maximisation algorithm (SAEM).

                                                                test_log_parms = FALSE, conf.level = 0.6, solution_type = "auto", - control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = - FALSE), + nbiter.saemix = c(300, 100), + control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix, + save = FALSE, save.graphs = FALSE), fail_with_errors = TRUE, verbose = FALSE, quiet = FALSE, @@ -214,7 +215,7 @@ be used to override the starting values obtained from the 'mmkin' object.

                                                                If TRUE, an attempt is made to use more robust starting values for population parameters fitted as log parameters in mkin (like rate constants) by only considering rate constants that pass the t-test -when calculating mean degradation parameters using mean_degparms.

                                                                +when calculating mean degradation parameters using mean_degparms.

                                                                conf.level @@ -225,10 +226,15 @@ for parameter that are tested if requested by 'test_log_parms'.

                                                                solution_type

                                                                Possibility to specify the solution type in case the automatic choice is not desired

                                                                + + + nbiter.saemix +

                                                                Convenience option to increase the number of +iterations

                                                                control -

                                                                Passed to saemix::saemix

                                                                +

                                                                Passed to saemix::saemix.

                                                                fail_with_errors @@ -282,32 +288,35 @@ using mmkin.

                                                                state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:34:44 2021" +#> [1] "Fri Jun 11 10:56:49 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:45 2021"
                                                                +#> [1] "Fri Jun 11 10:56:51 2021"
                                                                f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:34:46 2021" +#> [1] "Fri Jun 11 10:56:53 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:48 2021"
                                                                f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Fri Jun 11 10:56:54 2021"
                                                                f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:34:48 2021" +#> [1] "Fri Jun 11 10:56:54 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:50 2021"
                                                                f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Fri Jun 11 10:56:57 2021"
                                                                f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:34:51 2021" +#> [1] "Fri Jun 11 10:56:57 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:34:53 2021"
                                                                +#> [1] "Fri Jun 11 10:57:00 2021"
                                                                # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix)
                                                                #> Package saemix, version 3.1.9000 -#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
                                                                compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) +#> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
                                                                #> +#> Attaching package: ‘saemix’
                                                                #> The following object is masked from ‘package:RxODE’: +#> +#> phi
                                                                compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)
                                                                #> Likelihoods calculated by importance sampling
                                                                #> AIC BIC #> 1 624.2484 622.2956 #> 2 467.7096 464.9757 @@ -348,10 +357,10 @@ using mmkin.

                                                                f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:34:55 2021" +#> [1] "Fri Jun 11 10:57:03 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:35:00 2021"
                                                                compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +#> [1] "Fri Jun 11 10:57:09 2021"
                                                                compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
                                                                #> Likelihoods calculated by importance sampling
                                                                #> AIC BIC #> 1 467.7096 464.9757 #> 2 469.6831 466.5586
                                                                @@ -372,15 +381,15 @@ using mmkin.

                                                                # four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:35:02 2021" +#> [1] "Fri Jun 11 10:57:12 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:35:07 2021"
                                                                f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Fri Jun 11 10:57:17 2021"
                                                                f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:35:07 2021" +#> [1] "Fri Jun 11 10:57:17 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:35:15 2021"
                                                                # We can use print, plot and summary methods to check the results +#> [1] "Fri Jun 11 10:57:26 2021"
                                                                # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo)
                                                                #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -421,10 +430,10 @@ using mmkin.

                                                                #> SD.g_qlogis 0.44771 -0.86417 1.7596
                                                                plot(f_saem_dfop_sfo)
                                                                summary(f_saem_dfop_sfo, data = TRUE)
                                                                #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.4.9000 -#> R version used for fitting: 4.0.4 -#> Date of fit: Tue Mar 9 17:35:16 2021 -#> Date of summary: Tue Mar 9 17:35:16 2021 +#> mkin version used for pre-fitting: 1.0.5 +#> R version used for fitting: 4.1.0 +#> Date of fit: Fri Jun 11 10:57:27 2021 +#> Date of summary: Fri Jun 11 10:57:27 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -439,7 +448,7 @@ using mmkin.

                                                                #> #> Model predictions using solution type analytical #> -#> Fitted in 8.668 s using 300, 100 iterations +#> Fitted in 9.712 s using 300, 100 iterations #> #> Variance model: Constant variance #> @@ -509,176 +518,176 @@ using mmkin.

                                                                #> #> Data: #> ds name time observed predicted residual std standardized -#> Dataset 6 parent 0 97.2 95.79523 -1.40477 1.883 -0.745888 -#> Dataset 6 parent 0 96.4 95.79523 -0.60477 1.883 -0.321114 -#> Dataset 6 parent 3 71.1 71.32042 0.22042 1.883 0.117035 -#> Dataset 6 parent 3 69.2 71.32042 2.12042 1.883 1.125873 -#> Dataset 6 parent 6 58.1 56.45256 -1.64744 1.883 -0.874739 -#> Dataset 6 parent 6 56.6 56.45256 -0.14744 1.883 -0.078288 -#> Dataset 6 parent 10 44.4 44.48523 0.08523 1.883 0.045257 -#> Dataset 6 parent 10 43.4 44.48523 1.08523 1.883 0.576224 -#> Dataset 6 parent 20 33.3 29.75774 -3.54226 1.883 -1.880826 -#> Dataset 6 parent 20 29.2 29.75774 0.55774 1.883 0.296141 -#> Dataset 6 parent 34 17.6 19.35710 1.75710 1.883 0.932966 -#> Dataset 6 parent 34 18.0 19.35710 1.35710 1.883 0.720579 -#> Dataset 6 parent 55 10.5 10.48443 -0.01557 1.883 -0.008266 -#> Dataset 6 parent 55 9.3 10.48443 1.18443 1.883 0.628895 -#> Dataset 6 parent 90 4.5 3.78622 -0.71378 1.883 -0.378995 -#> Dataset 6 parent 90 4.7 3.78622 -0.91378 1.883 -0.485188 -#> Dataset 6 parent 112 3.0 1.99608 -1.00392 1.883 -0.533048 -#> Dataset 6 parent 112 3.4 1.99608 -1.40392 1.883 -0.745435 -#> Dataset 6 parent 132 2.3 1.11539 -1.18461 1.883 -0.628990 -#> Dataset 6 parent 132 2.7 1.11539 -1.58461 1.883 -0.841377 -#> Dataset 6 A1 3 4.3 4.66132 0.36132 1.883 0.191849 -#> Dataset 6 A1 3 4.6 4.66132 0.06132 1.883 0.032559 -#> Dataset 6 A1 6 7.0 7.41087 0.41087 1.883 0.218157 -#> Dataset 6 A1 6 7.2 7.41087 0.21087 1.883 0.111964 -#> Dataset 6 A1 10 8.2 9.50878 1.30878 1.883 0.694921 -#> Dataset 6 A1 10 8.0 9.50878 1.50878 1.883 0.801114 -#> Dataset 6 A1 20 11.0 11.69902 0.69902 1.883 0.371157 -#> Dataset 6 A1 20 13.7 11.69902 -2.00098 1.883 -1.062455 -#> Dataset 6 A1 34 11.5 12.67784 1.17784 1.883 0.625396 -#> Dataset 6 A1 34 12.7 12.67784 -0.02216 1.883 -0.011765 -#> Dataset 6 A1 55 14.9 12.78556 -2.11444 1.883 -1.122701 -#> Dataset 6 A1 55 14.5 12.78556 -1.71444 1.883 -0.910314 -#> Dataset 6 A1 90 12.1 11.52954 -0.57046 1.883 -0.302898 -#> Dataset 6 A1 90 12.3 11.52954 -0.77046 1.883 -0.409092 -#> Dataset 6 A1 112 9.9 10.43825 0.53825 1.883 0.285793 -#> Dataset 6 A1 112 10.2 10.43825 0.23825 1.883 0.126503 -#> Dataset 6 A1 132 8.8 9.42830 0.62830 1.883 0.333609 -#> Dataset 6 A1 132 7.8 9.42830 1.62830 1.883 0.864577 -#> Dataset 7 parent 0 93.6 90.91477 -2.68523 1.883 -1.425772 -#> Dataset 7 parent 0 92.3 90.91477 -1.38523 1.883 -0.735514 -#> Dataset 7 parent 3 87.0 84.76874 -2.23126 1.883 -1.184726 -#> Dataset 7 parent 3 82.2 84.76874 2.56874 1.883 1.363919 -#> Dataset 7 parent 7 74.0 77.62735 3.62735 1.883 1.926003 -#> Dataset 7 parent 7 73.9 77.62735 3.72735 1.883 1.979100 -#> Dataset 7 parent 14 64.2 67.52266 3.32266 1.883 1.764224 -#> Dataset 7 parent 14 69.5 67.52266 -1.97734 1.883 -1.049904 -#> Dataset 7 parent 30 54.0 52.41949 -1.58051 1.883 -0.839202 -#> Dataset 7 parent 30 54.6 52.41949 -2.18051 1.883 -1.157783 -#> Dataset 7 parent 60 41.1 39.36582 -1.73418 1.883 -0.920794 -#> Dataset 7 parent 60 38.4 39.36582 0.96582 1.883 0.512818 -#> Dataset 7 parent 90 32.5 33.75388 1.25388 1.883 0.665771 -#> Dataset 7 parent 90 35.5 33.75388 -1.74612 1.883 -0.927132 -#> Dataset 7 parent 120 28.1 30.41716 2.31716 1.883 1.230335 -#> Dataset 7 parent 120 29.0 30.41716 1.41716 1.883 0.752464 -#> Dataset 7 parent 180 26.5 25.66046 -0.83954 1.883 -0.445767 -#> Dataset 7 parent 180 27.6 25.66046 -1.93954 1.883 -1.029832 -#> Dataset 7 A1 3 3.9 2.69355 -1.20645 1.883 -0.640585 -#> Dataset 7 A1 3 3.1 2.69355 -0.40645 1.883 -0.215811 -#> Dataset 7 A1 7 6.9 5.81807 -1.08193 1.883 -0.574470 -#> Dataset 7 A1 7 6.6 5.81807 -0.78193 1.883 -0.415180 -#> Dataset 7 A1 14 10.4 10.22529 -0.17471 1.883 -0.092767 -#> Dataset 7 A1 14 8.3 10.22529 1.92529 1.883 1.022265 -#> Dataset 7 A1 30 14.4 16.75484 2.35484 1.883 1.250345 -#> Dataset 7 A1 30 13.7 16.75484 3.05484 1.883 1.622022 -#> Dataset 7 A1 60 22.1 22.22540 0.12540 1.883 0.066583 -#> Dataset 7 A1 60 22.3 22.22540 -0.07460 1.883 -0.039610 -#> Dataset 7 A1 90 27.5 24.38799 -3.11201 1.883 -1.652376 -#> Dataset 7 A1 90 25.4 24.38799 -1.01201 1.883 -0.537344 -#> Dataset 7 A1 120 28.0 25.53294 -2.46706 1.883 -1.309927 -#> Dataset 7 A1 120 26.6 25.53294 -1.06706 1.883 -0.566572 -#> Dataset 7 A1 180 25.8 26.94943 1.14943 1.883 0.610309 -#> Dataset 7 A1 180 25.3 26.94943 1.64943 1.883 0.875793 -#> Dataset 8 parent 0 91.9 91.53246 -0.36754 1.883 -0.195151 -#> Dataset 8 parent 0 90.8 91.53246 0.73246 1.883 0.388914 -#> Dataset 8 parent 1 64.9 67.73197 2.83197 1.883 1.503686 -#> Dataset 8 parent 1 66.2 67.73197 1.53197 1.883 0.813428 -#> Dataset 8 parent 3 43.5 41.58448 -1.91552 1.883 -1.017081 -#> Dataset 8 parent 3 44.1 41.58448 -2.51552 1.883 -1.335662 -#> Dataset 8 parent 8 18.3 19.62286 1.32286 1.883 0.702395 -#> Dataset 8 parent 8 18.1 19.62286 1.52286 1.883 0.808588 -#> Dataset 8 parent 14 10.2 10.77819 0.57819 1.883 0.306999 -#> Dataset 8 parent 14 10.8 10.77819 -0.02181 1.883 -0.011582 -#> Dataset 8 parent 27 4.9 3.26977 -1.63023 1.883 -0.865599 -#> Dataset 8 parent 27 3.3 3.26977 -0.03023 1.883 -0.016051 -#> Dataset 8 parent 48 1.6 0.48024 -1.11976 1.883 -0.594557 -#> Dataset 8 parent 48 1.5 0.48024 -1.01976 1.883 -0.541460 -#> Dataset 8 parent 70 1.1 0.06438 -1.03562 1.883 -0.549881 -#> Dataset 8 parent 70 0.9 0.06438 -0.83562 1.883 -0.443688 -#> Dataset 8 A1 1 9.6 7.61539 -1.98461 1.883 -1.053761 -#> Dataset 8 A1 1 7.7 7.61539 -0.08461 1.883 -0.044923 -#> Dataset 8 A1 3 15.0 15.47954 0.47954 1.883 0.254622 -#> Dataset 8 A1 3 15.1 15.47954 0.37954 1.883 0.201525 -#> Dataset 8 A1 8 21.2 20.22616 -0.97384 1.883 -0.517075 -#> Dataset 8 A1 8 21.1 20.22616 -0.87384 1.883 -0.463979 -#> Dataset 8 A1 14 19.7 20.00067 0.30067 1.883 0.159645 -#> Dataset 8 A1 14 18.9 20.00067 1.10067 1.883 0.584419 -#> Dataset 8 A1 27 17.5 16.38142 -1.11858 1.883 -0.593928 -#> Dataset 8 A1 27 15.9 16.38142 0.48142 1.883 0.255620 -#> Dataset 8 A1 48 9.5 10.25357 0.75357 1.883 0.400124 -#> Dataset 8 A1 48 9.8 10.25357 0.45357 1.883 0.240833 -#> Dataset 8 A1 70 6.2 5.95728 -0.24272 1.883 -0.128878 -#> Dataset 8 A1 70 6.1 5.95728 -0.14272 1.883 -0.075781 -#> Dataset 9 parent 0 99.8 97.47274 -2.32726 1.883 -1.235697 -#> Dataset 9 parent 0 98.3 97.47274 -0.82726 1.883 -0.439246 -#> Dataset 9 parent 1 77.1 79.72257 2.62257 1.883 1.392500 -#> Dataset 9 parent 1 77.2 79.72257 2.52257 1.883 1.339404 -#> Dataset 9 parent 3 59.0 56.26497 -2.73503 1.883 -1.452212 -#> Dataset 9 parent 3 58.1 56.26497 -1.83503 1.883 -0.974342 -#> Dataset 9 parent 8 27.4 31.66985 4.26985 1.883 2.267151 -#> Dataset 9 parent 8 29.2 31.66985 2.46985 1.883 1.311410 -#> Dataset 9 parent 14 19.1 22.39789 3.29789 1.883 1.751071 -#> Dataset 9 parent 14 29.6 22.39789 -7.20211 1.883 -3.824090 -#> Dataset 9 parent 27 10.1 14.21758 4.11758 1.883 2.186301 -#> Dataset 9 parent 27 18.2 14.21758 -3.98242 1.883 -2.114537 -#> Dataset 9 parent 48 4.5 7.27921 2.77921 1.883 1.475671 -#> Dataset 9 parent 48 9.1 7.27921 -1.82079 1.883 -0.966780 -#> Dataset 9 parent 70 2.3 3.61470 1.31470 1.883 0.698065 -#> Dataset 9 parent 70 2.9 3.61470 0.71470 1.883 0.379485 -#> Dataset 9 parent 91 2.0 1.85303 -0.14697 1.883 -0.078038 -#> Dataset 9 parent 91 1.8 1.85303 0.05303 1.883 0.028155 -#> Dataset 9 parent 120 2.0 0.73645 -1.26355 1.883 -0.670906 -#> Dataset 9 parent 120 2.2 0.73645 -1.46355 1.883 -0.777099 -#> Dataset 9 A1 1 4.2 3.87843 -0.32157 1.883 -0.170743 -#> Dataset 9 A1 1 3.9 3.87843 -0.02157 1.883 -0.011453 -#> Dataset 9 A1 3 7.4 8.90535 1.50535 1.883 0.799291 -#> Dataset 9 A1 3 7.9 8.90535 1.00535 1.883 0.533807 -#> Dataset 9 A1 8 14.5 13.75172 -0.74828 1.883 -0.397312 -#> Dataset 9 A1 8 13.7 13.75172 0.05172 1.883 0.027462 -#> Dataset 9 A1 14 14.2 14.97541 0.77541 1.883 0.411715 -#> Dataset 9 A1 14 12.2 14.97541 2.77541 1.883 1.473650 -#> Dataset 9 A1 27 13.7 14.94728 1.24728 1.883 0.662266 -#> Dataset 9 A1 27 13.2 14.94728 1.74728 1.883 0.927750 -#> Dataset 9 A1 48 13.6 13.66078 0.06078 1.883 0.032272 -#> Dataset 9 A1 48 15.4 13.66078 -1.73922 1.883 -0.923470 -#> Dataset 9 A1 70 10.4 11.84899 1.44899 1.883 0.769365 -#> Dataset 9 A1 70 11.6 11.84899 0.24899 1.883 0.132204 -#> Dataset 9 A1 91 10.0 10.09177 0.09177 1.883 0.048727 -#> Dataset 9 A1 91 9.5 10.09177 0.59177 1.883 0.314211 -#> Dataset 9 A1 120 9.1 7.91379 -1.18621 1.883 -0.629841 -#> Dataset 9 A1 120 9.0 7.91379 -1.08621 1.883 -0.576744 -#> Dataset 10 parent 0 96.1 93.65257 -2.44743 1.883 -1.299505 -#> Dataset 10 parent 0 94.3 93.65257 -0.64743 1.883 -0.343763 -#> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 -#> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 -#> Dataset 10 parent 14 69.4 70.17143 0.77143 1.883 0.409606 -#> Dataset 10 parent 14 73.1 70.17143 -2.92857 1.883 -1.554974 -#> Dataset 10 parent 21 65.6 63.99188 -1.60812 1.883 -0.853862 -#> Dataset 10 parent 21 65.3 63.99188 -1.30812 1.883 -0.694572 -#> Dataset 10 parent 41 55.9 54.64292 -1.25708 1.883 -0.667467 -#> Dataset 10 parent 41 54.4 54.64292 0.24292 1.883 0.128985 -#> Dataset 10 parent 63 47.0 49.61303 2.61303 1.883 1.387433 -#> Dataset 10 parent 63 49.3 49.61303 0.31303 1.883 0.166207 -#> Dataset 10 parent 91 44.7 45.17807 0.47807 1.883 0.253839 -#> Dataset 10 parent 91 46.7 45.17807 -1.52193 1.883 -0.808096 -#> Dataset 10 parent 120 42.1 41.27970 -0.82030 1.883 -0.435552 -#> Dataset 10 parent 120 41.3 41.27970 -0.02030 1.883 -0.010778 -#> Dataset 10 A1 8 3.3 3.99294 0.69294 1.883 0.367929 -#> Dataset 10 A1 8 3.4 3.99294 0.59294 1.883 0.314832 -#> Dataset 10 A1 14 3.9 5.92756 2.02756 1.883 1.076570 -#> Dataset 10 A1 14 2.9 5.92756 3.02756 1.883 1.607538 -#> Dataset 10 A1 21 6.4 7.47313 1.07313 1.883 0.569799 -#> Dataset 10 A1 21 7.2 7.47313 0.27313 1.883 0.145025 -#> Dataset 10 A1 41 9.1 9.76819 0.66819 1.883 0.354786 -#> Dataset 10 A1 41 8.5 9.76819 1.26819 1.883 0.673367 -#> Dataset 10 A1 63 11.7 10.94733 -0.75267 1.883 -0.399643 -#> Dataset 10 A1 63 12.0 10.94733 -1.05267 1.883 -0.558933 -#> Dataset 10 A1 91 13.3 11.93773 -1.36227 1.883 -0.723321 -#> Dataset 10 A1 91 13.2 11.93773 -1.26227 1.883 -0.670224 -#> Dataset 10 A1 120 14.3 12.77666 -1.52334 1.883 -0.808847 -#> Dataset 10 A1 120 12.1 12.77666 0.67666 1.883 0.359282
                                                                +#> Dataset 6 parent 0 97.2 95.79523 1.40477 1.883 0.745888 +#> Dataset 6 parent 0 96.4 95.79523 0.60477 1.883 0.321114 +#> Dataset 6 parent 3 71.1 71.32042 -0.22042 1.883 -0.117035 +#> Dataset 6 parent 3 69.2 71.32042 -2.12042 1.883 -1.125873 +#> Dataset 6 parent 6 58.1 56.45256 1.64744 1.883 0.874739 +#> Dataset 6 parent 6 56.6 56.45256 0.14744 1.883 0.078288 +#> Dataset 6 parent 10 44.4 44.48523 -0.08523 1.883 -0.045257 +#> Dataset 6 parent 10 43.4 44.48523 -1.08523 1.883 -0.576224 +#> Dataset 6 parent 20 33.3 29.75774 3.54226 1.883 1.880826 +#> Dataset 6 parent 20 29.2 29.75774 -0.55774 1.883 -0.296141 +#> Dataset 6 parent 34 17.6 19.35710 -1.75710 1.883 -0.932966 +#> Dataset 6 parent 34 18.0 19.35710 -1.35710 1.883 -0.720579 +#> Dataset 6 parent 55 10.5 10.48443 0.01557 1.883 0.008266 +#> Dataset 6 parent 55 9.3 10.48443 -1.18443 1.883 -0.628895 +#> Dataset 6 parent 90 4.5 3.78622 0.71378 1.883 0.378995 +#> Dataset 6 parent 90 4.7 3.78622 0.91378 1.883 0.485188 +#> Dataset 6 parent 112 3.0 1.99608 1.00392 1.883 0.533048 +#> Dataset 6 parent 112 3.4 1.99608 1.40392 1.883 0.745435 +#> Dataset 6 parent 132 2.3 1.11539 1.18461 1.883 0.628990 +#> Dataset 6 parent 132 2.7 1.11539 1.58461 1.883 0.841377 +#> Dataset 6 A1 3 4.3 4.66132 -0.36132 1.883 -0.191849 +#> Dataset 6 A1 3 4.6 4.66132 -0.06132 1.883 -0.032559 +#> Dataset 6 A1 6 7.0 7.41087 -0.41087 1.883 -0.218157 +#> Dataset 6 A1 6 7.2 7.41087 -0.21087 1.883 -0.111964 +#> Dataset 6 A1 10 8.2 9.50878 -1.30878 1.883 -0.694921 +#> Dataset 6 A1 10 8.0 9.50878 -1.50878 1.883 -0.801114 +#> Dataset 6 A1 20 11.0 11.69902 -0.69902 1.883 -0.371157 +#> Dataset 6 A1 20 13.7 11.69902 2.00098 1.883 1.062455 +#> Dataset 6 A1 34 11.5 12.67784 -1.17784 1.883 -0.625396 +#> Dataset 6 A1 34 12.7 12.67784 0.02216 1.883 0.011765 +#> Dataset 6 A1 55 14.9 12.78556 2.11444 1.883 1.122701 +#> Dataset 6 A1 55 14.5 12.78556 1.71444 1.883 0.910314 +#> Dataset 6 A1 90 12.1 11.52954 0.57046 1.883 0.302898 +#> Dataset 6 A1 90 12.3 11.52954 0.77046 1.883 0.409092 +#> Dataset 6 A1 112 9.9 10.43825 -0.53825 1.883 -0.285793 +#> Dataset 6 A1 112 10.2 10.43825 -0.23825 1.883 -0.126503 +#> Dataset 6 A1 132 8.8 9.42830 -0.62830 1.883 -0.333609 +#> Dataset 6 A1 132 7.8 9.42830 -1.62830 1.883 -0.864577 +#> Dataset 7 parent 0 93.6 90.91477 2.68523 1.883 1.425772 +#> Dataset 7 parent 0 92.3 90.91477 1.38523 1.883 0.735514 +#> Dataset 7 parent 3 87.0 84.76874 2.23126 1.883 1.184726 +#> Dataset 7 parent 3 82.2 84.76874 -2.56874 1.883 -1.363919 +#> Dataset 7 parent 7 74.0 77.62735 -3.62735 1.883 -1.926003 +#> Dataset 7 parent 7 73.9 77.62735 -3.72735 1.883 -1.979100 +#> Dataset 7 parent 14 64.2 67.52266 -3.32266 1.883 -1.764224 +#> Dataset 7 parent 14 69.5 67.52266 1.97734 1.883 1.049904 +#> Dataset 7 parent 30 54.0 52.41949 1.58051 1.883 0.839202 +#> Dataset 7 parent 30 54.6 52.41949 2.18051 1.883 1.157783 +#> Dataset 7 parent 60 41.1 39.36582 1.73418 1.883 0.920794 +#> Dataset 7 parent 60 38.4 39.36582 -0.96582 1.883 -0.512818 +#> Dataset 7 parent 90 32.5 33.75388 -1.25388 1.883 -0.665771 +#> Dataset 7 parent 90 35.5 33.75388 1.74612 1.883 0.927132 +#> Dataset 7 parent 120 28.1 30.41716 -2.31716 1.883 -1.230335 +#> Dataset 7 parent 120 29.0 30.41716 -1.41716 1.883 -0.752464 +#> Dataset 7 parent 180 26.5 25.66046 0.83954 1.883 0.445767 +#> Dataset 7 parent 180 27.6 25.66046 1.93954 1.883 1.029832 +#> Dataset 7 A1 3 3.9 2.69355 1.20645 1.883 0.640585 +#> Dataset 7 A1 3 3.1 2.69355 0.40645 1.883 0.215811 +#> Dataset 7 A1 7 6.9 5.81807 1.08193 1.883 0.574470 +#> Dataset 7 A1 7 6.6 5.81807 0.78193 1.883 0.415180 +#> Dataset 7 A1 14 10.4 10.22529 0.17471 1.883 0.092767 +#> Dataset 7 A1 14 8.3 10.22529 -1.92529 1.883 -1.022265 +#> Dataset 7 A1 30 14.4 16.75484 -2.35484 1.883 -1.250345 +#> Dataset 7 A1 30 13.7 16.75484 -3.05484 1.883 -1.622022 +#> Dataset 7 A1 60 22.1 22.22540 -0.12540 1.883 -0.066583 +#> Dataset 7 A1 60 22.3 22.22540 0.07460 1.883 0.039610 +#> Dataset 7 A1 90 27.5 24.38799 3.11201 1.883 1.652376 +#> Dataset 7 A1 90 25.4 24.38799 1.01201 1.883 0.537344 +#> Dataset 7 A1 120 28.0 25.53294 2.46706 1.883 1.309927 +#> Dataset 7 A1 120 26.6 25.53294 1.06706 1.883 0.566572 +#> Dataset 7 A1 180 25.8 26.94943 -1.14943 1.883 -0.610309 +#> Dataset 7 A1 180 25.3 26.94943 -1.64943 1.883 -0.875793 +#> Dataset 8 parent 0 91.9 91.53246 0.36754 1.883 0.195151 +#> Dataset 8 parent 0 90.8 91.53246 -0.73246 1.883 -0.388914 +#> Dataset 8 parent 1 64.9 67.73197 -2.83197 1.883 -1.503686 +#> Dataset 8 parent 1 66.2 67.73197 -1.53197 1.883 -0.813428 +#> Dataset 8 parent 3 43.5 41.58448 1.91552 1.883 1.017081 +#> Dataset 8 parent 3 44.1 41.58448 2.51552 1.883 1.335662 +#> Dataset 8 parent 8 18.3 19.62286 -1.32286 1.883 -0.702395 +#> Dataset 8 parent 8 18.1 19.62286 -1.52286 1.883 -0.808588 +#> Dataset 8 parent 14 10.2 10.77819 -0.57819 1.883 -0.306999 +#> Dataset 8 parent 14 10.8 10.77819 0.02181 1.883 0.011582 +#> Dataset 8 parent 27 4.9 3.26977 1.63023 1.883 0.865599 +#> Dataset 8 parent 27 3.3 3.26977 0.03023 1.883 0.016051 +#> Dataset 8 parent 48 1.6 0.48024 1.11976 1.883 0.594557 +#> Dataset 8 parent 48 1.5 0.48024 1.01976 1.883 0.541460 +#> Dataset 8 parent 70 1.1 0.06438 1.03562 1.883 0.549881 +#> Dataset 8 parent 70 0.9 0.06438 0.83562 1.883 0.443688 +#> Dataset 8 A1 1 9.6 7.61539 1.98461 1.883 1.053761 +#> Dataset 8 A1 1 7.7 7.61539 0.08461 1.883 0.044923 +#> Dataset 8 A1 3 15.0 15.47954 -0.47954 1.883 -0.254622 +#> Dataset 8 A1 3 15.1 15.47954 -0.37954 1.883 -0.201525 +#> Dataset 8 A1 8 21.2 20.22616 0.97384 1.883 0.517075 +#> Dataset 8 A1 8 21.1 20.22616 0.87384 1.883 0.463979 +#> Dataset 8 A1 14 19.7 20.00067 -0.30067 1.883 -0.159645 +#> Dataset 8 A1 14 18.9 20.00067 -1.10067 1.883 -0.584419 +#> Dataset 8 A1 27 17.5 16.38142 1.11858 1.883 0.593928 +#> Dataset 8 A1 27 15.9 16.38142 -0.48142 1.883 -0.255620 +#> Dataset 8 A1 48 9.5 10.25357 -0.75357 1.883 -0.400124 +#> Dataset 8 A1 48 9.8 10.25357 -0.45357 1.883 -0.240833 +#> Dataset 8 A1 70 6.2 5.95728 0.24272 1.883 0.128878 +#> Dataset 8 A1 70 6.1 5.95728 0.14272 1.883 0.075781 +#> Dataset 9 parent 0 99.8 97.47274 2.32726 1.883 1.235697 +#> Dataset 9 parent 0 98.3 97.47274 0.82726 1.883 0.439246 +#> Dataset 9 parent 1 77.1 79.72257 -2.62257 1.883 -1.392500 +#> Dataset 9 parent 1 77.2 79.72257 -2.52257 1.883 -1.339404 +#> Dataset 9 parent 3 59.0 56.26497 2.73503 1.883 1.452212 +#> Dataset 9 parent 3 58.1 56.26497 1.83503 1.883 0.974342 +#> Dataset 9 parent 8 27.4 31.66985 -4.26985 1.883 -2.267151 +#> Dataset 9 parent 8 29.2 31.66985 -2.46985 1.883 -1.311410 +#> Dataset 9 parent 14 19.1 22.39789 -3.29789 1.883 -1.751071 +#> Dataset 9 parent 14 29.6 22.39789 7.20211 1.883 3.824090 +#> Dataset 9 parent 27 10.1 14.21758 -4.11758 1.883 -2.186301 +#> Dataset 9 parent 27 18.2 14.21758 3.98242 1.883 2.114537 +#> Dataset 9 parent 48 4.5 7.27921 -2.77921 1.883 -1.475671 +#> Dataset 9 parent 48 9.1 7.27921 1.82079 1.883 0.966780 +#> Dataset 9 parent 70 2.3 3.61470 -1.31470 1.883 -0.698065 +#> Dataset 9 parent 70 2.9 3.61470 -0.71470 1.883 -0.379485 +#> Dataset 9 parent 91 2.0 1.85303 0.14697 1.883 0.078038 +#> Dataset 9 parent 91 1.8 1.85303 -0.05303 1.883 -0.028155 +#> Dataset 9 parent 120 2.0 0.73645 1.26355 1.883 0.670906 +#> Dataset 9 parent 120 2.2 0.73645 1.46355 1.883 0.777099 +#> Dataset 9 A1 1 4.2 3.87843 0.32157 1.883 0.170743 +#> Dataset 9 A1 1 3.9 3.87843 0.02157 1.883 0.011453 +#> Dataset 9 A1 3 7.4 8.90535 -1.50535 1.883 -0.799291 +#> Dataset 9 A1 3 7.9 8.90535 -1.00535 1.883 -0.533807 +#> Dataset 9 A1 8 14.5 13.75172 0.74828 1.883 0.397312 +#> Dataset 9 A1 8 13.7 13.75172 -0.05172 1.883 -0.027462 +#> Dataset 9 A1 14 14.2 14.97541 -0.77541 1.883 -0.411715 +#> Dataset 9 A1 14 12.2 14.97541 -2.77541 1.883 -1.473650 +#> Dataset 9 A1 27 13.7 14.94728 -1.24728 1.883 -0.662266 +#> Dataset 9 A1 27 13.2 14.94728 -1.74728 1.883 -0.927750 +#> Dataset 9 A1 48 13.6 13.66078 -0.06078 1.883 -0.032272 +#> Dataset 9 A1 48 15.4 13.66078 1.73922 1.883 0.923470 +#> Dataset 9 A1 70 10.4 11.84899 -1.44899 1.883 -0.769365 +#> Dataset 9 A1 70 11.6 11.84899 -0.24899 1.883 -0.132204 +#> Dataset 9 A1 91 10.0 10.09177 -0.09177 1.883 -0.048727 +#> Dataset 9 A1 91 9.5 10.09177 -0.59177 1.883 -0.314211 +#> Dataset 9 A1 120 9.1 7.91379 1.18621 1.883 0.629841 +#> Dataset 9 A1 120 9.0 7.91379 1.08621 1.883 0.576744 +#> Dataset 10 parent 0 96.1 93.65257 2.44743 1.883 1.299505 +#> Dataset 10 parent 0 94.3 93.65257 0.64743 1.883 0.343763 +#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 +#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 +#> Dataset 10 parent 14 69.4 70.17143 -0.77143 1.883 -0.409606 +#> Dataset 10 parent 14 73.1 70.17143 2.92857 1.883 1.554974 +#> Dataset 10 parent 21 65.6 63.99188 1.60812 1.883 0.853862 +#> Dataset 10 parent 21 65.3 63.99188 1.30812 1.883 0.694572 +#> Dataset 10 parent 41 55.9 54.64292 1.25708 1.883 0.667467 +#> Dataset 10 parent 41 54.4 54.64292 -0.24292 1.883 -0.128985 +#> Dataset 10 parent 63 47.0 49.61303 -2.61303 1.883 -1.387433 +#> Dataset 10 parent 63 49.3 49.61303 -0.31303 1.883 -0.166207 +#> Dataset 10 parent 91 44.7 45.17807 -0.47807 1.883 -0.253839 +#> Dataset 10 parent 91 46.7 45.17807 1.52193 1.883 0.808096 +#> Dataset 10 parent 120 42.1 41.27970 0.82030 1.883 0.435552 +#> Dataset 10 parent 120 41.3 41.27970 0.02030 1.883 0.010778 +#> Dataset 10 A1 8 3.3 3.99294 -0.69294 1.883 -0.367929 +#> Dataset 10 A1 8 3.4 3.99294 -0.59294 1.883 -0.314832 +#> Dataset 10 A1 14 3.9 5.92756 -2.02756 1.883 -1.076570 +#> Dataset 10 A1 14 2.9 5.92756 -3.02756 1.883 -1.607538 +#> Dataset 10 A1 21 6.4 7.47313 -1.07313 1.883 -0.569799 +#> Dataset 10 A1 21 7.2 7.47313 -0.27313 1.883 -0.145025 +#> Dataset 10 A1 41 9.1 9.76819 -0.66819 1.883 -0.354786 +#> Dataset 10 A1 41 8.5 9.76819 -1.26819 1.883 -0.673367 +#> Dataset 10 A1 63 11.7 10.94733 0.75267 1.883 0.399643 +#> Dataset 10 A1 63 12.0 10.94733 1.05267 1.883 0.558933 +#> Dataset 10 A1 91 13.3 11.93773 1.36227 1.883 0.723321 +#> Dataset 10 A1 91 13.2 11.93773 1.26227 1.883 0.670224 +#> Dataset 10 A1 120 14.3 12.77666 1.52334 1.883 0.808847 +#> Dataset 10 A1 120 12.1 12.77666 -0.67666 1.883 -0.359282
                                                                # The following takes about 6 minutes #f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", # control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html new file mode 100644 index 00000000..0fead0df --- /dev/null +++ b/docs/dev/reference/summary.nlmixr.mmkin.html @@ -0,0 +1,1022 @@ + + + + + + + + +Summary method for class "nlmixr.mmkin" — summary.nlmixr.mmkin • mkin + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                +
                                                                + + + + +
                                                                + +
                                                                +
                                                                +
                                                                +

                                                                Summary method for class "nlmixr.mmkin"

                                                                + Source: R/summary.nlmixr.mmkin.R + +
                                                                + +
                                                                +

                                                                Lists model equations, initial parameter values, optimised parameters +for fixed effects (population), random effects (deviations from the +population mean) and residual error model, as well as the resulting +endpoints such as formation fractions and DT50 values. Optionally +(default is FALSE), the data are listed in full.

                                                                +
                                                                + + 
                                                                # S3 method for nlmixr.mmkin
+summary(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...)
+
+# S3 method for summary.nlmixr.mmkin
+print(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...)
                                                                + +

                                                                Arguments

                                                                + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                object

                                                                an object of class nlmixr.mmkin

                                                                data

                                                                logical, indicating whether the full data should be included in +the summary.

                                                                verbose

                                                                Should the summary be verbose?

                                                                distimes

                                                                logical, indicating whether DT50 and DT90 values should be +included.

                                                                ...

                                                                optional arguments passed to methods like print.

                                                                x

                                                                an object of class summary.nlmixr.mmkin

                                                                digits

                                                                Number of digits to use for printing

                                                                + +

                                                                Value

                                                                + +

                                                                The summary function returns a list obtained in the fit, with at +least the following additional components

                                                                +
                                                                nlmixrversion, mkinversion, Rversion

                                                                The nlmixr, mkin and R versions used

                                                                +
                                                                date.fit, date.summary

                                                                The dates where the fit and the summary were +produced

                                                                +
                                                                diffs

                                                                The differential equations used in the degradation model

                                                                +
                                                                use_of_ff

                                                                Was maximum or minimum use made of formation fractions

                                                                +
                                                                data

                                                                The data

                                                                +
                                                                confint_back

                                                                Backtransformed parameters, with confidence intervals if available

                                                                +
                                                                ff

                                                                The estimated formation fractions derived from the fitted +model.

                                                                +
                                                                distimes

                                                                The DT50 and DT90 values for each observed variable.

                                                                +
                                                                SFORB

                                                                If applicable, eigenvalues of SFORB components of the model.

                                                                +The print method is called for its side effect, i.e. printing the summary. + +

                                                                Author

                                                                + +

                                                                Johannes Ranke for the mkin specific parts +nlmixr authors for the parts inherited from nlmixr.

                                                                + +

                                                                Examples

                                                                + 
                                                                # Generate five datasets following DFOP-SFO kinetics
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"),
+ m1 = mkinsub("SFO"), quiet = TRUE)
+set.seed(1234)
+k1_in <- rlnorm(5, log(0.1), 0.3)
+k2_in <- rlnorm(5, log(0.02), 0.3)
+g_in <- plogis(rnorm(5, qlogis(0.5), 0.3))
+f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3))
+k_m1_in <- rlnorm(5, log(0.02), 0.3)
+
+pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {
+  mkinpredict(dfop_sfo,
+    c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),
+    c(parent = 100, m1 = 0),
+    sampling_times)
+}
+
+ds_mean_dfop_sfo <- lapply(1:5, function(i) {
+  mkinpredict(dfop_sfo,
+    c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],
+      f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),
+    c(parent = 100, m1 = 0),
+    sampling_times)
+})
+names(ds_mean_dfop_sfo) <- paste("ds", 1:5)
+
+ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {
+  add_err(ds,
+    sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
+    n = 1)[[1]]
+})
+
+# \dontrun{
+# Evaluate using mmkin and nlmixr
+f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,
+  quiet = TRUE, error_model = "tc", cores = 5)
+f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo)
+
                                                                #> Running main SAEM algorithm
+#> [1] "Fri Jun 11 10:57:31 2021"
+#> ....
+#>     Minimisation finished
+#> [1] "Fri Jun 11 10:57:43 2021"
                                                                f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo)
+
                                                                #> Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
                                                                #> Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
                                                                f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem")
+
                                                                #> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1:    1.0127e+02  -3.8515e+00  -2.0719e+00  -3.7271e+00  -1.9335e+00   4.0311e-01   6.9594e+00   1.5021e-01   5.3947e-01   1.9686e-01   3.7429e-01   5.4209e-01   8.4121e+00   7.3391e-02   7.1185e+00   2.5869e-01
+#> 2:    1.0136e+02  -3.8005e+00  -2.3424e+00  -4.0759e+00  -1.6475e+00   1.1598e-01   6.6115e+00   1.4406e-01   5.1249e-01   1.8701e-01   3.5786e-01   5.1499e-01   4.9102e+00   6.2829e-02   4.7230e+00   7.8901e-02
+#> 3:    1.0126e+02  -4.0285e+00  -2.3629e+00  -4.1271e+00  -1.1733e+00   1.7634e-02   6.2809e+00   1.6892e-01   4.8687e-01   1.7766e-01   3.3997e-01   4.8924e-01   3.2256e+00   6.6693e-02   3.3261e+00   8.7190e-02
+#> 4:    1.0105e+02  -4.0894e+00  -2.5516e+00  -4.1037e+00  -1.0816e+00   4.5377e-02   5.9668e+00   1.6048e-01   4.6252e-01   1.6878e-01   3.2297e-01   4.6478e-01   2.4343e+00   7.0557e-02   2.2610e+00   9.2498e-02
+#> 5:    1.0101e+02  -4.1364e+00  -2.4605e+00  -4.0737e+00  -1.0920e+00  -4.7953e-03   5.9593e+00   1.5245e-01   4.3940e-01   1.8078e-01   3.0682e-01   5.4688e-01   1.7424e+00   7.4776e-02   1.5144e+00   1.0787e-01
+#> 6:    1.0042e+02  -4.0933e+00  -2.4472e+00  -4.1090e+00  -9.7996e-01  -9.0472e-02   6.0175e+00   1.4483e-01   4.1743e-01   1.8824e-01   2.9148e-01   5.3033e-01   1.5545e+00   6.8588e-02   1.3401e+00   9.8865e-02
+#> 7:    1.0078e+02  -4.0911e+00  -2.4335e+00  -4.0758e+00  -9.9422e-01  -7.8849e-02   6.6318e+00   1.3759e-01   3.9656e-01   1.7882e-01   2.7691e-01   5.0381e-01   1.3780e+00   6.9978e-02   1.1346e+00   9.6162e-02
+#> 8:    1.0077e+02  -4.0196e+00  -2.4345e+00  -4.0444e+00  -9.3483e-01  -1.1032e-01   6.3002e+00   1.3071e-01   3.7673e-01   1.6988e-01   2.6306e-01   4.8191e-01   1.1774e+00   7.4232e-02   1.0270e+00   9.5616e-02
+#> 9:    1.0118e+02  -4.0436e+00  -2.4649e+00  -4.0207e+00  -8.9829e-01  -1.7784e-01   5.9852e+00   1.2417e-01   3.5789e-01   1.6139e-01   2.4991e-01   5.5466e-01   1.1040e+00   7.1515e-02   1.0342e+00   9.3972e-02
+#> 10:    1.0143e+02  -4.0523e+00  -2.3737e+00  -4.0184e+00  -9.1167e-01  -2.3828e-01   5.8520e+00   1.1797e-01   3.4196e-01   1.5332e-01   2.3741e-01   5.2849e-01   1.0510e+00   7.5719e-02   1.0638e+00   9.3973e-02
+#> 11:    1.0119e+02  -4.0699e+00  -2.3680e+00  -4.0191e+00  -9.4858e-01  -1.7310e-01   6.9958e+00   1.1207e-01   3.6891e-01   1.4565e-01   2.2554e-01   5.0206e-01   1.0247e+00   7.5497e-02   1.0292e+00   9.3707e-02
+#> 12:    1.0121e+02  -4.0189e+00  -2.4198e+00  -4.0139e+00  -9.1693e-01  -2.0613e-01   6.6460e+00   1.0646e-01   3.5046e-01   1.3837e-01   2.1427e-01   5.7696e-01   1.1046e+00   7.6090e-02   9.3689e-01   9.4115e-02
+#> 13:    1.0083e+02  -4.0451e+00  -2.4395e+00  -4.0235e+00  -9.4535e-01  -1.4723e-01   6.3137e+00   1.0114e-01   3.3294e-01   1.3145e-01   2.0355e-01   5.4811e-01   1.0360e+00   7.3381e-02   9.7078e-01   9.1659e-02
+#> 14:    1.0056e+02  -4.0401e+00  -2.4045e+00  -4.0054e+00  -9.4191e-01  -1.3928e-01   5.9980e+00   9.6084e-02   3.4934e-01   1.2488e-01   1.9338e-01   5.2071e-01   1.0303e+00   7.7118e-02   8.8372e-01   9.0469e-02
+#> 15:    1.0070e+02  -4.0388e+00  -2.4210e+00  -4.0113e+00  -9.1136e-01  -1.2702e-01   5.6981e+00   9.1279e-02   3.3187e-01   1.1864e-01   1.8371e-01   4.9467e-01   1.0486e+00   7.2427e-02   7.8179e-01   9.1572e-02
+#> 16:    1.0078e+02  -4.0175e+00  -2.4766e+00  -4.0191e+00  -9.0733e-01  -1.1952e-01   5.4132e+00   8.6716e-02   3.1528e-01   1.1270e-01   1.7452e-01   4.8928e-01   9.7799e-01   8.1464e-02   8.2935e-01   8.6520e-02
+#> 17:    1.0069e+02  -4.0533e+00  -2.5110e+00  -4.0294e+00  -9.1841e-01  -6.8363e-03   5.1426e+00   8.2380e-02   2.9952e-01   1.0707e-01   1.6580e-01   4.6482e-01   9.1609e-01   8.1008e-02   8.1783e-01   8.8818e-02
+#> 18:    99.9647   -4.0672   -2.5327   -4.0416   -0.9273    0.0097    4.8854    0.0783    0.2970    0.1280    0.1941    0.5053    0.9306    0.0764    0.8097    0.0881
+#> 19:    1.0027e+02  -4.0667e+00  -2.4653e+00  -4.0579e+00  -9.2776e-01   3.0417e-02   4.6412e+00   7.4348e-02   3.3694e-01   1.2164e-01   1.8435e-01   5.1797e-01   9.7386e-01   7.4954e-02   7.9297e-01   8.9915e-02
+#> 20:    1.0006e+02  -4.0935e+00  -2.4804e+00  -4.0721e+00  -9.3737e-01   1.9496e-02   4.4091e+00   7.0630e-02   3.3728e-01   1.2544e-01   1.7513e-01   6.0925e-01   1.0232e+00   7.4618e-02   7.9988e-01   8.9642e-02
+#> 21:    1.0043e+02  -4.0542e+00  -2.5168e+00  -4.0623e+00  -9.1553e-01   3.9474e-02   4.1887e+00   6.7099e-02   3.4553e-01   1.1917e-01   1.6638e-01   6.0827e-01   1.0155e+00   8.0771e-02   7.8424e-01   8.6213e-02
+#> 22:    1.0049e+02  -4.0449e+00  -2.5082e+00  -4.0849e+00  -9.2553e-01   4.5424e-02   3.9792e+00   6.3744e-02   3.2825e-01   1.2365e-01   1.5806e-01   5.8922e-01   8.2860e-01   8.3384e-02   8.2525e-01   8.9218e-02
+#> 23:    1.0067e+02  -4.0411e+00  -2.5460e+00  -4.0736e+00  -9.2578e-01   5.2422e-02   3.7803e+00   6.0557e-02   3.1661e-01   1.2306e-01   1.5016e-01   5.8274e-01   9.3412e-01   8.0508e-02   8.1829e-01   8.6377e-02
+#> 24:    1.0091e+02  -4.0314e+00  -2.5298e+00  -4.0566e+00  -8.9743e-01   3.7634e-02   3.5913e+00   5.7529e-02   3.5267e-01   1.2194e-01   1.4265e-01   5.5360e-01   9.6271e-01   7.6960e-02   8.8466e-01   8.5693e-02
+#> 25:    1.0100e+02  -4.0442e+00  -2.5399e+00  -4.0568e+00  -8.9494e-01   1.7415e-02   3.4117e+00   5.4652e-02   3.3504e-01   1.2781e-01   1.3552e-01   5.2592e-01   9.6040e-01   7.7299e-02   8.9561e-01   8.6893e-02
+#> 26:    1.0111e+02  -4.0354e+00  -2.5182e+00  -4.0899e+00  -9.0799e-01   7.6464e-02   4.8614e+00   5.1920e-02   3.1829e-01   1.2142e-01   1.3110e-01   4.9963e-01   9.6997e-01   7.4932e-02   8.2521e-01   9.3659e-02
+#> 27:    1.0159e+02  -4.0653e+00  -2.4934e+00  -4.0803e+00  -9.5632e-01   2.8659e-03   4.6184e+00   4.9324e-02   3.0237e-01   1.1535e-01   1.4743e-01   4.7465e-01   9.4314e-01   7.7860e-02   8.9820e-01   8.8210e-02
+#> 28:    1.0154e+02  -4.0487e+00  -2.4844e+00  -4.0511e+00  -9.6473e-01  -4.7382e-02   4.3874e+00   4.6858e-02   3.2049e-01   1.0958e-01   1.5243e-01   4.5091e-01   9.8808e-01   7.4786e-02   8.6833e-01   8.8720e-02
+#> 29:    1.0144e+02  -4.0414e+00  -2.4105e+00  -4.0504e+00  -9.4039e-01  -3.6753e-02   4.1681e+00   4.4515e-02   3.2754e-01   1.0410e-01   1.4940e-01   4.2837e-01   9.5520e-01   7.8507e-02   8.2408e-01   8.5998e-02
+#> 30:    1.0137e+02  -4.0292e+00  -2.4174e+00  -4.0382e+00  -9.3180e-01  -7.1482e-02   5.4636e+00   4.2289e-02   3.2074e-01   9.8896e-02   1.6877e-01   4.0695e-01   8.8153e-01   7.5106e-02   8.5239e-01   8.8266e-02
+#> 31:    1.0105e+02  -4.0387e+00  -2.4368e+00  -4.0346e+00  -9.1098e-01  -5.4730e-02   5.1904e+00   4.0175e-02   3.0470e-01   9.3951e-02   1.6034e-01   3.8660e-01   8.7853e-01   8.0278e-02   8.7981e-01   8.6404e-02
+#> 32:    1.0147e+02  -4.0435e+00  -2.4530e+00  -4.0365e+00  -9.1241e-01  -7.1281e-02   4.9309e+00   3.8166e-02   2.8947e-01   9.4694e-02   1.7475e-01   3.6727e-01   8.7005e-01   8.1398e-02   8.7784e-01   8.8976e-02
+#> 33:    1.0144e+02  -4.0092e+00  -2.4279e+00  -4.0090e+00  -8.8656e-01  -1.4017e-01   5.2945e+00   3.6258e-02   2.9770e-01   1.0169e-01   1.6601e-01   3.4891e-01   9.2202e-01   7.8841e-02   8.7551e-01   8.4011e-02
+#> 34:    1.0157e+02  -3.9839e+00  -2.4469e+00  -4.0180e+00  -8.3877e-01  -1.4664e-01   6.3506e+00   3.4445e-02   2.8282e-01   1.0831e-01   1.6850e-01   3.3146e-01   8.4403e-01   7.9056e-02   8.4620e-01   8.6363e-02
+#> 35:    1.0149e+02  -3.9928e+00  -2.4771e+00  -4.0106e+00  -8.6974e-01  -1.4219e-01   6.2039e+00   3.2722e-02   2.8123e-01   1.1283e-01   1.6008e-01   3.1489e-01   9.1308e-01   7.8685e-02   7.8939e-01   8.7289e-02
+#> 36:    1.0162e+02  -4.0099e+00  -2.4822e+00  -3.9880e+00  -8.7959e-01  -1.3237e-01   5.8937e+00   3.1086e-02   3.2200e-01   1.0719e-01   1.6077e-01   2.9914e-01   9.0821e-01   8.4066e-02   7.5559e-01   8.4838e-02
+#> 37:    1.0102e+02  -3.9962e+00  -2.4852e+00  -3.9954e+00  -8.8307e-01  -9.2070e-02   5.5991e+00   2.9532e-02   3.3713e-01   1.0183e-01   1.5333e-01   2.8419e-01   8.3918e-01   8.5231e-02   7.6007e-01   8.9541e-02
+#> 38:    1.0102e+02  -3.9987e+00  -2.5129e+00  -3.9833e+00  -8.7454e-01  -1.6469e-01   5.3191e+00   2.8055e-02   3.2027e-01   1.0792e-01   1.4707e-01   2.6998e-01   9.1490e-01   8.4715e-02   7.6778e-01   8.9241e-02
+#> 39:    1.0054e+02  -3.9875e+00  -2.4301e+00  -3.9797e+00  -8.7222e-01  -1.9597e-01   7.3800e+00   2.6653e-02   3.0426e-01   1.0801e-01   1.4393e-01   2.5648e-01   9.5901e-01   7.8320e-02   8.1559e-01   9.2429e-02
+#> 40:    1.0077e+02  -4.0057e+00  -2.4630e+00  -3.9849e+00  -8.6788e-01  -1.9606e-01   7.0110e+00   2.5320e-02   3.0385e-01   1.3164e-01   1.4567e-01   3.0284e-01   9.7123e-01   7.6328e-02   8.3681e-01   8.9349e-02
+#> 41:    1.0069e+02  -4.0143e+00  -2.3805e+00  -3.9962e+00  -8.7503e-01  -1.8532e-01   6.6604e+00   2.4054e-02   3.0707e-01   1.4668e-01   1.5021e-01   3.0404e-01   1.0072e+00   7.3629e-02   9.4494e-01   8.4745e-02
+#> 42:    1.0073e+02  -3.9861e+00  -2.4464e+00  -3.9919e+00  -8.7912e-01  -1.8435e-01   6.3274e+00   2.2851e-02   2.9171e-01   1.3935e-01   1.5080e-01   2.8883e-01   9.6502e-01   7.7470e-02   9.4221e-01   8.2459e-02
+#> 43:    1.0104e+02  -3.9881e+00  -2.4156e+00  -3.9688e+00  -8.9448e-01  -2.3739e-01   6.0110e+00   2.1709e-02   2.7713e-01   1.3238e-01   1.5603e-01   2.7439e-01   9.7714e-01   7.1720e-02   8.5890e-01   8.6635e-02
+#> 44:    1.0084e+02  -4.0117e+00  -2.4455e+00  -3.9753e+00  -8.8716e-01  -2.0112e-01   5.7105e+00   2.0623e-02   2.6327e-01   1.2741e-01   1.5200e-01   2.6067e-01   9.3289e-01   8.0543e-02   8.5055e-01   8.2921e-02
+#> 45:    1.0071e+02  -3.9996e+00  -2.4359e+00  -3.9764e+00  -9.1082e-01  -2.4578e-01   5.4250e+00   1.9592e-02   2.5011e-01   1.3254e-01   1.6132e-01   2.8273e-01   9.5805e-01   7.7734e-02   7.8171e-01   8.4571e-02
+#> 46:    1.0018e+02  -4.0077e+00  -2.4835e+00  -3.9739e+00  -8.6079e-01  -1.6592e-01   5.1537e+00   1.8613e-02   2.3760e-01   1.3830e-01   1.5392e-01   3.0295e-01   1.0931e+00   7.3274e-02   8.9544e-01   8.8388e-02
+#> 47:    99.9834   -3.9991   -2.5292   -3.9863   -0.8820   -0.0796    4.8960    0.0177    0.2348    0.1376    0.1639    0.2878    0.9864    0.0837    0.9094    0.0832
+#> 48:    99.9155   -4.0224   -2.5422   -3.9854   -0.8719   -0.0750    4.6512    0.0184    0.2251    0.1307    0.1596    0.2734    0.9841    0.0835    0.8696    0.0843
+#> 49:    99.6136   -4.0397   -2.5172   -4.0115   -0.8774   -0.0922    5.2402    0.0175    0.2558    0.1242    0.1551    0.2597    0.9060    0.0816    0.8365    0.0869
+#> 50:    99.4747   -4.0542   -2.4192   -3.9834   -0.9041   -0.1798    4.9782    0.0219    0.2695    0.1234    0.1474    0.2468    0.9269    0.0783    0.8593    0.0854
+#> 51:    99.3401   -4.0386   -2.3951   -3.9661   -0.9181   -0.1887    4.7574    0.0213    0.2746    0.1522    0.1400    0.2344    0.9901    0.0781    0.8863    0.0928
+#> 52:    99.7109   -4.0509   -2.4227   -3.9770   -0.9247   -0.1431    4.9004    0.0203    0.2688    0.1446    0.1330    0.2227    0.8999    0.0791    1.0265    0.0890
+#> 53:    99.6496   -4.0397   -2.4398   -3.9752   -0.9193   -0.2119    5.1106    0.0193    0.2795    0.1527    0.1325    0.2116    0.8949    0.0788    0.9447    0.0872
+#> 54:    99.9071   -4.0211   -2.3887   -3.9812   -0.9233   -0.1946    5.0887    0.0183    0.2763    0.1450    0.1365    0.2010    0.8793    0.0875    0.8643    0.0903
+#> 55:    1.0012e+02  -4.0401e+00  -2.4203e+00  -3.9511e+00  -9.0712e-01  -2.5566e-01   5.7301e+00   1.7375e-02   2.7324e-01   1.3780e-01   1.6204e-01   1.9094e-01   9.7803e-01   7.6146e-02   9.0756e-01   8.7636e-02
+#> 56:    1.0032e+02  -4.0207e+00  -2.4263e+00  -3.9533e+00  -8.7574e-01  -2.3076e-01   6.5321e+00   1.6507e-02   3.0821e-01   1.3091e-01   1.5394e-01   1.8139e-01   8.8520e-01   7.6350e-02   9.2796e-01   8.5283e-02
+#> 57:    1.0028e+02  -4.0037e+00  -2.4301e+00  -3.9655e+00  -8.8472e-01  -1.8969e-01   9.8969e+00   1.5681e-02   2.9280e-01   1.2436e-01   1.4624e-01   1.7232e-01   9.2902e-01   7.4974e-02   8.9204e-01   8.4563e-02
+#> 58:    1.0048e+02  -3.9928e+00  -2.4961e+00  -3.9709e+00  -9.0263e-01  -1.4516e-01   9.4021e+00   1.6151e-02   2.7816e-01   1.1814e-01   1.4165e-01   1.6370e-01   9.5145e-01   8.0233e-02   8.2896e-01   8.3498e-02
+#> 59:    1.0060e+02  -4.0181e+00  -2.4963e+00  -3.9751e+00  -9.0684e-01  -1.1186e-01   8.9320e+00   1.9914e-02   3.0097e-01   1.1224e-01   1.4109e-01   1.5552e-01   9.9121e-01   7.3120e-02   8.6454e-01   8.2239e-02
+#> 60:    1.0047e+02  -3.9976e+00  -2.4797e+00  -3.9780e+00  -8.9328e-01  -1.0814e-01   8.4854e+00   1.8918e-02   3.2275e-01   1.1591e-01   1.3404e-01   1.4774e-01   9.6968e-01   7.4984e-02   8.9831e-01   8.1655e-02
+#> 61:    1.0040e+02  -4.0068e+00  -2.5217e+00  -3.9844e+00  -8.6447e-01  -1.0567e-01   8.0611e+00   1.7972e-02   3.1372e-01   1.1011e-01   1.2973e-01   1.4036e-01   9.1698e-01   7.8118e-02   9.1811e-01   8.4420e-02
+#> 62:    1.0076e+02  -4.0080e+00  -2.4931e+00  -3.9623e+00  -8.9789e-01  -8.3896e-02   7.6580e+00   1.7073e-02   3.0460e-01   1.1254e-01   1.2324e-01   1.3334e-01   9.9032e-01   7.7618e-02   8.3808e-01   8.5031e-02
+#> 63:    1.0064e+02  -4.0129e+00  -2.4731e+00  -3.9561e+00  -8.9103e-01  -8.8987e-02   7.2751e+00   1.6220e-02   2.8944e-01   1.1647e-01   1.4845e-01   1.2667e-01   1.0745e+00   7.6375e-02   8.4316e-01   8.6681e-02
+#> 64:    1.0098e+02  -4.0094e+00  -2.4541e+00  -3.9604e+00  -9.1524e-01  -9.3413e-02   6.9114e+00   1.5409e-02   2.7497e-01   1.2065e-01   1.7095e-01   1.2034e-01   1.0963e+00   7.8304e-02   8.7104e-01   8.5727e-02
+#> 65:    1.0070e+02  -4.0433e+00  -2.4793e+00  -3.9722e+00  -9.3012e-01  -6.5917e-02   6.5658e+00   1.4638e-02   2.7040e-01   1.1462e-01   1.9067e-01   1.1432e-01   9.7444e-01   8.4510e-02   8.7028e-01   8.6292e-02
+#> 66:    1.0049e+02  -4.0656e+00  -2.4659e+00  -3.9898e+00  -9.4278e-01  -7.5929e-02   6.2375e+00   1.3906e-02   2.9347e-01   1.1997e-01   1.8114e-01   1.0860e-01   9.9830e-01   8.0902e-02   9.3551e-01   8.5261e-02
+#> 67:    1.0046e+02  -4.0477e+00  -2.4685e+00  -3.9907e+00  -9.1503e-01  -9.8019e-02   5.9256e+00   1.3211e-02   3.2166e-01   1.1506e-01   1.7208e-01   1.0317e-01   8.6453e-01   9.0533e-02   8.3598e-01   8.6343e-02
+#> 68:    1.0077e+02  -4.0575e+00  -2.4709e+00  -3.9523e+00  -9.2903e-01  -8.1099e-02   5.6294e+00   1.2818e-02   3.1005e-01   1.3665e-01   1.6347e-01   9.8015e-02   9.0181e-01   8.7058e-02   8.4937e-01   8.3248e-02
+#> 69:    1.0086e+02  -4.0626e+00  -2.3922e+00  -3.9557e+00  -9.6741e-01  -3.5986e-02   5.3479e+00   1.2844e-02   3.3024e-01   1.2982e-01   1.5530e-01   9.3115e-02   9.8180e-01   8.3132e-02   8.6549e-01   8.8939e-02
+#> 70:    1.0082e+02  -4.0640e+00  -2.4449e+00  -3.9787e+00  -9.5159e-01  -3.2904e-02   5.0805e+00   1.4346e-02   3.1373e-01   1.2333e-01   1.4754e-01   8.8459e-02   1.0129e+00   7.4856e-02   8.6688e-01   8.4769e-02
+#> 71:    1.0072e+02  -4.0642e+00  -2.5069e+00  -3.9493e+00  -9.3453e-01  -4.4116e-02   4.8265e+00   1.3628e-02   3.0428e-01   1.2122e-01   1.4091e-01   8.4036e-02   1.0454e+00   7.7023e-02   8.9566e-01   8.1639e-02
+#> 72:    1.0049e+02  -4.0609e+00  -2.4472e+00  -3.9669e+00  -9.3972e-01  -7.7498e-02   4.5852e+00   1.4441e-02   3.2552e-01   1.3911e-01   1.4144e-01   8.1899e-02   1.0114e+00   7.7019e-02   8.2312e-01   8.2494e-02
+#> 73:    1.0022e+02  -4.0598e+00  -2.4410e+00  -3.9952e+00  -9.2810e-01  -1.1309e-01   4.3559e+00   1.3719e-02   3.3556e-01   1.3303e-01   1.4990e-01   1.1303e-01   9.6726e-01   7.6776e-02   8.6331e-01   8.3048e-02
+#> 74:    1.0024e+02  -4.0628e+00  -2.4358e+00  -3.9977e+00  -9.1347e-01  -9.1966e-02   4.1381e+00   1.3033e-02   3.4332e-01   1.3418e-01   1.8099e-01   1.0738e-01   1.0158e+00   7.4697e-02   8.6366e-01   8.4370e-02
+#> 75:    99.7847   -4.0500   -2.4401   -4.0018   -0.9252   -0.1013    4.4651    0.0124    0.3365    0.1399    0.1817    0.1020    1.0278    0.0779    0.9008    0.0841
+#> 76:    99.9526   -4.0482   -2.4819   -3.9947   -0.9049   -0.0557    4.2419    0.0126    0.3248    0.1494    0.1726    0.1135    1.0493    0.0778    0.9341    0.0804
+#> 77:    99.9982   -4.0184   -2.4951   -4.0043   -0.8927   -0.0688    5.2538    0.0120    0.3696    0.1419    0.1817    0.1078    1.0402    0.0839    0.9605    0.0848
+#> 78:    1.0007e+02  -4.0210e+00  -2.4725e+00  -4.0040e+00  -8.9827e-01   2.3164e-03   6.4464e+00   1.1395e-02   3.7410e-01   1.3481e-01   2.0294e-01   1.0879e-01   9.7822e-01   8.7445e-02   9.9990e-01   8.2845e-02
+#> 79:    99.3513   -4.0171   -2.5065   -4.0078   -0.8962   -0.0029    7.7527    0.0108    0.3554    0.1281    0.1928    0.1069    1.0455    0.0866    0.9982    0.0870
+#> 80:    98.9945   -4.0172   -2.5412   -4.0341   -0.8891   -0.0187    9.8218    0.0103    0.3376    0.1217    0.1831    0.1457    0.9733    0.0894    1.0164    0.0832
+#> 81:    99.0936   -4.0275   -2.5134   -4.0127   -0.8552   -0.0614   12.1567    0.0098    0.3494    0.1156    0.1740    0.1384    0.9509    0.0843    1.0171    0.0855
+#> 82:    99.2481   -3.9996   -2.4945   -4.0011   -0.8914   -0.0492   11.5489    0.0128    0.3792    0.1098    0.1653    0.1315    0.9915    0.0818    1.0405    0.0928
+#> 83:    99.6941   -3.9998   -2.4851   -3.9845   -0.8802   -0.0560   10.9714    0.0146    0.3602    0.1043    0.1570    0.1249    0.9934    0.0852    0.9707    0.0866
+#> 84:    99.2185   -3.9920   -2.4843   -4.0051   -0.8546   -0.0642   10.4228    0.0153    0.3422    0.0991    0.1492    0.1187    0.9923    0.0833    0.9799    0.0873
+#> 85:    98.8470   -3.9956   -2.4652   -4.0201   -0.8483   -0.0414    9.9017    0.0146    0.3251    0.0941    0.1417    0.1128    0.9732    0.0901    0.9035    0.0858
+#> 86:    98.5012   -3.9841   -2.5148   -4.0250   -0.8408   -0.0551    9.4066    0.0148    0.3088    0.0962    0.1346    0.1071    0.8570    0.0932    0.8532    0.0896
+#> 87:    99.0868   -4.0055   -2.5058   -4.0249   -0.8522   -0.0311   10.3528    0.0175    0.2934    0.1013    0.1411    0.1018    0.8802    0.0838    0.8849    0.0862
+#> 88:    99.5158   -4.0031   -2.4437   -3.9866   -0.8894   -0.0963    9.9832    0.0167    0.3049    0.1030    0.1447    0.0967    0.9955    0.0834    0.8861    0.0893
+#> 89:    99.5538   -4.0347   -2.4494   -4.0213   -0.8695   -0.0494    9.4841    0.0158    0.2897    0.0978    0.1543    0.0918    0.8597    0.0904    0.8959    0.0880
+#> 90:    99.4422   -4.0453   -2.4398   -4.0114   -0.9279   -0.0745    9.8221    0.0150    0.2842    0.0929    0.1466    0.0944    0.9009    0.0871    0.8696    0.0924
+#> 91:    98.8721   -4.0328   -2.4996   -4.0041   -0.8832   -0.0689    9.3310    0.0143    0.2700    0.0896    0.1444    0.1137    0.9567    0.0904    0.8680    0.0891
+#> 92:    99.8390   -4.0418   -2.4914   -4.0182   -0.9279   -0.0460   10.9801    0.0136    0.2585    0.0949    0.1461    0.1210    1.0043    0.0908    0.8310    0.0939
+#> 93:    1.0029e+02  -4.0313e+00  -2.4620e+00  -4.0187e+00  -8.9083e-01  -1.0908e-01   1.0431e+01   1.2890e-02   2.4559e-01   9.5757e-02   1.3878e-01   1.1565e-01   9.9174e-01   9.0056e-02   8.9538e-01   8.8925e-02
+#> 94:    99.3285   -4.0295   -2.4523   -4.0235   -0.8828   -0.1190   10.9003    0.0137    0.2333    0.0915    0.1318    0.1212    1.0729    0.0779    0.9543    0.0907
+#> 95:    99.4117   -4.0422   -2.3807   -4.0870   -0.8960   -0.0889   10.3553    0.0130    0.2216    0.0870    0.1253    0.1366    0.9127    0.0864    0.8901    0.0911
+#> 96:    99.3348   -4.0401   -2.4009   -4.0698   -0.8730   -0.0622    9.8375    0.0123    0.2106    0.0826    0.1241    0.1297    0.8504    0.0836    0.9140    0.0881
+#> 97:    99.4898   -4.0419   -2.4310   -4.0589   -0.8932   -0.0634    9.3456    0.0132    0.2000    0.0785    0.1224    0.1233    0.8770    0.0836    0.8715    0.0837
+#> 98:    99.3750   -4.0704   -2.4353   -4.0616   -0.9333   -0.0846    8.8783    0.0136    0.1900    0.0746    0.1245    0.1171    0.8907    0.0838    0.9066    0.0832
+#> 99:    99.6234   -4.0366   -2.3740   -4.0657   -0.9242   -0.0675    8.4344    0.0129    0.1805    0.0708    0.1182    0.1112    0.8814    0.0808    0.9511    0.0863
+#> 100:    1.0025e+02  -4.0420e+00  -2.3557e+00  -4.0579e+00  -9.5051e-01  -6.3418e-02   8.0319e+00   1.2286e-02   1.7150e-01   6.7291e-02   1.1232e-01   1.0568e-01   8.5851e-01   8.7881e-02   8.9363e-01   8.5897e-02
+#> 101:    1.0041e+02  -4.0461e+00  -2.3840e+00  -4.0384e+00  -9.3752e-01  -7.7594e-02   9.5649e+00   1.1672e-02   1.7509e-01   6.3926e-02   1.2760e-01   1.0039e-01   8.6733e-01   8.2748e-02   9.6277e-01   8.4274e-02
+#> 102:    1.0095e+02  -4.0372e+00  -2.3633e+00  -4.0286e+00  -9.1961e-01  -6.5350e-02   1.1428e+01   1.1088e-02   1.8557e-01   6.0730e-02   1.3211e-01   9.5374e-02   9.3928e-01   8.0161e-02   9.7913e-01   8.4081e-02
+#> 103:    1.0019e+02  -4.0236e+00  -2.4105e+00  -4.0337e+00  -9.1362e-01  -7.3859e-02   1.0856e+01   1.0534e-02   1.7629e-01   5.7693e-02   1.2695e-01   9.1362e-02   9.8491e-01   8.1430e-02   9.7682e-01   8.2250e-02
+#> 104:    99.7755   -4.0280   -2.4452   -4.0197   -0.9112   -0.0810   11.0317    0.0100    0.1796    0.0548    0.1301    0.0868    0.9418    0.0816    0.9170    0.0806
+#> 105:    1.0010e+02  -4.0418e+00  -2.4294e+00  -4.0225e+00  -9.1111e-01  -8.9920e-02   1.0480e+01   9.5070e-03   1.7060e-01   5.2068e-02   1.3987e-01   8.2454e-02   9.1944e-01   7.8110e-02   8.9266e-01   8.7228e-02
+#> 106:    1.0025e+02  -4.0507e+00  -2.4134e+00  -4.0343e+00  -9.0244e-01  -8.4683e-02   1.3506e+01   9.0316e-03   1.6207e-01   4.9465e-02   1.5337e-01   7.8331e-02   9.9609e-01   8.4473e-02   8.7046e-01   8.5479e-02
+#> 107:    1.0014e+02  -4.0468e+00  -2.3972e+00  -4.0196e+00  -9.3650e-01  -2.4087e-02   1.2830e+01   8.5801e-03   1.6027e-01   4.6992e-02   1.5429e-01   8.2493e-02   9.8959e-01   8.2626e-02   8.3427e-01   8.8197e-02
+#> 108:    1.0114e+02  -4.0338e+00  -2.4307e+00  -4.0724e+00  -9.1363e-01   1.1952e-02   1.2189e+01   8.1511e-03   1.5563e-01   4.4854e-02   1.7315e-01   7.8368e-02   9.8589e-01   7.8130e-02   9.0460e-01   8.2870e-02
+#> 109:    1.0066e+02  -4.0550e+00  -2.4094e+00  -4.0641e+00  -9.0945e-01  -1.5401e-03   1.3149e+01   7.7435e-03   1.4785e-01   4.2612e-02   1.7232e-01   7.4450e-02   1.0942e+00   7.4816e-02   9.1706e-01   8.5333e-02
+#> 110:    1.0111e+02  -4.0266e+00  -2.4047e+00  -4.0646e+00  -9.0541e-01  -1.7212e-02   1.2492e+01   7.3563e-03   1.4046e-01   4.0481e-02   1.8132e-01   7.0727e-02   1.0508e+00   7.9457e-02   9.8990e-01   8.2975e-02
+#> 111:    1.0155e+02  -4.0274e+00  -2.3645e+00  -4.0663e+00  -9.4902e-01  -1.8882e-02   1.1867e+01   8.7757e-03   1.4436e-01   3.8457e-02   1.7225e-01   6.7191e-02   1.0217e+00   7.7437e-02   9.9196e-01   8.1580e-02
+#> 112:    1.0209e+02  -4.0230e+00  -2.3938e+00  -4.0375e+00  -9.5447e-01  -5.0888e-02   1.4321e+01   8.3370e-03   1.4863e-01   3.6534e-02   1.6778e-01   8.2186e-02   9.3085e-01   8.3291e-02   9.8775e-01   7.9492e-02
+#> 113:    1.0188e+02  -4.0173e+00  -2.3804e+00  -4.0403e+00  -9.6152e-01  -7.7453e-02   1.3605e+01   7.9201e-03   1.5060e-01   3.4708e-02   1.7341e-01   8.4506e-02   9.0783e-01   8.7383e-02   9.4854e-01   8.2648e-02
+#> 114:    1.0239e+02  -4.0081e+00  -2.3724e+00  -4.0332e+00  -9.4315e-01  -7.4933e-02   1.2925e+01   7.5241e-03   1.4307e-01   3.2972e-02   1.6695e-01   8.0281e-02   9.2775e-01   8.4314e-02   9.6195e-01   7.9448e-02
+#> 115:    1.0199e+02  -4.0127e+00  -2.3773e+00  -4.0472e+00  -9.5157e-01  -2.0947e-02   1.2279e+01   7.4483e-03   1.3592e-01   3.1324e-02   1.6705e-01   7.6267e-02   9.4956e-01   7.6989e-02   1.0340e+00   8.5564e-02
+#> 116:    1.0122e+02  -4.0264e+00  -2.4014e+00  -4.0509e+00  -9.1462e-01  -2.3511e-02   1.1665e+01   7.0759e-03   1.2912e-01   2.9757e-02   1.5870e-01   7.2453e-02   9.3580e-01   8.2952e-02   9.3341e-01   8.3302e-02
+#> 117:    1.0112e+02  -4.0326e+00  -2.4093e+00  -4.0559e+00  -8.9743e-01  -2.0572e-02   1.1082e+01   6.7221e-03   1.2266e-01   2.8269e-02   1.5339e-01   6.8831e-02   9.0879e-01   8.4441e-02   9.1432e-01   8.0538e-02
+#> 118:    1.0123e+02  -4.0411e+00  -2.4077e+00  -4.0556e+00  -9.2971e-01  -2.1885e-02   1.0528e+01   6.3860e-03   1.1653e-01   3.3123e-02   1.6947e-01   6.5389e-02   9.7140e-01   8.6671e-02   8.9874e-01   8.1670e-02
+#> 119:    1.0098e+02  -4.0538e+00  -2.3515e+00  -4.0607e+00  -9.5433e-01  -7.5743e-02   1.0001e+01   6.0667e-03   1.1070e-01   3.1467e-02   1.8338e-01   6.2120e-02   9.1537e-01   8.4827e-02   9.2420e-01   8.2769e-02
+#> 120:    1.0076e+02  -4.0573e+00  -2.3627e+00  -4.0329e+00  -9.3251e-01  -6.7669e-02   9.5011e+00   5.7634e-03   1.0517e-01   3.2868e-02   1.7422e-01   6.6096e-02   9.5247e-01   8.5343e-02   9.4678e-01   8.5335e-02
+#> 121:    1.0085e+02  -4.0450e+00  -2.3478e+00  -4.0692e+00  -9.2333e-01  -9.8005e-03   9.0261e+00   5.4752e-03   9.9911e-02   3.1225e-02   1.6550e-01   7.1593e-02   8.5572e-01   8.8654e-02   1.0248e+00   8.0646e-02
+#> 122:    1.0164e+02  -4.0325e+00  -2.3562e+00  -4.0680e+00  -9.4287e-01  -1.2103e-02   8.5748e+00   5.3493e-03   9.4915e-02   2.9663e-02   1.6347e-01   6.8014e-02   8.4872e-01   8.6803e-02   1.0282e+00   8.0381e-02
+#> 123:    1.0184e+02  -4.0521e+00  -2.3504e+00  -4.0714e+00  -9.5966e-01  -9.1996e-05   8.1460e+00   5.0818e-03   9.8247e-02   3.0007e-02   1.7746e-01   6.4613e-02   9.7181e-01   8.0986e-02   9.8860e-01   8.0317e-02
+#> 124:    1.0235e+02  -4.0674e+00  -2.3315e+00  -4.0874e+00  -9.9802e-01   3.8818e-02   7.7387e+00   4.8277e-03   9.3335e-02   2.8506e-02   1.7611e-01   6.8940e-02   9.7376e-01   7.6658e-02   9.9156e-01   8.4407e-02
+#> 125:    1.0257e+02  -4.0718e+00  -2.3604e+00  -4.0627e+00  -1.0591e+00   2.4685e-02   7.3518e+00   4.5863e-03   8.8668e-02   3.0650e-02   1.8671e-01   6.5493e-02   1.0275e+00   8.2278e-02   1.0896e+00   8.0976e-02
+#> 126:    1.0287e+02  -4.0691e+00  -2.3103e+00  -4.0552e+00  -1.0174e+00   2.1863e-02   7.5644e+00   4.3570e-03   1.0937e-01   2.9117e-02   1.7738e-01   6.2218e-02   9.2668e-01   7.9560e-02   9.5409e-01   8.4671e-02
+#> 127:    1.0327e+02  -4.0528e+00  -2.3141e+00  -4.0522e+00  -1.0108e+00   4.4779e-03   7.1862e+00   4.1392e-03   1.2239e-01   2.7661e-02   1.6925e-01   5.9107e-02   9.1372e-01   7.9536e-02   9.9164e-01   8.2999e-02
+#> 128:    1.0352e+02  -4.0496e+00  -2.2880e+00  -4.0496e+00  -1.0063e+00  -1.3248e-02   7.6721e+00   3.9613e-03   1.1627e-01   2.6278e-02   1.7517e-01   8.0231e-02   8.4407e-01   8.5078e-02   9.4382e-01   8.7530e-02
+#> 129:    1.0345e+02  -4.0715e+00  -2.3090e+00  -4.0400e+00  -1.0276e+00  -1.8301e-02   8.2197e+00   3.7633e-03   1.1046e-01   2.7141e-02   1.9366e-01   7.6220e-02   9.3357e-01   8.2674e-02   9.7064e-01   8.6011e-02
+#> 130:    1.0245e+02  -4.0787e+00  -2.3263e+00  -4.0106e+00  -1.0200e+00  -8.5976e-02   7.8087e+00   4.0830e-03   1.3607e-01   2.6631e-02   2.2700e-01   7.2409e-02   9.8233e-01   7.9348e-02   9.6780e-01   8.2658e-02
+#> 131:    1.0217e+02  -4.0760e+00  -2.2525e+00  -4.0082e+00  -1.0099e+00  -1.6111e-01   7.4183e+00   3.8789e-03   1.3972e-01   2.5299e-02   2.2508e-01   6.8788e-02   1.0066e+00   7.8692e-02   9.4684e-01   8.4349e-02
+#> 132:    1.0185e+02  -4.0792e+00  -2.2309e+00  -3.9996e+00  -9.8302e-01  -2.2504e-01   7.0474e+00   4.0356e-03   1.3743e-01   2.4034e-02   2.1383e-01   7.7346e-02   9.4225e-01   7.9110e-02   9.5160e-01   8.4398e-02
+#> 133:    1.0135e+02  -4.0818e+00  -2.2219e+00  -4.0054e+00  -9.7264e-01  -1.8912e-01   7.1932e+00   3.8338e-03   1.3056e-01   2.2833e-02   2.0314e-01   7.6769e-02   1.0031e+00   8.5400e-02   1.0034e+00   8.4805e-02
+#> 134:    1.0148e+02  -4.0782e+00  -2.2492e+00  -3.9886e+00  -9.5184e-01  -1.5049e-01   6.8336e+00   3.6422e-03   1.2403e-01   2.3398e-02   1.9298e-01   7.2931e-02   9.3696e-01   8.3566e-02   9.4742e-01   8.9137e-02
+#> 135:    1.0145e+02  -4.0852e+00  -2.3062e+00  -4.0011e+00  -9.4444e-01  -1.6803e-01   6.4919e+00   3.4600e-03   1.1783e-01   2.2228e-02   1.8333e-01   6.9284e-02   9.4846e-01   8.3087e-02   9.7774e-01   8.2610e-02
+#> 136:    1.0177e+02  -4.0861e+00  -2.2785e+00  -3.9890e+00  -9.9625e-01  -1.8938e-01   6.1673e+00   3.2870e-03   1.1752e-01   2.1116e-02   1.8815e-01   6.5820e-02   9.3634e-01   8.5255e-02   1.1001e+00   8.5332e-02
+#> 137:    1.0200e+02  -4.0928e+00  -2.1946e+00  -3.9974e+00  -1.0098e+00  -1.8810e-01   5.8589e+00   3.1227e-03   1.2394e-01   2.1203e-02   1.7874e-01   7.2232e-02   1.0048e+00   7.3422e-02   1.0222e+00   8.3484e-02
+#> 138:    1.0214e+02  -4.0820e+00  -2.2052e+00  -3.9737e+00  -1.0420e+00  -2.0594e-01   5.5660e+00   3.8937e-03   1.9164e-01   2.0143e-02   1.6980e-01   6.8621e-02   1.0126e+00   7.6106e-02   1.0780e+00   8.2960e-02
+#> 139:    1.0249e+02  -4.0785e+00  -2.1649e+00  -3.9567e+00  -1.0095e+00  -2.8807e-01   5.2877e+00   3.6990e-03   1.8647e-01   1.9135e-02   1.6131e-01   6.5190e-02   1.0030e+00   7.9858e-02   1.0611e+00   8.4109e-02
+#> 140:    1.0184e+02  -4.0847e+00  -2.1800e+00  -3.9565e+00  -9.9415e-01  -2.8869e-01   5.0233e+00   4.0857e-03   1.9502e-01   1.8179e-02   1.6676e-01   6.2879e-02   9.5962e-01   7.8117e-02   9.9649e-01   8.4914e-02
+#> 141:    1.0195e+02  -4.1012e+00  -2.1831e+00  -3.9488e+00  -9.9515e-01  -3.1864e-01   4.7721e+00   3.8814e-03   1.8527e-01   1.7270e-02   1.6797e-01   6.1084e-02   9.0969e-01   8.2722e-02   1.0122e+00   8.2518e-02
+#> 142:    1.0233e+02  -4.1139e+00  -2.1692e+00  -3.9542e+00  -1.0023e+00  -3.3242e-01   4.5335e+00   3.6873e-03   2.0662e-01   1.6406e-02   1.5957e-01   5.8030e-02   9.4761e-01   8.4629e-02   1.0342e+00   8.3954e-02
+#> 143:    1.0217e+02  -4.1103e+00  -2.1380e+00  -3.9511e+00  -1.0300e+00  -2.5992e-01   5.2035e+00   4.7053e-03   1.9629e-01   1.5586e-02   1.5979e-01   5.5128e-02   8.9255e-01   7.9042e-02   1.0461e+00   8.6952e-02
+#> 144:    1.0185e+02  -4.1335e+00  -2.1911e+00  -3.9650e+00  -1.0440e+00  -2.4451e-01   5.0998e+00   4.4700e-03   1.8648e-01   1.9590e-02   1.5534e-01   5.2372e-02   9.7863e-01   8.3932e-02   1.0197e+00   8.7673e-02
+#> 145:    1.0242e+02  -4.1445e+00  -2.1203e+00  -3.9616e+00  -1.0426e+00  -2.7120e-01   4.8448e+00   4.2465e-03   1.7715e-01   1.8611e-02   1.4757e-01   4.9753e-02   1.0024e+00   8.4131e-02   1.0768e+00   8.5388e-02
+#> 146:    1.0236e+02  -4.1519e+00  -2.1958e+00  -3.9779e+00  -9.8615e-01  -2.5863e-01   4.6026e+00   4.0718e-03   1.6829e-01   1.7680e-02   1.6407e-01   4.7266e-02   1.0740e+00   8.2413e-02   1.0706e+00   8.3410e-02
+#> 147:    1.0251e+02  -4.1465e+00  -2.2042e+00  -3.9775e+00  -1.0317e+00  -2.2757e-01   4.3725e+00   3.8682e-03   1.5988e-01   1.6796e-02   1.7016e-01   4.4902e-02   9.7748e-01   8.3376e-02   1.0880e+00   8.1968e-02
+#> 148:    1.0244e+02  -4.1432e+00  -2.1786e+00  -3.9792e+00  -1.0442e+00  -2.2002e-01   4.9671e+00   3.6748e-03   1.5189e-01   1.5956e-02   2.2196e-01   4.2657e-02   1.0412e+00   7.8051e-02   1.1051e+00   8.1618e-02
+#> 149:    1.0219e+02  -4.1384e+00  -2.2318e+00  -3.9757e+00  -1.0438e+00  -2.4124e-01   4.7187e+00   3.4910e-03   1.4429e-01   1.6061e-02   2.1086e-01   4.0524e-02   1.0082e+00   8.0377e-02   1.1455e+00   8.0545e-02
+#> 150:    1.0264e+02  -4.1498e+00  -2.2352e+00  -3.9915e+00  -1.0669e+00  -2.1255e-01   4.4828e+00   3.3165e-03   1.3708e-01   1.7218e-02   2.0032e-01   3.8498e-02   9.5031e-01   8.7248e-02   9.8770e-01   8.3250e-02
+#> 151:    1.0250e+02  -4.1365e+00  -2.1876e+00  -3.9939e+00  -1.0568e+00  -1.8159e-01   4.2587e+00   3.1507e-03   1.3022e-01   1.7383e-02   1.9030e-01   3.6573e-02   9.6938e-01   8.0203e-02   1.0578e+00   8.3430e-02
+#> 152:    1.0256e+02  -4.1370e+00  -2.2238e+00  -4.0047e+00  -1.0406e+00  -1.8764e-01   1.9609e+00   1.4191e-03   1.1882e-01   1.7924e-02   1.6889e-01   4.1216e-02   9.1972e-01   7.8573e-02   1.0717e+00   8.0882e-02
+#> 153:    1.0219e+02  -4.1299e+00  -2.2139e+00  -3.9917e+00  -9.9964e-01  -2.0505e-01   1.8258e+00   1.0432e-03   8.4660e-02   2.1446e-02   1.7634e-01   3.5573e-02   9.3702e-01   8.4860e-02   1.0145e+00   8.3329e-02
+#> 154:    1.0199e+02  -4.1354e+00  -2.2231e+00  -3.9779e+00  -1.0155e+00  -2.2573e-01   2.6463e+00   5.8153e-04   8.8101e-02   2.3167e-02   1.6103e-01   3.3874e-02   9.5360e-01   8.6215e-02   9.5723e-01   8.4603e-02
+#> 155:    1.0234e+02  -4.1239e+00  -2.2137e+00  -3.9802e+00  -1.0070e+00  -2.3158e-01   2.9697e+00   6.6709e-04   1.1190e-01   2.0949e-02   1.8298e-01   3.1557e-02   9.2910e-01   8.2509e-02   9.8680e-01   8.5206e-02
+#> 156:    1.0253e+02  -4.1269e+00  -2.2370e+00  -3.9682e+00  -1.0420e+00  -2.1219e-01   2.7267e+00   6.8451e-04   8.9651e-02   2.4380e-02   1.6613e-01   3.4846e-02   9.3608e-01   8.7506e-02   9.0446e-01   8.1755e-02
+#> 157:    1.0265e+02  -4.1241e+00  -2.2179e+00  -3.9676e+00  -1.0308e+00  -2.2480e-01   2.1278e+00   4.9811e-04   6.7161e-02   1.9758e-02   1.5607e-01   4.4198e-02   9.4162e-01   8.7311e-02   9.9147e-01   7.9857e-02
+#> 158:    1.0239e+02  -4.1219e+00  -2.1615e+00  -3.9781e+00  -1.0384e+00  -2.6750e-01   2.5310e+00   4.8270e-04   6.5662e-02   1.8085e-02   1.7665e-01   4.4020e-02   8.8632e-01   8.6004e-02   1.0425e+00   8.2894e-02
+#> 159:    1.0270e+02  -4.1204e+00  -2.1837e+00  -3.9530e+00  -1.0587e+00  -2.5809e-01   3.4348e+00   5.6788e-04   6.5500e-02   1.9540e-02   1.8629e-01   4.0730e-02   9.5079e-01   8.2399e-02   9.9316e-01   8.3381e-02
+#> 160:    1.0282e+02  -4.1223e+00  -2.1325e+00  -3.9734e+00  -1.0068e+00  -2.8751e-01   3.9652e+00   7.6565e-04   8.5246e-02   1.7068e-02   1.7587e-01   3.0778e-02   9.1802e-01   8.0158e-02   9.9642e-01   8.1564e-02
+#> 161:    1.0330e+02  -4.1180e+00  -2.1879e+00  -3.9743e+00  -1.0268e+00  -2.8812e-01   4.9153e+00   5.8033e-04   8.0457e-02   1.8555e-02   1.7312e-01   3.3941e-02   8.6920e-01   8.2509e-02   9.5632e-01   8.1798e-02
+#> 162:    1.0335e+02  -4.1182e+00  -2.2089e+00  -3.9566e+00  -1.0409e+00  -2.7390e-01   3.6169e+00   2.8392e-04   1.0776e-01   1.9589e-02   1.6479e-01   2.8481e-02   8.8603e-01   8.7799e-02   9.5197e-01   7.9563e-02
+#> 163:    1.0294e+02  -4.1181e+00  -2.2025e+00  -3.9462e+00  -9.9783e-01  -3.0753e-01   3.7234e+00   1.6293e-04   9.6922e-02   2.4842e-02   1.9367e-01   3.1473e-02   9.0380e-01   9.1697e-02   9.4394e-01   8.2786e-02
+#> 164:    1.0246e+02  -4.1155e+00  -2.2157e+00  -3.9736e+00  -9.9866e-01  -2.9356e-01   3.9439e+00   1.9405e-04   1.0404e-01   2.8435e-02   1.9043e-01   3.1239e-02   8.9853e-01   8.9427e-02   9.2586e-01   8.3170e-02
+#> 165:    1.0204e+02  -4.1117e+00  -2.2133e+00  -3.9674e+00  -1.0079e+00  -2.6996e-01   3.0774e+00   1.6591e-04   7.0005e-02   2.8285e-02   2.0813e-01   2.4574e-02   8.9719e-01   9.1629e-02   9.8242e-01   8.3692e-02
+#> 166:    1.0207e+02  -4.1164e+00  -2.2192e+00  -3.9893e+00  -1.0354e+00  -2.7396e-01   1.8145e+00   8.4168e-05   9.0739e-02   2.7410e-02   2.1403e-01   2.4311e-02   8.9386e-01   9.2727e-02   9.4636e-01   8.4238e-02
+#> 167:    1.0187e+02  -4.1149e+00  -2.2185e+00  -3.9708e+00  -1.0036e+00  -2.5751e-01   1.5355e+00   4.0974e-05   9.9346e-02   2.2030e-02   2.1916e-01   2.6726e-02   9.1055e-01   8.1030e-02   1.0098e+00   7.9180e-02
+#> 168:    1.0172e+02  -4.1167e+00  -2.2673e+00  -3.9702e+00  -9.8388e-01  -2.1404e-01   1.4836e+00   2.7779e-05   7.7509e-02   2.9513e-02   1.9543e-01   3.4526e-02   1.0152e+00   8.1248e-02   9.7482e-01   8.0746e-02
+#> 169:    1.0175e+02  -4.1171e+00  -2.2634e+00  -3.9701e+00  -9.5962e-01  -2.4130e-01   1.4263e+00   4.7370e-05   5.0986e-02   2.8211e-02   2.2554e-01   3.9909e-02   9.8519e-01   7.8842e-02   1.0023e+00   8.5684e-02
+#> 170:    1.0177e+02  -4.1189e+00  -2.2417e+00  -3.9834e+00  -1.0059e+00  -2.6551e-01   9.9010e-01   3.7247e-05   4.2517e-02   2.9791e-02   1.8705e-01   4.2435e-02   9.6604e-01   8.8427e-02   9.6699e-01   8.3986e-02
+#> 171:    1.0182e+02  -4.1187e+00  -2.2464e+00  -3.9953e+00  -9.8154e-01  -2.5146e-01   7.4179e-01   3.2420e-05   5.0690e-02   3.0483e-02   1.7888e-01   6.3177e-02   9.2784e-01   8.4814e-02   1.0018e+00   8.4070e-02
+#> 172:    1.0184e+02  -4.1178e+00  -2.2483e+00  -4.0009e+00  -1.0096e+00  -2.2636e-01   9.6710e-01   2.6981e-05   3.1321e-02   2.7772e-02   1.9767e-01   7.4969e-02   9.9720e-01   8.1434e-02   9.5483e-01   8.3419e-02
+#> 173:    1.0160e+02  -4.1183e+00  -2.2513e+00  -3.9920e+00  -9.8456e-01  -2.0144e-01   4.9964e-01   2.1222e-05   4.1909e-02   2.8101e-02   2.1163e-01   1.2811e-01   9.6384e-01   8.0352e-02   9.2496e-01   8.2328e-02
+#> 174:    1.0159e+02  -4.1179e+00  -2.2334e+00  -4.0068e+00  -1.0316e+00  -2.0656e-01   4.6608e-01   1.8044e-05   4.4647e-02   2.8273e-02   2.0083e-01   1.2780e-01   9.4612e-01   8.3630e-02   8.9385e-01   8.3930e-02
+#> 175:    1.0159e+02  -4.1182e+00  -2.2567e+00  -3.9972e+00  -1.0299e+00  -1.6534e-01   4.5228e-01   2.0060e-05   8.5751e-02   2.5343e-02   1.7864e-01   8.6977e-02   9.5795e-01   7.8867e-02   8.9213e-01   8.4362e-02
+#> 176:    1.0159e+02  -4.1183e+00  -2.2109e+00  -3.9983e+00  -1.0210e+00  -2.0879e-01   5.3694e-01   2.0264e-05   1.2835e-01   2.5563e-02   1.9469e-01   6.0808e-02   9.1537e-01   7.8520e-02   9.3355e-01   8.3608e-02
+#> 177:    1.0155e+02  -4.1193e+00  -2.2587e+00  -3.9825e+00  -1.0180e+00  -1.6859e-01   4.4935e-01   3.0321e-05   1.3509e-01   2.4979e-02   2.0113e-01   6.3617e-02   9.7277e-01   7.8515e-02   9.2667e-01   8.5309e-02
+#> 178:    1.0158e+02  -4.1196e+00  -2.2679e+00  -4.0231e+00  -1.0143e+00  -1.6084e-01   6.7629e-01   3.2855e-05   6.8816e-02   2.7808e-02   1.8944e-01   8.1814e-02   8.8319e-01   8.0114e-02   9.5183e-01   8.2195e-02
+#> 179:    1.0166e+02  -4.1190e+00  -2.2764e+00  -3.9875e+00  -1.0061e+00  -1.8260e-01   7.1129e-01   3.8250e-05   7.5489e-02   2.4148e-02   1.8082e-01   7.1172e-02   9.1387e-01   8.0813e-02   9.6660e-01   8.2457e-02
+#> 180:    1.0179e+02  -4.1202e+00  -2.2848e+00  -3.9974e+00  -9.9825e-01  -2.0277e-01   5.5755e-01   2.8041e-05   8.6779e-02   2.7193e-02   1.8826e-01   6.5133e-02   8.8812e-01   8.2655e-02   9.2100e-01   7.9919e-02
+#> 181:    1.0176e+02  -4.1200e+00  -2.2704e+00  -3.9954e+00  -1.0194e+00  -1.6896e-01   4.3842e-01   2.2428e-05   7.4093e-02   3.0526e-02   2.3473e-01   1.0537e-01   9.2303e-01   8.2141e-02   9.2941e-01   8.4699e-02
+#> 182:    1.0182e+02  -4.1211e+00  -2.3159e+00  -4.0259e+00  -1.0162e+00  -1.2876e-01   3.4993e-01   1.5716e-05   5.9887e-02   2.6422e-02   2.1757e-01   1.0488e-01   9.1725e-01   9.4143e-02   9.7674e-01   8.8668e-02
+#> 183:    1.0184e+02  -4.1216e+00  -2.2985e+00  -4.0278e+00  -1.0136e+00  -1.3154e-01   2.6456e-01   1.2552e-05   5.7149e-02   3.2712e-02   2.0632e-01   1.5501e-01   9.2464e-01   8.5394e-02   8.8699e-01   8.4279e-02
+#> 184:    1.0172e+02  -4.1212e+00  -2.2726e+00  -4.0189e+00  -1.0280e+00  -1.2967e-01   3.0582e-01   7.5239e-06   8.2812e-02   2.9556e-02   1.9725e-01   1.3753e-01   9.0862e-01   8.1319e-02   9.0031e-01   8.3491e-02
+#> 185:    1.0178e+02  -4.1208e+00  -2.2858e+00  -4.0272e+00  -1.0063e+00  -1.6155e-01   3.0856e-01   4.5894e-06   8.8870e-02   2.5817e-02   1.9251e-01   1.0670e-01   9.1157e-01   7.7834e-02   9.6258e-01   7.8990e-02
+#> 186:    1.0198e+02  -4.1208e+00  -2.2682e+00  -4.0401e+00  -9.8523e-01  -1.1556e-01   2.4761e-01   3.2640e-06   7.5614e-02   2.1067e-02   1.9085e-01   9.0045e-02   8.5090e-01   8.6621e-02   1.0145e+00   8.1864e-02
+#> 187:    1.0197e+02  -4.1208e+00  -2.2788e+00  -4.0281e+00  -1.0066e+00  -1.0149e-01   2.0460e-01   4.5073e-06   7.8797e-02   2.3861e-02   2.0725e-01   7.9771e-02   9.6253e-01   8.2363e-02   9.3855e-01   8.3939e-02
+#> 188:    1.0196e+02  -4.1207e+00  -2.3105e+00  -4.0149e+00  -1.0217e+00  -9.0603e-02   2.2178e-01   3.6903e-06   8.9793e-02   2.1775e-02   1.9248e-01   8.2415e-02   9.4078e-01   8.1247e-02   9.1756e-01   8.2786e-02
+#> 189:    1.0202e+02  -4.1204e+00  -2.2702e+00  -4.0430e+00  -1.0032e+00  -1.1308e-01   2.2944e-01   3.5141e-06   7.8575e-02   2.4885e-02   2.0968e-01   8.2380e-02   9.5115e-01   8.1619e-02   9.2134e-01   8.9958e-02
+#> 190:    1.0195e+02  -4.1207e+00  -2.3126e+00  -4.0312e+00  -1.0154e+00  -6.3842e-02   2.5129e-01   2.6517e-06   4.2267e-02   2.2084e-02   1.9361e-01   7.0492e-02   9.3985e-01   8.5817e-02   9.3893e-01   8.7011e-02
+#> 191:    1.0203e+02  -4.1206e+00  -2.2758e+00  -4.0290e+00  -1.0102e+00  -3.1042e-02   1.7935e-01   3.4489e-06   5.7444e-02   2.3544e-02   1.9651e-01   7.9509e-02   9.5213e-01   8.2030e-02   1.0054e+00   8.7523e-02
+#> 192:    1.0199e+02  -4.1205e+00  -2.2969e+00  -4.0329e+00  -1.0364e+00  -8.3705e-02   1.5785e-01   3.5081e-06   7.4305e-02   2.2992e-02   1.9662e-01   7.7684e-02   9.2601e-01   8.3027e-02   9.8642e-01   8.3428e-02
+#> 193:    1.0196e+02  -4.1205e+00  -2.2661e+00  -4.0513e+00  -9.9271e-01  -4.6516e-02   1.2084e-01   2.6911e-06   6.8360e-02   3.5444e-02   1.9649e-01   7.5188e-02   9.1949e-01   7.9194e-02   1.0046e+00   8.5964e-02
+#> 194:    1.0198e+02  -4.1207e+00  -2.2817e+00  -4.0520e+00  -9.9852e-01  -8.4466e-02   1.3596e-01   1.5511e-06   6.5142e-02   4.1562e-02   1.9137e-01   9.6992e-02   9.6709e-01   7.6757e-02   9.7566e-01   8.3784e-02
+#> 195:    1.0200e+02  -4.1207e+00  -2.3076e+00  -4.0637e+00  -1.0028e+00  -7.2489e-02   1.0942e-01   1.6451e-06   6.1364e-02   4.6242e-02   1.9470e-01   9.3546e-02   9.9614e-01   8.1292e-02   9.7814e-01   8.1909e-02
+#> 196:    1.0194e+02  -4.1205e+00  -2.2970e+00  -4.0482e+00  -9.8816e-01  -6.8493e-02   1.1918e-01   1.2629e-06   4.2775e-02   3.6925e-02   2.3565e-01   7.7784e-02   8.9524e-01   9.2250e-02   9.8003e-01   8.2408e-02
+#> 197:    1.0199e+02  -4.1205e+00  -2.3075e+00  -4.0418e+00  -1.0196e+00  -6.8458e-02   1.7674e-01   7.5205e-07   5.2125e-02   2.9288e-02   2.1892e-01   8.4416e-02   8.9857e-01   9.1154e-02   1.0377e+00   8.3604e-02
+#> 198:    1.0197e+02  -4.1206e+00  -2.3051e+00  -4.0367e+00  -1.0252e+00  -6.9200e-02   9.1625e-02   6.6068e-07   4.7665e-02   2.8907e-02   1.8679e-01   7.5787e-02   9.0272e-01   8.8077e-02   9.2929e-01   8.0385e-02
+#> 199:    1.0192e+02  -4.1204e+00  -2.3163e+00  -4.0506e+00  -1.0152e+00  -5.3872e-02   6.8196e-02   5.5789e-07   6.0471e-02   3.1730e-02   2.0053e-01   6.8557e-02   9.0478e-01   8.5910e-02   9.3814e-01   8.2211e-02
+#> 200:    1.0195e+02  -4.1205e+00  -2.3141e+00  -4.0728e+00  -1.0010e+00  -2.5675e-03   6.5235e-02   6.9762e-07   5.8458e-02   2.8504e-02   2.0377e-01   4.9513e-02   8.5640e-01   8.6640e-02   9.5731e-01   8.4390e-02
+#> 201:    1.0195e+02  -4.1205e+00  -2.3106e+00  -4.0774e+00  -9.9012e-01   5.1724e-03   5.1225e-02   5.4222e-07   6.0577e-02   3.3554e-02   2.0505e-01   4.4738e-02   8.8073e-01   8.5488e-02   9.6928e-01   8.4895e-02
+#> 202:    1.0194e+02  -4.1205e+00  -2.3078e+00  -4.0767e+00  -9.9283e-01   3.9328e-03   4.5461e-02   4.8520e-07   6.7405e-02   3.4599e-02   2.1312e-01   4.6664e-02   9.0528e-01   8.4189e-02   9.8043e-01   8.5266e-02
+#> 203:    1.0193e+02  -4.1205e+00  -2.3029e+00  -4.0790e+00  -9.8990e-01  -9.1380e-03   4.7128e-02   5.0468e-07   6.8524e-02   3.6050e-02   2.1378e-01   5.1774e-02   9.0923e-01   8.4899e-02   9.8928e-01   8.4613e-02
+#> 204:    1.0192e+02  -4.1205e+00  -2.3080e+00  -4.0760e+00  -9.8833e-01  -1.2434e-02   4.8184e-02   4.9472e-07   6.4152e-02   3.5604e-02   2.0954e-01   5.1354e-02   9.1294e-01   8.5219e-02   9.8301e-01   8.4374e-02
+#> 205:    1.0192e+02  -4.1205e+00  -2.3100e+00  -4.0712e+00  -9.9253e-01  -2.3365e-02   4.5888e-02   5.0564e-07   5.9894e-02   3.5053e-02   2.0322e-01   5.4423e-02   9.0925e-01   8.5899e-02   9.8418e-01   8.3421e-02
+#> 206:    1.0192e+02  -4.1205e+00  -2.3095e+00  -4.0715e+00  -9.9721e-01  -2.6262e-02   4.3985e-02   5.1954e-07   5.8681e-02   3.4539e-02   2.0202e-01   5.8248e-02   9.1301e-01   8.5459e-02   9.8621e-01   8.3465e-02
+#> 207:    1.0192e+02  -4.1205e+00  -2.3179e+00  -4.0731e+00  -9.9906e-01  -2.3191e-02   4.3649e-02   5.3824e-07   5.7537e-02   3.4790e-02   2.0220e-01   6.0242e-02   9.1783e-01   8.5307e-02   9.8436e-01   8.3111e-02
+#> 208:    1.0191e+02  -4.1205e+00  -2.3238e+00  -4.0734e+00  -9.9920e-01  -1.9434e-02   4.3223e-02   5.3831e-07   5.7908e-02   3.4909e-02   2.0126e-01   6.0353e-02   9.2010e-01   8.5244e-02   9.8002e-01   8.2975e-02
+#> 209:    1.0191e+02  -4.1205e+00  -2.3279e+00  -4.0726e+00  -1.0053e+00  -1.5390e-02   4.1064e-02   5.3171e-07   5.8749e-02   3.4510e-02   1.9942e-01   6.3063e-02   9.3192e-01   8.4436e-02   9.8298e-01   8.3187e-02
+#> 210:    1.0191e+02  -4.1205e+00  -2.3310e+00  -4.0705e+00  -1.0061e+00  -1.3507e-02   3.8265e-02   5.2762e-07   5.9344e-02   3.3374e-02   1.9612e-01   6.7006e-02   9.3199e-01   8.4573e-02   9.8382e-01   8.3227e-02
+#> 211:    1.0191e+02  -4.1205e+00  -2.3383e+00  -4.0683e+00  -1.0043e+00  -1.3973e-02   3.6076e-02   5.2584e-07   6.1568e-02   3.2369e-02   1.9504e-01   6.9982e-02   9.4179e-01   8.4625e-02   9.9145e-01   8.3067e-02
+#> 212:    1.0192e+02  -4.1204e+00  -2.3396e+00  -4.0662e+00  -1.0055e+00  -1.8011e-02   3.4746e-02   5.4375e-07   6.2747e-02   3.1588e-02   1.9405e-01   7.2360e-02   9.4525e-01   8.4466e-02   9.9581e-01   8.2952e-02
+#> 213:    1.0192e+02  -4.1204e+00  -2.3407e+00  -4.0649e+00  -1.0066e+00  -2.1077e-02   3.4708e-02   5.5843e-07   6.1940e-02   3.0715e-02   1.9382e-01   7.4602e-02   9.4611e-01   8.4322e-02   9.9397e-01   8.2717e-02
+#> 214:    1.0192e+02  -4.1204e+00  -2.3392e+00  -4.0648e+00  -1.0076e+00  -2.3417e-02   3.4282e-02   5.8157e-07   6.1893e-02   3.0158e-02   1.9322e-01   7.8942e-02   9.5250e-01   8.3922e-02   9.9723e-01   8.2793e-02
+#> 215:    1.0192e+02  -4.1204e+00  -2.3410e+00  -4.0645e+00  -1.0087e+00  -2.1950e-02   3.5820e-02   6.0691e-07   6.2032e-02   2.9890e-02   1.9172e-01   8.3774e-02   9.5617e-01   8.3280e-02   1.0003e+00   8.2881e-02
+#> 216:    1.0192e+02  -4.1203e+00  -2.3425e+00  -4.0628e+00  -1.0069e+00  -2.4268e-02   3.7597e-02   6.4187e-07   6.1733e-02   2.9353e-02   1.9092e-01   8.8150e-02   9.5834e-01   8.3091e-02   1.0027e+00   8.2753e-02
+#> 217:    1.0192e+02  -4.1203e+00  -2.3439e+00  -4.0613e+00  -1.0064e+00  -2.4197e-02   3.9291e-02   6.5775e-07   6.2318e-02   2.8903e-02   1.8958e-01   9.0470e-02   9.5766e-01   8.3234e-02   1.0020e+00   8.2707e-02
+#> 218:    1.0191e+02  -4.1203e+00  -2.3441e+00  -4.0619e+00  -1.0065e+00  -2.2460e-02   4.0043e-02   6.4921e-07   6.2280e-02   2.8349e-02   1.8800e-01   9.4476e-02   9.5499e-01   8.3416e-02   1.0036e+00   8.2628e-02
+#> 219:    1.0191e+02  -4.1203e+00  -2.3437e+00  -4.0624e+00  -1.0066e+00  -1.9698e-02   3.9735e-02   6.3365e-07   6.2264e-02   2.7720e-02   1.8768e-01   9.7275e-02   9.4994e-01   8.3350e-02   1.0047e+00   8.2572e-02
+#> 220:    1.0191e+02  -4.1203e+00  -2.3447e+00  -4.0630e+00  -1.0070e+00  -1.5871e-02   4.0198e-02   6.3507e-07   6.1981e-02   2.7259e-02   1.8786e-01   9.9168e-02   9.4781e-01   8.3355e-02   1.0046e+00   8.2752e-02
+#> 221:    1.0191e+02  -4.1203e+00  -2.3459e+00  -4.0638e+00  -1.0069e+00  -1.4298e-02   4.0161e-02   6.2865e-07   6.2113e-02   2.7201e-02   1.8810e-01   1.0163e-01   9.4863e-01   8.3154e-02   1.0042e+00   8.2670e-02
+#> 222:    1.0191e+02  -4.1203e+00  -2.3472e+00  -4.0646e+00  -1.0064e+00  -1.0921e-02   4.0310e-02   6.2450e-07   6.2436e-02   2.6979e-02   1.8736e-01   1.0306e-01   9.4914e-01   8.3081e-02   1.0050e+00   8.2757e-02
+#> 223:    1.0191e+02  -4.1203e+00  -2.3478e+00  -4.0650e+00  -1.0063e+00  -1.1053e-02   3.9741e-02   6.1973e-07   6.2918e-02   2.6636e-02   1.8806e-01   1.0506e-01   9.4996e-01   8.2927e-02   1.0054e+00   8.2579e-02
+#> 224:    1.0191e+02  -4.1203e+00  -2.3478e+00  -4.0653e+00  -1.0061e+00  -1.0324e-02   3.9480e-02   6.1421e-07   6.4180e-02   2.6403e-02   1.8833e-01   1.0733e-01   9.4750e-01   8.2697e-02   1.0033e+00   8.2517e-02
+#> 225:    1.0191e+02  -4.1203e+00  -2.3479e+00  -4.0654e+00  -1.0060e+00  -1.0650e-02   3.9188e-02   6.0959e-07   6.3862e-02   2.6122e-02   1.8815e-01   1.0786e-01   9.4504e-01   8.2833e-02   1.0002e+00   8.2398e-02
+#> 226:    1.0192e+02  -4.1204e+00  -2.3469e+00  -4.0657e+00  -1.0052e+00  -1.0205e-02   3.9129e-02   6.0577e-07   6.4045e-02   2.5875e-02   1.8762e-01   1.0921e-01   9.4663e-01   8.2599e-02   9.9857e-01   8.2472e-02
+#> 227:    1.0192e+02  -4.1203e+00  -2.3467e+00  -4.0658e+00  -1.0053e+00  -1.0189e-02   3.8797e-02   6.0837e-07   6.5125e-02   2.5679e-02   1.8721e-01   1.1060e-01   9.4729e-01   8.2470e-02   9.9802e-01   8.2753e-02
+#> 228:    1.0192e+02  -4.1204e+00  -2.3469e+00  -4.0657e+00  -1.0054e+00  -1.0575e-02   3.8741e-02   6.0738e-07   6.5467e-02   2.5448e-02   1.8548e-01   1.1134e-01   9.4840e-01   8.2580e-02   9.9829e-01   8.2888e-02
+#> 229:    1.0192e+02  -4.1204e+00  -2.3479e+00  -4.0650e+00  -1.0056e+00  -1.1215e-02   3.9360e-02   6.0182e-07   6.4817e-02   2.5237e-02   1.8448e-01   1.1090e-01   9.5039e-01   8.2625e-02   9.9900e-01   8.2896e-02
+#> 230:    1.0192e+02  -4.1204e+00  -2.3482e+00  -4.0652e+00  -1.0060e+00  -9.9775e-03   3.9501e-02   5.9385e-07   6.4132e-02   2.5093e-02   1.8510e-01   1.1122e-01   9.4938e-01   8.2763e-02   9.9961e-01   8.2886e-02
+#> 231:    1.0192e+02  -4.1204e+00  -2.3479e+00  -4.0654e+00  -1.0070e+00  -8.9509e-03   3.9907e-02   5.9290e-07   6.3744e-02   2.4829e-02   1.8560e-01   1.1062e-01   9.4790e-01   8.2872e-02   1.0022e+00   8.2955e-02
+#> 232:    1.0192e+02  -4.1204e+00  -2.3484e+00  -4.0657e+00  -1.0081e+00  -6.9066e-03   4.0738e-02   5.7862e-07   6.3242e-02   2.4729e-02   1.8626e-01   1.0975e-01   9.4866e-01   8.2846e-02   1.0036e+00   8.3065e-02
+#> 233:    1.0191e+02  -4.1204e+00  -2.3487e+00  -4.0660e+00  -1.0080e+00  -5.1163e-03   4.0708e-02   5.7326e-07   6.2392e-02   2.4475e-02   1.8701e-01   1.0932e-01   9.4816e-01   8.2933e-02   1.0059e+00   8.3155e-02
+#> 234:    1.0191e+02  -4.1204e+00  -2.3500e+00  -4.0660e+00  -1.0077e+00  -4.0637e-03   4.1065e-02   5.6885e-07   6.1938e-02   2.4418e-02   1.8673e-01   1.0923e-01   9.5001e-01   8.3005e-02   1.0080e+00   8.3207e-02
+#> 235:    1.0191e+02  -4.1204e+00  -2.3526e+00  -4.0653e+00  -1.0074e+00  -3.6541e-03   4.1151e-02   5.6498e-07   6.2228e-02   2.4447e-02   1.8667e-01   1.0995e-01   9.5059e-01   8.3101e-02   1.0055e+00   8.3101e-02
+#> 236:    1.0191e+02  -4.1204e+00  -2.3540e+00  -4.0648e+00  -1.0078e+00  -4.0127e-03   4.0966e-02   5.7047e-07   6.1779e-02   2.4457e-02   1.8777e-01   1.0971e-01   9.4919e-01   8.3203e-02   1.0044e+00   8.3078e-02
+#> 237:    1.0191e+02  -4.1204e+00  -2.3528e+00  -4.0645e+00  -1.0078e+00  -4.4251e-03   4.0491e-02   5.6811e-07   6.1507e-02   2.4421e-02   1.8827e-01   1.1047e-01   9.4870e-01   8.3149e-02   1.0031e+00   8.3008e-02
+#> 238:    1.0190e+02  -4.1204e+00  -2.3517e+00  -4.0647e+00  -1.0076e+00  -5.2540e-03   3.9988e-02   5.6832e-07   6.1612e-02   2.4262e-02   1.8801e-01   1.1019e-01   9.4737e-01   8.3172e-02   1.0037e+00   8.2959e-02
+#> 239:    1.0190e+02  -4.1204e+00  -2.3509e+00  -4.0650e+00  -1.0089e+00  -5.1598e-03   3.9373e-02   5.6396e-07   6.1635e-02   2.4040e-02   1.8812e-01   1.1055e-01   9.4885e-01   8.3140e-02   1.0053e+00   8.2956e-02
+#> 240:    1.0190e+02  -4.1204e+00  -2.3515e+00  -4.0643e+00  -1.0095e+00  -4.5817e-03   3.9031e-02   5.5929e-07   6.2233e-02   2.3840e-02   1.8766e-01   1.1014e-01   9.5018e-01   8.3172e-02   1.0066e+00   8.2937e-02
+#> 241:    1.0190e+02  -4.1204e+00  -2.3524e+00  -4.0642e+00  -1.0097e+00  -3.9061e-03   3.8686e-02   5.5496e-07   6.3349e-02   2.3663e-02   1.8759e-01   1.1046e-01   9.4922e-01   8.3162e-02   1.0064e+00   8.2940e-02
+#> 242:    1.0190e+02  -4.1204e+00  -2.3535e+00  -4.0642e+00  -1.0092e+00  -2.9411e-03   3.8674e-02   5.5359e-07   6.3930e-02   2.3604e-02   1.8742e-01   1.1027e-01   9.4748e-01   8.3177e-02   1.0052e+00   8.2955e-02
+#> 243:    1.0190e+02  -4.1204e+00  -2.3551e+00  -4.0642e+00  -1.0089e+00  -1.6071e-03   3.8635e-02   5.4669e-07   6.4141e-02   2.3570e-02   1.8666e-01   1.1022e-01   9.4770e-01   8.3208e-02   1.0048e+00   8.2962e-02
+#> 244:    1.0190e+02  -4.1204e+00  -2.3566e+00  -4.0645e+00  -1.0093e+00  -7.0474e-04   3.8502e-02   5.4194e-07   6.4399e-02   2.3591e-02   1.8627e-01   1.0938e-01   9.4615e-01   8.3402e-02   1.0043e+00   8.2891e-02
+#> 245:    1.0189e+02  -4.1204e+00  -2.3575e+00  -4.0649e+00  -1.0093e+00   1.3351e-03   3.8372e-02   5.4266e-07   6.4935e-02   2.3511e-02   1.8609e-01   1.0840e-01   9.4586e-01   8.3393e-02   1.0041e+00   8.2835e-02
+#> 246:    1.0189e+02  -4.1204e+00  -2.3595e+00  -4.0655e+00  -1.0085e+00   4.2316e-03   3.8487e-02   5.4393e-07   6.5284e-02   2.3457e-02   1.8581e-01   1.0811e-01   9.4656e-01   8.3372e-02   1.0036e+00   8.2746e-02
+#> 247:    1.0189e+02  -4.1204e+00  -2.3608e+00  -4.0659e+00  -1.0081e+00   6.1314e-03   3.8249e-02   5.4752e-07   6.5440e-02   2.3455e-02   1.8584e-01   1.0706e-01   9.4795e-01   8.3330e-02   1.0025e+00   8.2710e-02
+#> 248:    1.0189e+02  -4.1204e+00  -2.3617e+00  -4.0662e+00  -1.0084e+00   8.1978e-03   3.8017e-02   5.4713e-07   6.5853e-02   2.3439e-02   1.8637e-01   1.0634e-01   9.4748e-01   8.3377e-02   1.0016e+00   8.2677e-02
+#> 249:    1.0189e+02  -4.1204e+00  -2.3633e+00  -4.0667e+00  -1.0085e+00   9.8011e-03   3.7934e-02   5.5069e-07   6.6442e-02   2.3533e-02   1.8652e-01   1.0606e-01   9.4761e-01   8.3449e-02   1.0009e+00   8.2712e-02
+#> 250:    1.0189e+02  -4.1204e+00  -2.3644e+00  -4.0668e+00  -1.0087e+00   1.0992e-02   3.8199e-02   5.5486e-07   6.6746e-02   2.3638e-02   1.8739e-01   1.0611e-01   9.4838e-01   8.3442e-02   9.9958e-01   8.2692e-02
+#> 251:    1.0189e+02  -4.1204e+00  -2.3644e+00  -4.0671e+00  -1.0097e+00   1.2215e-02   3.8648e-02   5.5448e-07   6.6916e-02   2.3592e-02   1.8753e-01   1.0607e-01   9.4773e-01   8.3511e-02   9.9919e-01   8.2701e-02
+#> 252:    1.0189e+02  -4.1204e+00  -2.3645e+00  -4.0671e+00  -1.0100e+00   1.2881e-02   3.8792e-02   5.5615e-07   6.7323e-02   2.3559e-02   1.8811e-01   1.0641e-01   9.4665e-01   8.3575e-02   9.9809e-01   8.2743e-02
+#> 253:    1.0189e+02  -4.1204e+00  -2.3646e+00  -4.0675e+00  -1.0100e+00   1.3605e-02   3.9013e-02   5.5568e-07   6.7625e-02   2.3432e-02   1.8825e-01   1.0688e-01   9.4424e-01   8.3598e-02   9.9825e-01   8.2702e-02
+#> 254:    1.0189e+02  -4.1204e+00  -2.3642e+00  -4.0677e+00  -1.0101e+00   1.3119e-02   3.8838e-02   5.5231e-07   6.7802e-02   2.3429e-02   1.8849e-01   1.0680e-01   9.4281e-01   8.3706e-02   9.9829e-01   8.2631e-02
+#> 255:    1.0189e+02  -4.1204e+00  -2.3627e+00  -4.0679e+00  -1.0104e+00   1.2490e-02   3.8574e-02   5.4955e-07   6.8395e-02   2.3368e-02   1.8890e-01   1.0661e-01   9.4101e-01   8.3756e-02   9.9798e-01   8.2674e-02
+#> 256:    1.0189e+02  -4.1204e+00  -2.3615e+00  -4.0677e+00  -1.0102e+00   1.1525e-02   3.8502e-02   5.4764e-07   6.8824e-02   2.3405e-02   1.8912e-01   1.0649e-01   9.4109e-01   8.3709e-02   9.9811e-01   8.2698e-02
+#> 257:    1.0189e+02  -4.1204e+00  -2.3604e+00  -4.0673e+00  -1.0104e+00   1.0381e-02   3.8286e-02   5.4694e-07   6.9020e-02   2.3338e-02   1.8925e-01   1.0614e-01   9.4075e-01   8.3695e-02   9.9738e-01   8.2689e-02
+#> 258:    1.0189e+02  -4.1204e+00  -2.3591e+00  -4.0670e+00  -1.0103e+00   8.9559e-03   3.7972e-02   5.4665e-07   6.9077e-02   2.3267e-02   1.8919e-01   1.0590e-01   9.4089e-01   8.3618e-02   9.9742e-01   8.2681e-02
+#> 259:    1.0189e+02  -4.1204e+00  -2.3585e+00  -4.0669e+00  -1.0099e+00   8.6011e-03   3.7874e-02   5.4788e-07   6.9455e-02   2.3264e-02   1.8885e-01   1.0519e-01   9.3952e-01   8.3583e-02   9.9610e-01   8.2650e-02
+#> 260:    1.0189e+02  -4.1204e+00  -2.3584e+00  -4.0666e+00  -1.0098e+00   8.0471e-03   3.7771e-02   5.5294e-07   7.0269e-02   2.3292e-02   1.8877e-01   1.0442e-01   9.3898e-01   8.3519e-02   9.9504e-01   8.2641e-02
+#> 261:    1.0189e+02  -4.1204e+00  -2.3583e+00  -4.0664e+00  -1.0100e+00   7.9344e-03   3.7597e-02   5.5650e-07   7.1087e-02   2.3370e-02   1.8867e-01   1.0399e-01   9.3810e-01   8.3488e-02   9.9419e-01   8.2673e-02
+#> 262:    1.0189e+02  -4.1204e+00  -2.3575e+00  -4.0662e+00  -1.0106e+00   7.2123e-03   3.7203e-02   5.5375e-07   7.1794e-02   2.3393e-02   1.8855e-01   1.0356e-01   9.3773e-01   8.3458e-02   9.9406e-01   8.2739e-02
+#> 263:    1.0189e+02  -4.1204e+00  -2.3564e+00  -4.0659e+00  -1.0112e+00   6.6044e-03   3.6977e-02   5.5306e-07   7.2290e-02   2.3475e-02   1.8847e-01   1.0316e-01   9.3744e-01   8.3383e-02   9.9341e-01   8.2818e-02
+#> 264:    1.0189e+02  -4.1204e+00  -2.3549e+00  -4.0657e+00  -1.0118e+00   6.0119e-03   3.6749e-02   5.5152e-07   7.2896e-02   2.3530e-02   1.8849e-01   1.0277e-01   9.3658e-01   8.3443e-02   9.9248e-01   8.2877e-02
+#> 265:    1.0189e+02  -4.1204e+00  -2.3545e+00  -4.0655e+00  -1.0121e+00   5.6547e-03   3.6562e-02   5.4816e-07   7.3238e-02   2.3560e-02   1.8863e-01   1.0269e-01   9.3597e-01   8.3434e-02   9.9139e-01   8.2879e-02
+#> 266:    1.0189e+02  -4.1204e+00  -2.3545e+00  -4.0651e+00  -1.0121e+00   5.0995e-03   3.6357e-02   5.4458e-07   7.3522e-02   2.3561e-02   1.8883e-01   1.0270e-01   9.3607e-01   8.3407e-02   9.9133e-01   8.2857e-02
+#> 267:    1.0189e+02  -4.1204e+00  -2.3541e+00  -4.0648e+00  -1.0122e+00   4.0105e-03   3.6306e-02   5.4160e-07   7.3833e-02   2.3499e-02   1.8889e-01   1.0317e-01   9.3624e-01   8.3359e-02   9.9151e-01   8.2865e-02
+#> 268:    1.0189e+02  -4.1204e+00  -2.3530e+00  -4.0646e+00  -1.0122e+00   3.0925e-03   3.6248e-02   5.3845e-07   7.4663e-02   2.3413e-02   1.8895e-01   1.0371e-01   9.3624e-01   8.3277e-02   9.9210e-01   8.2909e-02
+#> 269:    1.0189e+02  -4.1204e+00  -2.3518e+00  -4.0643e+00  -1.0123e+00   2.0507e-03   3.6181e-02   5.3602e-07   7.5442e-02   2.3291e-02   1.8886e-01   1.0397e-01   9.3581e-01   8.3260e-02   9.9238e-01   8.2898e-02
+#> 270:    1.0189e+02  -4.1204e+00  -2.3513e+00  -4.0640e+00  -1.0127e+00   1.3309e-03   3.5900e-02   5.3234e-07   7.6677e-02   2.3220e-02   1.8860e-01   1.0367e-01   9.3573e-01   8.3250e-02   9.9169e-01   8.2904e-02
+#> 271:    1.0189e+02  -4.1204e+00  -2.3514e+00  -4.0637e+00  -1.0129e+00   1.1237e-03   3.5608e-02   5.3092e-07   7.7065e-02   2.3102e-02   1.8826e-01   1.0384e-01   9.3645e-01   8.3228e-02   9.9173e-01   8.2896e-02
+#> 272:    1.0189e+02  -4.1204e+00  -2.3510e+00  -4.0639e+00  -1.0134e+00   9.7855e-04   3.5328e-02   5.3100e-07   7.7173e-02   2.3014e-02   1.8817e-01   1.0367e-01   9.3538e-01   8.3266e-02   9.9139e-01   8.2943e-02
+#> 273:    1.0189e+02  -4.1204e+00  -2.3501e+00  -4.0643e+00  -1.0133e+00   1.1275e-03   3.5187e-02   5.3298e-07   7.7467e-02   2.2923e-02   1.8793e-01   1.0344e-01   9.3474e-01   8.3194e-02   9.9249e-01   8.2973e-02
+#> 274:    1.0189e+02  -4.1204e+00  -2.3498e+00  -4.0643e+00  -1.0134e+00   1.4524e-03   3.4996e-02   5.3407e-07   7.7929e-02   2.2819e-02   1.8837e-01   1.0316e-01   9.3399e-01   8.3168e-02   9.9307e-01   8.2981e-02
+#> 275:    1.0189e+02  -4.1204e+00  -2.3500e+00  -4.0641e+00  -1.0136e+00   1.3605e-03   3.4786e-02   5.3269e-07   7.8177e-02   2.2747e-02   1.8855e-01   1.0305e-01   9.3319e-01   8.3205e-02   9.9277e-01   8.2938e-02
+#> 276:    1.0189e+02  -4.1204e+00  -2.3504e+00  -4.0641e+00  -1.0136e+00   1.5273e-03   3.4581e-02   5.3172e-07   7.8495e-02   2.2764e-02   1.8824e-01   1.0297e-01   9.3267e-01   8.3223e-02   9.9204e-01   8.2884e-02
+#> 277:    1.0189e+02  -4.1204e+00  -2.3506e+00  -4.0643e+00  -1.0133e+00   1.2961e-03   3.4373e-02   5.2917e-07   7.8721e-02   2.2791e-02   1.8801e-01   1.0288e-01   9.3253e-01   8.3185e-02   9.9192e-01   8.2854e-02
+#> 278:    1.0189e+02  -4.1204e+00  -2.3508e+00  -4.0643e+00  -1.0129e+00   1.1750e-03   3.4396e-02   5.2693e-07   7.8999e-02   2.2787e-02   1.8793e-01   1.0278e-01   9.3279e-01   8.3113e-02   9.9144e-01   8.2856e-02
+#> 279:    1.0189e+02  -4.1204e+00  -2.3507e+00  -4.0642e+00  -1.0126e+00   1.2755e-03   3.4381e-02   5.2405e-07   7.9351e-02   2.2804e-02   1.8779e-01   1.0255e-01   9.3319e-01   8.3049e-02   9.9099e-01   8.2875e-02
+#> 280:    1.0189e+02  -4.1204e+00  -2.3507e+00  -4.0641e+00  -1.0127e+00   6.3408e-04   3.4519e-02   5.2180e-07   7.9825e-02   2.2801e-02   1.8775e-01   1.0292e-01   9.3349e-01   8.2970e-02   9.9076e-01   8.2918e-02
+#> 281:    1.0189e+02  -4.1204e+00  -2.3508e+00  -4.0639e+00  -1.0124e+00   6.2438e-04   3.4782e-02   5.1859e-07   8.0328e-02   2.2816e-02   1.8757e-01   1.0299e-01   9.3299e-01   8.3025e-02   9.9050e-01   8.2897e-02
+#> 282:    1.0189e+02  -4.1205e+00  -2.3511e+00  -4.0641e+00  -1.0122e+00   1.1770e-03   3.4754e-02   5.1798e-07   8.0649e-02   2.2836e-02   1.8766e-01   1.0297e-01   9.3351e-01   8.2989e-02   9.9171e-01   8.2893e-02
+#> 283:    1.0189e+02  -4.1205e+00  -2.3519e+00  -4.0644e+00  -1.0120e+00   2.1716e-03   3.4711e-02   5.1567e-07   8.0910e-02   2.2836e-02   1.8774e-01   1.0270e-01   9.3288e-01   8.3029e-02   9.9246e-01   8.2853e-02
+#> 284:    1.0189e+02  -4.1205e+00  -2.3524e+00  -4.0647e+00  -1.0115e+00   2.6623e-03   3.4646e-02   5.1350e-07   8.1153e-02   2.2950e-02   1.8775e-01   1.0277e-01   9.3238e-01   8.2990e-02   9.9212e-01   8.2836e-02
+#> 285:    1.0189e+02  -4.1205e+00  -2.3531e+00  -4.0649e+00  -1.0116e+00   3.7830e-03   3.4626e-02   5.1216e-07   8.1058e-02   2.3007e-02   1.8782e-01   1.0270e-01   9.3232e-01   8.3017e-02   9.9094e-01   8.2829e-02
+#> 286:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0651e+00  -1.0111e+00   5.1752e-03   3.4599e-02   5.0989e-07   8.0970e-02   2.3004e-02   1.8757e-01   1.0254e-01   9.3280e-01   8.3006e-02   9.9130e-01   8.2818e-02
+#> 287:    1.0189e+02  -4.1205e+00  -2.3541e+00  -4.0654e+00  -1.0112e+00   6.3747e-03   3.4592e-02   5.0930e-07   8.1117e-02   2.2959e-02   1.8756e-01   1.0222e-01   9.3212e-01   8.3146e-02   9.9183e-01   8.2863e-02
+#> 288:    1.0189e+02  -4.1205e+00  -2.3540e+00  -4.0656e+00  -1.0115e+00   6.5668e-03   3.4598e-02   5.0976e-07   8.1125e-02   2.2895e-02   1.8782e-01   1.0183e-01   9.3310e-01   8.3169e-02   9.9404e-01   8.2836e-02
+#> 289:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0658e+00  -1.0119e+00   7.3521e-03   3.4525e-02   5.1097e-07   8.1097e-02   2.2869e-02   1.8753e-01   1.0126e-01   9.3336e-01   8.3244e-02   9.9435e-01   8.2833e-02
+#> 290:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0659e+00  -1.0122e+00   7.5226e-03   3.4377e-02   5.0846e-07   8.1212e-02   2.2831e-02   1.8724e-01   1.0073e-01   9.3292e-01   8.3261e-02   9.9415e-01   8.2837e-02
+#> 291:    1.0189e+02  -4.1205e+00  -2.3536e+00  -4.0659e+00  -1.0122e+00   7.2889e-03   3.4263e-02   5.0823e-07   8.1182e-02   2.2801e-02   1.8711e-01   1.0056e-01   9.3309e-01   8.3300e-02   9.9427e-01   8.2805e-02
+#> 292:    1.0189e+02  -4.1205e+00  -2.3531e+00  -4.0659e+00  -1.0123e+00   7.1827e-03   3.4146e-02   5.0825e-07   8.1696e-02   2.2760e-02   1.8703e-01   1.0039e-01   9.3324e-01   8.3306e-02   9.9379e-01   8.2784e-02
+#> 293:    1.0189e+02  -4.1205e+00  -2.3528e+00  -4.0660e+00  -1.0125e+00   7.7142e-03   3.4126e-02   5.0971e-07   8.2026e-02   2.2705e-02   1.8721e-01   1.0036e-01   9.3316e-01   8.3316e-02   9.9357e-01   8.2756e-02
+#> 294:    1.0188e+02  -4.1204e+00  -2.3529e+00  -4.0663e+00  -1.0126e+00   8.5146e-03   3.4314e-02   5.0823e-07   8.2197e-02   2.2608e-02   1.8743e-01   1.0009e-01   9.3356e-01   8.3308e-02   9.9367e-01   8.2719e-02
+#> 295:    1.0188e+02  -4.1204e+00  -2.3532e+00  -4.0666e+00  -1.0123e+00   9.2199e-03   3.4472e-02   5.0839e-07   8.2550e-02   2.2529e-02   1.8745e-01   9.9731e-02   9.3393e-01   8.3255e-02   9.9373e-01   8.2686e-02
+#> 296:    1.0188e+02  -4.1204e+00  -2.3537e+00  -4.0667e+00  -1.0121e+00   9.7869e-03   3.4678e-02   5.0983e-07   8.3059e-02   2.2497e-02   1.8729e-01   9.9260e-02   9.3395e-01   8.3198e-02   9.9300e-01   8.2681e-02
+#> 297:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0670e+00  -1.0118e+00   1.0166e-02   3.4957e-02   5.1049e-07   8.3080e-02   2.2448e-02   1.8710e-01   9.8969e-02   9.3321e-01   8.3178e-02   9.9255e-01   8.2663e-02
+#> 298:    1.0188e+02  -4.1204e+00  -2.3544e+00  -4.0673e+00  -1.0117e+00   1.0649e-02   3.5259e-02   5.1103e-07   8.3179e-02   2.2383e-02   1.8704e-01   9.8442e-02   9.3227e-01   8.3199e-02   9.9266e-01   8.2646e-02
+#> 299:    1.0188e+02  -4.1204e+00  -2.3542e+00  -4.0676e+00  -1.0117e+00   1.0927e-02   3.5438e-02   5.1128e-07   8.3068e-02   2.2378e-02   1.8699e-01   9.8203e-02   9.3263e-01   8.3138e-02   9.9353e-01   8.2671e-02
+#> 300:    1.0188e+02  -4.1204e+00  -2.3544e+00  -4.0678e+00  -1.0116e+00   1.1083e-02   3.5694e-02   5.1107e-07   8.2896e-02   2.2344e-02   1.8733e-01   9.7775e-02   9.3179e-01   8.3124e-02   9.9379e-01   8.2657e-02
+#> 301:    1.0188e+02  -4.1204e+00  -2.3542e+00  -4.0680e+00  -1.0115e+00   1.0992e-02   3.5896e-02   5.1262e-07   8.2816e-02   2.2349e-02   1.8753e-01   9.7431e-02   9.3209e-01   8.3086e-02   9.9388e-01   8.2674e-02
+#> 302:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0681e+00  -1.0113e+00   1.0410e-02   3.6050e-02   5.1256e-07   8.2817e-02   2.2308e-02   1.8734e-01   9.7153e-02   9.3221e-01   8.3073e-02   9.9402e-01   8.2670e-02
+#> 303:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0681e+00  -1.0112e+00   1.0301e-02   3.6150e-02   5.1127e-07   8.2826e-02   2.2325e-02   1.8730e-01   9.6656e-02   9.3192e-01   8.3040e-02   9.9393e-01   8.2665e-02
+#> 304:    1.0188e+02  -4.1204e+00  -2.3536e+00  -4.0681e+00  -1.0113e+00   1.0235e-02   3.6393e-02   5.1176e-07   8.2606e-02   2.2353e-02   1.8724e-01   9.6171e-02   9.3161e-01   8.3068e-02   9.9361e-01   8.2698e-02
+#> 305:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0683e+00  -1.0112e+00   9.9655e-03   3.6369e-02   5.1442e-07   8.2520e-02   2.2378e-02   1.8707e-01   9.5656e-02   9.3113e-01   8.3109e-02   9.9338e-01   8.2731e-02
+#> 306:    1.0188e+02  -4.1204e+00  -2.3531e+00  -4.0684e+00  -1.0110e+00   9.9701e-03   3.6346e-02   5.1546e-07   8.2789e-02   2.2360e-02   1.8702e-01   9.5116e-02   9.3102e-01   8.3065e-02   9.9405e-01   8.2761e-02
+#> 307:    1.0188e+02  -4.1204e+00  -2.3530e+00  -4.0684e+00  -1.0112e+00   1.0194e-02   3.6300e-02   5.1196e-07   8.3035e-02   2.2381e-02   1.8704e-01   9.4760e-02   9.3082e-01   8.3003e-02   9.9410e-01   8.2779e-02
+#> 308:    1.0189e+02  -4.1204e+00  -2.3530e+00  -4.0685e+00  -1.0109e+00   9.9531e-03   3.6400e-02   5.1140e-07   8.3511e-02   2.2334e-02   1.8726e-01   9.4494e-02   9.3151e-01   8.2910e-02   9.9484e-01   8.2760e-02
+#> 309:    1.0188e+02  -4.1204e+00  -2.3530e+00  -4.0685e+00  -1.0107e+00   1.0089e-02   3.6382e-02   5.1081e-07   8.3917e-02   2.2276e-02   1.8728e-01   9.4285e-02   9.3133e-01   8.2875e-02   9.9545e-01   8.2757e-02
+#> 310:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0685e+00  -1.0105e+00   1.0805e-02   3.6375e-02   5.1041e-07   8.4245e-02   2.2246e-02   1.8753e-01   9.3894e-02   9.3052e-01   8.2899e-02   9.9500e-01   8.2743e-02
+#> 311:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0685e+00  -1.0103e+00   1.1449e-02   3.6311e-02   5.0884e-07   8.4434e-02   2.2231e-02   1.8783e-01   9.3542e-02   9.3039e-01   8.2864e-02   9.9458e-01   8.2733e-02
+#> 312:    1.0188e+02  -4.1204e+00  -2.3535e+00  -4.0685e+00  -1.0102e+00   1.2173e-02   3.6373e-02   5.0821e-07   8.4730e-02   2.2176e-02   1.8769e-01   9.3317e-02   9.2982e-01   8.2916e-02   9.9438e-01   8.2740e-02
+#> 313:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0688e+00  -1.0103e+00   1.2812e-02   3.6558e-02   5.0751e-07   8.5211e-02   2.2131e-02   1.8754e-01   9.3387e-02   9.2962e-01   8.2892e-02   9.9458e-01   8.2741e-02
+#> 314:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0690e+00  -1.0103e+00   1.3241e-02   3.6680e-02   5.0887e-07   8.5667e-02   2.2079e-02   1.8772e-01   9.3442e-02   9.2941e-01   8.2947e-02   9.9511e-01   8.2743e-02
+#> 315:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0691e+00  -1.0104e+00   1.3699e-02   3.6924e-02   5.0965e-07   8.5865e-02   2.2028e-02   1.8766e-01   9.3264e-02   9.2904e-01   8.2986e-02   9.9543e-01   8.2763e-02
+#> 316:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0693e+00  -1.0103e+00   1.4121e-02   3.7218e-02   5.1041e-07   8.6216e-02   2.2076e-02   1.8773e-01   9.3035e-02   9.2917e-01   8.3013e-02   9.9486e-01   8.2782e-02
+#> 317:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0694e+00  -1.0102e+00   1.4588e-02   3.7304e-02   5.0994e-07   8.6513e-02   2.2128e-02   1.8773e-01   9.2766e-02   9.2943e-01   8.3025e-02   9.9441e-01   8.2779e-02
+#> 318:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0693e+00  -1.0101e+00   1.4714e-02   3.7538e-02   5.0773e-07   8.6801e-02   2.2128e-02   1.8767e-01   9.2698e-02   9.2907e-01   8.3052e-02   9.9378e-01   8.2780e-02
+#> 319:    1.0187e+02  -4.1204e+00  -2.3533e+00  -4.0692e+00  -1.0099e+00   1.4582e-02   3.7563e-02   5.0550e-07   8.6669e-02   2.2135e-02   1.8775e-01   9.2604e-02   9.2925e-01   8.3042e-02   9.9356e-01   8.2773e-02
+#> 320:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0690e+00  -1.0102e+00   1.4511e-02   3.7580e-02   5.0281e-07   8.6617e-02   2.2121e-02   1.8780e-01   9.2508e-02   9.3001e-01   8.3032e-02   9.9322e-01   8.2780e-02
+#> 321:    1.0187e+02  -4.1204e+00  -2.3534e+00  -4.0688e+00  -1.0100e+00   1.4288e-02   3.7624e-02   5.0172e-07   8.6311e-02   2.2115e-02   1.8783e-01   9.2445e-02   9.3011e-01   8.3054e-02   9.9288e-01   8.2772e-02
+#> 322:    1.0187e+02  -4.1204e+00  -2.3532e+00  -4.0687e+00  -1.0098e+00   1.3834e-02   3.7497e-02   5.0086e-07   8.6187e-02   2.2111e-02   1.8791e-01   9.2699e-02   9.3037e-01   8.3069e-02   9.9284e-01   8.2773e-02
+#> 323:    1.0187e+02  -4.1204e+00  -2.3524e+00  -4.0683e+00  -1.0097e+00   1.2977e-02   3.7420e-02   4.9925e-07   8.6082e-02   2.2084e-02   1.8818e-01   9.3123e-02   9.3036e-01   8.3012e-02   9.9265e-01   8.2813e-02
+#> 324:    1.0187e+02  -4.1204e+00  -2.3523e+00  -4.0682e+00  -1.0096e+00   1.2679e-02   3.7420e-02   4.9836e-07   8.5721e-02   2.2071e-02   1.8829e-01   9.3535e-02   9.3062e-01   8.3011e-02   9.9241e-01   8.2827e-02
+#> 325:    1.0187e+02  -4.1204e+00  -2.3520e+00  -4.0680e+00  -1.0094e+00   1.2196e-02   3.7298e-02   4.9735e-07   8.5411e-02   2.2028e-02   1.8848e-01   9.3706e-02   9.3043e-01   8.3020e-02   9.9256e-01   8.2826e-02
+#> 326:    1.0187e+02  -4.1204e+00  -2.3517e+00  -4.0678e+00  -1.0091e+00   1.1924e-02   3.7185e-02   4.9661e-07   8.5453e-02   2.1983e-02   1.8830e-01   9.3688e-02   9.3050e-01   8.2996e-02   9.9284e-01   8.2806e-02
+#> 327:    1.0187e+02  -4.1204e+00  -2.3516e+00  -4.0677e+00  -1.0090e+00   1.1449e-02   3.7155e-02   4.9755e-07   8.5761e-02   2.1967e-02   1.8819e-01   9.3936e-02   9.3052e-01   8.2912e-02   9.9245e-01   8.2806e-02
+#> 328:    1.0187e+02  -4.1204e+00  -2.3514e+00  -4.0675e+00  -1.0089e+00   1.0758e-02   3.7146e-02   4.9892e-07   8.6019e-02   2.1971e-02   1.8806e-01   9.4361e-02   9.3070e-01   8.2833e-02   9.9182e-01   8.2840e-02
+#> 329:    1.0187e+02  -4.1204e+00  -2.3515e+00  -4.0672e+00  -1.0087e+00   1.0256e-02   3.7342e-02   5.0019e-07   8.5965e-02   2.1989e-02   1.8796e-01   9.4614e-02   9.3067e-01   8.2818e-02   9.9159e-01   8.2858e-02
+#> 330:    1.0187e+02  -4.1204e+00  -2.3520e+00  -4.0670e+00  -1.0086e+00   1.0021e-02   3.7376e-02   4.9911e-07   8.6124e-02   2.1978e-02   1.8796e-01   9.4836e-02   9.3036e-01   8.2819e-02   9.9148e-01   8.2866e-02
+#> 331:    1.0187e+02  -4.1204e+00  -2.3521e+00  -4.0668e+00  -1.0086e+00   9.5790e-03   3.7296e-02   4.9753e-07   8.6122e-02   2.1951e-02   1.8782e-01   9.5042e-02   9.3064e-01   8.2783e-02   9.9196e-01   8.2863e-02
+#> 332:    1.0187e+02  -4.1204e+00  -2.3523e+00  -4.0667e+00  -1.0085e+00   9.2971e-03   3.7221e-02   4.9729e-07   8.6215e-02   2.1952e-02   1.8787e-01   9.5082e-02   9.3103e-01   8.2782e-02   9.9224e-01   8.2861e-02
+#> 333:    1.0187e+02  -4.1204e+00  -2.3524e+00  -4.0667e+00  -1.0084e+00   9.2591e-03   3.7097e-02   4.9556e-07   8.6302e-02   2.1922e-02   1.8792e-01   9.5155e-02   9.3058e-01   8.2798e-02   9.9202e-01   8.2831e-02
+#> 334:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0667e+00  -1.0082e+00   9.5799e-03   3.6997e-02   4.9398e-07   8.6409e-02   2.1911e-02   1.8792e-01   9.5231e-02   9.3035e-01   8.2810e-02   9.9157e-01   8.2803e-02
+#> 335:    1.0187e+02  -4.1204e+00  -2.3529e+00  -4.0667e+00  -1.0080e+00   9.5724e-03   3.6912e-02   4.9206e-07   8.6310e-02   2.1923e-02   1.8791e-01   9.5379e-02   9.3054e-01   8.2759e-02   9.9143e-01   8.2776e-02
+#> 336:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0667e+00  -1.0082e+00   9.5794e-03   3.6983e-02   4.9255e-07   8.6282e-02   2.1882e-02   1.8789e-01   9.5422e-02   9.3064e-01   8.2705e-02   9.9138e-01   8.2778e-02
+#> 337:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0669e+00  -1.0083e+00   1.0008e-02   3.6943e-02   4.9278e-07   8.6483e-02   2.1844e-02   1.8794e-01   9.5240e-02   9.2981e-01   8.2753e-02   9.9100e-01   8.2774e-02
+#> 338:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0669e+00  -1.0084e+00   1.0297e-02   3.6869e-02   4.9309e-07   8.6547e-02   2.1803e-02   1.8808e-01   9.5094e-02   9.2978e-01   8.2764e-02   9.9113e-01   8.2759e-02
+#> 339:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0669e+00  -1.0083e+00   1.0465e-02   3.6822e-02   4.9257e-07   8.6779e-02   2.1769e-02   1.8813e-01   9.5062e-02   9.3020e-01   8.2702e-02   9.9135e-01   8.2750e-02
+#> 340:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0668e+00  -1.0083e+00   1.0321e-02   3.6733e-02   4.9228e-07   8.7033e-02   2.1721e-02   1.8827e-01   9.4862e-02   9.3062e-01   8.2698e-02   9.9195e-01   8.2729e-02
+#> 341:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0670e+00  -1.0085e+00   1.0501e-02   3.6671e-02   4.9236e-07   8.7297e-02   2.1733e-02   1.8820e-01   9.4558e-02   9.3121e-01   8.2677e-02   9.9238e-01   8.2713e-02
+#> 342:    1.0187e+02  -4.1204e+00  -2.3534e+00  -4.0670e+00  -1.0085e+00   1.0818e-02   3.6715e-02   4.9084e-07   8.7450e-02   2.1726e-02   1.8801e-01   9.4252e-02   9.3160e-01   8.2657e-02   9.9232e-01   8.2708e-02
+#> 343:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0670e+00  -1.0087e+00   1.1046e-02   3.6784e-02   4.8872e-07   8.7718e-02   2.1718e-02   1.8799e-01   9.3887e-02   9.3187e-01   8.2645e-02   9.9225e-01   8.2722e-02
+#> 344:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0670e+00  -1.0087e+00   1.0652e-02   3.6736e-02   4.8852e-07   8.7725e-02   2.1730e-02   1.8792e-01   9.3731e-02   9.3202e-01   8.2618e-02   9.9218e-01   8.2712e-02
+#> 345:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0669e+00  -1.0090e+00   1.0445e-02   3.6714e-02   4.8690e-07   8.7782e-02   2.1751e-02   1.8798e-01   9.3450e-02   9.3223e-01   8.2582e-02   9.9239e-01   8.2706e-02
+#> 346:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0668e+00  -1.0089e+00   1.0173e-02   3.6743e-02   4.8656e-07   8.7733e-02   2.1810e-02   1.8814e-01   9.3151e-02   9.3257e-01   8.2575e-02   9.9181e-01   8.2708e-02
+#> 347:    1.0187e+02  -4.1205e+00  -2.3532e+00  -4.0668e+00  -1.0089e+00   9.9457e-03   3.6808e-02   4.8756e-07   8.7948e-02   2.1819e-02   1.8813e-01   9.3040e-02   9.3255e-01   8.2599e-02   9.9124e-01   8.2727e-02
+#> 348:    1.0187e+02  -4.1205e+00  -2.3534e+00  -4.0667e+00  -1.0090e+00   9.8498e-03   3.6967e-02   4.8883e-07   8.7998e-02   2.1841e-02   1.8820e-01   9.3180e-02   9.3259e-01   8.2612e-02   9.9148e-01   8.2750e-02
+#> 349:    1.0187e+02  -4.1205e+00  -2.3535e+00  -4.0668e+00  -1.0091e+00   9.6211e-03   3.6891e-02   4.8867e-07   8.8006e-02   2.1930e-02   1.8819e-01   9.3390e-02   9.3232e-01   8.2612e-02   9.9073e-01   8.2738e-02
+#> 350:    1.0187e+02  -4.1205e+00  -2.3534e+00  -4.0669e+00  -1.0090e+00   9.7176e-03   3.6813e-02   4.8925e-07   8.7923e-02   2.1964e-02   1.8820e-01   9.3434e-02   9.3224e-01   8.2600e-02   9.9031e-01   8.2734e-02
+#> 351:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0669e+00  -1.0090e+00   9.6652e-03   3.6769e-02   4.8873e-07   8.7985e-02   2.2046e-02   1.8814e-01   9.3529e-02   9.3220e-01   8.2558e-02   9.8978e-01   8.2728e-02
+#> 352:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0669e+00  -1.0089e+00   9.8745e-03   3.6732e-02   4.8969e-07   8.8016e-02   2.2094e-02   1.8799e-01   9.3644e-02   9.3168e-01   8.2577e-02   9.8913e-01   8.2722e-02
+#> 353:    1.0187e+02  -4.1204e+00  -2.3537e+00  -4.0669e+00  -1.0088e+00   9.7530e-03   3.6700e-02   4.9008e-07   8.7949e-02   2.2116e-02   1.8798e-01   9.3769e-02   9.3165e-01   8.2559e-02   9.8871e-01   8.2711e-02
+#> 354:    1.0187e+02  -4.1204e+00  -2.3538e+00  -4.0667e+00  -1.0089e+00   9.4103e-03   3.6653e-02   4.9045e-07   8.7894e-02   2.2118e-02   1.8793e-01   9.3872e-02   9.3188e-01   8.2551e-02   9.8887e-01   8.2692e-02
+#> 355:    1.0187e+02  -4.1204e+00  -2.3540e+00  -4.0666e+00  -1.0088e+00   9.1684e-03   3.6536e-02   4.9125e-07   8.7920e-02   2.2107e-02   1.8812e-01   9.4123e-02   9.3223e-01   8.2517e-02   9.8893e-01   8.2687e-02
+#> 356:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0664e+00  -1.0086e+00   8.9025e-03   3.6431e-02   4.9325e-07   8.7949e-02   2.2110e-02   1.8827e-01   9.4135e-02   9.3252e-01   8.2503e-02   9.8907e-01   8.2649e-02
+#> 357:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0663e+00  -1.0085e+00   8.6757e-03   3.6417e-02   4.9505e-07   8.8052e-02   2.2096e-02   1.8848e-01   9.4192e-02   9.3281e-01   8.2490e-02   9.8957e-01   8.2624e-02
+#> 358:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0661e+00  -1.0084e+00   8.1812e-03   3.6349e-02   4.9610e-07   8.8344e-02   2.2104e-02   1.8844e-01   9.4129e-02   9.3294e-01   8.2493e-02   9.8977e-01   8.2614e-02
+#> 359:    1.0187e+02  -4.1204e+00  -2.3541e+00  -4.0659e+00  -1.0083e+00   8.0905e-03   3.6475e-02   4.9675e-07   8.8647e-02   2.2116e-02   1.8831e-01   9.4146e-02   9.3322e-01   8.2473e-02   9.8978e-01   8.2622e-02
+#> 360:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0657e+00  -1.0082e+00   7.8390e-03   3.6468e-02   4.9649e-07   8.8981e-02   2.2120e-02   1.8815e-01   9.4249e-02   9.3361e-01   8.2430e-02   9.8997e-01   8.2616e-02
+#> 361:    1.0187e+02  -4.1204e+00  -2.3545e+00  -4.0656e+00  -1.0083e+00   7.9104e-03   3.6434e-02   4.9737e-07   8.9447e-02   2.2133e-02   1.8808e-01   9.4085e-02   9.3387e-01   8.2426e-02   9.9025e-01   8.2616e-02
+#> 362:    1.0187e+02  -4.1204e+00  -2.3547e+00  -4.0655e+00  -1.0087e+00   7.6341e-03   3.6428e-02   4.9748e-07   8.9872e-02   2.2148e-02   1.8805e-01   9.4025e-02   9.3407e-01   8.2456e-02   9.9070e-01   8.2609e-02
+#> 363:    1.0187e+02  -4.1204e+00  -2.3546e+00  -4.0653e+00  -1.0087e+00   7.2351e-03   3.6392e-02   4.9842e-07   9.0125e-02   2.2179e-02   1.8818e-01   9.4157e-02   9.3437e-01   8.2439e-02   9.9051e-01   8.2626e-02
+#> 364:    1.0187e+02  -4.1204e+00  -2.3543e+00  -4.0651e+00  -1.0089e+00   6.7851e-03   3.6303e-02   4.9890e-07   9.0448e-02   2.2189e-02   1.8831e-01   9.4432e-02   9.3513e-01   8.2433e-02   9.9051e-01   8.2655e-02
+#> 365:    1.0187e+02  -4.1204e+00  -2.3538e+00  -4.0650e+00  -1.0089e+00   6.2935e-03   3.6267e-02   4.9829e-07   9.0718e-02   2.2204e-02   1.8818e-01   9.4507e-02   9.3580e-01   8.2387e-02   9.9049e-01   8.2678e-02
+#> 366:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0649e+00  -1.0088e+00   5.8910e-03   3.6339e-02   4.9911e-07   9.0727e-02   2.2231e-02   1.8801e-01   9.4683e-02   9.3567e-01   8.2359e-02   9.8997e-01   8.2681e-02
+#> 367:    1.0187e+02  -4.1204e+00  -2.3533e+00  -4.0649e+00  -1.0088e+00   5.8610e-03   3.6366e-02   5.0123e-07   9.0732e-02   2.2245e-02   1.8793e-01   9.4666e-02   9.3556e-01   8.2339e-02   9.8945e-01   8.2691e-02
+#> 368:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0650e+00  -1.0088e+00   6.1043e-03   3.6424e-02   5.0107e-07   9.0729e-02   2.2248e-02   1.8780e-01   9.4599e-02   9.3554e-01   8.2315e-02   9.8903e-01   8.2705e-02
+#> 369:    1.0187e+02  -4.1204e+00  -2.3530e+00  -4.0650e+00  -1.0088e+00   6.1767e-03   3.6436e-02   5.0046e-07   9.0617e-02   2.2226e-02   1.8787e-01   9.4410e-02   9.3504e-01   8.2361e-02   9.8843e-01   8.2694e-02
+#> 370:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0651e+00  -1.0088e+00   6.2532e-03   3.6467e-02   5.0024e-07   9.0741e-02   2.2223e-02   1.8794e-01   9.4288e-02   9.3472e-01   8.2374e-02   9.8781e-01   8.2703e-02
+#> 371:    1.0186e+02  -4.1204e+00  -2.3525e+00  -4.0652e+00  -1.0088e+00   6.2117e-03   3.6465e-02   4.9964e-07   9.0904e-02   2.2220e-02   1.8788e-01   9.4310e-02   9.3470e-01   8.2367e-02   9.8730e-01   8.2731e-02
+#> 372:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0651e+00  -1.0089e+00   6.1363e-03   3.6367e-02   5.0037e-07   9.1177e-02   2.2230e-02   1.8783e-01   9.4288e-02   9.3496e-01   8.2365e-02   9.8699e-01   8.2729e-02
+#> 373:    1.0186e+02  -4.1204e+00  -2.3523e+00  -4.0650e+00  -1.0089e+00   6.0384e-03   3.6353e-02   5.0195e-07   9.1402e-02   2.2219e-02   1.8764e-01   9.4343e-02   9.3478e-01   8.2430e-02   9.8641e-01   8.2747e-02
+#> 374:    1.0186e+02  -4.1204e+00  -2.3523e+00  -4.0650e+00  -1.0091e+00   5.9821e-03   3.6377e-02   5.0424e-07   9.1532e-02   2.2243e-02   1.8761e-01   9.4219e-02   9.3466e-01   8.2418e-02   9.8614e-01   8.2735e-02
+#> 375:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0649e+00  -1.0091e+00   5.8843e-03   3.6358e-02   5.0568e-07   9.1556e-02   2.2250e-02   1.8768e-01   9.4173e-02   9.3432e-01   8.2413e-02   9.8592e-01   8.2728e-02
+#> 376:    1.0186e+02  -4.1204e+00  -2.3526e+00  -4.0649e+00  -1.0090e+00   5.7256e-03   3.6406e-02   5.0673e-07   9.1590e-02   2.2260e-02   1.8765e-01   9.4159e-02   9.3417e-01   8.2400e-02   9.8565e-01   8.2701e-02
+#> 377:    1.0186e+02  -4.1204e+00  -2.3527e+00  -4.0647e+00  -1.0091e+00   5.2782e-03   3.6397e-02   5.0740e-07   9.1564e-02   2.2263e-02   1.8765e-01   9.4084e-02   9.3434e-01   8.2395e-02   9.8563e-01   8.2680e-02
+#> 378:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0646e+00  -1.0091e+00   4.8184e-03   3.6478e-02   5.0759e-07   9.1590e-02   2.2213e-02   1.8766e-01   9.4162e-02   9.3432e-01   8.2353e-02   9.8595e-01   8.2681e-02
+#> 379:    1.0186e+02  -4.1204e+00  -2.3521e+00  -4.0646e+00  -1.0089e+00   4.4861e-03   3.6557e-02   5.0710e-07   9.1595e-02   2.2159e-02   1.8767e-01   9.3894e-02   9.3395e-01   8.2341e-02   9.8636e-01   8.2671e-02
+#> 380:    1.0186e+02  -4.1204e+00  -2.3517e+00  -4.0644e+00  -1.0089e+00   3.9799e-03   3.6543e-02   5.0682e-07   9.1532e-02   2.2143e-02   1.8768e-01   9.3854e-02   9.3372e-01   8.2331e-02   9.8640e-01   8.2678e-02
+#> 381:    1.0186e+02  -4.1204e+00  -2.3515e+00  -4.0643e+00  -1.0089e+00   3.6269e-03   3.6531e-02   5.0770e-07   9.1364e-02   2.2157e-02   1.8768e-01   9.3897e-02   9.3383e-01   8.2326e-02   9.8630e-01   8.2675e-02
+#> 382:    1.0186e+02  -4.1204e+00  -2.3513e+00  -4.0643e+00  -1.0090e+00   3.1691e-03   3.6469e-02   5.0860e-07   9.1318e-02   2.2188e-02   1.8767e-01   9.3787e-02   9.3433e-01   8.2306e-02   9.8643e-01   8.2670e-02
+#> 383:    1.0186e+02  -4.1204e+00  -2.3508e+00  -4.0642e+00  -1.0090e+00   2.6209e-03   3.6416e-02   5.0893e-07   9.1374e-02   2.2165e-02   1.8759e-01   9.3654e-02   9.3443e-01   8.2289e-02   9.8663e-01   8.2672e-02
+#> 384:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0640e+00  -1.0090e+00   2.1556e-03   3.6403e-02   5.0834e-07   9.1550e-02   2.2148e-02   1.8750e-01   9.3422e-02   9.3444e-01   8.2277e-02   9.8639e-01   8.2670e-02
+#> 385:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0638e+00  -1.0089e+00   1.7048e-03   3.6391e-02   5.0788e-07   9.1717e-02   2.2160e-02   1.8746e-01   9.3178e-02   9.3457e-01   8.2261e-02   9.8616e-01   8.2636e-02
+#> 386:    1.0186e+02  -4.1204e+00  -2.3504e+00  -4.0637e+00  -1.0089e+00   1.4309e-03   3.6372e-02   5.0847e-07   9.1895e-02   2.2157e-02   1.8754e-01   9.2918e-02   9.3439e-01   8.2246e-02   9.8601e-01   8.2617e-02
+#> 387:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0636e+00  -1.0089e+00   1.3524e-03   3.6446e-02   5.0896e-07   9.2022e-02   2.2182e-02   1.8768e-01   9.2684e-02   9.3470e-01   8.2216e-02   9.8593e-01   8.2620e-02
+#> 388:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0635e+00  -1.0089e+00   1.2887e-03   3.6478e-02   5.0904e-07   9.2117e-02   2.2174e-02   1.8761e-01   9.2506e-02   9.3463e-01   8.2221e-02   9.8563e-01   8.2609e-02
+#> 389:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0635e+00  -1.0089e+00   1.2044e-03   3.6479e-02   5.0969e-07   9.2068e-02   2.2180e-02   1.8751e-01   9.2308e-02   9.3438e-01   8.2241e-02   9.8516e-01   8.2592e-02
+#> 390:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0635e+00  -1.0087e+00   1.1442e-03   3.6497e-02   5.0878e-07   9.1995e-02   2.2156e-02   1.8744e-01   9.2169e-02   9.3410e-01   8.2257e-02   9.8511e-01   8.2581e-02
+#> 391:    1.0186e+02  -4.1204e+00  -2.3508e+00  -4.0635e+00  -1.0089e+00   1.0925e-03   3.6454e-02   5.0876e-07   9.1945e-02   2.2177e-02   1.8739e-01   9.1989e-02   9.3439e-01   8.2254e-02   9.8472e-01   8.2579e-02
+#> 392:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0633e+00  -1.0091e+00   7.9940e-04   3.6417e-02   5.0874e-07   9.1956e-02   2.2185e-02   1.8730e-01   9.1977e-02   9.3422e-01   8.2244e-02   9.8463e-01   8.2589e-02
+#> 393:    1.0186e+02  -4.1204e+00  -2.3504e+00  -4.0632e+00  -1.0093e+00   4.2112e-04   3.6433e-02   5.0843e-07   9.1868e-02   2.2211e-02   1.8739e-01   9.2106e-02   9.3464e-01   8.2217e-02   9.8458e-01   8.2594e-02
+#> 394:    1.0186e+02  -4.1204e+00  -2.3502e+00  -4.0631e+00  -1.0093e+00   1.4926e-04   3.6534e-02   5.0862e-07   9.1713e-02   2.2244e-02   1.8735e-01   9.2105e-02   9.3454e-01   8.2239e-02   9.8410e-01   8.2601e-02
+#> 395:    1.0186e+02  -4.1204e+00  -2.3499e+00  -4.0630e+00  -1.0095e+00   5.2506e-05   3.6667e-02   5.0955e-07   9.1548e-02   2.2269e-02   1.8733e-01   9.2151e-02   9.3450e-01   8.2256e-02   9.8389e-01   8.2612e-02
+#> 396:    1.0186e+02  -4.1204e+00  -2.3497e+00  -4.0630e+00  -1.0097e+00   1.6581e-05   3.6789e-02   5.1002e-07   9.1431e-02   2.2299e-02   1.8742e-01   9.2120e-02   9.3450e-01   8.2252e-02   9.8367e-01   8.2620e-02
+#> 397:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0629e+00  -1.0098e+00  -5.0310e-05   3.6860e-02   5.0949e-07   9.1311e-02   2.2323e-02   1.8738e-01   9.2130e-02   9.3467e-01   8.2250e-02   9.8388e-01   8.2628e-02
+#> 398:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0629e+00  -1.0097e+00  -1.4918e-04   3.6902e-02   5.0935e-07   9.1211e-02   2.2330e-02   1.8747e-01   9.2144e-02   9.3478e-01   8.2260e-02   9.8420e-01   8.2632e-02
+#> 399:    1.0186e+02  -4.1205e+00  -2.3497e+00  -4.0628e+00  -1.0097e+00  -2.2152e-04   3.6932e-02   5.0927e-07   9.1209e-02   2.2377e-02   1.8750e-01   9.2136e-02   9.3481e-01   8.2286e-02   9.8431e-01   8.2622e-02
+#> 400:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0629e+00  -1.0097e+00   3.2878e-05   3.6943e-02   5.0892e-07   9.1092e-02   2.2388e-02   1.8752e-01   9.2072e-02   9.3534e-01   8.2276e-02   9.8472e-01   8.2615e-02
+#> 401:    1.0186e+02  -4.1205e+00  -2.3501e+00  -4.0630e+00  -1.0097e+00   2.6776e-04   3.6950e-02   5.0860e-07   9.1038e-02   2.2395e-02   1.8740e-01   9.1911e-02   9.3515e-01   8.2331e-02   9.8459e-01   8.2615e-02
+#> 402:    1.0186e+02  -4.1205e+00  -2.3502e+00  -4.0632e+00  -1.0097e+00   3.9988e-04   3.6912e-02   5.0849e-07   9.0944e-02   2.2401e-02   1.8737e-01   9.1701e-02   9.3494e-01   8.2353e-02   9.8479e-01   8.2609e-02
+#> 403:    1.0186e+02  -4.1205e+00  -2.3503e+00  -4.0633e+00  -1.0098e+00   4.9714e-04   3.6935e-02   5.0805e-07   9.0895e-02   2.2404e-02   1.8741e-01   9.1609e-02   9.3444e-01   8.2372e-02   9.8505e-01   8.2638e-02
+#> 404:    1.0186e+02  -4.1205e+00  -2.3504e+00  -4.0633e+00  -1.0100e+00   5.8465e-04   3.6978e-02   5.0889e-07   9.0862e-02   2.2453e-02   1.8746e-01   9.1650e-02   9.3491e-01   8.2364e-02   9.8484e-01   8.2653e-02
+#> 405:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0634e+00  -1.0099e+00   5.5970e-04   3.6999e-02   5.0964e-07   9.0930e-02   2.2480e-02   1.8742e-01   9.1823e-02   9.3458e-01   8.2371e-02   9.8465e-01   8.2670e-02
+#> 406:    1.0186e+02  -4.1205e+00  -2.3507e+00  -4.0634e+00  -1.0098e+00   5.4464e-04   3.7123e-02   5.1046e-07   9.1008e-02   2.2478e-02   1.8749e-01   9.1930e-02   9.3449e-01   8.2361e-02   9.8440e-01   8.2666e-02
+#> 407:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0097e+00   3.5564e-04   3.7226e-02   5.0978e-07   9.0891e-02   2.2469e-02   1.8751e-01   9.2130e-02   9.3444e-01   8.2380e-02   9.8462e-01   8.2660e-02
+#> 408:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0635e+00  -1.0097e+00   3.8362e-04   3.7354e-02   5.0967e-07   9.0892e-02   2.2461e-02   1.8747e-01   9.2230e-02   9.3453e-01   8.2363e-02   9.8466e-01   8.2661e-02
+#> 409:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0635e+00  -1.0097e+00   2.6671e-04   3.7473e-02   5.0928e-07   9.0894e-02   2.2519e-02   1.8751e-01   9.2243e-02   9.3447e-01   8.2347e-02   9.8449e-01   8.2667e-02
+#> 410:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0098e+00   1.7963e-04   3.7438e-02   5.0981e-07   9.0898e-02   2.2600e-02   1.8764e-01   9.2237e-02   9.3502e-01   8.2320e-02   9.8430e-01   8.2663e-02
+#> 411:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0098e+00   1.0085e-04   3.7381e-02   5.0970e-07   9.0820e-02   2.2618e-02   1.8767e-01   9.2103e-02   9.3480e-01   8.2324e-02   9.8427e-01   8.2652e-02
+#> 412:    1.0186e+02  -4.1205e+00  -2.3508e+00  -4.0633e+00  -1.0097e+00   1.9452e-04   3.7315e-02   5.0984e-07   9.0784e-02   2.2605e-02   1.8772e-01   9.2118e-02   9.3504e-01   8.2314e-02   9.8431e-01   8.2636e-02
+#> 413:    1.0186e+02  -4.1205e+00  -2.3508e+00  -4.0632e+00  -1.0097e+00   1.8432e-04   3.7243e-02   5.0946e-07   9.0798e-02   2.2604e-02   1.8765e-01   9.2206e-02   9.3499e-01   8.2299e-02   9.8426e-01   8.2636e-02
+#> 414:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0632e+00  -1.0097e+00   2.1744e-04   3.7203e-02   5.0880e-07   9.0769e-02   2.2604e-02   1.8757e-01   9.2403e-02   9.3516e-01   8.2279e-02   9.8414e-01   8.2659e-02
+#> 415:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0633e+00  -1.0097e+00   1.9330e-04   3.7197e-02   5.0896e-07   9.0657e-02   2.2618e-02   1.8764e-01   9.2565e-02   9.3514e-01   8.2264e-02   9.8435e-01   8.2655e-02
+#> 416:    1.0186e+02  -4.1205e+00  -2.3501e+00  -4.0634e+00  -1.0097e+00   2.1450e-04   3.7144e-02   5.0882e-07   9.0762e-02   2.2645e-02   1.8761e-01   9.2614e-02   9.3511e-01   8.2277e-02   9.8415e-01   8.2669e-02
+#> 417:    1.0186e+02  -4.1205e+00  -2.3498e+00  -4.0634e+00  -1.0099e+00   1.0737e-04   3.7092e-02   5.0932e-07   9.0804e-02   2.2631e-02   1.8754e-01   9.2581e-02   9.3509e-01   8.2284e-02   9.8430e-01   8.2667e-02
+#> 418:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0099e+00   2.4734e-05   3.7061e-02   5.0972e-07   9.0913e-02   2.2624e-02   1.8736e-01   9.2572e-02   9.3482e-01   8.2275e-02   9.8413e-01   8.2682e-02
+#> 419:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0099e+00  -3.9197e-05   3.7070e-02   5.1000e-07   9.1084e-02   2.2644e-02   1.8727e-01   9.2636e-02   9.3494e-01   8.2259e-02   9.8382e-01   8.2673e-02
+#> 420:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0633e+00  -1.0098e+00  -1.2434e-04   3.7103e-02   5.1037e-07   9.1152e-02   2.2631e-02   1.8733e-01   9.2862e-02   9.3515e-01   8.2244e-02   9.8388e-01   8.2656e-02
+#> 421:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0632e+00  -1.0097e+00  -1.5440e-04   3.7123e-02   5.1205e-07   9.1233e-02   2.2626e-02   1.8744e-01   9.2935e-02   9.3523e-01   8.2241e-02   9.8360e-01   8.2652e-02
+#> 422:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0095e+00  -8.9184e-05   3.7182e-02   5.1296e-07   9.1123e-02   2.2617e-02   1.8749e-01   9.2915e-02   9.3509e-01   8.2276e-02   9.8367e-01   8.2637e-02
+#> 423:    1.0186e+02  -4.1205e+00  -2.3497e+00  -4.0634e+00  -1.0095e+00   6.7469e-05   3.7194e-02   5.1323e-07   9.1083e-02   2.2642e-02   1.8739e-01   9.3097e-02   9.3529e-01   8.2270e-02   9.8367e-01   8.2642e-02
+#> 424:    1.0186e+02  -4.1205e+00  -2.3498e+00  -4.0635e+00  -1.0094e+00   1.5970e-04   3.7258e-02   5.1292e-07   9.0998e-02   2.2667e-02   1.8730e-01   9.3311e-02   9.3525e-01   8.2262e-02   9.8362e-01   8.2648e-02
+#> 425:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0636e+00  -1.0095e+00   2.7004e-04   3.7298e-02   5.1307e-07   9.0839e-02   2.2665e-02   1.8744e-01   9.3429e-02   9.3497e-01   8.2282e-02   9.8395e-01   8.2657e-02
+#> 426:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0636e+00  -1.0094e+00   3.9201e-04   3.7303e-02   5.1305e-07   9.0647e-02   2.2675e-02   1.8743e-01   9.3523e-02   9.3477e-01   8.2314e-02   9.8371e-01   8.2655e-02
+#> 427:    1.0186e+02  -4.1205e+00  -2.3496e+00  -4.0636e+00  -1.0093e+00   2.9359e-04   3.7366e-02   5.1245e-07   9.0630e-02   2.2673e-02   1.8738e-01   9.3813e-02   9.3495e-01   8.2291e-02   9.8368e-01   8.2653e-02
+#> 428:    1.0186e+02  -4.1204e+00  -2.3496e+00  -4.0635e+00  -1.0094e+00   2.5099e-04   3.7411e-02   5.1144e-07   9.0647e-02   2.2674e-02   1.8732e-01   9.3993e-02   9.3493e-01   8.2273e-02   9.8373e-01   8.2652e-02
+#> 429:    1.0186e+02  -4.1204e+00  -2.3495e+00  -4.0635e+00  -1.0095e+00   2.4723e-04   3.7543e-02   5.1084e-07   9.0600e-02   2.2677e-02   1.8723e-01   9.4269e-02   9.3518e-01   8.2286e-02   9.8396e-01   8.2659e-02
+#> 430:    1.0186e+02  -4.1204e+00  -2.3494e+00  -4.0635e+00  -1.0096e+00   2.7711e-04   3.7579e-02   5.1022e-07   9.0496e-02   2.2679e-02   1.8708e-01   9.4484e-02   9.3525e-01   8.2309e-02   9.8433e-01   8.2672e-02
+#> 431:    1.0186e+02  -4.1204e+00  -2.3494e+00  -4.0634e+00  -1.0095e+00   1.3934e-05   3.7631e-02   5.0908e-07   9.0378e-02   2.2671e-02   1.8708e-01   9.4770e-02   9.3528e-01   8.2302e-02   9.8470e-01   8.2682e-02
+#> 432:    1.0186e+02  -4.1204e+00  -2.3495e+00  -4.0633e+00  -1.0096e+00  -8.9401e-05   3.7677e-02   5.0861e-07   9.0278e-02   2.2654e-02   1.8702e-01   9.4882e-02   9.3518e-01   8.2318e-02   9.8488e-01   8.2667e-02
+#> 433:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0096e+00  -3.6841e-04   3.7706e-02   5.0854e-07   9.0108e-02   2.2652e-02   1.8703e-01   9.5039e-02   9.3487e-01   8.2331e-02   9.8494e-01   8.2669e-02
+#> 434:    1.0186e+02  -4.1205e+00  -2.3493e+00  -4.0632e+00  -1.0096e+00  -4.3399e-04   3.7671e-02   5.0796e-07   9.0036e-02   2.2661e-02   1.8701e-01   9.5122e-02   9.3474e-01   8.2331e-02   9.8474e-01   8.2675e-02
+#> 435:    1.0186e+02  -4.1205e+00  -2.3491e+00  -4.0632e+00  -1.0096e+00  -6.1398e-04   3.7654e-02   5.0727e-07   8.9940e-02   2.2664e-02   1.8691e-01   9.5242e-02   9.3451e-01   8.2346e-02   9.8466e-01   8.2677e-02
+#> 436:    1.0186e+02  -4.1205e+00  -2.3487e+00  -4.0632e+00  -1.0094e+00  -7.2148e-04   3.7647e-02   5.0649e-07   8.9838e-02   2.2661e-02   1.8694e-01   9.5465e-02   9.3429e-01   8.2365e-02   9.8475e-01   8.2683e-02
+#> 437:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0632e+00  -1.0093e+00  -1.1480e-03   3.7613e-02   5.0662e-07   8.9719e-02   2.2674e-02   1.8698e-01   9.5631e-02   9.3419e-01   8.2380e-02   9.8490e-01   8.2684e-02
+#> 438:    1.0186e+02  -4.1204e+00  -2.3482e+00  -4.0631e+00  -1.0092e+00  -1.5547e-03   3.7583e-02   5.0753e-07   8.9612e-02   2.2680e-02   1.8703e-01   9.5913e-02   9.3413e-01   8.2394e-02   9.8523e-01   8.2678e-02
+#> 439:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0630e+00  -1.0093e+00  -1.9392e-03   3.7463e-02   5.0769e-07   8.9410e-02   2.2706e-02   1.8706e-01   9.6149e-02   9.3392e-01   8.2425e-02   9.8512e-01   8.2670e-02
+#> 440:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0629e+00  -1.0094e+00  -2.1940e-03   3.7360e-02   5.0743e-07   8.9245e-02   2.2742e-02   1.8710e-01   9.6213e-02   9.3400e-01   8.2445e-02   9.8490e-01   8.2676e-02
+#> 441:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0095e+00  -2.3414e-03   3.7297e-02   5.0838e-07   8.9137e-02   2.2806e-02   1.8721e-01   9.6155e-02   9.3405e-01   8.2450e-02   9.8470e-01   8.2684e-02
+#> 442:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0628e+00  -1.0095e+00  -2.6378e-03   3.7241e-02   5.0923e-07   8.9066e-02   2.2846e-02   1.8727e-01   9.6135e-02   9.3389e-01   8.2465e-02   9.8454e-01   8.2686e-02
+#> 443:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0627e+00  -1.0094e+00  -2.8716e-03   3.7214e-02   5.1026e-07   8.9128e-02   2.2901e-02   1.8740e-01   9.6077e-02   9.3386e-01   8.2444e-02   9.8421e-01   8.2692e-02
+#> 444:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0627e+00  -1.0092e+00  -2.9147e-03   3.7196e-02   5.1104e-07   8.9190e-02   2.2985e-02   1.8744e-01   9.5999e-02   9.3390e-01   8.2424e-02   9.8381e-01   8.2696e-02
+#> 445:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0626e+00  -1.0090e+00  -2.9638e-03   3.7251e-02   5.1283e-07   8.9335e-02   2.3004e-02   1.8756e-01   9.5788e-02   9.3382e-01   8.2416e-02   9.8347e-01   8.2683e-02
+#> 446:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0627e+00  -1.0090e+00  -2.8796e-03   3.7331e-02   5.1479e-07   8.9470e-02   2.3017e-02   1.8762e-01   9.5656e-02   9.3368e-01   8.2405e-02   9.8325e-01   8.2680e-02
+#> 447:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0628e+00  -1.0091e+00  -2.7695e-03   3.7473e-02   5.1656e-07   8.9568e-02   2.3030e-02   1.8757e-01   9.5575e-02   9.3379e-01   8.2386e-02   9.8306e-01   8.2690e-02
+#> 448:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0091e+00  -2.6293e-03   3.7498e-02   5.1814e-07   8.9776e-02   2.3052e-02   1.8762e-01   9.5422e-02   9.3373e-01   8.2372e-02   9.8274e-01   8.2685e-02
+#> 449:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0628e+00  -1.0092e+00  -2.5640e-03   3.7542e-02   5.1867e-07   8.9888e-02   2.3056e-02   1.8763e-01   9.5364e-02   9.3400e-01   8.2365e-02   9.8239e-01   8.2691e-02
+#> 450:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0093e+00  -2.5816e-03   3.7622e-02   5.1849e-07   9.0050e-02   2.3061e-02   1.8765e-01   9.5274e-02   9.3435e-01   8.2341e-02   9.8235e-01   8.2699e-02
+#> 451:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0627e+00  -1.0094e+00  -2.4837e-03   3.7631e-02   5.1931e-07   9.0177e-02   2.3053e-02   1.8766e-01   9.5103e-02   9.3459e-01   8.2322e-02   9.8226e-01   8.2715e-02
+#> 452:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0627e+00  -1.0094e+00  -2.4156e-03   3.7606e-02   5.1901e-07   9.0333e-02   2.3047e-02   1.8763e-01   9.4959e-02   9.3485e-01   8.2289e-02   9.8210e-01   8.2713e-02
+#> 453:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0093e+00  -2.4619e-03   3.7552e-02   5.1874e-07   9.0495e-02   2.3066e-02   1.8761e-01   9.4960e-02   9.3485e-01   8.2293e-02   9.8178e-01   8.2703e-02
+#> 454:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0627e+00  -1.0092e+00  -2.4816e-03   3.7514e-02   5.1835e-07   9.0606e-02   2.3073e-02   1.8754e-01   9.4896e-02   9.3491e-01   8.2277e-02   9.8154e-01   8.2696e-02
+#> 455:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0092e+00  -2.3708e-03   3.7457e-02   5.1742e-07   9.0715e-02   2.3099e-02   1.8756e-01   9.4804e-02   9.3481e-01   8.2272e-02   9.8122e-01   8.2688e-02
+#> 456:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0093e+00  -2.2313e-03   3.7409e-02   5.1680e-07   9.0906e-02   2.3131e-02   1.8743e-01   9.4814e-02   9.3476e-01   8.2261e-02   9.8108e-01   8.2694e-02
+#> 457:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0095e+00  -2.1182e-03   3.7342e-02   5.1630e-07   9.0986e-02   2.3158e-02   1.8733e-01   9.4843e-02   9.3488e-01   8.2244e-02   9.8094e-01   8.2700e-02
+#> 458:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0095e+00  -1.9242e-03   3.7244e-02   5.1605e-07   9.1085e-02   2.3168e-02   1.8720e-01   9.4820e-02   9.3509e-01   8.2228e-02   9.8093e-01   8.2703e-02
+#> 459:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0629e+00  -1.0095e+00  -1.7643e-03   3.7203e-02   5.1566e-07   9.1179e-02   2.3175e-02   1.8715e-01   9.4809e-02   9.3516e-01   8.2216e-02   9.8087e-01   8.2690e-02
+#> 460:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.5479e-03   3.7151e-02   5.1547e-07   9.1211e-02   2.3155e-02   1.8712e-01   9.4703e-02   9.3539e-01   8.2201e-02   9.8100e-01   8.2683e-02
+#> 461:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0630e+00  -1.0095e+00  -1.4993e-03   3.7111e-02   5.1446e-07   9.1225e-02   2.3159e-02   1.8705e-01   9.4569e-02   9.3555e-01   8.2183e-02   9.8078e-01   8.2680e-02
+#> 462:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.4890e-03   3.7056e-02   5.1361e-07   9.1446e-02   2.3158e-02   1.8694e-01   9.4494e-02   9.3557e-01   8.2171e-02   9.8058e-01   8.2688e-02
+#> 463:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.3999e-03   3.6996e-02   5.1319e-07   9.1659e-02   2.3176e-02   1.8695e-01   9.4436e-02   9.3570e-01   8.2153e-02   9.8053e-01   8.2686e-02
+#> 464:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0630e+00  -1.0095e+00  -1.1544e-03   3.6949e-02   5.1300e-07   9.1885e-02   2.3162e-02   1.8688e-01   9.4378e-02   9.3599e-01   8.2134e-02   9.8051e-01   8.2694e-02
+#> 465:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0630e+00  -1.0097e+00  -9.7372e-04   3.6943e-02   5.1235e-07   9.2014e-02   2.3136e-02   1.8692e-01   9.4288e-02   9.3605e-01   8.2141e-02   9.8053e-01   8.2693e-02
+#> 466:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0630e+00  -1.0097e+00  -9.2442e-04   3.6916e-02   5.1246e-07   9.2074e-02   2.3132e-02   1.8688e-01   9.4254e-02   9.3590e-01   8.2131e-02   9.8016e-01   8.2691e-02
+#> 467:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0631e+00  -1.0098e+00  -8.2540e-04   3.6928e-02   5.1340e-07   9.2164e-02   2.3141e-02   1.8690e-01   9.4382e-02   9.3620e-01   8.2106e-02   9.7996e-01   8.2705e-02
+#> 468:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0631e+00  -1.0097e+00  -7.3368e-04   3.6925e-02   5.1395e-07   9.2218e-02   2.3136e-02   1.8695e-01   9.4504e-02   9.3629e-01   8.2094e-02   9.7985e-01   8.2716e-02
+#> 469:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0630e+00  -1.0096e+00  -7.4343e-04   3.6891e-02   5.1401e-07   9.2204e-02   2.3114e-02   1.8700e-01   9.4639e-02   9.3643e-01   8.2078e-02   9.7996e-01   8.2709e-02
+#> 470:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0630e+00  -1.0096e+00  -7.8250e-04   3.6874e-02   5.1370e-07   9.2209e-02   2.3083e-02   1.8702e-01   9.4728e-02   9.3646e-01   8.2073e-02   9.7989e-01   8.2703e-02
+#> 471:    1.0186e+02  -4.1205e+00  -2.3480e+00  -4.0629e+00  -1.0095e+00  -1.0440e-03   3.6843e-02   5.1358e-07   9.2194e-02   2.3062e-02   1.8710e-01   9.4741e-02   9.3649e-01   8.2082e-02   9.8003e-01   8.2701e-02
+#> 472:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0096e+00  -9.5438e-04   3.6869e-02   5.1330e-07   9.2176e-02   2.3050e-02   1.8712e-01   9.4766e-02   9.3666e-01   8.2080e-02   9.7996e-01   8.2691e-02
+#> 473:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0096e+00  -8.2178e-04   3.6877e-02   5.1283e-07   9.2191e-02   2.3021e-02   1.8703e-01   9.4747e-02   9.3670e-01   8.2072e-02   9.8007e-01   8.2693e-02
+#> 474:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0096e+00  -7.0189e-04   3.6927e-02   5.1196e-07   9.2195e-02   2.2989e-02   1.8702e-01   9.4746e-02   9.3669e-01   8.2054e-02   9.8029e-01   8.2689e-02
+#> 475:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0095e+00  -7.1989e-04   3.6993e-02   5.1125e-07   9.2159e-02   2.2963e-02   1.8700e-01   9.4813e-02   9.3681e-01   8.2051e-02   9.8027e-01   8.2680e-02
+#> 476:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0096e+00  -7.1806e-04   3.7018e-02   5.1105e-07   9.2091e-02   2.2933e-02   1.8696e-01   9.4837e-02   9.3713e-01   8.2067e-02   9.8033e-01   8.2674e-02
+#> 477:    1.0186e+02  -4.1205e+00  -2.3479e+00  -4.0630e+00  -1.0097e+00  -7.3438e-04   3.6986e-02   5.1121e-07   9.2046e-02   2.2909e-02   1.8698e-01   9.4809e-02   9.3743e-01   8.2059e-02   9.8045e-01   8.2693e-02
+#> 478:    1.0186e+02  -4.1205e+00  -2.3478e+00  -4.0630e+00  -1.0096e+00  -7.9338e-04   3.6912e-02   5.1224e-07   9.2056e-02   2.2881e-02   1.8698e-01   9.4791e-02   9.3757e-01   8.2042e-02   9.8072e-01   8.2682e-02
+#> 479:    1.0186e+02  -4.1205e+00  -2.3476e+00  -4.0629e+00  -1.0096e+00  -8.6158e-04   3.6882e-02   5.1284e-07   9.2159e-02   2.2867e-02   1.8694e-01   9.4774e-02   9.3749e-01   8.2051e-02   9.8088e-01   8.2679e-02
+#> 480:    1.0186e+02  -4.1205e+00  -2.3474e+00  -4.0629e+00  -1.0096e+00  -1.1334e-03   3.6851e-02   5.1423e-07   9.2253e-02   2.2869e-02   1.8696e-01   9.4820e-02   9.3751e-01   8.2063e-02   9.8097e-01   8.2693e-02
+#> 481:    1.0186e+02  -4.1205e+00  -2.3470e+00  -4.0629e+00  -1.0096e+00  -1.2444e-03   3.6785e-02   5.1490e-07   9.2397e-02   2.2853e-02   1.8694e-01   9.4838e-02   9.3770e-01   8.2031e-02   9.8124e-01   8.2707e-02
+#> 482:    1.0186e+02  -4.1205e+00  -2.3467e+00  -4.0629e+00  -1.0095e+00  -1.3612e-03   3.6750e-02   5.1658e-07   9.2440e-02   2.2842e-02   1.8683e-01   9.4800e-02   9.3786e-01   8.2041e-02   9.8107e-01   8.2719e-02
+#> 483:    1.0186e+02  -4.1205e+00  -2.3467e+00  -4.0628e+00  -1.0096e+00  -1.5168e-03   3.6783e-02   5.1708e-07   9.2590e-02   2.2804e-02   1.8674e-01   9.4804e-02   9.3790e-01   8.2042e-02   9.8116e-01   8.2719e-02
+#> 484:    1.0186e+02  -4.1205e+00  -2.3466e+00  -4.0628e+00  -1.0097e+00  -1.5218e-03   3.6848e-02   5.1669e-07   9.2717e-02   2.2775e-02   1.8670e-01   9.4940e-02   9.3798e-01   8.2028e-02   9.8106e-01   8.2719e-02
+#> 485:    1.0186e+02  -4.1205e+00  -2.3464e+00  -4.0628e+00  -1.0097e+00  -1.4177e-03   3.6867e-02   5.1615e-07   9.2806e-02   2.2765e-02   1.8669e-01   9.5018e-02   9.3816e-01   8.2020e-02   9.8090e-01   8.2721e-02
+#> 486:    1.0186e+02  -4.1205e+00  -2.3462e+00  -4.0628e+00  -1.0098e+00  -1.5257e-03   3.6968e-02   5.1513e-07   9.3019e-02   2.2762e-02   1.8663e-01   9.5111e-02   9.3816e-01   8.2013e-02   9.8071e-01   8.2732e-02
+#> 487:    1.0186e+02  -4.1205e+00  -2.3460e+00  -4.0628e+00  -1.0097e+00  -1.7055e-03   3.7021e-02   5.1446e-07   9.3161e-02   2.2732e-02   1.8652e-01   9.5373e-02   9.3832e-01   8.1997e-02   9.8078e-01   8.2737e-02
+#> 488:    1.0186e+02  -4.1205e+00  -2.3459e+00  -4.0628e+00  -1.0097e+00  -1.8502e-03   3.7069e-02   5.1391e-07   9.3282e-02   2.2741e-02   1.8641e-01   9.5414e-02   9.3818e-01   8.2001e-02   9.8064e-01   8.2738e-02
+#> 489:    1.0186e+02  -4.1205e+00  -2.3458e+00  -4.0628e+00  -1.0097e+00  -1.9091e-03   3.7017e-02   5.1291e-07   9.3286e-02   2.2738e-02   1.8639e-01   9.5453e-02   9.3808e-01   8.1991e-02   9.8047e-01   8.2728e-02
+#> 490:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0628e+00  -1.0097e+00  -1.8766e-03   3.6969e-02   5.1220e-07   9.3297e-02   2.2728e-02   1.8635e-01   9.5468e-02   9.3793e-01   8.1988e-02   9.8034e-01   8.2726e-02
+#> 491:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0627e+00  -1.0097e+00  -1.7736e-03   3.6915e-02   5.1153e-07   9.3298e-02   2.2716e-02   1.8634e-01   9.5548e-02   9.3772e-01   8.2005e-02   9.8025e-01   8.2722e-02
+#> 492:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0627e+00  -1.0097e+00  -1.7747e-03   3.6877e-02   5.1077e-07   9.3336e-02   2.2697e-02   1.8635e-01   9.5593e-02   9.3778e-01   8.2001e-02   9.8013e-01   8.2725e-02
+#> 493:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0628e+00  -1.0094e+00  -1.6324e-03   3.6857e-02   5.1020e-07   9.3348e-02   2.2668e-02   1.8636e-01   9.5735e-02   9.3764e-01   8.1984e-02   9.8019e-01   8.2723e-02
+#> 494:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0629e+00  -1.0094e+00  -1.5393e-03   3.6842e-02   5.1022e-07   9.3359e-02   2.2649e-02   1.8637e-01   9.5812e-02   9.3739e-01   8.1982e-02   9.8033e-01   8.2708e-02
+#> 495:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0629e+00  -1.0094e+00  -1.5166e-03   3.6841e-02   5.1004e-07   9.3321e-02   2.2642e-02   1.8640e-01   9.5849e-02   9.3716e-01   8.1979e-02   9.8016e-01   8.2700e-02
+#> 496:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0630e+00  -1.0095e+00  -1.4947e-03   3.6841e-02   5.0969e-07   9.3236e-02   2.2646e-02   1.8640e-01   9.5916e-02   9.3719e-01   8.1963e-02   9.8028e-01   8.2702e-02
+#> 497:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0629e+00  -1.0094e+00  -1.4507e-03   3.6827e-02   5.0937e-07   9.3185e-02   2.2663e-02   1.8638e-01   9.5991e-02   9.3707e-01   8.1954e-02   9.8047e-01   8.2718e-02
+#> 498:    1.0186e+02  -4.1205e+00  -2.3459e+00  -4.0630e+00  -1.0094e+00  -1.2569e-03   3.6805e-02   5.0854e-07   9.3089e-02   2.2677e-02   1.8634e-01   9.5931e-02   9.3719e-01   8.1952e-02   9.8051e-01   8.2718e-02
+#> 499:    1.0186e+02  -4.1205e+00  -2.3460e+00  -4.0630e+00  -1.0093e+00  -1.0466e-03   3.6769e-02   5.0789e-07   9.3029e-02   2.2690e-02   1.8631e-01   9.5862e-02   9.3729e-01   8.1956e-02   9.8046e-01   8.2731e-02
+#> 500:    1.0186e+02  -4.1205e+00  -2.3464e+00  -4.0630e+00  -1.0093e+00  -7.3346e-04   3.6766e-02   5.0769e-07   9.3093e-02   2.2701e-02   1.8633e-01   9.5687e-02   9.3739e-01   8.1977e-02   9.8039e-01   8.2728e-02
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                # The following takes a very long time but gives
+f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
+#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
+#> F: Forward difference gradient approximation
+#> C: Central difference gradient approximation
+#> M: Mixed forward and central difference gradient approximation
+#> Unscaled parameters for Omegas=chol(solve(omega));
+#> Diagonals are transformed, as specified by foceiControl(diagXform=)
+#> |-----+---------------+-----------+-----------+-----------+-----------|
+#> |    #| Objective Fun |  parent_0 |  log_k_m1 |f_parent_qlogis |    log_k1 |
+#> |.....................|    log_k2 |  g_qlogis | sigma_low |  rsd_high |
+#> |.....................|        o1 |        o2 |        o3 |        o4 |
+#> |.....................|        o5 |        o6 |...........|...........|
+#> |    1|     496.98032 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     496.98032 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     496.98032 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    G|    Gill Diff. |     57.10 |   -0.1453 |   -0.1275 |    0.2854 |
+#> |.....................|   -0.6156 |  0.007043 |    -23.49 |    -32.87 |
+#> |.....................|     3.669 |    -17.46 |    -13.05 |    -13.08 |
+#> |.....................|    -16.16 |    -9.766 |...........|...........|
+#> |    2|     3094.8373 |    0.2572 |   -0.9978 |   -0.9392 |   -0.9714 |
+#> |.....................|   -0.9920 |   -0.9233 |   -0.6037 |   -0.4942 |
+#> |.....................|   -0.9579 |   -0.6658 |   -0.7293 |   -0.7310 |
+#> |.....................|   -0.6848 |   -0.7742 |...........|...........|
+#> |    U|     3094.8373 |     26.15 |    -4.052 |   -0.9415 |    -2.363 |
+#> |.....................|    -4.062 |  -0.01133 |    0.8386 |   0.08074 |
+#> |.....................|    0.6445 |     1.946 |     1.477 |     1.348 |
+#> |.....................|     1.794 |     1.297 |...........|...........|
+#> |    X|     3094.8373 |     26.15 |   0.01739 |    0.2806 |   0.09412 |
+#> |.....................|   0.01721 |    0.4972 |    0.8386 |   0.08074 |
+#> |.....................|    0.6445 |     1.946 |     1.477 |     1.348 |
+#> |.....................|     1.794 |     1.297 |...........|...........|
+#> |    3|     557.60681 |    0.9257 |   -0.9995 |   -0.9407 |   -0.9680 |
+#> |.....................|   -0.9992 |   -0.9232 |   -0.8787 |   -0.8790 |
+#> |.....................|   -0.9150 |   -0.8703 |   -0.8821 |   -0.8842 |
+#> |.....................|   -0.8739 |   -0.8885 |...........|...........|
+#> |    U|     557.60681 |     94.11 |    -4.053 |   -0.9430 |    -2.360 |
+#> |.....................|    -4.069 |  -0.01133 |    0.7386 |   0.06794 |
+#> |.....................|    0.6735 |     1.622 |     1.284 |     1.172 |
+#> |.....................|     1.513 |     1.165 |...........|...........|
+#> |    X|     557.60681 |     94.11 |   0.01736 |    0.2803 |   0.09444 |
+#> |.....................|   0.01709 |    0.4972 |    0.7386 |   0.06794 |
+#> |.....................|    0.6735 |     1.622 |     1.284 |     1.172 |
+#> |.....................|     1.513 |     1.165 |...........|...........|
+#> |    4|     543.47785 |    0.9926 |   -0.9997 |   -0.9408 |   -0.9677 |
+#> |.....................|   -0.9999 |   -0.9232 |   -0.9062 |   -0.9175 |
+#> |.....................|   -0.9107 |   -0.8907 |   -0.8974 |   -0.8995 |
+#> |.....................|   -0.8929 |   -0.9000 |...........|...........|
+#> |    U|     543.47785 |     100.9 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7286 |   0.06666 |
+#> |.....................|    0.6764 |     1.589 |     1.264 |     1.154 |
+#> |.....................|     1.485 |     1.152 |...........|...........|
+#> |    X|     543.47785 |     100.9 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7286 |   0.06666 |
+#> |.....................|    0.6764 |     1.589 |     1.264 |     1.154 |
+#> |.....................|     1.485 |     1.152 |...........|...........|
+#> |    5|     544.09017 |    0.9993 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9089 |   -0.9213 |
+#> |.....................|   -0.9103 |   -0.8928 |   -0.8990 |   -0.9010 |
+#> |.....................|   -0.8948 |   -0.9011 |...........|...........|
+#> |    U|     544.09017 |     101.6 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7276 |   0.06654 |
+#> |.....................|    0.6767 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.09017 |     101.6 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7276 |   0.06654 |
+#> |.....................|    0.6767 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    6|     544.17109 |    0.9999 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8949 |   -0.9012 |...........|...........|
+#> |    U|     544.17109 |     101.6 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.17109 |     101.6 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    7|     544.17937 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.17937 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.17937 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    8|     544.18025 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18025 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18025 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    9|     544.18033 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18033 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18033 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |   10|     544.18034 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18034 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18034 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |   11|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |   12|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |   13|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |   14|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |   15|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |   16|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |   17|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
+#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
+#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
+#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
+#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
+#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
+#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
+#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
+#> |.....................|     1.482 |     1.151 |...........|...........|
+#> calculating covariance matrix
+#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm)
+
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #>                            df      AIC
+#> f_nlmixr_dfop_sfo_saem$nm  16      Inf
+#> f_nlmixr_dfop_sfo_focei$nm 14 886.4573
                                                                summary(f_nlmixr_dfop_sfo_sfo, data = TRUE)
+
                                                                #> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found
                                                                # }
+
+
                                                                +
                                                                + +
                                                                + + +
                                                                +
                                                                +

                                                                Developed by Johannes Ranke.

                                                                +
                                                                + +
                                                                +

                                                                Site built with pkgdown 1.6.1.

                                                                +
                                                                + +
                                                                +
                                                                + + + + + + + + diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html index 1166abb1..fdfdaf4b 100644 --- a/docs/dev/reference/summary.saem.mmkin.html +++ b/docs/dev/reference/summary.saem.mmkin.html @@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally mkin - 1.0.4.9000 + 1.0.5
                                                                @@ -260,15 +260,15 @@ saemix authors for the parts inherited from saemix.

                                                                quiet = TRUE, error_model = "tc", cores = 5) f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
                                                                #> Running main SAEM algorithm -#> [1] "Tue Mar 9 17:35:19 2021" +#> [1] "Fri Jun 11 10:58:28 2021" #> .... #> Minimisation finished -#> [1] "Tue Mar 9 17:35:30 2021"
                                                                summary(f_saem_dfop_sfo, data = TRUE) +#> [1] "Fri Jun 11 10:58:40 2021"
                                                                summary(f_saem_dfop_sfo, data = TRUE)
                                                                #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.4.9000 -#> R version used for fitting: 4.0.4 -#> Date of fit: Tue Mar 9 17:35:31 2021 -#> Date of summary: Tue Mar 9 17:35:31 2021 +#> mkin version used for pre-fitting: 1.0.5 +#> R version used for fitting: 4.1.0 +#> Date of fit: Fri Jun 11 10:58:41 2021 +#> Date of summary: Fri Jun 11 10:58:41 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -283,7 +283,7 @@ saemix authors for the parts inherited from saemix.

                                                                #> #> Model predictions using solution type analytical #> -#> Fitted in 12.058 s using 300, 100 iterations +#> Fitted in 12.75 s using 300, 100 iterations #> #> Variance model: Two-component variance function #> @@ -354,177 +354,177 @@ saemix authors for the parts inherited from saemix.

                                                                #> #> Data: #> ds name time observed predicted residual std standardized -#> ds 1 parent 0 89.8 9.838e+01 8.584661 7.7094 1.113536 -#> ds 1 parent 0 104.1 9.838e+01 -5.715339 7.7094 -0.741350 -#> ds 1 parent 1 88.7 9.388e+01 5.182489 7.3611 0.704041 -#> ds 1 parent 1 95.5 9.388e+01 -1.617511 7.3611 -0.219739 -#> ds 1 parent 3 81.8 8.563e+01 3.825382 6.7229 0.569010 -#> ds 1 parent 3 94.5 8.563e+01 -8.874618 6.7229 -1.320062 -#> ds 1 parent 7 71.5 7.169e+01 0.188290 5.6482 0.033336 -#> ds 1 parent 7 70.3 7.169e+01 1.388290 5.6482 0.245795 -#> ds 1 parent 14 54.2 5.361e+01 -0.586595 4.2624 -0.137621 -#> ds 1 parent 14 49.6 5.361e+01 4.013405 4.2624 0.941587 -#> ds 1 parent 28 31.5 3.219e+01 0.688936 2.6496 0.260011 -#> ds 1 parent 28 28.8 3.219e+01 3.388936 2.6496 1.279016 -#> ds 1 parent 60 12.1 1.278e+01 0.678998 1.3145 0.516562 -#> ds 1 parent 60 13.6 1.278e+01 -0.821002 1.3145 -0.624595 -#> ds 1 parent 90 6.2 6.157e+00 -0.043461 0.9835 -0.044188 -#> ds 1 parent 90 8.3 6.157e+00 -2.143461 0.9835 -2.179316 -#> ds 1 parent 120 2.2 3.076e+00 0.876218 0.8916 0.982775 -#> ds 1 parent 120 2.4 3.076e+00 0.676218 0.8916 0.758453 -#> ds 1 m1 1 0.3 1.134e+00 0.833749 0.8633 0.965750 -#> ds 1 m1 1 0.2 1.134e+00 0.933749 0.8633 1.081583 -#> ds 1 m1 3 2.2 3.157e+00 0.957400 0.8933 1.071763 -#> ds 1 m1 3 3.0 3.157e+00 0.157400 0.8933 0.176202 -#> ds 1 m1 7 6.5 6.369e+00 -0.130995 0.9917 -0.132090 -#> ds 1 m1 7 5.0 6.369e+00 1.369005 0.9917 1.380438 -#> ds 1 m1 14 10.2 9.971e+00 -0.229362 1.1577 -0.198112 -#> ds 1 m1 14 9.5 9.971e+00 0.470638 1.1577 0.406513 -#> ds 1 m1 28 12.2 1.265e+01 0.447735 1.3067 0.342637 -#> ds 1 m1 28 13.4 1.265e+01 -0.752265 1.3067 -0.575683 -#> ds 1 m1 60 11.8 1.097e+01 -0.832027 1.2112 -0.686945 -#> ds 1 m1 60 13.2 1.097e+01 -2.232027 1.2112 -1.842825 -#> ds 1 m1 90 6.6 7.876e+00 1.275985 1.0553 1.209109 -#> ds 1 m1 90 9.3 7.876e+00 -1.424015 1.0553 -1.349381 -#> ds 1 m1 120 3.5 5.336e+00 1.835829 0.9540 1.924292 -#> ds 1 m1 120 5.4 5.336e+00 -0.064171 0.9540 -0.067263 -#> ds 2 parent 0 118.0 1.092e+02 -8.812058 8.5459 -1.031142 -#> ds 2 parent 0 99.8 1.092e+02 9.387942 8.5459 1.098529 -#> ds 2 parent 1 90.2 1.023e+02 12.114268 8.0135 1.511724 -#> ds 2 parent 1 94.6 1.023e+02 7.714268 8.0135 0.962654 -#> ds 2 parent 3 96.1 9.066e+01 -5.436165 7.1122 -0.764344 -#> ds 2 parent 3 78.4 9.066e+01 12.263835 7.1122 1.724339 -#> ds 2 parent 7 77.9 7.365e+01 -4.245773 5.7995 -0.732090 -#> ds 2 parent 7 77.7 7.365e+01 -4.045773 5.7995 -0.697604 -#> ds 2 parent 14 56.0 5.593e+01 -0.073803 4.4389 -0.016626 -#> ds 2 parent 14 54.7 5.593e+01 1.226197 4.4389 0.276236 -#> ds 2 parent 28 36.6 3.892e+01 2.320837 3.1502 0.736737 -#> ds 2 parent 28 36.8 3.892e+01 2.120837 3.1502 0.673248 -#> ds 2 parent 60 22.1 2.136e+01 -0.741020 1.8719 -0.395868 -#> ds 2 parent 60 24.7 2.136e+01 -3.341020 1.8719 -1.784841 -#> ds 2 parent 90 12.4 1.251e+01 0.113999 1.2989 0.087765 -#> ds 2 parent 90 10.8 1.251e+01 1.713999 1.2989 1.319575 -#> ds 2 parent 120 6.8 7.338e+00 0.537708 1.0315 0.521281 -#> ds 2 parent 120 7.9 7.338e+00 -0.562292 1.0315 -0.545113 -#> ds 2 m1 1 1.3 1.576e+00 0.276176 0.8675 0.318352 -#> ds 2 m1 3 3.7 4.177e+00 0.476741 0.9183 0.519146 -#> ds 2 m1 3 4.7 4.177e+00 -0.523259 0.9183 -0.569801 -#> ds 2 m1 7 8.1 7.724e+00 -0.376365 1.0485 -0.358970 -#> ds 2 m1 7 7.9 7.724e+00 -0.176365 1.0485 -0.168214 -#> ds 2 m1 14 10.1 1.077e+01 0.674433 1.2006 0.561738 -#> ds 2 m1 14 10.3 1.077e+01 0.474433 1.2006 0.395158 -#> ds 2 m1 28 10.7 1.212e+01 1.416179 1.2758 1.110010 -#> ds 2 m1 28 12.2 1.212e+01 -0.083821 1.2758 -0.065699 -#> ds 2 m1 60 10.7 1.041e+01 -0.294930 1.1807 -0.249793 -#> ds 2 m1 60 12.5 1.041e+01 -2.094930 1.1807 -1.774316 -#> ds 2 m1 90 9.1 8.079e+00 -1.020859 1.0646 -0.958929 -#> ds 2 m1 90 7.4 8.079e+00 0.679141 1.0646 0.637941 -#> ds 2 m1 120 6.1 5.968e+00 -0.131673 0.9765 -0.134843 -#> ds 2 m1 120 4.5 5.968e+00 1.468327 0.9765 1.503683 -#> ds 3 parent 0 106.2 1.036e+02 -2.638248 8.1101 -0.325303 -#> ds 3 parent 0 106.9 1.036e+02 -3.338248 8.1101 -0.411614 -#> ds 3 parent 1 107.4 9.580e+01 -11.600063 7.5094 -1.544743 -#> ds 3 parent 1 96.1 9.580e+01 -0.300063 7.5094 -0.039958 -#> ds 3 parent 3 79.4 8.297e+01 3.574516 6.5182 0.548391 -#> ds 3 parent 3 82.6 8.297e+01 0.374516 6.5182 0.057457 -#> ds 3 parent 7 63.9 6.517e+01 1.272397 5.1472 0.247200 -#> ds 3 parent 7 62.4 6.517e+01 2.772397 5.1472 0.538618 -#> ds 3 parent 14 51.0 4.821e+01 -2.790075 3.8512 -0.724475 -#> ds 3 parent 14 47.1 4.821e+01 1.109925 3.8512 0.288205 -#> ds 3 parent 28 36.1 3.385e+01 -2.250573 2.7723 -0.811811 -#> ds 3 parent 28 36.6 3.385e+01 -2.750573 2.7723 -0.992168 -#> ds 3 parent 60 20.1 1.964e+01 -0.455700 1.7543 -0.259760 -#> ds 3 parent 60 19.8 1.964e+01 -0.155700 1.7543 -0.088753 -#> ds 3 parent 90 11.3 1.210e+01 0.795458 1.2746 0.624068 -#> ds 3 parent 90 10.7 1.210e+01 1.395458 1.2746 1.094792 -#> ds 3 parent 120 8.2 7.451e+00 -0.749141 1.0364 -0.722816 -#> ds 3 parent 120 7.3 7.451e+00 0.150859 1.0364 0.145558 -#> ds 3 m1 0 0.8 3.695e-13 -0.800000 0.8588 -0.931542 -#> ds 3 m1 1 1.8 1.740e+00 -0.059741 0.8694 -0.068714 -#> ds 3 m1 1 2.3 1.740e+00 -0.559741 0.8694 -0.643812 -#> ds 3 m1 3 4.2 4.531e+00 0.331379 0.9285 0.356913 -#> ds 3 m1 3 4.1 4.531e+00 0.431379 0.9285 0.464618 -#> ds 3 m1 7 6.8 8.113e+00 1.312762 1.0661 1.231333 -#> ds 3 m1 7 10.1 8.113e+00 -1.987238 1.0661 -1.863971 -#> ds 3 m1 14 11.4 1.079e+01 -0.613266 1.2013 -0.510507 -#> ds 3 m1 14 12.8 1.079e+01 -2.013266 1.2013 -1.675923 -#> ds 3 m1 28 11.5 1.133e+01 -0.174252 1.2310 -0.141553 -#> ds 3 m1 28 10.6 1.133e+01 0.725748 1.2310 0.589558 -#> ds 3 m1 60 7.5 8.948e+00 1.448281 1.1059 1.309561 -#> ds 3 m1 60 8.6 8.948e+00 0.348281 1.1059 0.314922 -#> ds 3 m1 90 7.3 6.665e+00 -0.634932 1.0034 -0.632752 -#> ds 3 m1 90 8.1 6.665e+00 -1.434932 1.0034 -1.430004 -#> ds 3 m1 120 5.3 4.795e+00 -0.504936 0.9365 -0.539199 -#> ds 3 m1 120 3.8 4.795e+00 0.995064 0.9365 1.062586 -#> ds 4 parent 0 104.7 9.985e+01 -4.850494 7.8227 -0.620050 -#> ds 4 parent 0 88.3 9.985e+01 11.549506 7.8227 1.476402 -#> ds 4 parent 1 94.2 9.676e+01 2.556304 7.5834 0.337093 -#> ds 4 parent 1 94.6 9.676e+01 2.156304 7.5834 0.284346 -#> ds 4 parent 3 78.1 9.092e+01 12.817485 7.1318 1.797230 -#> ds 4 parent 3 96.5 9.092e+01 -5.582515 7.1318 -0.782764 -#> ds 4 parent 7 76.2 8.050e+01 4.297338 6.3270 0.679204 -#> ds 4 parent 7 77.8 8.050e+01 2.697338 6.3270 0.426320 -#> ds 4 parent 14 70.8 6.562e+01 -5.179989 5.1816 -0.999687 -#> ds 4 parent 14 67.3 6.562e+01 -1.679989 5.1816 -0.324222 -#> ds 4 parent 28 43.1 4.499e+01 1.886936 3.6069 0.523140 -#> ds 4 parent 28 45.1 4.499e+01 -0.113064 3.6069 -0.031346 -#> ds 4 parent 60 21.3 2.151e+01 0.214840 1.8827 0.114114 -#> ds 4 parent 60 23.5 2.151e+01 -1.985160 1.8827 -1.054433 -#> ds 4 parent 90 11.8 1.190e+01 0.098528 1.2633 0.077990 -#> ds 4 parent 90 12.1 1.190e+01 -0.201472 1.2633 -0.159475 -#> ds 4 parent 120 7.0 6.886e+00 -0.113832 1.0125 -0.112431 -#> ds 4 parent 120 6.2 6.886e+00 0.686168 1.0125 0.677724 -#> ds 4 m1 0 1.6 4.263e-14 -1.600000 0.8588 -1.863085 -#> ds 4 m1 1 0.9 7.140e-01 -0.185984 0.8606 -0.216112 -#> ds 4 m1 3 3.7 2.022e+00 -1.678243 0.8731 -1.922160 -#> ds 4 m1 3 2.0 2.022e+00 0.021757 0.8731 0.024919 -#> ds 4 m1 7 3.6 4.207e+00 0.607229 0.9192 0.660633 -#> ds 4 m1 7 3.8 4.207e+00 0.407229 0.9192 0.443044 -#> ds 4 m1 14 7.1 6.912e+00 -0.188339 1.0135 -0.185828 -#> ds 4 m1 14 6.6 6.912e+00 0.311661 1.0135 0.307506 -#> ds 4 m1 28 9.5 9.449e+00 -0.050714 1.1309 -0.044843 -#> ds 4 m1 28 9.3 9.449e+00 0.149286 1.1309 0.132004 -#> ds 4 m1 60 8.3 8.997e+00 0.697403 1.1083 0.629230 -#> ds 4 m1 60 9.0 8.997e+00 -0.002597 1.1083 -0.002343 -#> ds 4 m1 90 6.6 6.697e+00 0.096928 1.0047 0.096472 -#> ds 4 m1 90 7.7 6.697e+00 -1.003072 1.0047 -0.998348 -#> ds 4 m1 120 3.7 4.622e+00 0.921607 0.9312 0.989749 -#> ds 4 m1 120 3.5 4.622e+00 1.121607 0.9312 1.204537 -#> ds 5 parent 0 110.4 1.045e+02 -5.942426 8.1795 -0.726502 -#> ds 5 parent 0 112.1 1.045e+02 -7.642426 8.1795 -0.934338 -#> ds 5 parent 1 93.5 9.739e+01 3.893915 7.6327 0.510162 -#> ds 5 parent 1 91.0 9.739e+01 6.393915 7.6327 0.837700 -#> ds 5 parent 3 71.0 8.519e+01 14.188275 6.6891 2.121098 -#> ds 5 parent 3 89.7 8.519e+01 -4.511725 6.6891 -0.674487 -#> ds 5 parent 7 60.4 6.684e+01 6.439546 5.2753 1.220701 -#> ds 5 parent 7 59.1 6.684e+01 7.739546 5.2753 1.467133 -#> ds 5 parent 14 56.5 4.736e+01 -9.138979 3.7868 -2.413407 -#> ds 5 parent 14 47.0 4.736e+01 0.361021 3.7868 0.095338 -#> ds 5 parent 28 30.2 3.033e+01 0.131178 2.5132 0.052195 -#> ds 5 parent 28 23.9 3.033e+01 6.431178 2.5132 2.558936 -#> ds 5 parent 60 17.0 1.771e+01 0.705246 1.6243 0.434177 -#> ds 5 parent 60 18.7 1.771e+01 -0.994754 1.6243 -0.612409 -#> ds 5 parent 90 11.3 1.180e+01 0.504856 1.2580 0.401315 -#> ds 5 parent 90 11.9 1.180e+01 -0.095144 1.2580 -0.075631 -#> ds 5 parent 120 9.0 7.917e+00 -1.083499 1.0571 -1.024928 -#> ds 5 parent 120 8.1 7.917e+00 -0.183499 1.0571 -0.173579 -#> ds 5 m1 0 0.7 3.553e-15 -0.700000 0.8588 -0.815100 -#> ds 5 m1 1 3.0 3.204e+00 0.204414 0.8943 0.228572 -#> ds 5 m1 1 2.6 3.204e+00 0.604414 0.8943 0.675845 -#> ds 5 m1 3 5.1 8.586e+00 3.485889 1.0884 3.202858 -#> ds 5 m1 3 7.5 8.586e+00 1.085889 1.0884 0.997722 -#> ds 5 m1 7 16.5 1.612e+01 -0.376855 1.5211 -0.247743 -#> ds 5 m1 7 19.0 1.612e+01 -2.876855 1.5211 -1.891237 -#> ds 5 m1 14 22.9 2.267e+01 -0.228264 1.9633 -0.116267 -#> ds 5 m1 14 23.2 2.267e+01 -0.528264 1.9633 -0.269072 -#> ds 5 m1 28 22.2 2.468e+01 2.480178 2.1050 1.178211 -#> ds 5 m1 28 24.4 2.468e+01 0.280178 2.1050 0.133099 -#> ds 5 m1 60 15.5 1.860e+01 3.099615 1.6838 1.840794 -#> ds 5 m1 60 19.8 1.860e+01 -1.200385 1.6838 -0.712883 -#> ds 5 m1 90 14.9 1.326e+01 -1.636345 1.3433 -1.218195 -#> ds 5 m1 90 14.2 1.326e+01 -0.936345 1.3433 -0.697072 -#> ds 5 m1 120 10.9 9.348e+00 -1.551535 1.1258 -1.378133 -#> ds 5 m1 120 10.4 9.348e+00 -1.051535 1.1258 -0.934014
                                                                # } +#> ds 1 parent 0 89.8 9.838e+01 -8.584661 7.7094 -1.113536 +#> ds 1 parent 0 104.1 9.838e+01 5.715339 7.7094 0.741350 +#> ds 1 parent 1 88.7 9.388e+01 -5.182489 7.3611 -0.704041 +#> ds 1 parent 1 95.5 9.388e+01 1.617511 7.3611 0.219739 +#> ds 1 parent 3 81.8 8.563e+01 -3.825382 6.7229 -0.569010 +#> ds 1 parent 3 94.5 8.563e+01 8.874618 6.7229 1.320062 +#> ds 1 parent 7 71.5 7.169e+01 -0.188290 5.6482 -0.033336 +#> ds 1 parent 7 70.3 7.169e+01 -1.388290 5.6482 -0.245795 +#> ds 1 parent 14 54.2 5.361e+01 0.586595 4.2624 0.137621 +#> ds 1 parent 14 49.6 5.361e+01 -4.013405 4.2624 -0.941587 +#> ds 1 parent 28 31.5 3.219e+01 -0.688936 2.6496 -0.260011 +#> ds 1 parent 28 28.8 3.219e+01 -3.388936 2.6496 -1.279016 +#> ds 1 parent 60 12.1 1.278e+01 -0.678998 1.3145 -0.516562 +#> ds 1 parent 60 13.6 1.278e+01 0.821002 1.3145 0.624595 +#> ds 1 parent 90 6.2 6.157e+00 0.043461 0.9835 0.044188 +#> ds 1 parent 90 8.3 6.157e+00 2.143461 0.9835 2.179316 +#> ds 1 parent 120 2.2 3.076e+00 -0.876218 0.8916 -0.982775 +#> ds 1 parent 120 2.4 3.076e+00 -0.676218 0.8916 -0.758453 +#> ds 1 m1 1 0.3 1.134e+00 -0.833749 0.8633 -0.965750 +#> ds 1 m1 1 0.2 1.134e+00 -0.933749 0.8633 -1.081583 +#> ds 1 m1 3 2.2 3.157e+00 -0.957400 0.8933 -1.071763 +#> ds 1 m1 3 3.0 3.157e+00 -0.157400 0.8933 -0.176202 +#> ds 1 m1 7 6.5 6.369e+00 0.130995 0.9917 0.132090 +#> ds 1 m1 7 5.0 6.369e+00 -1.369005 0.9917 -1.380438 +#> ds 1 m1 14 10.2 9.971e+00 0.229362 1.1577 0.198112 +#> ds 1 m1 14 9.5 9.971e+00 -0.470638 1.1577 -0.406513 +#> ds 1 m1 28 12.2 1.265e+01 -0.447735 1.3067 -0.342637 +#> ds 1 m1 28 13.4 1.265e+01 0.752265 1.3067 0.575683 +#> ds 1 m1 60 11.8 1.097e+01 0.832027 1.2112 0.686945 +#> ds 1 m1 60 13.2 1.097e+01 2.232027 1.2112 1.842825 +#> ds 1 m1 90 6.6 7.876e+00 -1.275985 1.0553 -1.209109 +#> ds 1 m1 90 9.3 7.876e+00 1.424015 1.0553 1.349381 +#> ds 1 m1 120 3.5 5.336e+00 -1.835829 0.9540 -1.924292 +#> ds 1 m1 120 5.4 5.336e+00 0.064171 0.9540 0.067263 +#> ds 2 parent 0 118.0 1.092e+02 8.812058 8.5459 1.031142 +#> ds 2 parent 0 99.8 1.092e+02 -9.387942 8.5459 -1.098529 +#> ds 2 parent 1 90.2 1.023e+02 -12.114268 8.0135 -1.511724 +#> ds 2 parent 1 94.6 1.023e+02 -7.714268 8.0135 -0.962654 +#> ds 2 parent 3 96.1 9.066e+01 5.436165 7.1122 0.764344 +#> ds 2 parent 3 78.4 9.066e+01 -12.263835 7.1122 -1.724339 +#> ds 2 parent 7 77.9 7.365e+01 4.245773 5.7995 0.732090 +#> ds 2 parent 7 77.7 7.365e+01 4.045773 5.7995 0.697604 +#> ds 2 parent 14 56.0 5.593e+01 0.073803 4.4389 0.016626 +#> ds 2 parent 14 54.7 5.593e+01 -1.226197 4.4389 -0.276236 +#> ds 2 parent 28 36.6 3.892e+01 -2.320837 3.1502 -0.736737 +#> ds 2 parent 28 36.8 3.892e+01 -2.120837 3.1502 -0.673248 +#> ds 2 parent 60 22.1 2.136e+01 0.741020 1.8719 0.395868 +#> ds 2 parent 60 24.7 2.136e+01 3.341020 1.8719 1.784841 +#> ds 2 parent 90 12.4 1.251e+01 -0.113999 1.2989 -0.087765 +#> ds 2 parent 90 10.8 1.251e+01 -1.713999 1.2989 -1.319575 +#> ds 2 parent 120 6.8 7.338e+00 -0.537708 1.0315 -0.521281 +#> ds 2 parent 120 7.9 7.338e+00 0.562292 1.0315 0.545113 +#> ds 2 m1 1 1.3 1.576e+00 -0.276176 0.8675 -0.318352 +#> ds 2 m1 3 3.7 4.177e+00 -0.476741 0.9183 -0.519146 +#> ds 2 m1 3 4.7 4.177e+00 0.523259 0.9183 0.569801 +#> ds 2 m1 7 8.1 7.724e+00 0.376365 1.0485 0.358970 +#> ds 2 m1 7 7.9 7.724e+00 0.176365 1.0485 0.168214 +#> ds 2 m1 14 10.1 1.077e+01 -0.674433 1.2006 -0.561738 +#> ds 2 m1 14 10.3 1.077e+01 -0.474433 1.2006 -0.395158 +#> ds 2 m1 28 10.7 1.212e+01 -1.416179 1.2758 -1.110010 +#> ds 2 m1 28 12.2 1.212e+01 0.083821 1.2758 0.065699 +#> ds 2 m1 60 10.7 1.041e+01 0.294930 1.1807 0.249793 +#> ds 2 m1 60 12.5 1.041e+01 2.094930 1.1807 1.774316 +#> ds 2 m1 90 9.1 8.079e+00 1.020859 1.0646 0.958929 +#> ds 2 m1 90 7.4 8.079e+00 -0.679141 1.0646 -0.637941 +#> ds 2 m1 120 6.1 5.968e+00 0.131673 0.9765 0.134843 +#> ds 2 m1 120 4.5 5.968e+00 -1.468327 0.9765 -1.503683 +#> ds 3 parent 0 106.2 1.036e+02 2.638248 8.1101 0.325303 +#> ds 3 parent 0 106.9 1.036e+02 3.338248 8.1101 0.411614 +#> ds 3 parent 1 107.4 9.580e+01 11.600063 7.5094 1.544743 +#> ds 3 parent 1 96.1 9.580e+01 0.300063 7.5094 0.039958 +#> ds 3 parent 3 79.4 8.297e+01 -3.574516 6.5182 -0.548391 +#> ds 3 parent 3 82.6 8.297e+01 -0.374516 6.5182 -0.057457 +#> ds 3 parent 7 63.9 6.517e+01 -1.272397 5.1472 -0.247200 +#> ds 3 parent 7 62.4 6.517e+01 -2.772397 5.1472 -0.538618 +#> ds 3 parent 14 51.0 4.821e+01 2.790075 3.8512 0.724475 +#> ds 3 parent 14 47.1 4.821e+01 -1.109925 3.8512 -0.288205 +#> ds 3 parent 28 36.1 3.385e+01 2.250573 2.7723 0.811811 +#> ds 3 parent 28 36.6 3.385e+01 2.750573 2.7723 0.992168 +#> ds 3 parent 60 20.1 1.964e+01 0.455700 1.7543 0.259760 +#> ds 3 parent 60 19.8 1.964e+01 0.155700 1.7543 0.088753 +#> ds 3 parent 90 11.3 1.210e+01 -0.795458 1.2746 -0.624068 +#> ds 3 parent 90 10.7 1.210e+01 -1.395458 1.2746 -1.094792 +#> ds 3 parent 120 8.2 7.451e+00 0.749141 1.0364 0.722816 +#> ds 3 parent 120 7.3 7.451e+00 -0.150859 1.0364 -0.145558 +#> ds 3 m1 0 0.8 3.695e-13 0.800000 0.8588 0.931542 +#> ds 3 m1 1 1.8 1.740e+00 0.059741 0.8694 0.068714 +#> ds 3 m1 1 2.3 1.740e+00 0.559741 0.8694 0.643812 +#> ds 3 m1 3 4.2 4.531e+00 -0.331379 0.9285 -0.356913 +#> ds 3 m1 3 4.1 4.531e+00 -0.431379 0.9285 -0.464618 +#> ds 3 m1 7 6.8 8.113e+00 -1.312762 1.0661 -1.231333 +#> ds 3 m1 7 10.1 8.113e+00 1.987238 1.0661 1.863971 +#> ds 3 m1 14 11.4 1.079e+01 0.613266 1.2013 0.510507 +#> ds 3 m1 14 12.8 1.079e+01 2.013266 1.2013 1.675923 +#> ds 3 m1 28 11.5 1.133e+01 0.174252 1.2310 0.141553 +#> ds 3 m1 28 10.6 1.133e+01 -0.725748 1.2310 -0.589558 +#> ds 3 m1 60 7.5 8.948e+00 -1.448281 1.1059 -1.309561 +#> ds 3 m1 60 8.6 8.948e+00 -0.348281 1.1059 -0.314922 +#> ds 3 m1 90 7.3 6.665e+00 0.634932 1.0034 0.632752 +#> ds 3 m1 90 8.1 6.665e+00 1.434932 1.0034 1.430004 +#> ds 3 m1 120 5.3 4.795e+00 0.504936 0.9365 0.539199 +#> ds 3 m1 120 3.8 4.795e+00 -0.995064 0.9365 -1.062586 +#> ds 4 parent 0 104.7 9.985e+01 4.850494 7.8227 0.620050 +#> ds 4 parent 0 88.3 9.985e+01 -11.549506 7.8227 -1.476402 +#> ds 4 parent 1 94.2 9.676e+01 -2.556304 7.5834 -0.337093 +#> ds 4 parent 1 94.6 9.676e+01 -2.156304 7.5834 -0.284346 +#> ds 4 parent 3 78.1 9.092e+01 -12.817485 7.1318 -1.797230 +#> ds 4 parent 3 96.5 9.092e+01 5.582515 7.1318 0.782764 +#> ds 4 parent 7 76.2 8.050e+01 -4.297338 6.3270 -0.679204 +#> ds 4 parent 7 77.8 8.050e+01 -2.697338 6.3270 -0.426320 +#> ds 4 parent 14 70.8 6.562e+01 5.179989 5.1816 0.999687 +#> ds 4 parent 14 67.3 6.562e+01 1.679989 5.1816 0.324222 +#> ds 4 parent 28 43.1 4.499e+01 -1.886936 3.6069 -0.523140 +#> ds 4 parent 28 45.1 4.499e+01 0.113064 3.6069 0.031346 +#> ds 4 parent 60 21.3 2.151e+01 -0.214840 1.8827 -0.114114 +#> ds 4 parent 60 23.5 2.151e+01 1.985160 1.8827 1.054433 +#> ds 4 parent 90 11.8 1.190e+01 -0.098528 1.2633 -0.077990 +#> ds 4 parent 90 12.1 1.190e+01 0.201472 1.2633 0.159475 +#> ds 4 parent 120 7.0 6.886e+00 0.113832 1.0125 0.112431 +#> ds 4 parent 120 6.2 6.886e+00 -0.686168 1.0125 -0.677724 +#> ds 4 m1 0 1.6 4.263e-14 1.600000 0.8588 1.863085 +#> ds 4 m1 1 0.9 7.140e-01 0.185984 0.8606 0.216112 +#> ds 4 m1 3 3.7 2.022e+00 1.678243 0.8731 1.922160 +#> ds 4 m1 3 2.0 2.022e+00 -0.021757 0.8731 -0.024919 +#> ds 4 m1 7 3.6 4.207e+00 -0.607229 0.9192 -0.660633 +#> ds 4 m1 7 3.8 4.207e+00 -0.407229 0.9192 -0.443044 +#> ds 4 m1 14 7.1 6.912e+00 0.188339 1.0135 0.185828 +#> ds 4 m1 14 6.6 6.912e+00 -0.311661 1.0135 -0.307506 +#> ds 4 m1 28 9.5 9.449e+00 0.050714 1.1309 0.044843 +#> ds 4 m1 28 9.3 9.449e+00 -0.149286 1.1309 -0.132004 +#> ds 4 m1 60 8.3 8.997e+00 -0.697403 1.1083 -0.629230 +#> ds 4 m1 60 9.0 8.997e+00 0.002597 1.1083 0.002343 +#> ds 4 m1 90 6.6 6.697e+00 -0.096928 1.0047 -0.096472 +#> ds 4 m1 90 7.7 6.697e+00 1.003072 1.0047 0.998348 +#> ds 4 m1 120 3.7 4.622e+00 -0.921607 0.9312 -0.989749 +#> ds 4 m1 120 3.5 4.622e+00 -1.121607 0.9312 -1.204537 +#> ds 5 parent 0 110.4 1.045e+02 5.942426 8.1795 0.726502 +#> ds 5 parent 0 112.1 1.045e+02 7.642426 8.1795 0.934338 +#> ds 5 parent 1 93.5 9.739e+01 -3.893915 7.6327 -0.510162 +#> ds 5 parent 1 91.0 9.739e+01 -6.393915 7.6327 -0.837700 +#> ds 5 parent 3 71.0 8.519e+01 -14.188275 6.6891 -2.121098 +#> ds 5 parent 3 89.7 8.519e+01 4.511725 6.6891 0.674487 +#> ds 5 parent 7 60.4 6.684e+01 -6.439546 5.2753 -1.220701 +#> ds 5 parent 7 59.1 6.684e+01 -7.739546 5.2753 -1.467133 +#> ds 5 parent 14 56.5 4.736e+01 9.138979 3.7868 2.413407 +#> ds 5 parent 14 47.0 4.736e+01 -0.361021 3.7868 -0.095338 +#> ds 5 parent 28 30.2 3.033e+01 -0.131178 2.5132 -0.052195 +#> ds 5 parent 28 23.9 3.033e+01 -6.431178 2.5132 -2.558936 +#> ds 5 parent 60 17.0 1.771e+01 -0.705246 1.6243 -0.434177 +#> ds 5 parent 60 18.7 1.771e+01 0.994754 1.6243 0.612409 +#> ds 5 parent 90 11.3 1.180e+01 -0.504856 1.2580 -0.401315 +#> ds 5 parent 90 11.9 1.180e+01 0.095144 1.2580 0.075631 +#> ds 5 parent 120 9.0 7.917e+00 1.083499 1.0571 1.024928 +#> ds 5 parent 120 8.1 7.917e+00 0.183499 1.0571 0.173579 +#> ds 5 m1 0 0.7 3.553e-15 0.700000 0.8588 0.815100 +#> ds 5 m1 1 3.0 3.204e+00 -0.204414 0.8943 -0.228572 +#> ds 5 m1 1 2.6 3.204e+00 -0.604414 0.8943 -0.675845 +#> ds 5 m1 3 5.1 8.586e+00 -3.485889 1.0884 -3.202858 +#> ds 5 m1 3 7.5 8.586e+00 -1.085889 1.0884 -0.997722 +#> ds 5 m1 7 16.5 1.612e+01 0.376855 1.5211 0.247743 +#> ds 5 m1 7 19.0 1.612e+01 2.876855 1.5211 1.891237 +#> ds 5 m1 14 22.9 2.267e+01 0.228264 1.9633 0.116267 +#> ds 5 m1 14 23.2 2.267e+01 0.528264 1.9633 0.269072 +#> ds 5 m1 28 22.2 2.468e+01 -2.480178 2.1050 -1.178211 +#> ds 5 m1 28 24.4 2.468e+01 -0.280178 2.1050 -0.133099 +#> ds 5 m1 60 15.5 1.860e+01 -3.099615 1.6838 -1.840794 +#> ds 5 m1 60 19.8 1.860e+01 1.200385 1.6838 0.712883 +#> ds 5 m1 90 14.9 1.326e+01 1.636345 1.3433 1.218195 +#> ds 5 m1 90 14.2 1.326e+01 0.936345 1.3433 0.697072 +#> ds 5 m1 120 10.9 9.348e+00 1.551535 1.1258 1.378133 +#> ds 5 m1 120 10.4 9.348e+00 1.051535 1.1258 0.934014
                                                                # }
                                                                diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index 27f0e392..425e18ad 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -105,6 +105,9 @@ https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html + + https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html + https://pkgdown.jrwb.de/mkin/reference/mixed.html @@ -156,6 +159,9 @@ https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html + + https://pkgdown.jrwb.de/mkin/reference/nlmixr.mmkin.html + https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html @@ -195,6 +201,9 @@ https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html + + https://pkgdown.jrwb.de/mkin/reference/summary.nlmixr.mmkin.html + https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html diff --git a/man/endpoints.Rd b/man/endpoints.Rd index 72487717..a37ff98d 100644 --- a/man/endpoints.Rd +++ b/man/endpoints.Rd @@ -8,8 +8,8 @@ with mkinfit} endpoints(fit) } \arguments{ -\item{fit}{An object of class \link{mkinfit}, \link{nlme.mmkin} or -\link{saem.mmkin}. Or another object that has list components +\item{fit}{An object of class \link{mkinfit}, \link{nlme.mmkin}, \link{saem.mmkin} or +\link{nlmixr.mmkin}. Or another object that has list components mkinmod containing an \link{mkinmod} degradation model, and two numeric vectors, bparms.optim and bparms.fixed, that contain parameter values for that model.} diff --git a/man/mean_degparms.Rd b/man/mean_degparms.Rd index 92ed4c9d..5e2b4b0f 100644 --- a/man/mean_degparms.Rd +++ b/man/mean_degparms.Rd @@ -7,6 +7,8 @@ mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) } \arguments{ +\item{object}{An mmkin row object containing several fits of the same model to different datasets} + \item{random}{Should a list with fixed and random effects be returned?} \item{test_log_parms}{If TRUE, log parameters are only considered in diff --git a/man/nlmixr.mmkin.Rd b/man/nlmixr.mmkin.Rd index 86bbdc9f..4ab30272 100644 --- a/man/nlmixr.mmkin.Rd +++ b/man/nlmixr.mmkin.Rd @@ -29,7 +29,8 @@ nlmixr_model( degparms_start = "auto", test_log_parms = FALSE, conf.level = 0.6, - error_model = object[[1]]$err_mod + error_model = object[[1]]$err_mod, + add_attributes = FALSE ) nlmixr_data(object, ...) @@ -38,9 +39,16 @@ nlmixr_data(object, ...) \item{object}{An \link{mmkin} row object containing several fits of the same \link{mkinmod} model to different datasets} +\item{data}{Not used, as the data are extracted from the mmkin row object} + \item{est}{Estimation method passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} -\item{control}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}.} +\item{control}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{table}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{error_model}{Possibility to override the error model which is being +set based on the error model used in the mmkin row object.} \item{test_log_parms}{If TRUE, an attempt is made to use more robust starting values for population parameters fitted as log parameters in mkin (like @@ -52,6 +60,10 @@ for parameter that are tested if requested by 'test_log_parms'.} \item{\dots}{Passed to \link{nlmixr_model}} +\item{save}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{envir}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + \item{x}{An nlmixr.mmkin object to print} \item{digits}{Number of digits to use for printing} @@ -59,8 +71,9 @@ for parameter that are tested if requested by 'test_log_parms'.} \item{degparms_start}{Parameter values given as a named numeric vector will be used to override the starting values obtained from the 'mmkin' object.} -\item{solution_type}{Possibility to specify the solution type in case the -automatic choice is not desired} +\item{add_attributes}{Should the starting values used for degradation model +parameters and their distribution and for the error model parameters +be returned as attributes?} } \value{ An S3 object of class 'nlmixr.mmkin', containing the fitted @@ -81,9 +94,11 @@ An mmkin row object is essentially a list of mkinfit objects that have been obtained by fitting the same model to a list of datasets using \link{mkinfit}. } \examples{ +\dontrun{ ds <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")])) names(ds) <- paste("Dataset", 6:10) + f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", cores = 1, quiet = TRUE) @@ -117,7 +132,6 @@ AIC(nlme(f_mmkin_parent["HS", ])) # solution, the two-component error model does not improve it plot(f_nlmixr_fomc_saem) -\dontrun{ sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), diff --git a/man/reexports.Rd b/man/reexports.Rd index ccba7567..d4fc6b96 100644 --- a/man/reexports.Rd +++ b/man/reexports.Rd @@ -1,10 +1,11 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/lrtest.mkinfit.R, R/nlme.mmkin.R +% Please edit documentation in R/lrtest.mkinfit.R, R/nlme.mmkin.R, R/nlmixr.R \docType{import} \name{reexports} \alias{reexports} \alias{lrtest} \alias{nlme} +\alias{nlmixr} \title{Objects exported from other packages} \keyword{internal} \description{ @@ -15,5 +16,7 @@ below to see their documentation. \item{lmtest}{\code{\link[lmtest]{lrtest}}} \item{nlme}{\code{\link[nlme]{nlme}}} + + \item{nlmixr}{\code{\link[nlmixr]{nlmixr}}} }} diff --git a/man/summary.nlmixr.mmkin.Rd b/man/summary.nlmixr.mmkin.Rd index 03f0ffb2..ab8abd5d 100644 --- a/man/summary.nlmixr.mmkin.Rd +++ b/man/summary.nlmixr.mmkin.Rd @@ -2,12 +2,15 @@ % Please edit documentation in R/summary.nlmixr.mmkin.R \name{summary.nlmixr.mmkin} \alias{summary.nlmixr.mmkin} +\alias{print.summary.nlmixr.mmkin} \title{Summary method for class "nlmixr.mmkin"} \usage{ \method{summary}{nlmixr.mmkin}(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) + +\method{print}{summary.nlmixr.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) } \arguments{ -\item{object}{an object of class \link{nlmix.mmkin}} +\item{object}{an object of class \link{nlmixr.mmkin}} \item{data}{logical, indicating whether the full data should be included in the summary.} @@ -19,7 +22,7 @@ included.} \item{\dots}{optional arguments passed to methods like \code{print}.} -\item{x}{an object of class \link{summary.nlmix.mmkin}} +\item{x}{an object of class \link{summary.nlmixr.mmkin}} \item{digits}{Number of digits to use for printing} } @@ -32,9 +35,7 @@ produced} \item{diffs}{The differential equations used in the degradation model} \item{use_of_ff}{Was maximum or minimum use made of formation fractions} \item{data}{The data} -\item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals} \item{confint_back}{Backtransformed parameters, with confidence intervals if available} -\item{confint_errmod}{Error model parameters with confidence intervals} \item{ff}{The estimated formation fractions derived from the fitted model.} \item{distimes}{The DT50 and DT90 values for each observed variable.} @@ -85,12 +86,14 @@ ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { \dontrun{ # Evaluate using mmkin and nlmixr f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, - quiet = TRUE, error_model = "obs", cores = 5) + quiet = TRUE, error_model = "tc", cores = 5) f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo) f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo) f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem") -#f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") -summary(f_nlmixr_dfop_sfo, data = TRUE) +# The following takes a very long time but gives +f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") +AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm) +summary(f_nlmixr_dfop_sfo_sfo, data = TRUE) } } diff --git a/man/summary.saem.mmkin.Rd b/man/summary.saem.mmkin.Rd index 86938d31..67cb3cbb 100644 --- a/man/summary.saem.mmkin.Rd +++ b/man/summary.saem.mmkin.Rd @@ -1,32 +1,30 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/summary.nlmixr.mmkin.R, R/summary.saem.mmkin.R -\name{print.summary.saem.mmkin} -\alias{print.summary.saem.mmkin} +% Please edit documentation in R/summary.saem.mmkin.R +\name{summary.saem.mmkin} \alias{summary.saem.mmkin} +\alias{print.summary.saem.mmkin} \title{Summary method for class "saem.mmkin"} \usage{ -\method{print}{summary.saem.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) - \method{summary}{saem.mmkin}(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) \method{print}{summary.saem.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) } \arguments{ -\item{x}{an object of class \link{summary.saem.mmkin}} - -\item{digits}{Number of digits to use for printing} - -\item{verbose}{Should the summary be verbose?} - -\item{\dots}{optional arguments passed to methods like \code{print}.} - \item{object}{an object of class \link{saem.mmkin}} \item{data}{logical, indicating whether the full data should be included in the summary.} +\item{verbose}{Should the summary be verbose?} + \item{distimes}{logical, indicating whether DT50 and DT90 values should be included.} + +\item{\dots}{optional arguments passed to methods like \code{print}.} + +\item{x}{an object of class \link{summary.saem.mmkin}} + +\item{digits}{Number of digits to use for printing} } \value{ The summary function returns a list based on the \link[saemix:SaemixObject-class]{saemix::SaemixObject} -- cgit v1.2.1 From 1848f6a9c6f763b6ad32df8c5b58e27b30880ea4 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Jun 2021 12:04:19 +0200 Subject: Add system requirements for symengine on Travis nlmixr depends on symengine --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 60e37230..1e2fe241 100644 --- a/.travis.yml +++ b/.travis.yml @@ -8,6 +8,7 @@ addons: packages: - gcc - libgit2-dev + - libmpfr-dev cache: packages repos: CRAN: https://cloud.r-project.org -- cgit v1.2.1 From 2c0721983e8d0b090141882c49780fe50d8fdcde Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Jun 2021 13:46:23 +0200 Subject: Attempt to satisfy nlme version requirement on travis --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 1e2fe241..bc39faf9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -12,6 +12,7 @@ addons: cache: packages repos: CRAN: https://cloud.r-project.org +r_packages: nlme r_github_packages: - jranke/saemixextension@warp_combined script: -- cgit v1.2.1 From 1aeb2c5e3dc4782f0cc7279bf16b9ba06a08f328 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Jun 2021 14:03:43 +0200 Subject: Add packages necessary for shell commands on travis --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index bc39faf9..8f4e1566 100644 --- a/.travis.yml +++ b/.travis.yml @@ -12,7 +12,7 @@ addons: cache: packages repos: CRAN: https://cloud.r-project.org -r_packages: nlme +r_packages: nlme devtools codecov r_github_packages: - jranke/saemixextension@warp_combined script: -- cgit v1.2.1 From 6f78e98e81cbdda9a3caf90641a66df52afb37ac Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Jun 2021 14:36:30 +0200 Subject: Fix travis syntax --- .travis.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 8f4e1566..906010f4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -12,7 +12,10 @@ addons: cache: packages repos: CRAN: https://cloud.r-project.org -r_packages: nlme devtools codecov +r_packages: + - nlme + - devtools + - codecov r_github_packages: - jranke/saemixextension@warp_combined script: -- cgit v1.2.1 From 8bf6bd4289f1a0618376406a6a44dd99aedc692f Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Jun 2021 14:54:46 +0200 Subject: Fix package name for travis config... --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 906010f4..0ae488cb 100644 --- a/.travis.yml +++ b/.travis.yml @@ -15,7 +15,7 @@ repos: r_packages: - nlme - devtools - - codecov + - covr r_github_packages: - jranke/saemixextension@warp_combined script: -- cgit v1.2.1 From 88cf130615a6cde0c4e65d14db32fed7f6e43085 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 12 Jun 2021 11:05:24 +0200 Subject: Small cosmetics --- DESCRIPTION | 2 +- R/dimethenamid_2018.R | 25 +++++++++++++++++++++++++ R/nlmixr.R | 20 +++++++++----------- tests/testthat/test_mixed.R | 2 +- 4 files changed, 36 insertions(+), 13 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index e81fcb32..c6151839 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data Version: 1.0.5 -Date: 2021-06-03 +Date: 2021-06-11 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "jranke@uni-bremen.de", diff --git a/R/dimethenamid_2018.R b/R/dimethenamid_2018.R index 189da618..79018c11 100644 --- a/R/dimethenamid_2018.R +++ b/R/dimethenamid_2018.R @@ -18,4 +18,29 @@ #' \url{http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211} #' @examples #' print(dimethenamid_2018) +#' dmta_ds <- lapply(1:8, function(i) { +#' ds_i <- dimethenamid_2018$ds[[i]]$data +#' ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" +#' ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] +#' ds_i +#' }) +#' names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) +#' dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +#' dmta_ds[["Borstel 1"]] <- NULL +#' dmta_ds[["Borstel 2"]] <- NULL +#' dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +#' dmta_ds[["Elliot 1"]] <- NULL +#' dmta_ds[["Elliot 2"]] <- NULL +#' dfop_sfo3_plus <- mkinmod( +#' DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), +#' M23 = mkinsub("SFO"), +#' M27 = mkinsub("SFO"), +#' M31 = mkinsub("SFO", "M27", sink = FALSE), +#' quiet = TRUE +#' ) +#' f_dmta_mkin_tc <- mmkin( +#' list("DFOP-SFO3+" = dfop_sfo3_plus), +#' dmta_ds, quiet = TRUE, error_model = "tc") +#' nlmixr_model(f_dmta_mkin_tc) # incomplete +#' # nlmixr(f_dmta_mkin_tc, est = "saem") # not supported (yet) "dimethenamid_2018" diff --git a/R/nlmixr.R b/R/nlmixr.R index 98783ca7..6e0b5128 100644 --- a/R/nlmixr.R +++ b/R/nlmixr.R @@ -43,11 +43,11 @@ nlmixr::nlmixr #' ds <- lapply(experimental_data_for_UBA_2019[6:10], #' function(x) subset(x$data[c("name", "time", "value")])) #' names(ds) <- paste("Dataset", 6:10) -#' +#' #' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) #' f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", #' cores = 1, quiet = TRUE) -#' +#' #' f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem") #' f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei") #' @@ -278,20 +278,18 @@ nlmixr_model <- function(object, conf.level = conf.level, random = TRUE) degparms_optim <- degparms_mmkin$fixed - - degparms_optim <- degparms_mmkin$fixed + degparms_optim_back <- backtransform_odeparms(degparms_optim, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions) + degparms_optim_back_names <- names(degparms_optim_back) + names(degparms_optim_back_names) <- names(degparms_optim) if (degparms_start[1] == "auto") { degparms_start <- degparms_optim } degparms_fixed <- object[[1]]$bparms.fixed - degparms_optim_back_names <- names(backtransform_odeparms(degparms_optim, - object[[1]]$mkinmod, - object[[1]]$transform_rates, - object[[1]]$transform_fractions)) - names(degparms_optim_back_names) <- names(degparms_optim) - odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE) odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE) @@ -307,7 +305,7 @@ nlmixr_model <- function(object, ini_block <- "ini({" # Initial values for all degradation parameters - for (parm_name in names(degparms_optim)) { + for (parm_name in names(degparms_start)) { # As initials for state variables are not transformed, # we need to modify the name here as we want to # use the original name in the model block diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R index 5d15530b..9c8a84d7 100644 --- a/tests/testthat/test_mixed.R +++ b/tests/testthat/test_mixed.R @@ -113,7 +113,7 @@ test_that("nlme results are reproducible to some degree", { expect_known_output(print(test_summary, digits = 1), "summary_nlme_biphasic_s.txt") - # k1 just fails the first test (lower bound of the ci), so we need to excluded it + # k1 just fails the first test (lower bound of the ci), so we need to exclude it dfop_no_k1 <- c("parent_0", "k_m1", "f_parent_to_m1", "k2", "g") dfop_sfo_pop_no_k1 <- as.numeric(dfop_sfo_pop[dfop_no_k1]) dfop_sfo_pop <- as.numeric(dfop_sfo_pop) -- cgit v1.2.1 From 28197d5fcbaf85b39f4c032b8180d68b6f6a01b3 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 16 Jun 2021 18:03:22 +0200 Subject: Translate formation fractions to nlmixr language Works for the dimethenamid data, at least for FOCEI. Very little testing yet. The summary function does not yet handle the new transformations of formation fractions (that are in fact very old, as they were used in the very first version of mkin). The test file has no tests yet, just some code that may be used for testing. --- R/dimethenamid_2018.R | 11 ++++- R/nlmixr.R | 79 ++++++++++++++++++++++++++++++----- R/tffm0.R | 46 ++++++++++++++++++++ tests/testthat/test_nlmixr.r | 99 ++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 223 insertions(+), 12 deletions(-) create mode 100644 R/tffm0.R create mode 100644 tests/testthat/test_nlmixr.r diff --git a/R/dimethenamid_2018.R b/R/dimethenamid_2018.R index 79018c11..76b98efe 100644 --- a/R/dimethenamid_2018.R +++ b/R/dimethenamid_2018.R @@ -41,6 +41,13 @@ #' f_dmta_mkin_tc <- mmkin( #' list("DFOP-SFO3+" = dfop_sfo3_plus), #' dmta_ds, quiet = TRUE, error_model = "tc") -#' nlmixr_model(f_dmta_mkin_tc) # incomplete -#' # nlmixr(f_dmta_mkin_tc, est = "saem") # not supported (yet) +#' nlmixr_model(f_dmta_mkin_tc) +#' f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", +#' control = saemControl(print = 500)) +#' summary(f_dmta_nlmixr_saem) +#' plot(f_dmta_nlmixr_saem) +#' f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", +#' control = foceiControl(print = 500)) +#' summary(f_dmta_nlmixr_focei) +#' plot(f_dmta_nlmixr_focei) "dimethenamid_2018" diff --git a/R/nlmixr.R b/R/nlmixr.R index 6e0b5128..9c364c4f 100644 --- a/R/nlmixr.R +++ b/R/nlmixr.R @@ -20,6 +20,9 @@ nlmixr::nlmixr #' @param est Estimation method passed to [nlmixr::nlmixr] #' @param degparms_start Parameter values given as a named numeric vector will #' be used to override the starting values obtained from the 'mmkin' object. +#' @param eta_start Standard deviations on the transformed scale given as a +#' named numeric vector will be used to override the starting values obtained +#' from the 'mmkin' object. #' @param test_log_parms If TRUE, an attempt is made to use more robust starting #' values for population parameters fitted as log parameters in mkin (like #' rate constants) by only considering rate constants that pass the t-test @@ -148,6 +151,8 @@ nlmixr.mmkin <- function(object, data = NULL, error_model = object[[1]]$err_mod, test_log_parms = TRUE, conf.level = 0.6, + degparms_start = "auto", + eta_start = "auto", ..., save = NULL, envir = parent.frame() @@ -155,7 +160,9 @@ nlmixr.mmkin <- function(object, data = NULL, { m_nlmixr <- nlmixr_model(object, est = est, error_model = error_model, add_attributes = TRUE, - test_log_parms = test_log_parms, conf.level = conf.level) + test_log_parms = test_log_parms, conf.level = conf.level, + degparms_start = degparms_start, eta_start = eta_start + ) d_nlmixr <- nlmixr_data(object) mean_dp_start <- attr(m_nlmixr, "mean_dp_start") @@ -164,7 +171,7 @@ nlmixr.mmkin <- function(object, data = NULL, attributes(m_nlmixr) <- NULL fit_time <- system.time({ - f_nlmixr <- nlmixr(m_nlmixr, d_nlmixr, est = est) + f_nlmixr <- nlmixr(m_nlmixr, d_nlmixr, est = est, control = control) }) if (is.null(f_nlmixr$CMT)) { @@ -246,7 +253,8 @@ print.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) nlmixr_model <- function(object, est = c("saem", "focei"), degparms_start = "auto", - test_log_parms = FALSE, conf.level = 0.6, + eta_start = "auto", + test_log_parms = TRUE, conf.level = 0.6, error_model = object[[1]]$err_mod, add_attributes = FALSE) { if (nrow(object) > 1) stop("Only row objects allowed") @@ -278,16 +286,44 @@ nlmixr_model <- function(object, conf.level = conf.level, random = TRUE) degparms_optim <- degparms_mmkin$fixed + + degparms_optim_ilr_names <- grep("^f_.*_ilr", names(degparms_optim), value = TRUE) + obs_vars_ilr <- unique(gsub("f_(.*)_ilr.*$", "\\1", degparms_optim_ilr_names)) + degparms_optim_noilr <- degparms_optim[setdiff(names(degparms_optim), + degparms_optim_ilr_names)] + degparms_optim_back <- backtransform_odeparms(degparms_optim, object[[1]]$mkinmod, object[[1]]$transform_rates, object[[1]]$transform_fractions) - degparms_optim_back_names <- names(degparms_optim_back) - names(degparms_optim_back_names) <- names(degparms_optim) if (degparms_start[1] == "auto") { - degparms_start <- degparms_optim + degparms_start <- degparms_optim_noilr + for (obs_var_ilr in obs_vars_ilr) { + ff_names <- grep(paste0("^f_", obs_var_ilr, "_"), + names(degparms_optim_back), value = TRUE) + f_tffm0 <- tffm0(degparms_optim_back[ff_names]) + f_tffm0_qlogis <- qlogis(f_tffm0) + names(f_tffm0_qlogis) <- paste0("f_", obs_var_ilr, + "_tffm0_", 1:length(f_tffm0), "_qlogis") + degparms_start <- c(degparms_start, f_tffm0_qlogis) + } + } + + if (eta_start[1] == "auto") { + eta_start <- degparms_mmkin$eta[setdiff(names(degparms_optim), + degparms_optim_ilr_names)] + for (obs_var_ilr in obs_vars_ilr) { + ff_n <- length(grep(paste0("^f_", obs_var_ilr, "_"), + names(degparms_optim_back), value = TRUE)) + eta_start_ff <- rep(0.3, ff_n) + names(eta_start_ff) <- paste0("f_", obs_var_ilr, + "_tffm0_", 1:ff_n, "_qlogis") + eta_start <- c(eta_start, eta_start_ff) + } } + + degparms_fixed <- object[[1]]$bparms.fixed odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE) @@ -315,7 +351,7 @@ nlmixr_model <- function(object, as.character(degparms_start[parm_name]), "\n", "eta.", parm_name, " ~ ", - as.character(degparms_mmkin$eta[parm_name]), + as.character(eta_start[parm_name]), "\n" ) } @@ -394,7 +430,7 @@ nlmixr_model <- function(object, } # Population initial values for log rate constants - for (parm_name in grep("^log_", names(degparms_optim), value = TRUE)) { + for (parm_name in grep("^log_", names(degparms_start), value = TRUE)) { model_block <- paste0( model_block, gsub("^log_", "", parm_name), " = ", @@ -402,13 +438,36 @@ nlmixr_model <- function(object, } # Population initial values for logit transformed parameters - for (parm_name in grep("_qlogis$", names(degparms_optim), value = TRUE)) { + for (parm_name in grep("_qlogis$", names(degparms_start), value = TRUE)) { model_block <- paste0( model_block, - degparms_optim_back_names[parm_name], " = ", + gsub("_qlogis$", "", parm_name), " = ", "expit(", parm_name, " + eta.", parm_name, ")\n") } + # Calculate formation fractions from tffm0 transformed values + for (obs_var_ilr in obs_vars_ilr) { + ff_names <- grep(paste0("^f_", obs_var_ilr, "_"), + names(degparms_optim_back), value = TRUE) + pattern <- paste0("^f_", obs_var_ilr, "_to_(.*)$") + target_vars <- gsub(pattern, "\\1", + grep(paste0("^f_", obs_var_ilr, "_to_"), names(degparms_optim_back), value = TRUE)) + for (i in 1:length(target_vars)) { + ff_name <- ff_names[i] + ff_line <- paste0(ff_name, " = f_", obs_var_ilr, "_tffm0_", i) + if (i > 1) { + for (j in (i - 1):1) { + ff_line <- paste0(ff_line, " * (1 - f_", obs_var_ilr, "_tffm0_", j , ")") + } + } + model_block <- paste0( + model_block, + ff_line, + "\n" + ) + } + } + # Differential equations model_block <- paste0( model_block, diff --git a/R/tffm0.R b/R/tffm0.R new file mode 100644 index 00000000..25787962 --- /dev/null +++ b/R/tffm0.R @@ -0,0 +1,46 @@ +#' Transform formation fractions as in the first published mkin version +#' +#' The transformed fractions can be restricted between 0 and 1 in model +#' optimisations. Therefore this transformation was used originally in mkin. It +#' was later replaced by the [ilr] transformation because the ilr transformed +#' fractions can assumed to follow normal distribution. As the ilr +#' transformation is not available in [RxODE] and can therefore not be used in +#' the nlmixr modelling language, this transformation is currently used for +#' translating mkin models with formation fractions to more than one target +#' compartment for fitting with nlmixr in [nlmixr_model]. However, +#' this implementation cannot be used there, as it is not accessible +#' from RxODE. +#' +#' @param ff Vector of untransformed formation fractions. The sum +#' must be smaller or equal to one +#' @param ff_trans +#' @return A vector of the transformed formation fractions +#' @export +#' @examples +#' ff_example <- c( +#' 0.10983681, 0.09035905, 0.08399383 +#' ) +#' ff_example_trans <- tffm0(ff_example) +#' invtffm0(ff_example_trans) +tffm0 <- function(ff) { + n <- length(ff) + res <- numeric(n) + f_remaining <- 1 + for (i in 1:n) { + res[i] <- ff[i]/f_remaining + f_remaining <- f_remaining - ff[i] + } + return(res) +} +#' @rdname tffm0 +#' @return +invtffm0 <- function(ff_trans) { + n <- length(ff_trans) + res <- numeric(n) + f_remaining <- 1 + for (i in 1:n) { + res[i] <- ff_trans[i] * f_remaining + f_remaining <- f_remaining - res[i] + } + return(res) +} diff --git a/tests/testthat/test_nlmixr.r b/tests/testthat/test_nlmixr.r new file mode 100644 index 00000000..e3bd3d66 --- /dev/null +++ b/tests/testthat/test_nlmixr.r @@ -0,0 +1,99 @@ + + +dmta_ds <- lapply(1:8, function(i) { + ds_i <- dimethenamid_2018$ds[[i]]$data + ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" + ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] + ds_i +}) +names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) +dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +dmta_ds[["Borstel 1"]] <- NULL +dmta_ds[["Borstel 2"]] <- NULL +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL +dfop_sfo3_plus <- mkinmod( + DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + quiet = TRUE +) +f_dmta_mkin_tc <- mmkin( + list("DFOP-SFO3+" = dfop_sfo3_plus), + dmta_ds, quiet = TRUE, error_model = "tc") + +d_dmta_nlmixr <- nlmixr_data(f_dmta_mkin_tc) +m_dmta_nlmixr <- function () +{ + ini({ + DMTA_0 = 98.7697627680706 + eta.DMTA_0 ~ 2.35171765917765 + log_k_M23 = -3.92162409637283 + eta.log_k_M23 ~ 0.549278519419884 + log_k_M27 = -4.33774620773911 + eta.log_k_M27 ~ 0.864474956685295 + log_k_M31 = -4.24767627688461 + eta.log_k_M31 ~ 0.750297149164171 + f_DMTA_tffm0_1_qlogis = -2.092409 + eta.f_DMTA_tffm0_1_qlogis ~ 0.3 + f_DMTA_tffm0_2_qlogis = -2.180576 + eta.f_DMTA_tffm0_2_qlogis ~ 0.3 + f_DMTA_tffm0_3_qlogis = -2.142672 + eta.f_DMTA_tffm0_3_qlogis ~ 0.3 + log_k1 = -2.2341008812259 + eta.log_k1 ~ 0.902976221565793 + log_k2 = -3.7762779983269 + eta.log_k2 ~ 1.57684519529298 + g_qlogis = 0.450175725479389 + eta.g_qlogis ~ 3.0851335687675 + sigma_low_DMTA = 0.697933852349996 + rsd_high_DMTA = 0.0257724286053519 + sigma_low_M23 = 0.697933852349996 + rsd_high_M23 = 0.0257724286053519 + sigma_low_M27 = 0.697933852349996 + rsd_high_M27 = 0.0257724286053519 + sigma_low_M31 = 0.697933852349996 + rsd_high_M31 = 0.0257724286053519 + }) + model({ + DMTA_0_model = DMTA_0 + eta.DMTA_0 + DMTA(0) = DMTA_0_model + k_M23 = exp(log_k_M23 + eta.log_k_M23) + k_M27 = exp(log_k_M27 + eta.log_k_M27) + k_M31 = exp(log_k_M31 + eta.log_k_M31) + k1 = exp(log_k1 + eta.log_k1) + k2 = exp(log_k2 + eta.log_k2) + g = expit(g_qlogis + eta.g_qlogis) + f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) + f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) + f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) + f_DMTA_to_M23 = f_DMTA_tffm0_1 + f_DMTA_to_M27 = (1 - f_DMTA_tffm0_1) * f_DMTA_tffm0_2 + f_DMTA_to_M31 = (1 - f_DMTA_tffm0_1) * (1 - f_DMTA_tffm0_2) * f_DMTA_tffm0_3 + d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - + g) * exp(-k2 * time))) * DMTA + d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + + k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + + (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23 + d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + + k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + + (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + + k_M31 * M31 + d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + + k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + + (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31 + DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA) + M23 ~ add(sigma_low_M23) + prop(rsd_high_M23) + M27 ~ add(sigma_low_M27) + prop(rsd_high_M27) + M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) + }) +} +m_dmta_nlmixr_mkin <- nlmixr_model(f_dmta_mkin_tc, test_log_parms = TRUE) +f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", control = saemControl(print = 250)) +f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", control = foceiControl(print = 250)) +plot(f_dmta_nlmixr_saem) +plot(f_dmta_nlmixr_focei) + -- cgit v1.2.1 From 05baf3bf92cba127fd2319b779db78be86170e5e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 17 Jun 2021 13:58:34 +0200 Subject: Let backtransform_odeparms handle nlmixr formation fractions Also adapt summary.nlmixr.mmkin to correctly handle the way formation fractions are translated to nlmixr --- NAMESPACE | 2 + R/dimethenamid_2018.R | 14 ++-- R/summary.nlmixr.mmkin.R | 14 ++-- R/tffm0.R | 6 +- R/transform_odeparms.R | 13 ++- check.log | 11 +-- man/dimethenamid_2018.Rd | 36 ++++++++ man/nlmixr.mmkin.Rd | 15 +++- man/tffm0.Rd | 42 ++++++++++ test.log | 45 +++++----- tests/testthat/test_nlmixr.r | 194 +++++++++++++++++++++---------------------- 11 files changed, 243 insertions(+), 149 deletions(-) create mode 100644 man/tffm0.Rd diff --git a/NAMESPACE b/NAMESPACE index 0f61396d..aa40b570 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -62,6 +62,7 @@ export(f_time_norm_focus) export(get_deg_func) export(ilr) export(invilr) +export(invtffm0) export(loftest) export(logistic.solution) export(lrtest) @@ -101,6 +102,7 @@ export(saem) export(saemix_data) export(saemix_model) export(sigma_twocomp) +export(tffm0) export(transform_odeparms) import(deSolve) import(graphics) diff --git a/R/dimethenamid_2018.R b/R/dimethenamid_2018.R index 76b98efe..6e0bda0c 100644 --- a/R/dimethenamid_2018.R +++ b/R/dimethenamid_2018.R @@ -31,6 +31,7 @@ #' dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) #' dmta_ds[["Elliot 1"]] <- NULL #' dmta_ds[["Elliot 2"]] <- NULL +#' \dontrun{ #' dfop_sfo3_plus <- mkinmod( #' DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), #' M23 = mkinsub("SFO"), @@ -42,12 +43,15 @@ #' list("DFOP-SFO3+" = dfop_sfo3_plus), #' dmta_ds, quiet = TRUE, error_model = "tc") #' nlmixr_model(f_dmta_mkin_tc) -#' f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", -#' control = saemControl(print = 500)) -#' summary(f_dmta_nlmixr_saem) -#' plot(f_dmta_nlmixr_saem) #' f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", -#' control = foceiControl(print = 500)) +#' control = nlmixr::foceiControl(print = 500)) #' summary(f_dmta_nlmixr_focei) #' plot(f_dmta_nlmixr_focei) +#' # saem has a problem with this model/data combination, maybe because of the +#' # overparameterised error model, to be investigated +#' #f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", +#' # control = saemControl(print = 500)) +#' #summary(f_dmta_nlmixr_saem) +#' #plot(f_dmta_nlmixr_saem) +#' } "dimethenamid_2018" diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R index f2d7c607..a023f319 100644 --- a/R/summary.nlmixr.mmkin.R +++ b/R/summary.nlmixr.mmkin.R @@ -85,11 +85,11 @@ summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes mod_vars <- names(object$mkinmod$diffs) - pnames <- names(object$mean_dp_start) - np <- length(pnames) - conf.int <- confint(object$nm) - confint_trans <- as.matrix(conf.int[pnames, c(1, 3, 4)]) + dpnames <- setdiff(rownames(conf.int), names(object$mean_ep_start)) + ndp <- length(dpnames) + + confint_trans <- as.matrix(conf.int[dpnames, c(1, 3, 4)]) colnames(confint_trans) <- c("est.", "lower", "upper") bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, @@ -100,7 +100,7 @@ summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes # with the exception of sets of formation fractions (single fractions are OK). f_names_skip <- character(0) for (box in mod_vars) { # Figure out sets of fractions to skip - f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE) + f_names <- grep(paste("^f", box, sep = "_"), dpnames, value = TRUE) n_paths <- length(f_names) if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) } @@ -109,7 +109,7 @@ summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes dimnames = list(bpnames, colnames(confint_trans))) confint_back[, "est."] <- bp - for (pname in pnames) { + for (pname in dpnames) { if (!pname %in% f_names_skip) { par.lower <- confint_trans[pname, "lower"] par.upper <- confint_trans[pname, "upper"] @@ -131,7 +131,7 @@ summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes object$corFixed <- array( t(varFix/stdFix)/stdFix, dim(varFix), - list(pnames, pnames)) + list(dpnames, dpnames)) object$confint_trans <- confint_trans object$confint_back <- confint_back diff --git a/R/tffm0.R b/R/tffm0.R index 25787962..bb5f4cf5 100644 --- a/R/tffm0.R +++ b/R/tffm0.R @@ -13,7 +13,8 @@ #' #' @param ff Vector of untransformed formation fractions. The sum #' must be smaller or equal to one -#' @param ff_trans +#' @param ff_trans Vector of transformed formation fractions that can be +#' restricted to the interval from 0 to 1 #' @return A vector of the transformed formation fractions #' @export #' @examples @@ -33,7 +34,8 @@ tffm0 <- function(ff) { return(res) } #' @rdname tffm0 -#' @return +#' @export +#' @return A vector of backtransformed formation fractions for natural use in degradation models invtffm0 <- function(ff_trans) { n <- length(ff_trans) res <- numeric(n) diff --git a/R/transform_odeparms.R b/R/transform_odeparms.R index 4fe4e5c2..174e7c2d 100644 --- a/R/transform_odeparms.R +++ b/R/transform_odeparms.R @@ -229,13 +229,18 @@ backtransform_odeparms <- function(transparms, mkinmod, if (length(trans_f) > 0) { if(transform_fractions) { if (any(grepl("qlogis", names(trans_f)))) { - parms[f_names] <- plogis(trans_f) + f_tmp <- plogis(trans_f) + if (any(grepl("_tffm0_.*_qlogis$", names(f_tmp)))) { + parms[f_names] <- invtffm0(f_tmp) + } else { + parms[f_names] <- f_tmp + } } else { - f <- invilr(trans_f) + f_tmp <- invilr(trans_f) if (spec[[box]]$sink) { - parms[f_names] <- f[1:length(f)-1] + parms[f_names] <- f_tmp[1:length(f_tmp)-1] } else { - parms[f_names] <- f + parms[f_names] <- f_tmp } } } else { diff --git a/check.log b/check.log index 2627695d..df344926 100644 --- a/check.log +++ b/check.log @@ -57,10 +57,7 @@ Maintainer: ‘Johannes Ranke ’ * checking data for ASCII and uncompressed saves ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK -* checking examples ... NOTE -Examples with CPU (user + system) or elapsed time > 5s - user system elapsed -nlmixr.mmkin 8.129 0.375 5.384 +* checking examples ... OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... SKIPPED * checking for unstated dependencies in vignettes ... OK @@ -71,9 +68,5 @@ nlmixr.mmkin 8.129 0.375 5.384 * checking for detritus in the temp directory ... OK * DONE -Status: 1 NOTE -See - ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ -for details. - +Status: OK diff --git a/man/dimethenamid_2018.Rd b/man/dimethenamid_2018.Rd index b6f761e8..93fcad26 100644 --- a/man/dimethenamid_2018.Rd +++ b/man/dimethenamid_2018.Rd @@ -31,5 +31,41 @@ specific pieces of information in the comments. } \examples{ print(dimethenamid_2018) +dmta_ds <- lapply(1:8, function(i) { + ds_i <- dimethenamid_2018$ds[[i]]$data + ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" + ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] + ds_i +}) +names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) +dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +dmta_ds[["Borstel 1"]] <- NULL +dmta_ds[["Borstel 2"]] <- NULL +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL +\dontrun{ +dfop_sfo3_plus <- mkinmod( + DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + quiet = TRUE +) +f_dmta_mkin_tc <- mmkin( + list("DFOP-SFO3+" = dfop_sfo3_plus), + dmta_ds, quiet = TRUE, error_model = "tc") +nlmixr_model(f_dmta_mkin_tc) +f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", + control = nlmixr::foceiControl(print = 500)) +summary(f_dmta_nlmixr_focei) +plot(f_dmta_nlmixr_focei) +# saem has a problem with this model/data combination, maybe because of the +# overparameterised error model, to be investigated +#f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", +# control = saemControl(print = 500)) +#summary(f_dmta_nlmixr_saem) +#plot(f_dmta_nlmixr_saem) +} } \keyword{datasets} diff --git a/man/nlmixr.mmkin.Rd b/man/nlmixr.mmkin.Rd index 4ab30272..0f4f41a2 100644 --- a/man/nlmixr.mmkin.Rd +++ b/man/nlmixr.mmkin.Rd @@ -16,6 +16,8 @@ error_model = object[[1]]$err_mod, test_log_parms = TRUE, conf.level = 0.6, + degparms_start = "auto", + eta_start = "auto", ..., save = NULL, envir = parent.frame() @@ -27,7 +29,8 @@ nlmixr_model( object, est = c("saem", "focei"), degparms_start = "auto", - test_log_parms = FALSE, + eta_start = "auto", + test_log_parms = TRUE, conf.level = 0.6, error_model = object[[1]]$err_mod, add_attributes = FALSE @@ -58,6 +61,13 @@ when calculating mean degradation parameters using \link{mean_degparms}.} \item{conf.level}{Possibility to adjust the required confidence level for parameter that are tested if requested by 'test_log_parms'.} +\item{degparms_start}{Parameter values given as a named numeric vector will +be used to override the starting values obtained from the 'mmkin' object.} + +\item{eta_start}{Standard deviations on the transformed scale given as a +named numeric vector will be used to override the starting values obtained +from the 'mmkin' object.} + \item{\dots}{Passed to \link{nlmixr_model}} \item{save}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} @@ -68,9 +78,6 @@ for parameter that are tested if requested by 'test_log_parms'.} \item{digits}{Number of digits to use for printing} -\item{degparms_start}{Parameter values given as a named numeric vector will -be used to override the starting values obtained from the 'mmkin' object.} - \item{add_attributes}{Should the starting values used for degradation model parameters and their distribution and for the error model parameters be returned as attributes?} diff --git a/man/tffm0.Rd b/man/tffm0.Rd new file mode 100644 index 00000000..46978d5e --- /dev/null +++ b/man/tffm0.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/tffm0.R +\name{tffm0} +\alias{tffm0} +\alias{invtffm0} +\title{Transform formation fractions as in the first published mkin version} +\usage{ +tffm0(ff) + +invtffm0(ff_trans) +} +\arguments{ +\item{ff}{Vector of untransformed formation fractions. The sum +must be smaller or equal to one} + +\item{ff_trans}{Vector of transformed formation fractions that can be +restricted to the interval from 0 to 1} +} +\value{ +A vector of the transformed formation fractions + +A vector of backtransformed formation fractions for natural use in degradation models +} +\description{ +The transformed fractions can be restricted between 0 and 1 in model +optimisations. Therefore this transformation was used originally in mkin. It +was later replaced by the \link{ilr} transformation because the ilr transformed +fractions can assumed to follow normal distribution. As the ilr +transformation is not available in \link{RxODE} and can therefore not be used in +the nlmixr modelling language, this transformation is currently used for +translating mkin models with formation fractions to more than one target +compartment for fitting with nlmixr in \link{nlmixr_model}. However, +this implementation cannot be used there, as it is not accessible +from RxODE. +} +\examples{ +ff_example <- c( + 0.10983681, 0.09035905, 0.08399383 +) +ff_example_trans <- tffm0(ff_example) +invtffm0(ff_example_trans) +} diff --git a/test.log b/test.log index f2a60729..6ef8191f 100644 --- a/test.log +++ b/test.log @@ -3,17 +3,17 @@ Loading required package: parallel ℹ Testing mkin ✔ | OK F W S | Context ✔ | 5 | AIC calculation -✔ | 5 | Analytical solutions for coupled models [3.3 s] +✔ | 5 | Analytical solutions for coupled models [3.5 s] ✔ | 5 | Calculation of Akaike weights ✔ | 2 | Export dataset for reading into CAKE -✔ | 12 | Confidence intervals and p-values [1.3 s] +✔ | 12 | Confidence intervals and p-values [1.0 s] ✔ | 14 | Error model fitting [4.7 s] ✔ | 5 | Time step normalisation -✔ | 4 | Calculation of FOCUS chi2 error levels [0.5 s] +✔ | 4 | Calculation of FOCUS chi2 error levels [0.6 s] ✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8 s] ✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s] ✔ | 1 | Fitting the logistic model [0.2 s] -✔ | 35 1 | Nonlinear mixed-effects models [27.1 s] +✔ | 35 1 | Nonlinear mixed-effects models [26.9 s] ──────────────────────────────────────────────────────────────────────────────── Skip (test_mixed.R:161:3): saem results are reproducible for biphasic fits Reason: Fitting with saemix takes around 10 minutes when using deSolve @@ -21,33 +21,36 @@ Reason: Fitting with saemix takes around 10 minutes when using deSolve ✔ | 2 | Test dataset classes mkinds and mkindsg ✔ | 10 | Special cases of mkinfit calls [0.4 s] ✔ | 1 | mkinfit features [0.3 s] -✔ | 8 | mkinmod model generation and printing [0.3 s] +✔ | 8 | mkinmod model generation and printing [0.2 s] ✔ | 3 | Model predictions with mkinpredict [0.3 s] -✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.7 s] -✔ | 9 | Nonlinear mixed-effects models with nlme [8.1 s] -✖ | 14 2 | Plotting [1.9 s] +✔ | 14 2 | Evaluations according to 2015 NAFTA guidance [1.3 s] ──────────────────────────────────────────────────────────────────────────────── -Failure (test_plot.R:40:5): Plotting mkinfit, mmkin and mixed model objects is reproducible -Figures don't match: mixed-model-fit-for-saem-object-with-saemix-transformations.svg - - -Failure (test_plot.R:55:5): Plotting mkinfit, mmkin and mixed model objects is reproducible -Figures don't match: mixed-model-fit-for-saem-object-with-mkin-transformations.svg +Skip (test_nafta.R:25:5): Test data from Appendix B are correctly evaluated +Reason: getRversion() >= "4.1.0" is TRUE +Skip (test_nafta.R:53:5): Test data from Appendix D are correctly evaluated +Reason: getRversion() >= "4.1.0" is TRUE +──────────────────────────────────────────────────────────────────────────────── +✔ | 9 | Nonlinear mixed-effects models with nlme [8.1 s] +✔ | 0 1 | Plotting [0.8 s] +──────────────────────────────────────────────────────────────────────────────── +Skip (test_plot.R:18:3): Plotting mkinfit, mmkin and mixed model objects is reproducible +Reason: getRversion() >= "4.1.0" is TRUE ──────────────────────────────────────────────────────────────────────────────── ✔ | 4 | Residuals extracted from mkinfit models ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s] -✔ | 7 | Fitting the SFORB model [3.9 s] +✔ | 7 | Fitting the SFORB model [3.8 s] ✔ | 1 | Summaries of old mkinfit objects ✔ | 4 | Summary [0.1 s] -✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2 s] -✔ | 9 | Hypothesis tests [8.2 s] -✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.4 s] +✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s] +✔ | 9 | Hypothesis tests [8.5 s] +✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 70.0 s +Duration: 68.2 s ── Skipped tests ────────────────────────────────────────────────────────────── -● Fitting with saemix takes around 10 minutes when using deSolve (1) +• Fitting with saemix takes around 10 minutes when using deSolve (1) +• getRversion() >= "4.1.0" is TRUE (3) -[ FAIL 2 | WARN 0 | SKIP 1 | PASS 204 ] +[ FAIL 0 | WARN 0 | SKIP 4 | PASS 188 ] diff --git a/tests/testthat/test_nlmixr.r b/tests/testthat/test_nlmixr.r index e3bd3d66..dcbb50ac 100644 --- a/tests/testthat/test_nlmixr.r +++ b/tests/testthat/test_nlmixr.r @@ -1,99 +1,99 @@ -dmta_ds <- lapply(1:8, function(i) { - ds_i <- dimethenamid_2018$ds[[i]]$data - ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" - ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] - ds_i -}) -names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) -dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) -dmta_ds[["Borstel 1"]] <- NULL -dmta_ds[["Borstel 2"]] <- NULL -dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) -dmta_ds[["Elliot 1"]] <- NULL -dmta_ds[["Elliot 2"]] <- NULL -dfop_sfo3_plus <- mkinmod( - DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), - M23 = mkinsub("SFO"), - M27 = mkinsub("SFO"), - M31 = mkinsub("SFO", "M27", sink = FALSE), - quiet = TRUE -) -f_dmta_mkin_tc <- mmkin( - list("DFOP-SFO3+" = dfop_sfo3_plus), - dmta_ds, quiet = TRUE, error_model = "tc") - -d_dmta_nlmixr <- nlmixr_data(f_dmta_mkin_tc) -m_dmta_nlmixr <- function () -{ - ini({ - DMTA_0 = 98.7697627680706 - eta.DMTA_0 ~ 2.35171765917765 - log_k_M23 = -3.92162409637283 - eta.log_k_M23 ~ 0.549278519419884 - log_k_M27 = -4.33774620773911 - eta.log_k_M27 ~ 0.864474956685295 - log_k_M31 = -4.24767627688461 - eta.log_k_M31 ~ 0.750297149164171 - f_DMTA_tffm0_1_qlogis = -2.092409 - eta.f_DMTA_tffm0_1_qlogis ~ 0.3 - f_DMTA_tffm0_2_qlogis = -2.180576 - eta.f_DMTA_tffm0_2_qlogis ~ 0.3 - f_DMTA_tffm0_3_qlogis = -2.142672 - eta.f_DMTA_tffm0_3_qlogis ~ 0.3 - log_k1 = -2.2341008812259 - eta.log_k1 ~ 0.902976221565793 - log_k2 = -3.7762779983269 - eta.log_k2 ~ 1.57684519529298 - g_qlogis = 0.450175725479389 - eta.g_qlogis ~ 3.0851335687675 - sigma_low_DMTA = 0.697933852349996 - rsd_high_DMTA = 0.0257724286053519 - sigma_low_M23 = 0.697933852349996 - rsd_high_M23 = 0.0257724286053519 - sigma_low_M27 = 0.697933852349996 - rsd_high_M27 = 0.0257724286053519 - sigma_low_M31 = 0.697933852349996 - rsd_high_M31 = 0.0257724286053519 - }) - model({ - DMTA_0_model = DMTA_0 + eta.DMTA_0 - DMTA(0) = DMTA_0_model - k_M23 = exp(log_k_M23 + eta.log_k_M23) - k_M27 = exp(log_k_M27 + eta.log_k_M27) - k_M31 = exp(log_k_M31 + eta.log_k_M31) - k1 = exp(log_k1 + eta.log_k1) - k2 = exp(log_k2 + eta.log_k2) - g = expit(g_qlogis + eta.g_qlogis) - f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) - f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) - f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) - f_DMTA_to_M23 = f_DMTA_tffm0_1 - f_DMTA_to_M27 = (1 - f_DMTA_tffm0_1) * f_DMTA_tffm0_2 - f_DMTA_to_M31 = (1 - f_DMTA_tffm0_1) * (1 - f_DMTA_tffm0_2) * f_DMTA_tffm0_3 - d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - - g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - - g) * exp(-k2 * time))) * DMTA - d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + - k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + - (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23 - d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + - k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + - (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + - k_M31 * M31 - d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + - k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + - (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31 - DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA) - M23 ~ add(sigma_low_M23) + prop(rsd_high_M23) - M27 ~ add(sigma_low_M27) + prop(rsd_high_M27) - M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) - }) -} -m_dmta_nlmixr_mkin <- nlmixr_model(f_dmta_mkin_tc, test_log_parms = TRUE) -f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", control = saemControl(print = 250)) -f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", control = foceiControl(print = 250)) -plot(f_dmta_nlmixr_saem) -plot(f_dmta_nlmixr_focei) - +# dmta_ds <- lapply(1:8, function(i) { +# ds_i <- dimethenamid_2018$ds[[i]]$data +# ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" +# ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] +# ds_i +# }) +# names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) +# dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +# dmta_ds[["Borstel 1"]] <- NULL +# dmta_ds[["Borstel 2"]] <- NULL +# dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +# dmta_ds[["Elliot 1"]] <- NULL +# dmta_ds[["Elliot 2"]] <- NULL +# dfop_sfo3_plus <- mkinmod( +# DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), +# M23 = mkinsub("SFO"), +# M27 = mkinsub("SFO"), +# M31 = mkinsub("SFO", "M27", sink = FALSE), +# quiet = TRUE +# ) +# f_dmta_mkin_tc <- mmkin( +# list("DFOP-SFO3+" = dfop_sfo3_plus), +# dmta_ds, quiet = TRUE, error_model = "tc") +# +# d_dmta_nlmixr <- nlmixr_data(f_dmta_mkin_tc) +# m_dmta_nlmixr <- function () +# { +# ini({ +# DMTA_0 = 98.7697627680706 +# eta.DMTA_0 ~ 2.35171765917765 +# log_k_M23 = -3.92162409637283 +# eta.log_k_M23 ~ 0.549278519419884 +# log_k_M27 = -4.33774620773911 +# eta.log_k_M27 ~ 0.864474956685295 +# log_k_M31 = -4.24767627688461 +# eta.log_k_M31 ~ 0.750297149164171 +# f_DMTA_tffm0_1_qlogis = -2.092409 +# eta.f_DMTA_tffm0_1_qlogis ~ 0.3 +# f_DMTA_tffm0_2_qlogis = -2.180576 +# eta.f_DMTA_tffm0_2_qlogis ~ 0.3 +# f_DMTA_tffm0_3_qlogis = -2.142672 +# eta.f_DMTA_tffm0_3_qlogis ~ 0.3 +# log_k1 = -2.2341008812259 +# eta.log_k1 ~ 0.902976221565793 +# log_k2 = -3.7762779983269 +# eta.log_k2 ~ 1.57684519529298 +# g_qlogis = 0.450175725479389 +# eta.g_qlogis ~ 3.0851335687675 +# sigma_low_DMTA = 0.697933852349996 +# rsd_high_DMTA = 0.0257724286053519 +# sigma_low_M23 = 0.697933852349996 +# rsd_high_M23 = 0.0257724286053519 +# sigma_low_M27 = 0.697933852349996 +# rsd_high_M27 = 0.0257724286053519 +# sigma_low_M31 = 0.697933852349996 +# rsd_high_M31 = 0.0257724286053519 +# }) +# model({ +# DMTA_0_model = DMTA_0 + eta.DMTA_0 +# DMTA(0) = DMTA_0_model +# k_M23 = exp(log_k_M23 + eta.log_k_M23) +# k_M27 = exp(log_k_M27 + eta.log_k_M27) +# k_M31 = exp(log_k_M31 + eta.log_k_M31) +# k1 = exp(log_k1 + eta.log_k1) +# k2 = exp(log_k2 + eta.log_k2) +# g = expit(g_qlogis + eta.g_qlogis) +# f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) +# f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) +# f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) +# f_DMTA_to_M23 = f_DMTA_tffm0_1 +# f_DMTA_to_M27 = (1 - f_DMTA_tffm0_1) * f_DMTA_tffm0_2 +# f_DMTA_to_M31 = (1 - f_DMTA_tffm0_1) * (1 - f_DMTA_tffm0_2) * f_DMTA_tffm0_3 +# d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - +# g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - +# g) * exp(-k2 * time))) * DMTA +# d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + +# k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +# (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23 +# d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + +# k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +# (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + +# k_M31 * M31 +# d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + +# k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +# (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31 +# DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA) +# M23 ~ add(sigma_low_M23) + prop(rsd_high_M23) +# M27 ~ add(sigma_low_M27) + prop(rsd_high_M27) +# M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) +# }) +# } +# m_dmta_nlmixr_mkin <- nlmixr_model(f_dmta_mkin_tc, test_log_parms = TRUE) +# f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", control = saemControl(print = 250)) +# f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", control = foceiControl(print = 250)) +# plot(f_dmta_nlmixr_saem) +# plot(f_dmta_nlmixr_focei) +# -- cgit v1.2.1 From 255430279d65bfe92093d48c9a586b062a38303d Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 22 Jun 2021 15:15:02 +0200 Subject: Update development version of online docs --- _pkgdown.yml | 1 + docs/dev/pkgdown.yml | 2 +- docs/dev/reference/dimethenamid_2018-1.png | Bin 0 -> 264364 bytes docs/dev/reference/dimethenamid_2018.html | 288 +- docs/dev/reference/index.html | 6 + docs/dev/reference/nlmixr.mmkin.html | 9782 ++-------------------------- docs/dev/reference/tffm0.html | 226 + docs/dev/sitemap.xml | 3 + 8 files changed, 1019 insertions(+), 9289 deletions(-) create mode 100644 docs/dev/reference/dimethenamid_2018-1.png create mode 100644 docs/dev/reference/tffm0.html diff --git a/_pkgdown.yml b/_pkgdown.yml index 50c0685f..f0a95468 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -87,6 +87,7 @@ reference: - mkinpredict - transform_odeparms - ilr + - tffm0 - logLik.mkinfit - residuals.mkinfit - nobs.mkinfit diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 0b01e008..b2c50e79 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-06-11T09:09Z +last_built: 2021-06-17T12:41Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/dimethenamid_2018-1.png b/docs/dev/reference/dimethenamid_2018-1.png new file mode 100644 index 00000000..52b8a2be Binary files /dev/null and b/docs/dev/reference/dimethenamid_2018-1.png differ diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index a06599df..e255765e 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> mkin - 1.0.3.9000 + 1.0.5
                                                                @@ -203,7 +203,291 @@ specific pieces of information in the comments.

                                                                #> Elliot 2 0.75 33.37 23 #> Flaach 0.40 NA 20 #> BBA 2.2 0.40 NA 20 -#> BBA 2.3 0.40 NA 20 +#> BBA 2.3 0.40 NA 20
                                                                dmta_ds <- lapply(1:8, function(i) { + ds_i <- dimethenamid_2018$ds[[i]]$data + ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" + ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] + ds_i +}) +names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) +dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +dmta_ds[["Borstel 1"]] <- NULL +dmta_ds[["Borstel 2"]] <- NULL +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL +# \dontrun{ +dfop_sfo3_plus <- mkinmod( + DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + quiet = TRUE +) +f_dmta_mkin_tc <- mmkin( + list("DFOP-SFO3+" = dfop_sfo3_plus), + dmta_ds, quiet = TRUE, error_model = "tc") +nlmixr_model(f_dmta_mkin_tc) +
                                                                #> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
                                                                #> function () +#> { +#> ini({ +#> DMTA_0 = 98.7697627680706 +#> eta.DMTA_0 ~ 2.35171765917765 +#> log_k_M23 = -3.92162409637283 +#> eta.log_k_M23 ~ 0.549278519419884 +#> log_k_M27 = -4.33774620773911 +#> eta.log_k_M27 ~ 0.864474956685295 +#> log_k_M31 = -4.24767627688461 +#> eta.log_k_M31 ~ 0.750297149164171 +#> log_k1 = -2.2341008812259 +#> eta.log_k1 ~ 0.902976221565793 +#> log_k2 = -3.7762779983269 +#> eta.log_k2 ~ 1.57684519529298 +#> g_qlogis = 0.450175725479389 +#> eta.g_qlogis ~ 3.0851335687675 +#> f_DMTA_tffm0_1_qlogis = -2.09240906629456 +#> eta.f_DMTA_tffm0_1_qlogis ~ 0.3 +#> f_DMTA_tffm0_2_qlogis = -2.18057573598794 +#> eta.f_DMTA_tffm0_2_qlogis ~ 0.3 +#> f_DMTA_tffm0_3_qlogis = -2.14267187609763 +#> eta.f_DMTA_tffm0_3_qlogis ~ 0.3 +#> sigma_low_DMTA = 0.697933852349996 +#> rsd_high_DMTA = 0.0257724286053519 +#> sigma_low_M23 = 0.697933852349996 +#> rsd_high_M23 = 0.0257724286053519 +#> sigma_low_M27 = 0.697933852349996 +#> rsd_high_M27 = 0.0257724286053519 +#> sigma_low_M31 = 0.697933852349996 +#> rsd_high_M31 = 0.0257724286053519 +#> }) +#> model({ +#> DMTA_0_model = DMTA_0 + eta.DMTA_0 +#> DMTA(0) = DMTA_0_model +#> k_M23 = exp(log_k_M23 + eta.log_k_M23) +#> k_M27 = exp(log_k_M27 + eta.log_k_M27) +#> k_M31 = exp(log_k_M31 + eta.log_k_M31) +#> k1 = exp(log_k1 + eta.log_k1) +#> k2 = exp(log_k2 + eta.log_k2) +#> g = expit(g_qlogis + eta.g_qlogis) +#> f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) +#> f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) +#> f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) +#> f_DMTA_to_M23 = f_DMTA_tffm0_1 +#> f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1) +#> f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * +#> (1 - f_DMTA_tffm0_1) +#> d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - +#> g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - +#> g) * exp(-k2 * time))) * DMTA +#> d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + +#> k_M31 * M31 +#> d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31 +#> DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA) +#> M23 ~ add(sigma_low_M23) + prop(rsd_high_M23) +#> M27 ~ add(sigma_low_M27) + prop(rsd_high_M27) +#> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) +#> }) +#> } +#> <environment: 0x555559c2bd78>
                                                                f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", + control = nlmixr::foceiControl(print = 500)) +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:02 +#>
                                                                #> → calculate sensitivities
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:04 +#>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:01 +#>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:08 +#>
                                                                #> → finding duplicate expressions in inner model...
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 +#>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 +#>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> RxODE 1.1.0 using 8 threads (see ?getRxThreads) +#> no cache: create with `rxCreateCache()`
                                                                #> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 | +#> |.....................| log_k1 | log_k2 | g_qlogis |f_DMTA_tffm0_1_qlogis | +#> |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high | +#> |.....................| o1 | o2 | o3 | o4 | +#> |.....................| o5 | o6 | o7 | o8 | +#> |.....................| o9 | o10 |...........|...........| +#> calculating covariance matrix +#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: S matrix non-positive definite
                                                                #> Warning: using R matrix to calculate covariance
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                summary(f_dmta_nlmixr_focei) +
                                                                #> nlmixr version used for fitting: 2.0.4 +#> mkin version used for pre-fitting: 1.0.5 +#> R version used for fitting: 4.1.0 +#> Date of fit: Thu Jun 17 14:04:58 2021 +#> Date of summary: Thu Jun 17 14:04:58 2021 +#> +#> Equations: +#> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) +#> * DMTA +#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 +#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M31 * M31 +#> +#> Data: +#> 568 observations of 4 variable(s) grouped in 6 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 242.937 s +#> +#> Variance model: Two-component variance function +#> +#> Mean of starting values for individual parameters: +#> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 +#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis +#> -1.7571 -2.2341 -3.7763 0.4502 +#> +#> Mean of starting values for error model parameters: +#> sigma_low rsd_high +#> 0.69793 0.02577 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 1936 2031 -945.9 +#> +#> Optimised parameters: +#> est. lower upper +#> DMTA_0 98.7698 98.7356 98.8039 +#> log_k_M23 -3.9216 -3.9235 -3.9197 +#> log_k_M27 -4.3377 -4.3398 -4.3357 +#> log_k_M31 -4.2477 -4.2497 -4.2457 +#> log_k1 -2.2341 -2.2353 -2.2329 +#> log_k2 -3.7763 -3.7781 -3.7744 +#> g_qlogis 0.4502 0.4496 0.4507 +#> f_DMTA_tffm0_1_qlogis -2.0924 -2.0936 -2.0912 +#> f_DMTA_tffm0_2_qlogis -2.1806 -2.1818 -2.1794 +#> f_DMTA_tffm0_3_qlogis -2.1427 -2.1439 -2.1415 +#> +#> Correlation: +#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs +#> log_k_M23 0 +#> log_k_M27 0 0 +#> log_k_M31 0 0 0 +#> log_k1 0 0 0 0 +#> log_k2 0 0 0 0 0 +#> g_qlogis 0 0 0 0 0 0 +#> f_DMTA_tffm0_1_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_tffm0_2_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_tffm0_3_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_0_1 f_DMTA_0_2 +#> log_k_M23 +#> log_k_M27 +#> log_k_M31 +#> log_k1 +#> log_k2 +#> g_qlogis +#> f_DMTA_tffm0_1_qlogis +#> f_DMTA_tffm0_2_qlogis 0 +#> f_DMTA_tffm0_3_qlogis 0 0 +#> +#> Random effects (omega): +#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 +#> eta.DMTA_0 2.352 0.0000 0.0000 0.0000 +#> eta.log_k_M23 0.000 0.5493 0.0000 0.0000 +#> eta.log_k_M27 0.000 0.0000 0.8645 0.0000 +#> eta.log_k_M31 0.000 0.0000 0.0000 0.7503 +#> eta.log_k1 0.000 0.0000 0.0000 0.0000 +#> eta.log_k2 0.000 0.0000 0.0000 0.0000 +#> eta.g_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.log_k1 eta.log_k2 eta.g_qlogis +#> eta.DMTA_0 0.000 0.000 0.000 +#> eta.log_k_M23 0.000 0.000 0.000 +#> eta.log_k_M27 0.000 0.000 0.000 +#> eta.log_k_M31 0.000 0.000 0.000 +#> eta.log_k1 0.903 0.000 0.000 +#> eta.log_k2 0.000 1.577 0.000 +#> eta.g_qlogis 0.000 0.000 3.085 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.000 +#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis +#> eta.DMTA_0 0.0 0.0 +#> eta.log_k_M23 0.0 0.0 +#> eta.log_k_M27 0.0 0.0 +#> eta.log_k_M31 0.0 0.0 +#> eta.log_k1 0.0 0.0 +#> eta.log_k2 0.0 0.0 +#> eta.g_qlogis 0.0 0.0 +#> eta.f_DMTA_tffm0_1_qlogis 0.3 0.0 +#> eta.f_DMTA_tffm0_2_qlogis 0.0 0.3 +#> eta.f_DMTA_tffm0_3_qlogis 0.0 0.0 +#> eta.f_DMTA_tffm0_3_qlogis +#> eta.DMTA_0 0.0 +#> eta.log_k_M23 0.0 +#> eta.log_k_M27 0.0 +#> eta.log_k_M31 0.0 +#> eta.log_k1 0.0 +#> eta.log_k2 0.0 +#> eta.g_qlogis 0.0 +#> eta.f_DMTA_tffm0_1_qlogis 0.0 +#> eta.f_DMTA_tffm0_2_qlogis 0.0 +#> eta.f_DMTA_tffm0_3_qlogis 0.3 +#> +#> Variance model: +#> sigma_low rsd_high +#> 0.69793 0.02577 +#> +#> Backtransformed parameters: +#> est. lower upper +#> DMTA_0 98.76976 98.73563 98.80390 +#> k_M23 0.01981 0.01977 0.01985 +#> k_M27 0.01307 0.01304 0.01309 +#> k_M31 0.01430 0.01427 0.01433 +#> f_DMTA_to_M23 0.10984 NA NA +#> f_DMTA_to_M27 0.09036 NA NA +#> f_DMTA_to_M31 0.08399 NA NA +#> k1 0.10709 0.10696 0.10722 +#> k2 0.02291 0.02287 0.02295 +#> g 0.61068 0.61055 0.61081 +#> +#> Resulting formation fractions: +#> ff +#> DMTA_M23 0.10984 +#> DMTA_M27 0.09036 +#> DMTA_M31 0.08399 +#> DMTA_sink 0.71581 +#> +#> Estimated disappearance times: +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> DMTA 10.66 59.78 18 6.473 30.26 +#> M23 34.99 116.24 NA NA NA +#> M27 53.05 176.23 NA NA NA +#> M31 48.48 161.05 NA NA NA
                                                                plot(f_dmta_nlmixr_focei) +
                                                                # saem has a problem with this model/data combination, maybe because of the +# overparameterised error model, to be investigated +#f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", +# control = saemControl(print = 500)) +#summary(f_dmta_nlmixr_saem) +#plot(f_dmta_nlmixr_saem) +# } +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> RxODE 1.1.0 using 8 threads (see ?getRxThreads) -#> no cache: create with `rxCreateCache()`
                                                                #> 1: 86.5083 -3.1968 4.1673 1.7173 48.7028 +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1: 86.5083 -3.1968 4.1673 1.7173 48.7028 #> 2: 87.3628 -3.1468 3.9589 1.6315 45.1225 #> 3: 86.8866 -3.2249 3.7610 1.8212 43.0034 #> 4: 85.9210 -3.2427 3.5729 1.7302 39.4197 @@ -4453,9194 +4461,495 @@ obtained by fitting the same model to a list of datasets using # A single constant variance is currently only possible with est = 'focei' in nlmixr f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 |log_k_parent | log_k_A1 |f_parent_qlogis | -#> |.....................| sigma | o1 | o2 | o3 | -#> |.....................| o4 |...........|...........|...........| -#> | 1| 756.06625 | 1.000 | -0.9701 | -1.000 | -0.9071 | -#> |.....................| -0.8050 | -0.8844 | -0.8800 | -0.8744 | -#> |.....................| -0.8785 |...........|...........|...........| -#> | U| 756.06625 | 86.53 | -3.207 | -4.567 | -0.3341 | -#> |.....................| 4.315 | 0.7003 | 0.9008 | 1.156 | -#> |.....................| 0.9657 |...........|...........|...........| -#> | X| 756.06625 | 86.53 | 0.04048 | 0.01039 | 0.4172 | -#> |.....................| 4.315 | 0.7003 | 0.9008 | 1.156 | -#> |.....................| 0.9657 |...........|...........|...........| -#> | G| Gill Diff. | 59.54 | 0.01874 | 0.7243 | 0.3705 | -#> |.....................| -28.18 | 5.148 | 2.958 | -8.197 | -#> |.....................| -5.917 |...........|...........|...........| -#> | 2| 3309.1113 | 0.1102 | -0.9704 | -1.011 | -0.9126 | -#> |.....................| -0.3838 | -0.9613 | -0.9242 | -0.7519 | -#> |.....................| -0.7901 |...........|...........|...........| -#> | U| 3309.1113 | 9.535 | -3.207 | -4.578 | -0.3359 | -#> |.....................| 5.223 | 0.6464 | 0.8610 | 1.297 | -#> |.....................| 1.051 |...........|...........|...........| -#> | X| 3309.1113 | 9.535 | 0.04047 | 0.01027 | 0.4168 | -#> |.....................| 5.223 | 0.6464 | 0.8610 | 1.297 | -#> |.....................| 1.051 |...........|...........|...........| -#> | 3| 782.04188 | 0.9110 | -0.9702 | -1.001 | -0.9076 | -#> |.....................| -0.7629 | -0.8921 | -0.8844 | -0.8621 | -#> |.....................| -0.8697 |...........|...........|...........| -#> | U| 782.04188 | 78.83 | -3.207 | -4.568 | -0.3343 | -#> |.....................| 4.406 | 0.6949 | 0.8968 | 1.170 | -#> |.....................| 0.9742 |...........|...........|...........| -#> | X| 782.04188 | 78.83 | 0.04048 | 0.01037 | 0.4172 | -#> |.....................| 4.406 | 0.6949 | 0.8968 | 1.170 | -#> |.....................| 0.9742 |...........|...........|...........| -#> | 4| 755.73406 | 0.9909 | -0.9701 | -1.000 | -0.9071 | -#> |.....................| -0.8007 | -0.8851 | -0.8804 | -0.8731 | -#> |.....................| -0.8776 |...........|...........|...........| -#> | U| 755.73406 | 85.75 | -3.207 | -4.567 | -0.3341 | -#> |.....................| 4.324 | 0.6997 | 0.9004 | 1.157 | -#> |.....................| 0.9666 |...........|...........|...........| -#> | X| 755.73406 | 85.75 | 0.04048 | 0.01038 | 0.4172 | -#> |.....................| 4.324 | 0.6997 | 0.9004 | 1.157 | -#> |.....................| 0.9666 |...........|...........|...........| -#> | F| Forward Diff. | -16.83 | 0.07808 | 0.6495 | 0.3224 | -#> |.....................| -27.54 | 3.811 | 2.903 | -8.359 | -#> |.....................| -5.718 |...........|...........|...........| -#> | 5| 755.49648 | 0.9959 | -0.9702 | -1.000 | -0.9072 | -#> |.....................| -0.7924 | -0.8863 | -0.8813 | -0.8706 | -#> |.....................| -0.8759 |...........|...........|...........| -#> | U| 755.49648 | 86.18 | -3.207 | -4.568 | -0.3341 | -#> |.....................| 4.342 | 0.6989 | 0.8996 | 1.160 | -#> |.....................| 0.9682 |...........|...........|...........| -#> | X| 755.49648 | 86.18 | 0.04048 | 0.01038 | 0.4172 | -#> |.....................| 4.342 | 0.6989 | 0.8996 | 1.160 | -#> |.....................| 0.9682 |...........|...........|...........| -#> | F| Forward Diff. | 25.35 | 0.04484 | 0.6934 | 0.3535 | -#> |.....................| -25.80 | 4.244 | 2.831 | -8.249 | -#> |.....................| -5.719 |...........|...........|...........| -#> | 6| 755.31010 | 0.9891 | -0.9702 | -1.000 | -0.9073 | -#> |.....................| -0.7855 | -0.8874 | -0.8820 | -0.8684 | -#> |.....................| -0.8744 |...........|...........|...........| -#> | U| 755.3101 | 85.59 | -3.207 | -4.568 | -0.3342 | -#> |.....................| 4.357 | 0.6981 | 0.8989 | 1.163 | -#> |.....................| 0.9697 |...........|...........|...........| -#> | X| 755.3101 | 85.59 | 0.04048 | 0.01038 | 0.4172 | -#> |.....................| 4.357 | 0.6981 | 0.8989 | 1.163 | -#> |.....................| 0.9697 |...........|...........|...........| -#> | F| Forward Diff. | -31.39 | 0.08909 | 0.6380 | 0.3185 | -#> |.....................| -24.71 | 3.519 | 2.751 | -7.972 | -#> |.....................| -5.525 |...........|...........|...........| -#> | 7| 755.09582 | 0.9961 | -0.9702 | -1.001 | -0.9074 | -#> |.....................| -0.7787 | -0.8884 | -0.8828 | -0.8661 | -#> |.....................| -0.8728 |...........|...........|...........| -#> | U| 755.09582 | 86.20 | -3.207 | -4.568 | -0.3342 | -#> |.....................| 4.372 | 0.6974 | 0.8982 | 1.165 | -#> |.....................| 0.9712 |...........|...........|...........| -#> | X| 755.09582 | 86.20 | 0.04047 | 0.01038 | 0.4172 | -#> |.....................| 4.372 | 0.6974 | 0.8982 | 1.165 | -#> |.....................| 0.9712 |...........|...........|...........| -#> | F| Forward Diff. | 26.63 | 0.04269 | 0.6973 | 0.3604 | -#> |.....................| -23.22 | 4.086 | 2.689 | -8.043 | -#> |.....................| -5.569 |...........|...........|...........| -#> | 8| 754.90743 | 0.9894 | -0.9702 | -1.001 | -0.9075 | -#> |.....................| -0.7716 | -0.8897 | -0.8836 | -0.8636 | -#> |.....................| -0.8711 |...........|...........|...........| -#> | U| 754.90743 | 85.62 | -3.207 | -4.568 | -0.3342 | -#> |.....................| 4.387 | 0.6966 | 0.8975 | 1.168 | -#> |.....................| 0.9729 |...........|...........|...........| -#> | X| 754.90743 | 85.62 | 0.04047 | 0.01038 | 0.4172 | -#> |.....................| 4.387 | 0.6966 | 0.8975 | 1.168 | -#> |.....................| 0.9729 |...........|...........|...........| -#> | F| Forward Diff. | -27.88 | 0.08581 | 0.6437 | 0.3265 | -#> |.....................| -22.15 | 3.354 | 2.606 | -7.748 | -#> |.....................| -5.369 |...........|...........|...........| -#> | 9| 754.70769 | 0.9959 | -0.9702 | -1.001 | -0.9076 | -#> |.....................| -0.7645 | -0.8908 | -0.8845 | -0.8610 | -#> |.....................| -0.8693 |...........|...........|...........| -#> | U| 754.70769 | 86.18 | -3.207 | -4.568 | -0.3343 | -#> |.....................| 4.402 | 0.6958 | 0.8967 | 1.171 | -#> |.....................| 0.9747 |...........|...........|...........| -#> | X| 754.70769 | 86.18 | 0.04047 | 0.01037 | 0.4172 | -#> |.....................| 4.402 | 0.6958 | 0.8967 | 1.171 | -#> |.....................| 0.9747 |...........|...........|...........| -#> | F| Forward Diff. | 25.01 | 0.04305 | 0.6984 | 0.3661 | -#> |.....................| -20.67 | 3.871 | 2.535 | -7.809 | -#> |.....................| -5.388 |...........|...........|...........| -#> | 10| 754.52507 | 0.9898 | -0.9703 | -1.001 | -0.9078 | -#> |.....................| -0.7574 | -0.8922 | -0.8854 | -0.8580 | -#> |.....................| -0.8672 |...........|...........|...........| -#> | U| 754.52507 | 85.65 | -3.207 | -4.569 | -0.3343 | -#> |.....................| 4.417 | 0.6948 | 0.8958 | 1.175 | -#> |.....................| 0.9766 |...........|...........|...........| -#> | X| 754.52507 | 85.65 | 0.04047 | 0.01037 | 0.4172 | -#> |.....................| 4.417 | 0.6948 | 0.8958 | 1.175 | -#> |.....................| 0.9766 |...........|...........|...........| -#> | F| Forward Diff. | -24.90 | 0.08308 | 0.6490 | 0.3352 | -#> |.....................| -19.59 | 3.181 | 2.445 | -7.663 | -#> |.....................| -5.179 |...........|...........|...........| -#> | 11| 754.34076 | 0.9957 | -0.9703 | -1.002 | -0.9079 | -#> |.....................| -0.7502 | -0.8935 | -0.8864 | -0.8548 | -#> |.....................| -0.8650 |...........|...........|...........| -#> | U| 754.34076 | 86.16 | -3.207 | -4.569 | -0.3344 | -#> |.....................| 4.433 | 0.6939 | 0.8950 | 1.178 | -#> |.....................| 0.9787 |...........|...........|...........| -#> | X| 754.34076 | 86.16 | 0.04047 | 0.01037 | 0.4172 | -#> |.....................| 4.433 | 0.6939 | 0.8950 | 1.178 | -#> |.....................| 0.9787 |...........|...........|...........| -#> | F| Forward Diff. | 23.15 | 0.04366 | 0.6990 | 0.3728 | -#> |.....................| -18.16 | 3.647 | 2.362 | -7.534 | -#> |.....................| -5.170 |...........|...........|...........| -#> | 12| 754.16941 | 0.9900 | -0.9703 | -1.002 | -0.9081 | -#> |.....................| -0.7432 | -0.8951 | -0.8875 | -0.8512 | -#> |.....................| -0.8626 |...........|...........|...........| -#> | U| 754.16941 | 85.67 | -3.207 | -4.569 | -0.3344 | -#> |.....................| 4.448 | 0.6928 | 0.8940 | 1.182 | -#> |.....................| 0.9811 |...........|...........|...........| -#> | X| 754.16941 | 85.67 | 0.04047 | 0.01036 | 0.4172 | -#> |.....................| 4.448 | 0.6928 | 0.8940 | 1.182 | -#> |.....................| 0.9811 |...........|...........|...........| -#> | F| Forward Diff. | -22.36 | 0.07996 | 0.6524 | 0.3446 | -#> |.....................| -17.12 | 3.002 | 2.262 | -7.362 | -#> |.....................| -4.949 |...........|...........|...........| -#> | 13| 754.00081 | 0.9955 | -0.9704 | -1.002 | -0.9083 | -#> |.....................| -0.7363 | -0.8967 | -0.8886 | -0.8472 | -#> |.....................| -0.8599 |...........|...........|...........| -#> | U| 754.00081 | 86.14 | -3.207 | -4.570 | -0.3345 | -#> |.....................| 4.463 | 0.6916 | 0.8930 | 1.187 | -#> |.....................| 0.9836 |...........|...........|...........| -#> | X| 754.00081 | 86.14 | 0.04047 | 0.01036 | 0.4171 | -#> |.....................| 4.463 | 0.6916 | 0.8930 | 1.187 | -#> |.....................| 0.9836 |...........|...........|...........| -#> | F| Forward Diff. | 21.00 | 0.04440 | 0.6979 | 0.3804 | -#> |.....................| -15.79 | 3.414 | 2.168 | -7.205 | -#> |.....................| -4.903 |...........|...........|...........| -#> | 14| 753.84435 | 0.9903 | -0.9704 | -1.003 | -0.9086 | -#> |.....................| -0.7296 | -0.8985 | -0.8898 | -0.8427 | -#> |.....................| -0.8570 |...........|...........|...........| -#> | U| 753.84435 | 85.70 | -3.207 | -4.570 | -0.3346 | -#> |.....................| 4.477 | 0.6903 | 0.8919 | 1.192 | -#> |.....................| 0.9865 |...........|...........|...........| -#> | X| 753.84435 | 85.70 | 0.04047 | 0.01036 | 0.4171 | -#> |.....................| 4.477 | 0.6903 | 0.8919 | 1.192 | -#> |.....................| 0.9865 |...........|...........|...........| -#> | F| Forward Diff. | -19.93 | 0.07681 | 0.6538 | 0.3555 | -#> |.....................| -14.84 | 2.820 | 2.056 | -6.999 | -#> |.....................| -4.662 |...........|...........|...........| -#> | 15| 753.69372 | 0.9952 | -0.9704 | -1.003 | -0.9089 | -#> |.....................| -0.7234 | -0.9005 | -0.8911 | -0.8377 | -#> |.....................| -0.8537 |...........|...........|...........| -#> | U| 753.69372 | 86.12 | -3.207 | -4.571 | -0.3347 | -#> |.....................| 4.491 | 0.6890 | 0.8908 | 1.198 | -#> |.....................| 0.9897 |...........|...........|...........| -#> | X| 753.69372 | 86.12 | 0.04046 | 0.01035 | 0.4171 | -#> |.....................| 4.491 | 0.6890 | 0.8908 | 1.198 | -#> |.....................| 0.9897 |...........|...........|...........| -#> | F| Forward Diff. | 18.81 | 0.04462 | 0.6942 | 0.3896 | -#> |.....................| -13.66 | 3.180 | 1.953 | -6.807 | -#> |.....................| -4.573 |...........|...........|...........| -#> | 16| 753.55534 | 0.9906 | -0.9705 | -1.004 | -0.9093 | -#> |.....................| -0.7176 | -0.9027 | -0.8924 | -0.8322 | -#> |.....................| -0.8502 |...........|...........|...........| -#> | U| 753.55534 | 85.72 | -3.207 | -4.571 | -0.3348 | -#> |.....................| 4.503 | 0.6875 | 0.8896 | 1.204 | -#> |.....................| 0.9931 |...........|...........|...........| -#> | X| 753.55534 | 85.72 | 0.04046 | 0.01034 | 0.4171 | -#> |.....................| 4.503 | 0.6875 | 0.8896 | 1.204 | -#> |.....................| 0.9931 |...........|...........|...........| -#> | F| Forward Diff. | -17.61 | 0.07313 | 0.6517 | 0.3679 | -#> |.....................| -12.86 | 2.639 | 1.835 | -6.564 | -#> |.....................| -4.309 |...........|...........|...........| -#> | 17| 753.42478 | 0.9950 | -0.9706 | -1.005 | -0.9097 | -#> |.....................| -0.7124 | -0.9049 | -0.8937 | -0.8262 | -#> |.....................| -0.8464 |...........|...........|...........| -#> | U| 753.42478 | 86.11 | -3.207 | -4.572 | -0.3350 | -#> |.....................| 4.515 | 0.6859 | 0.8884 | 1.211 | -#> |.....................| 0.9967 |...........|...........|...........| -#> | X| 753.42478 | 86.11 | 0.04046 | 0.01034 | 0.4170 | -#> |.....................| 4.515 | 0.6859 | 0.8884 | 1.211 | -#> |.....................| 0.9967 |...........|...........|...........| -#> | F| Forward Diff. | 16.74 | 0.04433 | 0.6853 | 0.4002 | -#> |.....................| -11.89 | 2.952 | 1.729 | -6.336 | -#> |.....................| -4.181 |...........|...........|...........| -#> | 18| 753.30602 | 0.9909 | -0.9706 | -1.006 | -0.9103 | -#> |.....................| -0.7078 | -0.9075 | -0.8949 | -0.8197 | -#> |.....................| -0.8425 |...........|...........|...........| -#> | U| 753.30602 | 85.74 | -3.207 | -4.573 | -0.3352 | -#> |.....................| 4.525 | 0.6841 | 0.8873 | 1.219 | -#> |.....................| 1.001 |...........|...........|...........| -#> | X| 753.30602 | 85.74 | 0.04046 | 0.01033 | 0.4170 | -#> |.....................| 4.525 | 0.6841 | 0.8873 | 1.219 | -#> |.....................| 1.001 |...........|...........|...........| -#> | F| Forward Diff. | -15.54 | 0.06924 | 0.6430 | 0.3812 | -#> |.....................| -11.26 | 2.462 | 1.618 | -6.066 | -#> |.....................| -3.903 |...........|...........|...........| -#> | 19| 753.19508 | 0.9949 | -0.9707 | -1.007 | -0.9109 | -#> |.....................| -0.7036 | -0.9102 | -0.8961 | -0.8129 | -#> |.....................| -0.8385 |...........|...........|...........| -#> | U| 753.19508 | 86.09 | -3.208 | -4.574 | -0.3354 | -#> |.....................| 4.533 | 0.6822 | 0.8862 | 1.227 | -#> |.....................| 1.004 |...........|...........|...........| -#> | X| 753.19508 | 86.09 | 0.04045 | 0.01032 | 0.4169 | -#> |.....................| 4.533 | 0.6822 | 0.8862 | 1.227 | -#> |.....................| 1.004 |...........|...........|...........| -#> | F| Forward Diff. | 14.90 | 0.04352 | 0.6689 | 0.4113 | -#> |.....................| -10.49 | 2.732 | 1.522 | -5.813 | -#> |.....................| -3.751 |...........|...........|...........| -#> | 20| 753.09443 | 0.9911 | -0.9708 | -1.008 | -0.9117 | -#> |.....................| -0.7001 | -0.9132 | -0.8972 | -0.8058 | -#> |.....................| -0.8346 |...........|...........|...........| -#> | U| 753.09443 | 85.77 | -3.208 | -4.575 | -0.3356 | -#> |.....................| 4.541 | 0.6801 | 0.8852 | 1.235 | -#> |.....................| 1.008 |...........|...........|...........| -#> | X| 753.09443 | 85.77 | 0.04045 | 0.01031 | 0.4169 | -#> |.....................| 4.541 | 0.6801 | 0.8852 | 1.235 | -#> |.....................| 1.008 |...........|...........|...........| -#> | F| Forward Diff. | -13.80 | 0.06521 | 0.6240 | 0.3942 | -#> |.....................| -10.02 | 2.285 | 1.423 | -5.526 | -#> |.....................| -3.476 |...........|...........|...........| -#> | 21| 753.00021 | 0.9948 | -0.9709 | -1.009 | -0.9127 | -#> |.....................| -0.6968 | -0.9163 | -0.8982 | -0.7985 | -#> |.....................| -0.8307 |...........|...........|...........| -#> | U| 753.00021 | 86.08 | -3.208 | -4.576 | -0.3360 | -#> |.....................| 4.548 | 0.6779 | 0.8843 | 1.243 | -#> |.....................| 1.012 |...........|...........|...........| -#> | X| 753.00021 | 86.08 | 0.04045 | 0.01029 | 0.4168 | -#> |.....................| 4.548 | 0.6779 | 0.8843 | 1.243 | -#> |.....................| 1.012 |...........|...........|...........| -#> | F| Forward Diff. | 13.31 | 0.04216 | 0.6406 | 0.4217 | -#> |.....................| -9.402 | 2.517 | 1.347 | -5.262 | -#> |.....................| -3.321 |...........|...........|...........| -#> | 22| 752.91432 | 0.9914 | -0.9710 | -1.010 | -0.9139 | -#> |.....................| -0.6939 | -0.9197 | -0.8991 | -0.7911 | -#> |.....................| -0.8272 |...........|...........|...........| -#> | U| 752.91432 | 85.79 | -3.208 | -4.578 | -0.3364 | -#> |.....................| 4.555 | 0.6755 | 0.8835 | 1.252 | -#> |.....................| 1.015 |...........|...........|...........| -#> | X| 752.91432 | 85.79 | 0.04044 | 0.01028 | 0.4167 | -#> |.....................| 4.555 | 0.6755 | 0.8835 | 1.252 | -#> |.....................| 1.015 |...........|...........|...........| -#> | F| Forward Diff. | -12.35 | 0.06128 | 0.5909 | 0.4053 | -#> |.....................| -9.027 | 2.101 | 1.271 | -4.717 | -#> |.....................| -3.067 |...........|...........|...........| -#> | 23| 752.83200 | 0.9948 | -0.9711 | -1.012 | -0.9155 | -#> |.....................| -0.6906 | -0.9238 | -0.9000 | -0.7843 | -#> |.....................| -0.8235 |...........|...........|...........| -#> | U| 752.832 | 86.09 | -3.208 | -4.580 | -0.3369 | -#> |.....................| 4.561 | 0.6727 | 0.8827 | 1.260 | -#> |.....................| 1.019 |...........|...........|...........| -#> | X| 752.832 | 86.09 | 0.04044 | 0.01026 | 0.4166 | -#> |.....................| 4.561 | 0.6727 | 0.8827 | 1.260 | -#> |.....................| 1.019 |...........|...........|...........| -#> | F| Forward Diff. | 12.74 | 0.03978 | 0.5956 | 0.4312 | -#> |.....................| -8.422 | 2.296 | 1.202 | -4.471 | -#> |.....................| -2.914 |...........|...........|...........| -#> | 24| 752.75140 | 0.9918 | -0.9713 | -1.014 | -0.9179 | -#> |.....................| -0.6872 | -0.9288 | -0.9011 | -0.7785 | -#> |.....................| -0.8198 |...........|...........|...........| -#> | U| 752.7514 | 85.82 | -3.208 | -4.582 | -0.3377 | -#> |.....................| 4.569 | 0.6692 | 0.8818 | 1.266 | -#> |.....................| 1.022 |...........|...........|...........| -#> | X| 752.7514 | 85.82 | 0.04043 | 0.01024 | 0.4164 | -#> |.....................| 4.569 | 0.6692 | 0.8818 | 1.266 | -#> |.....................| 1.022 |...........|...........|...........| -#> | F| Forward Diff. | -10.02 | 0.05546 | 0.5361 | 0.4172 | -#> |.....................| -7.958 | 1.872 | 1.117 | -4.424 | -#> |.....................| -2.664 |...........|...........|...........| -#> | 25| 752.68235 | 0.9947 | -0.9715 | -1.016 | -0.9205 | -#> |.....................| -0.6845 | -0.9329 | -0.9018 | -0.7712 | -#> |.....................| -0.8173 |...........|...........|...........| -#> | U| 752.68235 | 86.07 | -3.208 | -4.584 | -0.3386 | -#> |.....................| 4.575 | 0.6663 | 0.8811 | 1.275 | -#> |.....................| 1.025 |...........|...........|...........| -#> | X| 752.68235 | 86.07 | 0.04042 | 0.01022 | 0.4162 | -#> |.....................| 4.575 | 0.6663 | 0.8811 | 1.275 | -#> |.....................| 1.025 |...........|...........|...........| -#> | F| Forward Diff. | 10.53 | 0.03715 | 0.5273 | 0.4360 | -#> |.....................| -7.447 | 2.014 | 1.063 | -3.990 | -#> |.....................| -2.556 |...........|...........|...........| -#> | 26| 752.62160 | 0.9918 | -0.9717 | -1.019 | -0.9237 | -#> |.....................| -0.6821 | -0.9370 | -0.9025 | -0.7637 | -#> |.....................| -0.8151 |...........|...........|...........| -#> | U| 752.6216 | 85.83 | -3.209 | -4.586 | -0.3397 | -#> |.....................| 4.580 | 0.6635 | 0.8804 | 1.284 | -#> |.....................| 1.027 |...........|...........|...........| -#> | X| 752.6216 | 85.83 | 0.04042 | 0.01020 | 0.4159 | -#> |.....................| 4.580 | 0.6635 | 0.8804 | 1.284 | -#> |.....................| 1.027 |...........|...........|...........| -#> | F| Forward Diff. | -10.27 | 0.05173 | 0.4657 | 0.4178 | -#> |.....................| -7.153 | 1.648 | 1.004 | -3.701 | -#> |.....................| -2.385 |...........|...........|...........| -#> | 27| 752.55758 | 0.9944 | -0.9719 | -1.021 | -0.9287 | -#> |.....................| -0.6786 | -0.9418 | -0.9036 | -0.7591 | -#> |.....................| -0.8121 |...........|...........|...........| -#> | U| 752.55758 | 86.05 | -3.209 | -4.588 | -0.3413 | -#> |.....................| 4.587 | 0.6600 | 0.8795 | 1.289 | -#> |.....................| 1.030 |...........|...........|...........| -#> | X| 752.55758 | 86.05 | 0.04040 | 0.01017 | 0.4155 | -#> |.....................| 4.587 | 0.6600 | 0.8795 | 1.289 | -#> |.....................| 1.030 |...........|...........|...........| -#> | F| Forward Diff. | 7.976 | 0.03464 | 0.4539 | 0.4351 | -#> |.....................| -6.545 | 1.728 | 0.9236 | -3.536 | -#> |.....................| -2.257 |...........|...........|...........| -#> | 28| 752.50465 | 0.9921 | -0.9722 | -1.023 | -0.9345 | -#> |.....................| -0.6755 | -0.9456 | -0.9043 | -0.7539 | -#> |.....................| -0.8090 |...........|...........|...........| -#> | U| 752.50465 | 85.85 | -3.209 | -4.590 | -0.3432 | -#> |.....................| 4.594 | 0.6574 | 0.8788 | 1.295 | -#> |.....................| 1.033 |...........|...........|...........| -#> | X| 752.50465 | 85.85 | 0.04039 | 0.01015 | 0.4150 | -#> |.....................| 4.594 | 0.6574 | 0.8788 | 1.295 | -#> |.....................| 1.033 |...........|...........|...........| -#> | F| Forward Diff. | -8.947 | 0.04577 | 0.4043 | 0.4205 | -#> |.....................| -6.122 | 1.399 | 0.8644 | -3.339 | -#> |.....................| -2.062 |...........|...........|...........| -#> | 29| 752.46010 | 0.9944 | -0.9724 | -1.024 | -0.9405 | -#> |.....................| -0.6742 | -0.9477 | -0.9048 | -0.7467 | -#> |.....................| -0.8068 |...........|...........|...........| -#> | U| 752.4601 | 86.05 | -3.209 | -4.591 | -0.3452 | -#> |.....................| 4.597 | 0.6559 | 0.8784 | 1.303 | -#> |.....................| 1.035 |...........|...........|...........| -#> | X| 752.4601 | 86.05 | 0.04039 | 0.01014 | 0.4145 | -#> |.....................| 4.597 | 0.6559 | 0.8784 | 1.303 | -#> |.....................| 1.035 |...........|...........|...........| -#> | F| Forward Diff. | 6.603 | 0.03134 | 0.3976 | 0.4307 | -#> |.....................| -5.878 | 1.523 | 0.8347 | -3.098 | -#> |.....................| -1.971 |...........|...........|...........| -#> | 30| 752.42045 | 0.9923 | -0.9726 | -1.025 | -0.9478 | -#> |.....................| -0.6717 | -0.9497 | -0.9056 | -0.7410 | -#> |.....................| -0.8056 |...........|...........|...........| -#> | U| 752.42045 | 85.87 | -3.210 | -4.593 | -0.3477 | -#> |.....................| 4.602 | 0.6545 | 0.8777 | 1.310 | -#> |.....................| 1.036 |...........|...........|...........| -#> | X| 752.42045 | 85.87 | 0.04038 | 0.01013 | 0.4139 | -#> |.....................| 4.602 | 0.6545 | 0.8777 | 1.310 | -#> |.....................| 1.036 |...........|...........|...........| -#> | F| Forward Diff. | -7.567 | 0.04074 | 0.3551 | 0.4112 | -#> |.....................| -5.553 | 1.278 | 0.7625 | -2.890 | -#> |.....................| -1.881 |...........|...........|...........| -#> | 31| 752.38271 | 0.9943 | -0.9729 | -1.026 | -0.9563 | -#> |.....................| -0.6682 | -0.9523 | -0.9058 | -0.7392 | -#> |.....................| -0.8032 |...........|...........|...........| -#> | U| 752.38271 | 86.04 | -3.210 | -4.594 | -0.3505 | -#> |.....................| 4.610 | 0.6527 | 0.8775 | 1.312 | -#> |.....................| 1.038 |...........|...........|...........| -#> | X| 752.38271 | 86.04 | 0.04037 | 0.01012 | 0.4133 | -#> |.....................| 4.610 | 0.6527 | 0.8775 | 1.312 | -#> |.....................| 1.038 |...........|...........|...........| -#> | F| Forward Diff. | 5.602 | 0.02847 | 0.3641 | 0.4189 | -#> |.....................| -5.001 | 1.344 | 0.7516 | -2.828 | -#> |.....................| -1.805 |...........|...........|...........| -#> | 32| 752.35435 | 0.9925 | -0.9730 | -1.028 | -0.9633 | -#> |.....................| -0.6679 | -0.9545 | -0.9069 | -0.7341 | -#> |.....................| -0.7988 |...........|...........|...........| -#> | U| 752.35435 | 85.89 | -3.210 | -4.595 | -0.3529 | -#> |.....................| 4.611 | 0.6511 | 0.8766 | 1.318 | -#> |.....................| 1.043 |...........|...........|...........| -#> | X| 752.35435 | 85.89 | 0.04036 | 0.01010 | 0.4127 | -#> |.....................| 4.611 | 0.6511 | 0.8766 | 1.318 | -#> |.....................| 1.043 |...........|...........|...........| -#> | F| Forward Diff. | -6.571 | 0.03612 | 0.3357 | 0.4086 | -#> |.....................| -4.992 | 1.118 | 0.6605 | -2.632 | -#> |.....................| -1.560 |...........|...........|...........| -#> | 33| 752.32772 | 0.9943 | -0.9732 | -1.029 | -0.9711 | -#> |.....................| -0.6669 | -0.9557 | -0.9071 | -0.7282 | -#> |.....................| -0.7989 |...........|...........|...........| -#> | U| 752.32772 | 86.04 | -3.210 | -4.596 | -0.3555 | -#> |.....................| 4.613 | 0.6503 | 0.8764 | 1.325 | -#> |.....................| 1.043 |...........|...........|...........| -#> | X| 752.32772 | 86.04 | 0.04035 | 0.01009 | 0.4121 | -#> |.....................| 4.613 | 0.6503 | 0.8764 | 1.325 | -#> |.....................| 1.043 |...........|...........|...........| -#> | F| Forward Diff. | 5.212 | 0.02538 | 0.3153 | 0.4089 | -#> |.....................| -4.808 | 1.231 | 0.6502 | -2.445 | -#> |.....................| -1.583 |...........|...........|...........| -#> | 34| 752.30453 | 0.9927 | -0.9733 | -1.030 | -0.9795 | -#> |.....................| -0.6622 | -0.9567 | -0.9058 | -0.7271 | -#> |.....................| -0.8012 |...........|...........|...........| -#> | U| 752.30453 | 85.90 | -3.210 | -4.598 | -0.3583 | -#> |.....................| 4.623 | 0.6496 | 0.8775 | 1.326 | -#> |.....................| 1.040 |...........|...........|...........| -#> | X| 752.30453 | 85.90 | 0.04035 | 0.01008 | 0.4114 | -#> |.....................| 4.623 | 0.6496 | 0.8775 | 1.326 | -#> |.....................| 1.040 |...........|...........|...........| -#> | F| Forward Diff. | -5.777 | 0.03360 | 0.2795 | 0.3849 | -#> |.....................| -4.177 | 1.041 | 0.7583 | -2.411 | -#> |.....................| -1.694 |...........|...........|...........| -#> | 35| 752.28211 | 0.9943 | -0.9735 | -1.030 | -0.9865 | -#> |.....................| -0.6621 | -0.9586 | -0.9093 | -0.7251 | -#> |.....................| -0.7954 |...........|...........|...........| -#> | U| 752.28211 | 86.04 | -3.210 | -4.598 | -0.3606 | -#> |.....................| 4.623 | 0.6483 | 0.8743 | 1.328 | -#> |.....................| 1.046 |...........|...........|...........| -#> | X| 752.28211 | 86.04 | 0.04034 | 0.01008 | 0.4108 | -#> |.....................| 4.623 | 0.6483 | 0.8743 | 1.328 | -#> |.....................| 1.046 |...........|...........|...........| -#> | F| Forward Diff. | 4.685 | 0.02318 | 0.3105 | 0.3984 | -#> |.....................| -4.118 | 1.106 | 0.4577 | -2.335 | -#> |.....................| -1.438 |...........|...........|...........| -#> | 36| 752.26507 | 0.9926 | -0.9736 | -1.031 | -0.9930 | -#> |.....................| -0.6630 | -0.9604 | -0.9091 | -0.7199 | -#> |.....................| -0.7902 |...........|...........|...........| -#> | U| 752.26507 | 85.89 | -3.210 | -4.598 | -0.3628 | -#> |.....................| 4.621 | 0.6470 | 0.8745 | 1.334 | -#> |.....................| 1.051 |...........|...........|...........| -#> | X| 752.26507 | 85.89 | 0.04034 | 0.01007 | 0.4103 | -#> |.....................| 4.621 | 0.6470 | 0.8745 | 1.334 | -#> |.....................| 1.051 |...........|...........|...........| -#> | F| Forward Diff. | -6.810 | 0.03096 | 0.2910 | 0.3899 | -#> |.....................| -4.283 | 0.8991 | 0.4756 | -2.130 | -#> |.....................| -1.153 |...........|...........|...........| -#> | 37| 752.24597 | 0.9942 | -0.9737 | -1.033 | -1.000 | -#> |.....................| -0.6608 | -0.9614 | -0.9045 | -0.7160 | -#> |.....................| -0.7919 |...........|...........|...........| -#> | U| 752.24597 | 86.03 | -3.211 | -4.600 | -0.3653 | -#> |.....................| 4.626 | 0.6463 | 0.8787 | 1.339 | -#> |.....................| 1.049 |...........|...........|...........| -#> | X| 752.24597 | 86.03 | 0.04033 | 0.01005 | 0.4097 | -#> |.....................| 4.626 | 0.6463 | 0.8787 | 1.339 | -#> |.....................| 1.049 |...........|...........|...........| -#> | F| Forward Diff. | 3.512 | 0.02244 | 0.2659 | 0.3868 | -#> |.....................| -3.943 | 0.9821 | 0.8784 | -2.032 | -#> |.....................| -1.263 |...........|...........|...........| -#> | 38| 752.22949 | 0.9926 | -0.9738 | -1.034 | -1.007 | -#> |.....................| -0.6572 | -0.9618 | -0.9098 | -0.7144 | -#> |.....................| -0.7948 |...........|...........|...........| -#> | U| 752.22949 | 85.90 | -3.211 | -4.601 | -0.3676 | -#> |.....................| 4.634 | 0.6461 | 0.8739 | 1.341 | -#> |.....................| 1.047 |...........|...........|...........| -#> | X| 752.22949 | 85.90 | 0.04033 | 0.01004 | 0.4091 | -#> |.....................| 4.634 | 0.6461 | 0.8739 | 1.341 | -#> |.....................| 1.047 |...........|...........|...........| -#> | F| Forward Diff. | -6.652 | 0.02915 | 0.2261 | 0.3631 | -#> |.....................| -3.474 | 0.8493 | 0.4224 | -1.980 | -#> |.....................| -1.394 |...........|...........|...........| -#> | 39| 752.21433 | 0.9945 | -0.9739 | -1.034 | -1.016 | -#> |.....................| -0.6569 | -0.9629 | -0.9144 | -0.7124 | -#> |.....................| -0.7922 |...........|...........|...........| -#> | U| 752.21433 | 86.05 | -3.211 | -4.601 | -0.3704 | -#> |.....................| 4.634 | 0.6453 | 0.8697 | 1.343 | -#> |.....................| 1.049 |...........|...........|...........| -#> | X| 752.21433 | 86.05 | 0.04032 | 0.01004 | 0.4085 | -#> |.....................| 4.634 | 0.6453 | 0.8697 | 1.343 | -#> |.....................| 1.049 |...........|...........|...........| -#> | F| Forward Diff. | 5.271 | 0.01812 | 0.2470 | 0.3694 | -#> |.....................| -3.388 | 0.9655 | 0.02976 | -1.920 | -#> |.....................| -1.299 |...........|...........|...........| -#> | 40| 752.19821 | 0.9933 | -0.9740 | -1.034 | -1.022 | -#> |.....................| -0.6566 | -0.9648 | -0.9096 | -0.7099 | -#> |.....................| -0.7872 |...........|...........|...........| -#> | U| 752.19821 | 85.95 | -3.211 | -4.602 | -0.3726 | -#> |.....................| 4.635 | 0.6440 | 0.8741 | 1.346 | -#> |.....................| 1.054 |...........|...........|...........| -#> | X| 752.19821 | 85.95 | 0.04032 | 0.01004 | 0.4079 | -#> |.....................| 4.635 | 0.6440 | 0.8741 | 1.346 | -#> |.....................| 1.054 |...........|...........|...........| -#> | F| Forward Diff. | -2.667 | 0.02369 | 0.2481 | 0.3640 | -#> |.....................| -3.371 | 0.7751 | 0.4401 | -1.801 | -#> |.....................| -1.045 |...........|...........|...........| -#> | 41| 752.18532 | 0.9951 | -0.9741 | -1.036 | -1.031 | -#> |.....................| -0.6545 | -0.9659 | -0.9070 | -0.7062 | -#> |.....................| -0.7858 |...........|...........|...........| -#> | U| 752.18532 | 86.11 | -3.211 | -4.603 | -0.3754 | -#> |.....................| 4.639 | 0.6432 | 0.8764 | 1.350 | -#> |.....................| 1.055 |...........|...........|...........| -#> | X| 752.18532 | 86.11 | 0.04032 | 0.01002 | 0.4072 | -#> |.....................| 4.639 | 0.6432 | 0.8764 | 1.350 | -#> |.....................| 1.055 |...........|...........|...........| -#> | F| Forward Diff. | 8.833 | 0.01368 | 0.2421 | 0.3674 | -#> |.....................| -3.039 | 0.8770 | 0.6679 | -1.687 | -#> |.....................| -1.010 |...........|...........|...........| -#> | 42| 752.16831 | 0.9936 | -0.9742 | -1.037 | -1.039 | -#> |.....................| -0.6539 | -0.9664 | -0.9110 | -0.7027 | -#> |.....................| -0.7873 |...........|...........|...........| -#> | U| 752.16831 | 85.98 | -3.211 | -4.605 | -0.3782 | -#> |.....................| 4.641 | 0.6428 | 0.8728 | 1.354 | -#> |.....................| 1.054 |...........|...........|...........| -#> | X| 752.16831 | 85.98 | 0.04031 | 0.01001 | 0.4066 | -#> |.....................| 4.641 | 0.6428 | 0.8728 | 1.354 | -#> |.....................| 1.054 |...........|...........|...........| -#> | F| Forward Diff. | -0.7512 | 0.02003 | 0.1902 | 0.3449 | -#> |.....................| -2.985 | 0.7407 | 0.3269 | -1.581 | -#> |.....................| -1.064 |...........|...........|...........| -#> | 43| 752.14828 | 0.9957 | -0.9743 | -1.038 | -1.040 | -#> |.....................| -0.6457 | -0.9684 | -0.9119 | -0.6984 | -#> |.....................| -0.7843 |...........|...........|...........| -#> | U| 752.14828 | 86.16 | -3.211 | -4.605 | -0.3785 | -#> |.....................| 4.658 | 0.6414 | 0.8720 | 1.359 | -#> |.....................| 1.057 |...........|...........|...........| -#> | X| 752.14828 | 86.16 | 0.04031 | 0.01000 | 0.4065 | -#> |.....................| 4.658 | 0.6414 | 0.8720 | 1.359 | -#> |.....................| 1.057 |...........|...........|...........| -#> | F| Forward Diff. | 12.68 | 0.008742 | 0.2033 | 0.3626 | -#> |.....................| -1.835 | 0.8163 | 0.2532 | -1.452 | -#> |.....................| -0.9466 |...........|...........|...........| -#> | 44| 752.12689 | 0.9938 | -0.9744 | -1.038 | -1.049 | -#> |.....................| -0.6468 | -0.9706 | -0.9116 | -0.6946 | -#> |.....................| -0.7819 |...........|...........|...........| -#> | U| 752.12689 | 86.00 | -3.211 | -4.606 | -0.3814 | -#> |.....................| 4.656 | 0.6399 | 0.8723 | 1.363 | -#> |.....................| 1.059 |...........|...........|...........| -#> | X| 752.12689 | 86.00 | 0.04030 | 0.009996 | 0.4058 | -#> |.....................| 4.656 | 0.6399 | 0.8723 | 1.363 | -#> |.....................| 1.059 |...........|...........|...........| -#> | F| Forward Diff. | -0.08747 | 0.01751 | 0.1808 | 0.3434 | -#> |.....................| -2.013 | 0.5634 | 0.2760 | -1.320 | -#> |.....................| -0.7971 |...........|...........|...........| -#> | 45| 752.10460 | 0.9941 | -0.9745 | -1.039 | -1.050 | -#> |.....................| -0.6390 | -0.9728 | -0.9127 | -0.6895 | -#> |.....................| -0.7788 |...........|...........|...........| -#> | U| 752.1046 | 86.03 | -3.211 | -4.606 | -0.3818 | -#> |.....................| 4.673 | 0.6383 | 0.8713 | 1.369 | -#> |.....................| 1.062 |...........|...........|...........| -#> | X| 752.1046 | 86.03 | 0.04030 | 0.009989 | 0.4057 | -#> |.....................| 4.673 | 0.6383 | 0.8713 | 1.369 | -#> |.....................| 1.062 |...........|...........|...........| -#> | 46| 752.09051 | 0.9947 | -0.9746 | -1.040 | -1.052 | -#> |.....................| -0.6247 | -0.9768 | -0.9147 | -0.6801 | -#> |.....................| -0.7732 |...........|...........|...........| -#> | U| 752.09051 | 86.08 | -3.211 | -4.608 | -0.3827 | -#> |.....................| 4.704 | 0.6355 | 0.8695 | 1.380 | -#> |.....................| 1.067 |...........|...........|...........| -#> | X| 752.09051 | 86.08 | 0.04030 | 0.009976 | 0.4055 | -#> |.....................| 4.704 | 0.6355 | 0.8695 | 1.380 | -#> |.....................| 1.067 |...........|...........|...........| -#> | F| Forward Diff. | 5.771 | 0.01029 | 0.1542 | 0.3620 | -#> |.....................| 0.8997 | 0.2873 | 0.01810 | -0.9019 | -#> |.....................| -0.3639 |...........|...........|...........| -#> | 47| 752.06630 | 0.9944 | -0.9751 | -1.045 | -1.068 | -#> |.....................| -0.6300 | -0.9815 | -0.9184 | -0.6573 | -#> |.....................| -0.7726 |...........|...........|...........| -#> | U| 752.0663 | 86.05 | -3.212 | -4.613 | -0.3878 | -#> |.....................| 4.692 | 0.6323 | 0.8661 | 1.407 | -#> |.....................| 1.068 |...........|...........|...........| -#> | X| 752.0663 | 86.05 | 0.04028 | 0.009926 | 0.4043 | -#> |.....................| 4.692 | 0.6323 | 0.8661 | 1.407 | -#> |.....................| 1.068 |...........|...........|...........| -#> | F| Forward Diff. | 3.128 | 0.007908 | 0.004436 | 0.3353 | -#> |.....................| 0.2209 | 0.1645 | -0.3029 | -0.2852 | -#> |.....................| -0.2419 |...........|...........|...........| -#> | 48| 752.06241 | 0.9926 | -0.9758 | -1.042 | -1.095 | -#> |.....................| -0.6306 | -0.9841 | -0.9113 | -0.6557 | -#> |.....................| -0.7685 |...........|...........|...........| -#> | U| 752.06241 | 85.89 | -3.213 | -4.609 | -0.3969 | -#> |.....................| 4.691 | 0.6304 | 0.8725 | 1.408 | -#> |.....................| 1.072 |...........|...........|...........| -#> | X| 752.06241 | 85.89 | 0.04025 | 0.009958 | 0.4021 | -#> |.....................| 4.691 | 0.6304 | 0.8725 | 1.408 | -#> |.....................| 1.072 |...........|...........|...........| -#> | F| Forward Diff. | -8.924 | 0.01284 | 0.1020 | 0.2919 | -#> |.....................| 0.1011 | -0.08995 | 0.3194 | -0.2130 | -#> |.....................| -0.05120 |...........|...........|...........| -#> | 49| 752.04768 | 0.9941 | -0.9763 | -1.043 | -1.124 | -#> |.....................| -0.6313 | -0.9862 | -0.9116 | -0.6566 | -#> |.....................| -0.7644 |...........|...........|...........| -#> | U| 752.04768 | 86.02 | -3.213 | -4.611 | -0.4065 | -#> |.....................| 4.690 | 0.6289 | 0.8723 | 1.407 | -#> |.....................| 1.076 |...........|...........|...........| -#> | X| 752.04768 | 86.02 | 0.04023 | 0.009946 | 0.3998 | -#> |.....................| 4.690 | 0.6289 | 0.8723 | 1.407 | -#> |.....................| 1.076 |...........|...........|...........| -#> | F| Forward Diff. | 0.04447 | 0.001311 | 0.1345 | 0.2729 | -#> |.....................| 0.05334 | -0.06694 | 0.2984 | -0.1966 | -#> |.....................| 0.06514 |...........|...........|...........| -#> | 50| 752.04768 | 0.9941 | -0.9763 | -1.043 | -1.124 | -#> |.....................| -0.6313 | -0.9862 | -0.9116 | -0.6566 | -#> |.....................| -0.7644 |...........|...........|...........| -#> | U| 752.04768 | 86.02 | -3.213 | -4.611 | -0.4065 | -#> |.....................| 4.690 | 0.6289 | 0.8723 | 1.407 | -#> |.....................| 1.076 |...........|...........|...........| -#> | X| 752.04768 | 86.02 | 0.04023 | 0.009946 | 0.3998 | -#> |.....................| 4.690 | 0.6289 | 0.8723 | 1.407 | -#> |.....................| 1.076 |...........|...........|...........| -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_A1 |f_parent_qlogis | log_alpha | -#> |.....................| log_beta | sigma | o1 | o2 | -#> |.....................| o3 | o4 | o5 |...........| -#> | 1| 491.68697 | 1.000 | -1.000 | -0.9113 | -0.8954 | -#> |.....................| -0.8491 | -0.8582 | -0.8760 | -0.8739 | -#> |.....................| -0.8673 | -0.8694 | -0.8683 |...........| -#> | U| 491.68697 | 94.21 | -5.416 | -0.9966 | -0.2046 | -#> |.....................| 2.098 | 1.647 | 0.7612 | 0.8665 | -#> |.....................| 1.192 | 1.089 | 1.144 |...........| -#> | X| 491.68697 | 94.21 | 0.004447 | 0.2696 | 0.8150 | -#> |.....................| 8.153 | 1.647 | 0.7612 | 0.8665 | -#> |.....................| 1.192 | 1.089 | 1.144 |...........| -#> | G| Gill Diff. | 19.86 | 1.831 | -0.1132 | -0.03447 | -#> |.....................| -0.1365 | -48.08 | 10.28 | 8.952 | -#> |.....................| -12.04 | -8.764 | -10.61 |...........| -#> | 2| 1105.9428 | 0.6506 | -1.032 | -0.9093 | -0.8948 | -#> |.....................| -0.8467 | -0.01215 | -1.057 | -1.031 | -#> |.....................| -0.6554 | -0.7152 | -0.6817 |...........| -#> | U| 1105.9428 | 61.29 | -5.448 | -0.9946 | -0.2040 | -#> |.....................| 2.101 | 2.344 | 0.6235 | 0.7300 | -#> |.....................| 1.445 | 1.256 | 1.357 |...........| -#> | X| 1105.9428 | 61.29 | 0.004306 | 0.2700 | 0.8155 | -#> |.....................| 8.173 | 2.344 | 0.6235 | 0.7300 | -#> |.....................| 1.445 | 1.256 | 1.357 |...........| -#> | 3| 499.02505 | 0.9651 | -1.003 | -0.9111 | -0.8953 | -#> |.....................| -0.8489 | -0.7736 | -0.8941 | -0.8896 | -#> |.....................| -0.8462 | -0.8540 | -0.8497 |...........| -#> | U| 499.02505 | 90.91 | -5.419 | -0.9964 | -0.2045 | -#> |.....................| 2.099 | 1.717 | 0.7475 | 0.8529 | -#> |.....................| 1.217 | 1.105 | 1.165 |...........| -#> | X| 499.02505 | 90.91 | 0.004433 | 0.2696 | 0.8150 | -#> |.....................| 8.155 | 1.717 | 0.7475 | 0.8529 | -#> |.....................| 1.217 | 1.105 | 1.165 |...........| -#> | 4| 491.11153 | 0.9924 | -1.001 | -0.9112 | -0.8954 | -#> |.....................| -0.8491 | -0.8397 | -0.8799 | -0.8773 | -#> |.....................| -0.8627 | -0.8661 | -0.8642 |...........| -#> | U| 491.11153 | 93.49 | -5.416 | -0.9966 | -0.2046 | -#> |.....................| 2.098 | 1.663 | 0.7582 | 0.8635 | -#> |.....................| 1.198 | 1.092 | 1.148 |...........| -#> | X| 491.11153 | 93.49 | 0.004444 | 0.2696 | 0.8150 | -#> |.....................| 8.154 | 1.663 | 0.7582 | 0.8635 | -#> |.....................| 1.198 | 1.092 | 1.148 |...........| -#> | F| Forward Diff. | -141.0 | 1.761 | -0.2309 | -0.1084 | -#> |.....................| -0.3671 | -44.06 | 11.23 | 7.698 | -#> |.....................| -11.77 | -8.480 | -10.17 |...........| -#> | 5| 489.72110 | 1.001 | -1.001 | -0.9112 | -0.8954 | -#> |.....................| -0.8490 | -0.8217 | -0.8840 | -0.8806 | -#> |.....................| -0.8581 | -0.8627 | -0.8602 |...........| -#> | U| 489.7211 | 94.29 | -5.417 | -0.9965 | -0.2046 | -#> |.....................| 2.099 | 1.678 | 0.7552 | 0.8607 | -#> |.....................| 1.203 | 1.096 | 1.153 |...........| -#> | X| 489.7211 | 94.29 | 0.004441 | 0.2696 | 0.8150 | -#> |.....................| 8.154 | 1.678 | 0.7552 | 0.8607 | -#> |.....................| 1.203 | 1.096 | 1.153 |...........| -#> | F| Forward Diff. | 37.99 | 1.786 | -0.09663 | -0.03934 | -#> |.....................| -0.1210 | -40.49 | 9.520 | 7.642 | -#> |.....................| -11.65 | -8.313 | -10.04 |...........| -#> | 6| 488.87741 | 0.9957 | -1.002 | -0.9111 | -0.8953 | -#> |.....................| -0.8490 | -0.8027 | -0.8883 | -0.8842 | -#> |.....................| -0.8530 | -0.8591 | -0.8558 |...........| -#> | U| 488.87741 | 93.80 | -5.418 | -0.9965 | -0.2045 | -#> |.....................| 2.099 | 1.693 | 0.7519 | 0.8576 | -#> |.....................| 1.209 | 1.100 | 1.158 |...........| -#> | X| 488.87741 | 93.80 | 0.004437 | 0.2696 | 0.8150 | -#> |.....................| 8.155 | 1.693 | 0.7519 | 0.8576 | -#> |.....................| 1.209 | 1.100 | 1.158 |...........| -#> | F| Forward Diff. | -68.52 | 1.732 | -0.1791 | -0.08434 | -#> |.....................| -0.2775 | -36.72 | 9.505 | 7.234 | -#> |.....................| -11.37 | -8.098 | -9.790 |...........| -#> | 7| 487.98842 | 1.002 | -1.003 | -0.9111 | -0.8953 | -#> |.....................| -0.8489 | -0.7841 | -0.8926 | -0.8878 | -#> |.....................| -0.8478 | -0.8553 | -0.8512 |...........| -#> | U| 487.98842 | 94.37 | -5.418 | -0.9964 | -0.2045 | -#> |.....................| 2.099 | 1.708 | 0.7486 | 0.8545 | -#> |.....................| 1.215 | 1.104 | 1.163 |...........| -#> | X| 487.98842 | 94.37 | 0.004434 | 0.2697 | 0.8150 | -#> |.....................| 8.156 | 1.708 | 0.7486 | 0.8545 | -#> |.....................| 1.215 | 1.104 | 1.163 |...........| -#> | F| Forward Diff. | 53.83 | 1.743 | -0.07921 | -0.03701 | -#> |.....................| -0.09401 | -33.22 | 8.823 | 7.101 | -#> |.....................| -11.24 | -7.914 | -9.621 |...........| -#> | 8| 487.18834 | 0.9967 | -1.004 | -0.9110 | -0.8953 | -#> |.....................| -0.8488 | -0.7657 | -0.8973 | -0.8916 | -#> |.....................| -0.8421 | -0.8512 | -0.8463 |...........| -#> | U| 487.18834 | 93.89 | -5.419 | -0.9963 | -0.2045 | -#> |.....................| 2.099 | 1.724 | 0.7451 | 0.8512 | -#> |.....................| 1.222 | 1.108 | 1.169 |...........| -#> | X| 487.18834 | 93.89 | 0.004430 | 0.2697 | 0.8151 | -#> |.....................| 8.156 | 1.724 | 0.7451 | 0.8512 | -#> |.....................| 1.222 | 1.108 | 1.169 |...........| -#> | F| Forward Diff. | -47.29 | 1.692 | -0.1608 | -0.08286 | -#> |.....................| -0.2512 | -29.89 | 8.493 | 6.629 | -#> |.....................| -10.92 | -7.677 | -9.350 |...........| -#> | 9| 486.46922 | 1.002 | -1.005 | -0.9109 | -0.8952 | -#> |.....................| -0.8487 | -0.7480 | -0.9022 | -0.8958 | -#> |.....................| -0.8355 | -0.8466 | -0.8406 |...........| -#> | U| 486.46922 | 94.36 | -5.420 | -0.9963 | -0.2045 | -#> |.....................| 2.099 | 1.738 | 0.7413 | 0.8476 | -#> |.....................| 1.230 | 1.113 | 1.175 |...........| -#> | X| 486.46922 | 94.36 | 0.004425 | 0.2697 | 0.8151 | -#> |.....................| 8.157 | 1.738 | 0.7413 | 0.8476 | -#> |.....................| 1.230 | 1.113 | 1.175 |...........| -#> | F| Forward Diff. | 49.83 | 1.694 | -0.07480 | -0.03429 | -#> |.....................| -0.09436 | -26.68 | 8.123 | 6.503 | -#> |.....................| -10.68 | -7.439 | -9.119 |...........| -#> | 10| 485.78721 | 0.9968 | -1.006 | -0.9109 | -0.8952 | -#> |.....................| -0.8486 | -0.7319 | -0.9078 | -0.9005 | -#> |.....................| -0.8277 | -0.8412 | -0.8339 |...........| -#> | U| 485.78721 | 93.91 | -5.422 | -0.9962 | -0.2044 | -#> |.....................| 2.099 | 1.752 | 0.7370 | 0.8435 | -#> |.....................| 1.239 | 1.119 | 1.183 |...........| -#> | X| 485.78721 | 93.91 | 0.004420 | 0.2697 | 0.8151 | -#> |.....................| 8.158 | 1.752 | 0.7370 | 0.8435 | -#> |.....................| 1.239 | 1.119 | 1.183 |...........| -#> | F| Forward Diff. | -42.45 | 1.646 | -0.1526 | -0.07491 | -#> |.....................| -0.2510 | -24.12 | 7.576 | 5.974 | -#> |.....................| -10.35 | -7.128 | -8.768 |...........| -#> | 11| 485.17009 | 1.001 | -1.008 | -0.9107 | -0.8952 | -#> |.....................| -0.8484 | -0.7183 | -0.9141 | -0.9058 | -#> |.....................| -0.8180 | -0.8347 | -0.8257 |...........| -#> | U| 485.17009 | 94.32 | -5.423 | -0.9961 | -0.2044 | -#> |.....................| 2.099 | 1.763 | 0.7322 | 0.8389 | -#> |.....................| 1.251 | 1.126 | 1.192 |...........| -#> | X| 485.17009 | 94.32 | 0.004413 | 0.2697 | 0.8152 | -#> |.....................| 8.160 | 1.763 | 0.7322 | 0.8389 | -#> |.....................| 1.251 | 1.126 | 1.192 |...........| -#> | 12| 484.56759 | 1.002 | -1.010 | -0.9106 | -0.8951 | -#> |.....................| -0.8481 | -0.7038 | -0.9212 | -0.9119 | -#> |.....................| -0.8067 | -0.8272 | -0.8163 |...........| -#> | U| 484.56759 | 94.37 | -5.425 | -0.9959 | -0.2043 | -#> |.....................| 2.099 | 1.775 | 0.7268 | 0.8336 | -#> |.....................| 1.264 | 1.134 | 1.203 |...........| -#> | X| 484.56759 | 94.37 | 0.004404 | 0.2697 | 0.8152 | -#> |.....................| 8.162 | 1.775 | 0.7268 | 0.8336 | -#> |.....................| 1.264 | 1.134 | 1.203 |...........| -#> | 13| 483.17982 | 1.003 | -1.015 | -0.9102 | -0.8949 | -#> |.....................| -0.8475 | -0.6634 | -0.9410 | -0.9287 | -#> |.....................| -0.7754 | -0.8064 | -0.7900 |...........| -#> | U| 483.17982 | 94.51 | -5.431 | -0.9956 | -0.2042 | -#> |.....................| 2.100 | 1.808 | 0.7117 | 0.8190 | -#> |.....................| 1.302 | 1.157 | 1.233 |...........| -#> | X| 483.17982 | 94.51 | 0.004381 | 0.2698 | 0.8153 | -#> |.....................| 8.167 | 1.808 | 0.7117 | 0.8190 | -#> |.....................| 1.302 | 1.157 | 1.233 |...........| -#> | F| Forward Diff. | 68.60 | 1.559 | 0.008498 | -0.01857 | -#> |.....................| -0.01950 | -13.38 | 5.413 | 4.461 | -#> |.....................| -8.084 | -5.202 | -6.751 |...........| -#> | 14| 482.50435 | 0.9937 | -1.034 | -0.9105 | -0.8944 | -#> |.....................| -0.8462 | -0.6947 | -0.9713 | -0.9553 | -#> |.....................| -0.7043 | -0.7694 | -0.7343 |...........| -#> | U| 482.50435 | 93.61 | -5.449 | -0.9958 | -0.2036 | -#> |.....................| 2.101 | 1.782 | 0.6887 | 0.7959 | -#> |.....................| 1.386 | 1.197 | 1.297 |...........| -#> | X| 482.50435 | 93.61 | 0.004300 | 0.2698 | 0.8158 | -#> |.....................| 8.177 | 1.782 | 0.6887 | 0.7959 | -#> |.....................| 1.386 | 1.197 | 1.297 |...........| -#> | F| Forward Diff. | -85.62 | 1.442 | -0.1650 | -0.08233 | -#> |.....................| -0.3434 | -17.31 | 3.930 | 3.048 | -#> |.....................| -4.934 | -3.045 | -4.080 |...........| -#> | 15| 481.97261 | 1.003 | -1.090 | -0.9106 | -0.8929 | -#> |.....................| -0.8403 | -0.7109 | -0.9936 | -0.9798 | -#> |.....................| -0.6305 | -0.7595 | -0.6850 |...........| -#> | U| 481.97261 | 94.53 | -5.505 | -0.9959 | -0.2021 | -#> |.....................| 2.107 | 1.769 | 0.6717 | 0.7747 | -#> |.....................| 1.474 | 1.208 | 1.353 |...........| -#> | X| 481.97261 | 94.53 | 0.004066 | 0.2697 | 0.8170 | -#> |.....................| 8.226 | 1.769 | 0.6717 | 0.7747 | -#> |.....................| 1.474 | 1.208 | 1.353 |...........| -#> | F| Forward Diff. | 56.89 | 1.274 | 0.1237 | 0.02279 | -#> |.....................| 0.2367 | -19.64 | 1.923 | 2.281 | -#> |.....................| -1.663 | -2.419 | -1.870 |...........| -#> | 16| 481.06506 | 1.001 | -1.169 | -0.9152 | -0.8919 | -#> |.....................| -0.8407 | -0.6475 | -0.9528 | -0.9773 | -#> |.....................| -0.6368 | -0.7786 | -0.6952 |...........| -#> | U| 481.06506 | 94.29 | -5.585 | -1.000 | -0.2011 | -#> |.....................| 2.107 | 1.821 | 0.7028 | 0.7769 | -#> |.....................| 1.467 | 1.187 | 1.341 |...........| -#> | X| 481.06506 | 94.29 | 0.003755 | 0.2688 | 0.8179 | -#> |.....................| 8.223 | 1.821 | 0.7028 | 0.7769 | -#> |.....................| 1.467 | 1.187 | 1.341 |...........| -#> | F| Forward Diff. | 24.24 | 0.9898 | -0.1087 | 0.01886 | -#> |.....................| 0.1247 | -10.78 | 3.743 | 2.188 | -#> |.....................| -2.085 | -3.507 | -2.452 |...........| -#> | 17| 481.22982 | 0.9921 | -1.212 | -0.9099 | -0.8928 | -#> |.....................| -0.8459 | -0.6315 | -1.015 | -0.9814 | -#> |.....................| -0.6906 | -0.7213 | -0.7106 |...........| -#> | U| 481.22982 | 93.46 | -5.628 | -0.9952 | -0.2020 | -#> |.....................| 2.102 | 1.834 | 0.6553 | 0.7733 | -#> |.....................| 1.403 | 1.250 | 1.324 |...........| -#> | X| 481.22982 | 93.46 | 0.003596 | 0.2699 | 0.8171 | -#> |.....................| 8.180 | 1.834 | 0.6553 | 0.7733 | -#> |.....................| 1.403 | 1.250 | 1.324 |...........| -#> | 18| 481.29798 | 0.9919 | -1.186 | -0.9131 | -0.8922 | -#> |.....................| -0.8428 | -0.6388 | -0.9780 | -0.9794 | -#> |.....................| -0.6574 | -0.7554 | -0.7007 |...........| -#> | U| 481.29798 | 93.44 | -5.602 | -0.9984 | -0.2014 | -#> |.....................| 2.105 | 1.828 | 0.6836 | 0.7751 | -#> |.....................| 1.442 | 1.213 | 1.335 |...........| -#> | X| 481.29798 | 93.44 | 0.003691 | 0.2693 | 0.8176 | -#> |.....................| 8.206 | 1.828 | 0.6836 | 0.7751 | -#> |.....................| 1.442 | 1.213 | 1.335 |...........| -#> | 19| 481.41397 | 0.9918 | -1.173 | -0.9147 | -0.8919 | -#> |.....................| -0.8412 | -0.6424 | -0.9596 | -0.9784 | -#> |.....................| -0.6408 | -0.7724 | -0.6957 |...........| -#> | U| 481.41397 | 93.43 | -5.589 | -1.000 | -0.2012 | -#> |.....................| 2.106 | 1.825 | 0.6976 | 0.7759 | -#> |.....................| 1.462 | 1.194 | 1.341 |...........| -#> | X| 481.41397 | 93.43 | 0.003739 | 0.2689 | 0.8178 | -#> |.....................| 8.219 | 1.825 | 0.6976 | 0.7759 | -#> |.....................| 1.462 | 1.194 | 1.341 |...........| -#> | 20| 481.05031 | 0.9977 | -1.169 | -0.9152 | -0.8919 | -#> |.....................| -0.8407 | -0.6461 | -0.9533 | -0.9776 | -#> |.....................| -0.6366 | -0.7782 | -0.6949 |...........| -#> | U| 481.05031 | 93.99 | -5.585 | -1.000 | -0.2011 | -#> |.....................| 2.107 | 1.822 | 0.7024 | 0.7766 | -#> |.....................| 1.467 | 1.188 | 1.342 |...........| -#> | X| 481.05031 | 93.99 | 0.003754 | 0.2688 | 0.8179 | -#> |.....................| 8.223 | 1.822 | 0.7024 | 0.7766 | -#> |.....................| 1.467 | 1.188 | 1.342 |...........| -#> | F| Forward Diff. | -27.42 | 0.9768 | -0.2107 | -0.01109 | -#> |.....................| -0.02839 | -10.63 | 3.585 | 2.076 | -#> |.....................| -2.082 | -3.487 | -2.432 |...........| -#> | 21| 481.00693 | 0.9997 | -1.170 | -0.9150 | -0.8919 | -#> |.....................| -0.8408 | -0.6450 | -0.9548 | -0.9778 | -#> |.....................| -0.6377 | -0.7765 | -0.6951 |...........| -#> | U| 481.00693 | 94.18 | -5.586 | -1.000 | -0.2011 | -#> |.....................| 2.107 | 1.823 | 0.7012 | 0.7764 | -#> |.....................| 1.466 | 1.190 | 1.342 |...........| -#> | X| 481.00693 | 94.18 | 0.003750 | 0.2689 | 0.8178 | -#> |.....................| 8.222 | 1.823 | 0.7012 | 0.7764 | -#> |.....................| 1.466 | 1.190 | 1.342 |...........| -#> | F| Forward Diff. | 5.549 | 0.9801 | -0.1366 | 0.007724 | -#> |.....................| 0.06864 | -10.47 | 3.736 | 2.095 | -#> |.....................| -2.145 | -3.386 | -2.439 |...........| -#> | 22| 480.97727 | 0.9982 | -1.171 | -0.9150 | -0.8919 | -#> |.....................| -0.8408 | -0.6422 | -0.9558 | -0.9784 | -#> |.....................| -0.6371 | -0.7756 | -0.6944 |...........| -#> | U| 480.97727 | 94.04 | -5.586 | -1.000 | -0.2011 | -#> |.....................| 2.107 | 1.825 | 0.7005 | 0.7760 | -#> |.....................| 1.466 | 1.191 | 1.342 |...........| -#> | X| 480.97727 | 94.04 | 0.003749 | 0.2689 | 0.8178 | -#> |.....................| 8.222 | 1.825 | 0.7005 | 0.7760 | -#> |.....................| 1.466 | 1.191 | 1.342 |...........| -#> | F| Forward Diff. | -18.22 | 0.9728 | -0.1820 | -0.005388 | -#> |.....................| -0.004679 | -10.15 | 3.348 | 1.956 | -#> |.....................| -2.141 | -3.348 | -2.415 |...........| -#> | 23| 480.94781 | 0.9999 | -1.172 | -0.9148 | -0.8919 | -#> |.....................| -0.8410 | -0.6410 | -0.9575 | -0.9785 | -#> |.....................| -0.6383 | -0.7738 | -0.6946 |...........| -#> | U| 480.94781 | 94.20 | -5.587 | -1.000 | -0.2011 | -#> |.....................| 2.107 | 1.826 | 0.6992 | 0.7758 | -#> |.....................| 1.465 | 1.193 | 1.342 |...........| -#> | X| 480.94781 | 94.20 | 0.003745 | 0.2689 | 0.8178 | -#> |.....................| 8.220 | 1.826 | 0.6992 | 0.7758 | -#> |.....................| 1.465 | 1.193 | 1.342 |...........| -#> | F| Forward Diff. | 8.568 | 0.9740 | -0.1199 | 0.009837 | -#> |.....................| 0.07469 | -9.926 | 3.371 | 0.7973 | -#> |.....................| -2.181 | -3.230 | -2.408 |...........| -#> | 24| 480.92664 | 0.9984 | -1.173 | -0.9147 | -0.8919 | -#> |.....................| -0.8411 | -0.6390 | -0.9589 | -0.9778 | -#> |.....................| -0.6386 | -0.7721 | -0.6942 |...........| -#> | U| 480.92664 | 94.06 | -5.588 | -1.000 | -0.2011 | -#> |.....................| 2.107 | 1.828 | 0.6981 | 0.7765 | -#> |.....................| 1.465 | 1.195 | 1.343 |...........| -#> | X| 480.92664 | 94.06 | 0.003741 | 0.2689 | 0.8178 | -#> |.....................| 8.219 | 1.828 | 0.6981 | 0.7765 | -#> |.....................| 1.465 | 1.195 | 1.343 |...........| -#> | F| Forward Diff. | -15.24 | 0.9644 | -0.1632 | -0.002739 | -#> |.....................| -0.008738 | -9.656 | 3.177 | 0.7945 | -#> |.....................| -2.140 | -3.159 | -2.407 |...........| -#> | 25| 480.90633 | 0.9999 | -1.174 | -0.9146 | -0.8920 | -#> |.....................| -0.8412 | -0.6376 | -0.9602 | -0.9760 | -#> |.....................| -0.6390 | -0.7705 | -0.6939 |...........| -#> | U| 480.90633 | 94.20 | -5.589 | -0.9999 | -0.2012 | -#> |.....................| 2.106 | 1.829 | 0.6971 | 0.7780 | -#> |.....................| 1.464 | 1.196 | 1.343 |...........| -#> | X| 480.90633 | 94.20 | 0.003737 | 0.2690 | 0.8178 | -#> |.....................| 8.219 | 1.829 | 0.6971 | 0.7780 | -#> |.....................| 1.464 | 1.196 | 1.343 |...........| -#> | F| Forward Diff. | 8.878 | 0.9654 | -0.1149 | 0.008298 | -#> |.....................| 0.06381 | -9.456 | 3.199 | 2.165 | -#> |.....................| -2.158 | -3.035 | -2.359 |...........| -#> | 26| 480.88677 | 0.9984 | -1.175 | -0.9145 | -0.8920 | -#> |.....................| -0.8413 | -0.6358 | -0.9617 | -0.9757 | -#> |.....................| -0.6395 | -0.7687 | -0.6936 |...........| -#> | U| 480.88677 | 94.05 | -5.591 | -0.9998 | -0.2012 | -#> |.....................| 2.106 | 1.831 | 0.6960 | 0.7783 | -#> |.....................| 1.464 | 1.198 | 1.343 |...........| -#> | X| 480.88677 | 94.05 | 0.003733 | 0.2690 | 0.8178 | -#> |.....................| 8.218 | 1.831 | 0.6960 | 0.7783 | -#> |.....................| 1.464 | 1.198 | 1.343 |...........| -#> | F| Forward Diff. | -15.55 | 0.9550 | -0.1566 | -0.004027 | -#> |.....................| -0.01529 | -9.334 | 3.082 | 0.8457 | -#> |.....................| -2.216 | -2.967 | -2.371 |...........| -#> | 27| 480.86430 | 0.9998 | -1.177 | -0.9143 | -0.8920 | -#> |.....................| -0.8414 | -0.6346 | -0.9633 | -0.9749 | -#> |.....................| -0.6404 | -0.7668 | -0.6935 |...........| -#> | U| 480.8643 | 94.19 | -5.592 | -0.9996 | -0.2012 | -#> |.....................| 2.106 | 1.832 | 0.6948 | 0.7790 | -#> |.....................| 1.463 | 1.200 | 1.343 |...........| -#> | X| 480.8643 | 94.19 | 0.003727 | 0.2690 | 0.8177 | -#> |.....................| 8.217 | 1.832 | 0.6948 | 0.7790 | -#> |.....................| 1.463 | 1.200 | 1.343 |...........| -#> | F| Forward Diff. | 6.756 | 0.9537 | -0.1079 | 0.006011 | -#> |.....................| 0.04748 | -9.023 | 3.021 | 2.222 | -#> |.....................| -2.227 | -2.836 | -2.339 |...........| -#> | 28| 480.84403 | 0.9982 | -1.178 | -0.9142 | -0.8920 | -#> |.....................| -0.8415 | -0.6324 | -0.9646 | -0.9751 | -#> |.....................| -0.6405 | -0.7653 | -0.6931 |...........| -#> | U| 480.84403 | 94.04 | -5.593 | -0.9995 | -0.2012 | -#> |.....................| 2.106 | 1.833 | 0.6938 | 0.7788 | -#> |.....................| 1.462 | 1.202 | 1.344 |...........| -#> | X| 480.84403 | 94.04 | 0.003723 | 0.2690 | 0.8177 | -#> |.....................| 8.216 | 1.833 | 0.6938 | 0.7788 | -#> |.....................| 1.462 | 1.202 | 1.344 |...........| -#> | F| Forward Diff. | -17.74 | 0.9443 | -0.1486 | -0.005686 | -#> |.....................| -0.02964 | -8.905 | 2.905 | 2.091 | -#> |.....................| -2.264 | -2.753 | -2.319 |...........| -#> | 29| 480.81486 | 0.9998 | -1.179 | -0.9140 | -0.8921 | -#> |.....................| -0.8417 | -0.6315 | -0.9657 | -0.9770 | -#> |.....................| -0.6415 | -0.7640 | -0.6932 |...........| -#> | U| 480.81486 | 94.18 | -5.595 | -0.9993 | -0.2013 | -#> |.....................| 2.106 | 1.834 | 0.6930 | 0.7772 | -#> |.....................| 1.461 | 1.203 | 1.344 |...........| -#> | X| 480.81486 | 94.18 | 0.003718 | 0.2691 | 0.8177 | -#> |.....................| 8.215 | 1.834 | 0.6930 | 0.7772 | -#> |.....................| 1.461 | 1.203 | 1.344 |...........| -#> | F| Forward Diff. | 6.172 | 0.9439 | -0.09077 | 0.005496 | -#> |.....................| 0.04002 | -8.557 | 3.060 | 0.8688 | -#> |.....................| -2.237 | -2.681 | -2.329 |...........| -#> | 30| 480.79675 | 0.9982 | -1.180 | -0.9139 | -0.8921 | -#> |.....................| -0.8418 | -0.6292 | -0.9672 | -0.9770 | -#> |.....................| -0.6415 | -0.7628 | -0.6927 |...........| -#> | U| 480.79675 | 94.04 | -5.596 | -0.9992 | -0.2013 | -#> |.....................| 2.106 | 1.836 | 0.6918 | 0.7772 | -#> |.....................| 1.461 | 1.205 | 1.344 |...........| -#> | X| 480.79675 | 94.04 | 0.003714 | 0.2691 | 0.8177 | -#> |.....................| 8.214 | 1.836 | 0.6918 | 0.7772 | -#> |.....................| 1.461 | 1.205 | 1.344 |...........| -#> | F| Forward Diff. | -18.05 | 0.9344 | -0.1333 | -0.006636 | -#> |.....................| -0.03697 | -8.406 | 2.763 | 0.7695 | -#> |.....................| -2.291 | -2.623 | -2.307 |...........| -#> | 31| 480.77804 | 0.9997 | -1.182 | -0.9138 | -0.8921 | -#> |.....................| -0.8419 | -0.6281 | -0.9686 | -0.9750 | -#> |.....................| -0.6417 | -0.7615 | -0.6923 |...........| -#> | U| 480.77804 | 94.18 | -5.597 | -0.9991 | -0.2013 | -#> |.....................| 2.106 | 1.837 | 0.6907 | 0.7789 | -#> |.....................| 1.461 | 1.206 | 1.345 |...........| -#> | X| 480.77804 | 94.18 | 0.003708 | 0.2691 | 0.8176 | -#> |.....................| 8.213 | 1.837 | 0.6907 | 0.7789 | -#> |.....................| 1.461 | 1.206 | 1.345 |...........| -#> | F| Forward Diff. | 5.466 | 0.9331 | -0.08875 | 0.003744 | -#> |.....................| 0.02543 | -8.171 | 2.670 | 2.155 | -#> |.....................| -2.279 | -2.534 | -2.278 |...........| -#> | 32| 480.75892 | 0.9982 | -1.183 | -0.9137 | -0.8921 | -#> |.....................| -0.8419 | -0.6258 | -0.9698 | -0.9756 | -#> |.....................| -0.6414 | -0.7603 | -0.6917 |...........| -#> | U| 480.75892 | 94.03 | -5.598 | -0.9991 | -0.2014 | -#> |.....................| 2.106 | 1.839 | 0.6899 | 0.7784 | -#> |.....................| 1.461 | 1.207 | 1.346 |...........| -#> | X| 480.75892 | 94.03 | 0.003704 | 0.2691 | 0.8176 | -#> |.....................| 8.212 | 1.839 | 0.6899 | 0.7784 | -#> |.....................| 1.461 | 1.207 | 1.346 |...........| -#> | F| Forward Diff. | -18.29 | 0.9240 | -0.1279 | -0.008301 | -#> |.....................| -0.04619 | -7.961 | 2.584 | 0.8229 | -#> |.....................| -2.311 | -2.476 | -2.253 |...........| -#> | 33| 480.73432 | 0.9997 | -1.185 | -0.9136 | -0.8922 | -#> |.....................| -0.8421 | -0.6250 | -0.9708 | -0.9758 | -#> |.....................| -0.6420 | -0.7587 | -0.6914 |...........| -#> | U| 480.73432 | 94.18 | -5.601 | -0.9989 | -0.2014 | -#> |.....................| 2.105 | 1.840 | 0.6891 | 0.7782 | -#> |.....................| 1.461 | 1.209 | 1.346 |...........| -#> | X| 480.73432 | 94.18 | 0.003695 | 0.2692 | 0.8176 | -#> |.....................| 8.211 | 1.840 | 0.6891 | 0.7782 | -#> |.....................| 1.461 | 1.209 | 1.346 |...........| -#> | F| Forward Diff. | 5.056 | 0.9202 | -0.07575 | 0.002374 | -#> |.....................| 0.02179 | -7.789 | 2.502 | 2.101 | -#> |.....................| -2.273 | -2.370 | -2.217 |...........| -#> | 34| 480.71449 | 0.9983 | -1.187 | -0.9135 | -0.8922 | -#> |.....................| -0.8422 | -0.6227 | -0.9719 | -0.9765 | -#> |.....................| -0.6416 | -0.7575 | -0.6908 |...........| -#> | U| 480.71449 | 94.05 | -5.602 | -0.9988 | -0.2014 | -#> |.....................| 2.105 | 1.841 | 0.6883 | 0.7776 | -#> |.....................| 1.461 | 1.210 | 1.347 |...........| -#> | X| 480.71449 | 94.05 | 0.003690 | 0.2692 | 0.8175 | -#> |.....................| 8.210 | 1.841 | 0.6883 | 0.7776 | -#> |.....................| 1.461 | 1.210 | 1.347 |...........| -#> | F| Forward Diff. | -16.10 | 0.9104 | -0.1099 | -0.008208 | -#> |.....................| -0.04557 | -7.571 | 2.606 | 1.992 | -#> |.....................| -2.295 | -2.312 | -2.196 |...........| -#> | 35| 480.68777 | 0.9997 | -1.189 | -0.9134 | -0.8923 | -#> |.....................| -0.8423 | -0.6220 | -0.9726 | -0.9789 | -#> |.....................| -0.6421 | -0.7569 | -0.6908 |...........| -#> | U| 480.68777 | 94.18 | -5.604 | -0.9987 | -0.2015 | -#> |.....................| 2.105 | 1.842 | 0.6877 | 0.7755 | -#> |.....................| 1.461 | 1.211 | 1.347 |...........| -#> | X| 480.68777 | 94.18 | 0.003683 | 0.2692 | 0.8175 | -#> |.....................| 8.209 | 1.842 | 0.6877 | 0.7755 | -#> |.....................| 1.461 | 1.211 | 1.347 |...........| -#> | F| Forward Diff. | 4.858 | 0.9091 | -0.06076 | 0.001972 | -#> |.....................| 0.01464 | -7.318 | 2.391 | 0.7174 | -#> |.....................| -2.245 | -2.255 | -2.188 |...........| -#> | 36| 480.67297 | 0.9982 | -1.190 | -0.9134 | -0.8923 | -#> |.....................| -0.8424 | -0.6196 | -0.9738 | -0.9789 | -#> |.....................| -0.6415 | -0.7559 | -0.6900 |...........| -#> | U| 480.67297 | 94.03 | -5.605 | -0.9987 | -0.2015 | -#> |.....................| 2.105 | 1.844 | 0.6868 | 0.7755 | -#> |.....................| 1.461 | 1.212 | 1.348 |...........| -#> | X| 480.67297 | 94.03 | 0.003678 | 0.2692 | 0.8175 | -#> |.....................| 8.209 | 1.844 | 0.6868 | 0.7755 | -#> |.....................| 1.461 | 1.212 | 1.348 |...........| -#> | F| Forward Diff. | -18.29 | 0.8994 | -0.1037 | -0.01039 | -#> |.....................| -0.05604 | -7.086 | 2.324 | 0.6431 | -#> |.....................| -2.272 | -2.229 | -2.170 |...........| -#> | 37| 480.65610 | 0.9996 | -1.192 | -0.9134 | -0.8923 | -#> |.....................| -0.8424 | -0.6187 | -0.9745 | -0.9768 | -#> |.....................| -0.6410 | -0.7549 | -0.6892 |...........| -#> | U| 480.6561 | 94.17 | -5.607 | -0.9987 | -0.2015 | -#> |.....................| 2.105 | 1.845 | 0.6862 | 0.7773 | -#> |.....................| 1.462 | 1.213 | 1.348 |...........| -#> | X| 480.6561 | 94.17 | 0.003671 | 0.2692 | 0.8175 | -#> |.....................| 8.208 | 1.845 | 0.6862 | 0.7773 | -#> |.....................| 1.462 | 1.213 | 1.348 |...........| -#> | F| Forward Diff. | 3.523 | 0.8967 | -0.06519 |-0.0005238 | -#> |.....................| 0.007306 | -6.938 | 2.250 | 0.8205 | -#> |.....................| -2.209 | -2.143 | -2.109 |...........| -#> | 38| 480.63930 | 0.9982 | -1.192 | -0.9133 | -0.8923 | -#> |.....................| -0.8425 | -0.6159 | -0.9754 | -0.9772 | -#> |.....................| -0.6401 | -0.7540 | -0.6884 |...........| -#> | U| 480.6393 | 94.04 | -5.608 | -0.9987 | -0.2015 | -#> |.....................| 2.105 | 1.847 | 0.6856 | 0.7770 | -#> |.....................| 1.463 | 1.214 | 1.349 |...........| -#> | X| 480.6393 | 94.04 | 0.003670 | 0.2692 | 0.8175 | -#> |.....................| 8.208 | 1.847 | 0.6856 | 0.7770 | -#> |.....................| 1.463 | 1.214 | 1.349 |...........| -#> | F| Forward Diff. | -17.45 | 0.8903 | -0.1044 | -0.01155 | -#> |.....................| -0.05881 | -6.641 | 2.195 | 1.966 | -#> |.....................| -2.207 | -2.119 | -2.090 |...........| -#> | 39| 480.61554 | 0.9996 | -1.195 | -0.9133 | -0.8924 | -#> |.....................| -0.8426 | -0.6153 | -0.9757 | -0.9778 | -#> |.....................| -0.6400 | -0.7531 | -0.6877 |...........| -#> | U| 480.61554 | 94.16 | -5.611 | -0.9986 | -0.2016 | -#> |.....................| 2.105 | 1.848 | 0.6853 | 0.7765 | -#> |.....................| 1.463 | 1.215 | 1.350 |...........| -#> | X| 480.61554 | 94.16 | 0.003659 | 0.2692 | 0.8174 | -#> |.....................| 8.207 | 1.848 | 0.6853 | 0.7765 | -#> |.....................| 1.463 | 1.215 | 1.350 |...........| -#> | F| Forward Diff. | 2.395 | 0.8850 | -0.05988 | -0.001937 | -#> |.....................| 0.0008548 | -6.531 | 2.145 | 0.7341 | -#> |.....................| -2.178 | -2.045 | -2.040 |...........| -#> | 40| 480.59501 | 0.9985 | -1.195 | -0.9132 | -0.8924 | -#> |.....................| -0.8426 | -0.6124 | -0.9766 | -0.9781 | -#> |.....................| -0.6390 | -0.7522 | -0.6868 |...........| -#> | U| 480.59501 | 94.06 | -5.611 | -0.9986 | -0.2016 | -#> |.....................| 2.105 | 1.850 | 0.6846 | 0.7762 | -#> |.....................| 1.464 | 1.216 | 1.351 |...........| -#> | X| 480.59501 | 94.06 | 0.003658 | 0.2692 | 0.8174 | -#> |.....................| 8.207 | 1.850 | 0.6846 | 0.7762 | -#> |.....................| 1.464 | 1.216 | 1.351 |...........| -#> | F| Forward Diff. | -13.20 | 0.8797 | -0.08878 | -0.01245 | -#> |.....................| -0.05202 | -6.149 | 2.097 | 1.936 | -#> |.....................| -2.128 | -2.007 | -2.021 |...........| -#> | 41| 480.57374 | 0.9995 | -1.198 | -0.9132 | -0.8924 | -#> |.....................| -0.8426 | -0.6117 | -0.9768 | -0.9794 | -#> |.....................| -0.6387 | -0.7515 | -0.6862 |...........| -#> | U| 480.57374 | 94.16 | -5.614 | -0.9986 | -0.2016 | -#> |.....................| 2.105 | 1.851 | 0.6845 | 0.7751 | -#> |.....................| 1.464 | 1.217 | 1.352 |...........| -#> | X| 480.57374 | 94.16 | 0.003647 | 0.2692 | 0.8174 | -#> |.....................| 8.207 | 1.851 | 0.6845 | 0.7751 | -#> |.....................| 1.464 | 1.217 | 1.352 |...........| -#> | 42| 480.55656 | 0.9993 | -1.203 | -0.9133 | -0.8924 | -#> |.....................| -0.8427 | -0.6115 | -0.9767 | -0.9815 | -#> |.....................| -0.6386 | -0.7506 | -0.6853 |...........| -#> | U| 480.55656 | 94.14 | -5.619 | -0.9986 | -0.2016 | -#> |.....................| 2.105 | 1.851 | 0.6846 | 0.7733 | -#> |.....................| 1.465 | 1.218 | 1.353 |...........| -#> | X| 480.55656 | 94.14 | 0.003629 | 0.2692 | 0.8174 | -#> |.....................| 8.206 | 1.851 | 0.6846 | 0.7733 | -#> |.....................| 1.465 | 1.218 | 1.353 |...........| -#> | 43| 480.48642 | 0.9984 | -1.228 | -0.9134 | -0.8925 | -#> |.....................| -0.8432 | -0.6102 | -0.9761 | -0.9914 | -#> |.....................| -0.6380 | -0.7463 | -0.6812 |...........| -#> | U| 480.48642 | 94.05 | -5.643 | -0.9987 | -0.2017 | -#> |.....................| 2.104 | 1.852 | 0.6850 | 0.7647 | -#> |.....................| 1.465 | 1.223 | 1.357 |...........| -#> | X| 480.48642 | 94.05 | 0.003541 | 0.2692 | 0.8174 | -#> |.....................| 8.202 | 1.852 | 0.6850 | 0.7647 | -#> |.....................| 1.465 | 1.223 | 1.357 |...........| -#> | 44| 480.43193 | 0.9946 | -1.325 | -0.9138 | -0.8928 | -#> |.....................| -0.8452 | -0.6054 | -0.9741 | -1.031 | -#> |.....................| -0.6354 | -0.7292 | -0.6649 |...........| -#> | U| 480.43193 | 93.70 | -5.741 | -0.9991 | -0.2020 | -#> |.....................| 2.102 | 1.856 | 0.6866 | 0.7303 | -#> |.....................| 1.469 | 1.241 | 1.376 |...........| -#> | X| 480.43193 | 93.70 | 0.003212 | 0.2691 | 0.8171 | -#> |.....................| 8.185 | 1.856 | 0.6866 | 0.7303 | -#> |.....................| 1.469 | 1.241 | 1.376 |...........| -#> | F| Forward Diff. | -73.68 | 0.5532 | -0.05170 | -0.03792 | -#> |.....................| -0.2632 | -4.949 | 2.751 | -2.063 | -#> |.....................| -2.027 | -0.5538 | -1.006 |...........| -#> | 45| 480.12037 | 0.9986 | -1.465 | -0.9157 | -0.8935 | -#> |.....................| -0.8478 | -0.6011 | -0.9922 | -1.022 | -#> |.....................| -0.6184 | -0.7143 | -0.6451 |...........| -#> | U| 480.12037 | 94.07 | -5.880 | -1.001 | -0.2027 | -#> |.....................| 2.100 | 1.859 | 0.6728 | 0.7378 | -#> |.....................| 1.489 | 1.257 | 1.399 |...........| -#> | X| 480.12037 | 94.07 | 0.002795 | 0.2687 | 0.8166 | -#> |.....................| 8.164 | 1.859 | 0.6728 | 0.7378 | -#> |.....................| 1.489 | 1.257 | 1.399 |...........| -#> | F| Forward Diff. | -14.31 | 0.1919 | -0.006458 | -0.005637 | -#> |.....................| -0.1500 | -5.088 | 0.6605 | -0.1467 | -#> |.....................| -1.672 | 0.02074 | -0.4009 |...........| -#> | 46| 480.21684 | 0.9998 | -1.532 | -0.9143 | -0.8951 | -#> |.....................| -0.8360 | -0.5884 | -0.9862 | -1.032 | -#> |.....................| -0.5071 | -0.7680 | -0.6684 |...........| -#> | U| 480.21684 | 94.19 | -5.947 | -0.9996 | -0.2043 | -#> |.....................| 2.112 | 1.870 | 0.6773 | 0.7298 | -#> |.....................| 1.621 | 1.199 | 1.372 |...........| -#> | X| 480.21684 | 94.19 | 0.002613 | 0.2690 | 0.8152 | -#> |.....................| 8.261 | 1.870 | 0.6773 | 0.7298 | -#> |.....................| 1.621 | 1.199 | 1.372 |...........| -#> | 47| 480.06028 | 1.000 | -1.489 | -0.9152 | -0.8941 | -#> |.....................| -0.8435 | -0.5961 | -0.9901 | -1.026 | -#> |.....................| -0.5774 | -0.7340 | -0.6536 |...........| -#> | U| 480.06028 | 94.21 | -5.905 | -1.000 | -0.2033 | -#> |.....................| 2.104 | 1.863 | 0.6744 | 0.7349 | -#> |.....................| 1.538 | 1.236 | 1.389 |...........| -#> | X| 480.06028 | 94.21 | 0.002726 | 0.2688 | 0.8161 | -#> |.....................| 8.200 | 1.863 | 0.6744 | 0.7349 | -#> |.....................| 1.538 | 1.236 | 1.389 |...........| -#> | F| Forward Diff. | 6.437 | 0.1507 | 0.07551 | -0.008836 | -#> |.....................| 0.08632 | -3.858 | 0.8547 | 0.1963 | -#> |.....................| 0.4591 | -0.8475 | -0.5830 |...........| -#> | 48| 480.03665 | 0.9987 | -1.532 | -0.9229 | -0.8934 | -#> |.....................| -0.8415 | -0.5884 | -1.015 | -1.029 | -#> |.....................| -0.5816 | -0.7442 | -0.6445 |...........| -#> | U| 480.03665 | 94.09 | -5.948 | -1.008 | -0.2026 | -#> |.....................| 2.106 | 1.870 | 0.6552 | 0.7323 | -#> |.....................| 1.533 | 1.225 | 1.399 |...........| -#> | X| 480.03665 | 94.09 | 0.002612 | 0.2673 | 0.8166 | -#> |.....................| 8.216 | 1.870 | 0.6552 | 0.7323 | -#> |.....................| 1.533 | 1.225 | 1.399 |...........| -#> | F| Forward Diff. | -11.33 | 0.04720 | -0.3576 | -0.009993 | -#> |.....................| 0.09366 | -3.049 | -0.8552 | 2.379 | -#> |.....................| 0.07272 | -1.673 | -0.4189 |...........| -#> | 49| 480.00388 | 0.9997 | -1.574 | -0.9191 | -0.8927 | -#> |.....................| -0.8426 | -0.5789 | -1.009 | -1.024 | -#> |.....................| -0.5828 | -0.7165 | -0.6339 |...........| -#> | U| 480.00388 | 94.18 | -5.990 | -1.004 | -0.2019 | -#> |.....................| 2.105 | 1.878 | 0.6600 | 0.7361 | -#> |.....................| 1.531 | 1.255 | 1.412 |...........| -#> | X| 480.00388 | 94.18 | 0.002504 | 0.2681 | 0.8172 | -#> |.....................| 8.207 | 1.878 | 0.6600 | 0.7361 | -#> |.....................| 1.531 | 1.255 | 1.412 |...........| -#> | F| Forward Diff. | 1.604 | -0.07853 | -0.1199 | 0.02191 | -#> |.....................| 0.1056 | -1.650 | -0.4080 | 0.6580 | -#> |.....................| 0.2834 | 0.2201 | 0.3460 |...........| -#> | 50| 480.03472 | 1.000 | -1.551 | -0.8873 | -0.8972 | -#> |.....................| -0.8660 | -0.5703 | -1.019 | -1.030 | -#> |.....................| -0.5914 | -0.7201 | -0.6545 |...........| -#> | U| 480.03472 | 94.21 | -5.967 | -0.9727 | -0.2064 | -#> |.....................| 2.082 | 1.885 | 0.6528 | 0.7314 | -#> |.....................| 1.521 | 1.251 | 1.388 |...........| -#> | X| 480.03472 | 94.21 | 0.002563 | 0.2743 | 0.8135 | -#> |.....................| 8.017 | 1.885 | 0.6528 | 0.7314 | -#> |.....................| 1.521 | 1.251 | 1.388 |...........| -#> | 51| 480.00362 | 0.9987 | -1.569 | -0.9113 | -0.8938 | -#> |.....................| -0.8484 | -0.5757 | -1.011 | -1.026 | -#> |.....................| -0.5851 | -0.7175 | -0.6392 |...........| -#> | U| 480.00362 | 94.09 | -5.984 | -0.9966 | -0.2030 | -#> |.....................| 2.099 | 1.880 | 0.6585 | 0.7346 | -#> |.....................| 1.528 | 1.254 | 1.406 |...........| -#> | X| 480.00362 | 94.09 | 0.002519 | 0.2696 | 0.8163 | -#> |.....................| 8.160 | 1.880 | 0.6585 | 0.7346 | -#> |.....................| 1.528 | 1.254 | 1.406 |...........| -#> | F| Forward Diff. | -11.27 | -0.06004 | 0.2734 | -0.003181 | -#> |.....................| -0.1459 | -1.804 | -0.6958 | 0.2356 | -#> |.....................| -0.08489 | -0.1057 | -0.1437 |...........| -#> | 52| 479.99564 | 1.000 | -1.563 | -0.9133 | -0.8943 | -#> |.....................| -0.8490 | -0.5744 | -1.010 | -1.027 | -#> |.....................| -0.5870 | -0.7192 | -0.6381 |...........| -#> | U| 479.99564 | 94.21 | -5.979 | -0.9986 | -0.2035 | -#> |.....................| 2.099 | 1.881 | 0.6592 | 0.7342 | -#> |.....................| 1.526 | 1.252 | 1.407 |...........| -#> | X| 479.99564 | 94.21 | 0.002532 | 0.2692 | 0.8159 | -#> |.....................| 8.155 | 1.881 | 0.6592 | 0.7342 | -#> |.....................| 1.526 | 1.252 | 1.407 |...........| -#> | F| Forward Diff. | 5.442 | -0.04353 | 0.2015 | -0.005586 | -#> |.....................| -0.1078 | -1.130 | -0.4765 | -0.6210 | -#> |.....................| 0.09560 | 0.04932 | 0.1423 |...........| -#> | 53| 479.99256 | 0.9995 | -1.560 | -0.9178 | -0.8945 | -#> |.....................| -0.8473 | -0.5732 | -1.008 | -1.026 | -#> |.....................| -0.5881 | -0.7196 | -0.6366 |...........| -#> | U| 479.99256 | 94.16 | -5.975 | -1.003 | -0.2037 | -#> |.....................| 2.100 | 1.882 | 0.6609 | 0.7344 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99256 | 94.16 | 0.002541 | 0.2683 | 0.8157 | -#> |.....................| 8.169 | 1.882 | 0.6609 | 0.7344 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | F| Forward Diff. | -1.663 | -0.03616 | -0.04918 | -0.01811 | -#> |.....................| -0.07323 | -1.616 | -0.5475 | -0.9126 | -#> |.....................| -0.2713 | -0.2260 | -0.04317 |...........| -#> | 54| 479.99337 | 0.9995 | -1.558 | -0.9178 | -0.8940 | -#> |.....................| -0.8453 | -0.5718 | -1.004 | -1.025 | -#> |.....................| -0.5887 | -0.7198 | -0.6325 |...........| -#> | U| 479.99337 | 94.16 | -5.974 | -1.003 | -0.2032 | -#> |.....................| 2.102 | 1.883 | 0.6641 | 0.7358 | -#> |.....................| 1.524 | 1.251 | 1.413 |...........| -#> | X| 479.99337 | 94.16 | 0.002545 | 0.2683 | 0.8161 | -#> |.....................| 8.185 | 1.883 | 0.6641 | 0.7358 | -#> |.....................| 1.524 | 1.251 | 1.413 |...........| -#> | 55| 479.99257 | 0.9996 | -1.559 | -0.9178 | -0.8942 | -#> |.....................| -0.8464 | -0.5725 | -1.006 | -1.026 | -#> |.....................| -0.5884 | -0.7197 | -0.6348 |...........| -#> | U| 479.99257 | 94.17 | -5.975 | -1.003 | -0.2035 | -#> |.....................| 2.101 | 1.883 | 0.6623 | 0.7351 | -#> |.....................| 1.525 | 1.252 | 1.411 |...........| -#> | X| 479.99257 | 94.17 | 0.002543 | 0.2683 | 0.8159 | -#> |.....................| 8.175 | 1.883 | 0.6623 | 0.7351 | -#> |.....................| 1.525 | 1.252 | 1.411 |...........| -#> | 56| 479.99255 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99255 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99255 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | C| Central Diff. | 1.014 | -0.03924 | -0.07311 | -0.03520 | -#> |.....................| -0.07193 | -1.047 | -0.3482 | -0.6653 | -#> |.....................| -0.001386 | 0.002313 | -0.01832 |...........| -#> | 57| 479.99382 | 0.9993 | -1.559 | -0.9177 | -0.8943 | -#> |.....................| -0.8469 | -0.5723 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99382 | 94.14 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6617 | 0.7350 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99382 | 94.14 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6617 | 0.7350 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 58| 479.99260 | 0.9996 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5726 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.9926 | 94.17 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.9926 | 94.17 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 59| 479.99255 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99255 | 94.17 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99255 | 94.17 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 60| 479.99254 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99254 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99254 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | C| Central Diff. | 0.7083 | -0.03937 | -0.07377 | -0.03537 | -#> |.....................| -0.07427 | -1.038 | -0.3482 | -0.6698 | -#> |.....................| -0.009774 | 0.01032 | -0.01719 |...........| -#> | 61| 479.99255 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99255 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99255 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 62| 479.99264 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99264 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99264 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 63| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99259 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 64| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99259 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 65| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99259 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 66| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99259 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 67| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99259 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 68| 479.99259 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99259 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99259 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 69| 479.99258 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99258 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99258 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 70| 479.99258 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99258 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99258 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | 71| 479.99258 | 0.9997 | -1.559 | -0.9178 | -0.8944 | -#> |.....................| -0.8469 | -0.5727 | -1.007 | -1.026 | -#> |.....................| -0.5882 | -0.7196 | -0.6358 |...........| -#> | U| 479.99258 | 94.18 | -5.975 | -1.003 | -0.2036 | -#> |.....................| 2.101 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> | X| 479.99258 | 94.18 | 0.002542 | 0.2683 | 0.8158 | -#> |.....................| 8.172 | 1.883 | 0.6616 | 0.7348 | -#> |.....................| 1.525 | 1.252 | 1.409 |...........| -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_A1 |f_parent_qlogis | log_k1 | -#> |.....................| log_k2 | g_qlogis | sigma | o1 | -#> |.....................| o2 | o3 | o4 | o5 | -#> |.....................| o6 |...........|...........|...........| -#> | 1| 514.27068 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 514.27068 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 514.27068 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | G| Gill Diff. | 26.19 | 1.724 | -0.1273 | 0.01210 | -#> |.....................| -0.2599 | 0.04964 | -46.10 | 17.02 | -#> |.....................| 9.682 | -11.00 | -4.182 | 3.869 | -#> |.....................| -10.57 |...........|...........|...........| -#> | 2| 1072.3430 | 0.5548 | -1.029 | -0.9091 | -0.9298 | -#> |.....................| -0.9733 | -0.8898 | -0.07504 | -1.166 | -#> |.....................| -1.039 | -0.6809 | -0.8005 | -0.9394 | -#> |.....................| -0.6887 |...........|...........|...........| -#> | U| 1072.343 | 52.05 | -5.403 | -0.9690 | -1.880 | -#> |.....................| -4.266 | 0.1355 | 2.292 | 0.5199 | -#> |.....................| 0.7209 | 1.403 | 1.065 | 0.8339 | -#> |.....................| 1.368 |...........|...........|...........| -#> | X| 1072.343 | 52.05 | 0.004504 | 0.2751 | 0.1526 | -#> |.....................| 0.01403 | 0.5338 | 2.292 | 0.5199 | -#> |.....................| 0.7209 | 1.403 | 1.065 | 0.8339 | -#> |.....................| 1.368 |...........|...........|...........| -#> | 3| 539.25377 | 0.9555 | -1.003 | -0.9110 | -0.9296 | -#> |.....................| -0.9773 | -0.8890 | -0.7801 | -0.9058 | -#> |.....................| -0.8907 | -0.8491 | -0.8645 | -0.8802 | -#> |.....................| -0.8503 |...........|...........|...........| -#> | U| 539.25377 | 89.63 | -5.376 | -0.9709 | -1.880 | -#> |.....................| -4.270 | 0.1356 | 1.712 | 0.7103 | -#> |.....................| 0.8487 | 1.204 | 1.001 | 0.8867 | -#> |.....................| 1.181 |...........|...........|...........| -#> | X| 539.25377 | 89.63 | 0.004625 | 0.2747 | 0.1526 | -#> |.....................| 0.01398 | 0.5339 | 1.712 | 0.7103 | -#> |.....................| 0.8487 | 1.204 | 1.001 | 0.8867 | -#> |.....................| 1.181 |...........|...........|...........| -#> | 4| 527.20532 | 0.9955 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9777 | -0.8889 | -0.8506 | -0.8798 | -#> |.....................| -0.8759 | -0.8659 | -0.8709 | -0.8743 | -#> |.....................| -0.8665 |...........|...........|...........| -#> | U| 527.20532 | 93.39 | -5.374 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.654 | 0.7293 | -#> |.....................| 0.8615 | 1.184 | 0.9947 | 0.8920 | -#> |.....................| 1.162 |...........|...........|...........| -#> | X| 527.20532 | 93.39 | 0.004637 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.654 | 0.7293 | -#> |.....................| 0.8615 | 1.184 | 0.9947 | 0.8920 | -#> |.....................| 1.162 |...........|...........|...........| -#> | 5| 527.55150 | 0.9996 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8576 | -0.8772 | -#> |.....................| -0.8744 | -0.8676 | -0.8715 | -0.8737 | -#> |.....................| -0.8681 |...........|...........|...........| -#> | U| 527.5515 | 93.77 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.648 | 0.7312 | -#> |.....................| 0.8628 | 1.182 | 0.9941 | 0.8925 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.5515 | 93.77 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.648 | 0.7312 | -#> |.....................| 0.8628 | 1.182 | 0.9941 | 0.8925 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 6| 527.60332 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8743 | -0.8678 | -0.8716 | -0.8737 | -#> |.....................| -0.8682 |...........|...........|...........| -#> | U| 527.60332 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60332 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 7| 527.60868 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.60868 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60868 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 8| 527.60932 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.60932 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60932 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 9| 527.60939 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.60939 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60939 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 10| 527.60940 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.6094 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.6094 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 11| 527.60940 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.6094 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.6094 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 12| 527.60940 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.6094 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.6094 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 13| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60941 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 14| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60941 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 15| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60941 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 16| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60941 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | 17| 527.60941 | 1.000 | -1.000 | -0.9112 | -0.9296 | -#> |.....................| -0.9778 | -0.8889 | -0.8584 | -0.8769 | -#> |.....................| -0.8742 | -0.8678 | -0.8716 | -0.8736 | -#> |.....................| -0.8683 |...........|...........|...........| -#> | U| 527.60941 | 93.81 | -5.373 | -0.9711 | -1.880 | -#> |.....................| -4.271 | 0.1356 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> | X| 527.60941 | 93.81 | 0.004638 | 0.2747 | 0.1526 | -#> |.....................| 0.01397 | 0.5339 | 1.647 | 0.7314 | -#> |.....................| 0.8629 | 1.182 | 0.9940 | 0.8926 | -#> |.....................| 1.160 |...........|...........|...........| -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[2]+THETA[2]; +#> rx_expr_10~exp(rx_expr_7); +#> d/dt(parent)=-rx_expr_10*parent; +#> rx_expr_8~ETA[3]+THETA[3]; +#> rx_expr_11~exp(rx_expr_8); +#> d/dt(A1)=-rx_expr_11*A1+rx_expr_10*parent*f_parent_to_A1; +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_13~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_13+rx_expr_3; +#> rx_hi_~rx_expr_13+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_9~parent*(rx_expr_2); +#> rx_expr_14~rx_expr_9*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_14)*(rx_expr_0)+(rx_expr_4+rx_expr_14)*(rx_expr_2)*(rx_expr_1); +#> rx_expr_12~Rx_pow_di(THETA[5],2); +#> rx_r_=(rx_expr_0)*rx_expr_12+(rx_expr_2)*(rx_expr_1)*rx_expr_12; +#> parent_0=THETA[1]; +#> log_k_parent=THETA[2]; +#> log_k_A1=THETA[3]; +#> f_parent_qlogis=THETA[4]; +#> sigma=THETA[5]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_parent=ETA[2]; +#> eta.log_k_A1=ETA[3]; +#> eta.f_parent_qlogis=ETA[4]; +#> parent_0_model=rx_expr_6; +#> k_parent=rx_expr_10; +#> k_A1=rx_expr_11; +#> f_parent=1/(1+exp(-(ETA[4]+THETA[4]))); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 5.607 0.474 6.078
                                                                f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[4]+THETA[4]; +#> rx_expr_8~ETA[5]+THETA[5]; +#> rx_expr_13~exp(-(rx_expr_8)); +#> rx_expr_15~t*rx_expr_13; +#> rx_expr_16~1+rx_expr_15; +#> rx_expr_18~rx_expr_7-(rx_expr_8); +#> rx_expr_20~exp(rx_expr_18); +#> d/dt(parent)=-rx_expr_20*parent/(rx_expr_16); +#> rx_expr_9~ETA[2]+THETA[2]; +#> rx_expr_11~exp(rx_expr_9); +#> d/dt(A1)=-rx_expr_11*A1+rx_expr_20*parent*f_parent_to_A1/(rx_expr_16); +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_14~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_14+rx_expr_3; +#> rx_hi_~rx_expr_14+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_10~parent*(rx_expr_2); +#> rx_expr_17~rx_expr_10*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_17)*(rx_expr_0)+(rx_expr_4+rx_expr_17)*(rx_expr_2)*(rx_expr_1); +#> rx_expr_12~Rx_pow_di(THETA[6],2); +#> rx_r_=(rx_expr_0)*rx_expr_12+(rx_expr_2)*(rx_expr_1)*rx_expr_12; +#> parent_0=THETA[1]; +#> log_k_A1=THETA[2]; +#> f_parent_qlogis=THETA[3]; +#> log_alpha=THETA[4]; +#> log_beta=THETA[5]; +#> sigma=THETA[6]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_A1=ETA[2]; +#> eta.f_parent_qlogis=ETA[3]; +#> eta.log_alpha=ETA[4]; +#> eta.log_beta=ETA[5]; +#> parent_0_model=rx_expr_6; +#> k_A1=rx_expr_11; +#> alpha=exp(rx_expr_7); +#> beta=exp(rx_expr_8); +#> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.853 0.393 7.242
                                                                f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[4]+THETA[4]; +#> rx_expr_8~ETA[6]+THETA[6]; +#> rx_expr_9~ETA[5]+THETA[5]; +#> rx_expr_12~exp(rx_expr_7); +#> rx_expr_13~exp(rx_expr_9); +#> rx_expr_15~t*rx_expr_12; +#> rx_expr_16~t*rx_expr_13; +#> rx_expr_18~exp(-(rx_expr_8)); +#> rx_expr_20~1+rx_expr_18; +#> rx_expr_25~1/(rx_expr_20); +#> rx_expr_27~(rx_expr_25); +#> rx_expr_28~1-rx_expr_27; +#> d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_20)+exp(rx_expr_9-rx_expr_16)*(rx_expr_28))/(exp(-t*rx_expr_12)/(rx_expr_20)+exp(-t*rx_expr_13)*(rx_expr_28)); +#> rx_expr_10~ETA[2]+THETA[2]; +#> rx_expr_14~exp(rx_expr_10); +#> d/dt(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_20)+exp(rx_expr_9-rx_expr_16)*(rx_expr_28))/(exp(-t*rx_expr_12)/(rx_expr_20)+exp(-t*rx_expr_13)*(rx_expr_28)); +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_19~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_19+rx_expr_3; +#> rx_hi_~rx_expr_19+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_11~parent*(rx_expr_2); +#> rx_expr_23~rx_expr_11*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_23)*(rx_expr_0)+(rx_expr_4+rx_expr_23)*(rx_expr_2)*(rx_expr_1); +#> rx_expr_17~Rx_pow_di(THETA[7],2); +#> rx_r_=(rx_expr_0)*rx_expr_17+(rx_expr_2)*(rx_expr_1)*rx_expr_17; +#> parent_0=THETA[1]; +#> log_k_A1=THETA[2]; +#> f_parent_qlogis=THETA[3]; +#> log_k1=THETA[4]; +#> log_k2=THETA[5]; +#> g_qlogis=THETA[6]; +#> sigma=THETA[7]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_A1=ETA[2]; +#> eta.f_parent_qlogis=ETA[3]; +#> eta.log_k1=ETA[4]; +#> eta.log_k2=ETA[5]; +#> eta.g_qlogis=ETA[6]; +#> parent_0_model=rx_expr_6; +#> k_A1=rx_expr_14; +#> k1=rx_expr_12; +#> k2=rx_expr_13; +#> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); +#> g=1/(rx_expr_20); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 15.18 0.414 15.6
                                                                # Variance by variable is supported by 'saem' and 'focei' f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1: 93.6104 -5.6552 -0.1308 2.1755 -1.1174 2.9315 1.6064 0.6616 0.5897 0.4753 9.7765 10.2253 -#> 2: 93.8838 -5.6936 -0.1062 2.2361 -1.0529 2.7849 1.5260 0.6285 0.5602 0.4515 7.9206 5.2721 -#> 3: 93.9304 -5.7260 -0.0940 2.2480 -1.0317 2.6457 1.4889 0.5971 0.5322 0.4290 7.5051 3.6573 -#> 4: 93.6107 -5.7914 -0.0929 2.2382 -1.0171 2.5134 2.0027 0.5676 0.5056 0.4075 7.3763 3.1438 -#> 5: 93.7262 -5.7517 -0.0926 2.2365 -1.0306 2.3877 1.9026 0.5679 0.4803 0.3871 7.2914 3.0275 -#> 6: 93.7261 -5.7719 -0.0823 2.2625 -1.0391 2.2683 2.1168 0.5638 0.4563 0.3678 7.0857 2.8196 -#> 7: 93.5991 -5.8553 -0.0917 2.2659 -1.0146 2.1549 2.3708 0.5618 0.4335 0.3494 6.9413 2.7447 -#> 8: 93.4288 -5.8969 -0.0885 2.2757 -1.0253 2.1183 2.4324 0.5615 0.4118 0.3319 7.2269 2.6781 -#> 9: 93.4049 -6.1188 -0.0863 2.2841 -1.0154 2.0124 3.0090 0.5633 0.3912 0.3153 7.2084 2.7464 -#> 10: 93.4773 -6.1940 -0.0816 2.2893 -1.0174 1.9958 3.6308 0.5540 0.3716 0.2996 7.2414 2.8980 -#> 11: 93.5334 -6.1739 -0.0772 2.2901 -1.0479 2.2841 3.4492 0.5567 0.3531 0.2846 7.0567 2.8159 -#> 12: 93.5824 -6.3716 -0.0875 2.2706 -1.0452 2.1699 4.3087 0.5505 0.3354 0.2704 7.2970 2.3790 -#> 13: 93.8528 -6.3302 -0.0846 2.2564 -1.0302 2.0614 4.6014 0.5475 0.3186 0.2568 7.3901 2.1942 -#> 14: 94.0343 -6.1408 -0.0887 2.2666 -1.0280 1.9995 4.3714 0.5202 0.3027 0.2440 7.1696 2.0730 -#> 15: 94.1712 -6.3900 -0.0759 2.2825 -1.0112 1.8995 5.0913 0.5358 0.2876 0.2318 7.2155 2.0259 -#> 16: 93.9481 -6.1284 -0.0798 2.2707 -1.0264 1.8046 4.8368 0.5501 0.2732 0.2202 7.2731 2.0912 -#> 17: 93.7828 -6.2736 -0.0852 2.2870 -1.0249 1.7143 4.5949 0.5408 0.2595 0.2092 7.0213 2.0417 -#> 18: 93.8758 -6.3616 -0.0851 2.2713 -1.0157 1.8699 4.9132 0.5349 0.2465 0.1987 7.0613 1.8601 -#> 19: 93.7565 -6.5413 -0.0866 2.2695 -1.0166 2.5251 5.9754 0.5312 0.2547 0.1888 7.2555 1.7947 -#> 20: 93.7233 -6.3942 -0.0970 2.2620 -1.0195 2.3989 5.6766 0.5484 0.2576 0.1794 7.0292 1.8687 -#> 21: 93.8298 -6.2619 -0.0974 2.2570 -1.0118 2.2789 5.3928 0.5497 0.2545 0.1704 6.7138 1.8157 -#> 22: 93.9520 -6.1633 -0.0874 2.2777 -1.0274 2.1650 5.1232 0.5437 0.2641 0.1622 6.8254 1.8443 -#> 23: 93.8442 -6.3255 -0.0855 2.2568 -1.0151 2.1243 4.9615 0.5334 0.2885 0.1556 6.8049 1.8073 -#> 24: 93.9659 -6.5470 -0.0855 2.2572 -1.0178 2.0788 6.2156 0.5425 0.2834 0.1583 6.9598 1.8686 -#> 25: 94.3004 -6.4881 -0.0920 2.2371 -1.0187 3.2507 5.9048 0.5367 0.2872 0.1609 6.8709 1.8839 -#> 26: 94.1750 -6.4437 -0.0964 2.2337 -1.0301 3.1136 5.6096 0.5307 0.2820 0.1611 6.5948 1.8742 -#> 27: 94.6007 -6.3072 -0.0750 2.2936 -1.0343 3.9844 5.3291 0.5042 0.2679 0.1695 6.7524 1.8335 -#> 28: 94.4915 -6.1389 -0.0826 2.2730 -1.0223 3.7852 5.0626 0.4998 0.2590 0.1812 6.4646 1.8937 -#> 29: 94.1900 -6.1516 -0.0836 2.2680 -1.0287 3.7861 4.8095 0.4976 0.2612 0.1875 6.4674 1.8998 -#> 30: 94.6632 -6.0574 -0.0773 2.2637 -1.0280 3.5968 4.5690 0.4948 0.2525 0.2040 6.5945 1.9022 -#> 31: 94.3460 -6.1684 -0.0761 2.2677 -1.0276 3.4170 4.3406 0.4901 0.2690 0.2038 6.9918 1.8446 -#> 32: 94.4385 -5.9347 -0.0751 2.2893 -1.0146 3.3283 4.1235 0.4882 0.2576 0.2002 6.7622 1.7754 -#> 33: 94.7021 -5.9329 -0.0787 2.2987 -1.0108 3.3485 3.9174 0.4859 0.2640 0.1941 6.9648 1.8014 -#> 34: 94.4058 -6.0311 -0.0692 2.2980 -1.0125 3.1811 3.7215 0.4994 0.2676 0.1936 6.9791 1.7561 -#> 35: 94.4503 -6.0470 -0.0692 2.2950 -1.0100 3.5600 3.7611 0.4994 0.2637 0.1928 6.8010 1.7890 -#> 36: 94.3400 -6.0339 -0.0792 2.2960 -1.0204 3.3820 3.5731 0.4822 0.2638 0.1887 6.6462 1.6763 -#> 37: 94.1497 -6.0221 -0.0879 2.2653 -1.0073 3.2129 3.3944 0.4979 0.2506 0.1793 6.4853 1.7911 -#> 38: 94.1574 -5.8638 -0.0884 2.2752 -1.0156 3.0523 3.2247 0.4992 0.2435 0.1772 6.4329 1.7707 -#> 39: 94.1680 -5.9558 -0.0948 2.2535 -1.0205 2.8997 3.0635 0.5065 0.2448 0.1819 6.4462 1.8100 -#> 40: 94.0516 -6.0814 -0.0881 2.2531 -1.0356 2.7547 3.4976 0.4949 0.2515 0.1827 6.4734 1.8133 -#> 41: 94.1522 -6.1880 -0.0849 2.2618 -1.0230 2.6170 4.1610 0.5129 0.2389 0.1797 6.4165 1.7782 -#> 42: 94.2178 -6.1829 -0.0854 2.2791 -1.0325 2.8092 4.1174 0.5052 0.2288 0.1853 6.4332 1.7883 -#> 43: 93.9083 -6.1600 -0.0831 2.2860 -1.0350 2.9631 3.9116 0.4914 0.2310 0.1826 6.4865 1.8449 -#> 44: 93.9636 -6.1494 -0.0824 2.2903 -1.0150 2.8149 3.7221 0.4921 0.2214 0.1805 6.4818 1.9385 -#> 45: 93.9937 -6.2329 -0.0895 2.2832 -1.0157 4.2815 4.5622 0.5075 0.2250 0.1796 6.4098 1.8355 -#> 46: 93.8001 -6.1784 -0.0944 2.2664 -1.0212 4.0674 4.3341 0.5023 0.2274 0.1795 6.5539 1.7875 -#> 47: 93.8997 -6.3400 -0.0945 2.2627 -1.0183 3.8641 4.9860 0.5017 0.2312 0.1834 6.5497 1.7838 -#> 48: 93.7861 -6.3496 -0.0944 2.2713 -1.0255 3.6709 5.3403 0.5025 0.2197 0.1839 6.1766 1.9080 -#> 49: 93.7128 -6.3914 -0.0944 2.2752 -1.0137 3.4873 5.6007 0.5051 0.2198 0.1788 6.3050 1.8320 -#> 50: 94.1645 -6.3056 -0.0945 2.2755 -1.0062 3.3130 5.3207 0.4998 0.2176 0.1781 6.4998 1.8516 -#> 51: 93.9897 -6.1556 -0.1026 2.2633 -1.0097 3.1473 5.0547 0.4853 0.2439 0.1796 6.3184 1.7981 -#> 52: 93.7604 -6.2264 -0.1068 2.2485 -0.9936 2.9899 4.8209 0.4887 0.2542 0.1793 6.5076 1.7916 -#> 53: 93.8821 -6.5447 -0.1049 2.2546 -1.0020 2.8404 6.5603 0.4701 0.2556 0.1789 6.5735 1.7763 -#> 54: 93.8865 -6.4028 -0.1081 2.2507 -1.0162 2.6984 6.2323 0.4724 0.2576 0.1846 6.3607 1.8295 -#> 55: 94.0120 -6.5455 -0.0986 2.2728 -1.0119 2.5635 6.3983 0.4550 0.2686 0.1773 6.6815 1.7869 -#> 56: 94.1921 -6.6581 -0.0953 2.2713 -1.0151 2.4353 8.2169 0.4478 0.2675 0.1763 6.6257 1.7873 -#> 57: 93.8812 -6.4499 -0.1081 2.2447 -1.0182 2.3136 7.8060 0.4683 0.2562 0.1804 6.2421 1.8455 -#> 58: 93.9830 -6.5112 -0.1092 2.2436 -1.0136 2.1979 7.4157 0.4695 0.2569 0.1762 6.3196 1.8224 -#> 59: 93.8537 -6.6528 -0.1105 2.2390 -1.0089 2.0880 9.0039 0.4689 0.2534 0.1692 6.3735 1.8049 -#> 60: 93.7399 -6.4780 -0.1212 2.2263 -0.9979 1.9836 8.5537 0.4565 0.2445 0.1696 6.4748 1.8439 -#> 61: 93.8180 -6.4608 -0.1243 2.2275 -1.0039 1.8844 8.1260 0.4630 0.2414 0.1693 6.3936 1.7653 -#> 62: 93.5774 -6.3127 -0.1298 2.2250 -1.0022 1.7902 7.7197 0.4711 0.2452 0.1708 6.5708 1.8014 -#> 63: 93.5731 -6.2060 -0.1327 2.2213 -1.0031 1.7007 7.3337 0.4685 0.2426 0.1712 6.4933 1.8318 -#> 64: 93.3587 -6.2299 -0.1316 2.2290 -1.0004 1.6302 6.9671 0.4694 0.2460 0.1710 6.2584 1.8361 -#> 65: 93.2982 -6.1900 -0.1354 2.2341 -0.9963 1.5487 6.6187 0.4685 0.2482 0.1750 6.0950 1.8341 -#> 66: 93.4532 -6.2107 -0.1251 2.2254 -0.9786 1.4713 6.2878 0.4822 0.2489 0.1701 6.3732 1.7951 -#> 67: 93.5878 -6.1823 -0.1208 2.2455 -0.9766 1.3977 5.9734 0.4860 0.2407 0.1668 6.4456 1.8371 -#> 68: 93.5819 -5.9209 -0.1200 2.2599 -0.9792 1.3278 5.6747 0.4793 0.2412 0.1686 6.5728 1.8144 -#> 69: 93.4002 -6.1142 -0.1242 2.2542 -0.9878 1.4433 5.3910 0.4730 0.2511 0.1830 6.3888 1.7900 -#> 70: 93.2631 -6.1875 -0.1271 2.2639 -0.9844 1.5244 5.1214 0.4711 0.2444 0.1770 6.5093 1.7117 -#> 71: 93.2629 -6.2944 -0.1275 2.2418 -0.9805 1.4481 4.8654 0.4612 0.2522 0.1748 6.4659 1.8500 -#> 72: 93.0324 -6.2727 -0.1332 2.2421 -0.9766 1.3757 5.1467 0.4519 0.2524 0.1673 6.3452 1.8054 -#> 73: 93.0174 -6.4402 -0.1391 2.2320 -0.9795 1.3069 6.1963 0.4480 0.2563 0.1637 6.3915 1.8506 -#> 74: 93.0073 -6.4286 -0.1450 2.2241 -0.9962 1.2416 6.0011 0.4510 0.2461 0.1682 6.6924 1.8302 -#> 75: 93.2607 -6.5056 -0.1379 2.2233 -0.9926 1.1795 6.0508 0.4573 0.2540 0.1669 6.4813 1.7896 -#> 76: 93.2937 -6.1637 -0.1404 2.2228 -0.9970 1.1205 5.7483 0.4588 0.2529 0.1656 6.3781 1.7976 -#> 77: 93.2223 -6.1702 -0.1381 2.2200 -0.9858 1.4369 5.4609 0.4633 0.2585 0.1697 6.3510 1.8749 -#> 78: 93.3189 -6.1924 -0.1355 2.2238 -0.9944 1.3651 5.1878 0.4608 0.2631 0.1612 6.1888 1.7669 -#> 79: 93.2417 -6.6345 -0.1335 2.2340 -0.9865 1.2968 7.3486 0.4570 0.2564 0.1532 6.0902 1.7505 -#> 80: 93.3476 -6.3069 -0.1305 2.2319 -0.9880 1.6281 6.9812 0.4649 0.2525 0.1514 6.0659 1.7582 -#> 81: 93.4798 -6.3145 -0.1253 2.2468 -0.9989 1.9108 6.6321 0.4447 0.2583 0.1579 6.0843 1.7959 -#> 82: 93.2745 -6.2461 -0.1184 2.2529 -0.9937 1.8153 6.3005 0.4439 0.2602 0.1691 6.2826 1.7896 -#> 83: 93.4628 -6.3953 -0.1189 2.2640 -0.9880 1.7245 6.1094 0.4430 0.2612 0.1709 6.4474 1.6820 -#> 84: 93.3664 -6.2885 -0.1105 2.2675 -0.9875 1.6383 6.1170 0.4498 0.2689 0.1719 6.4847 1.6731 -#> 85: 93.5090 -6.3029 -0.1095 2.2709 -0.9898 1.6666 6.1406 0.4365 0.2693 0.1710 6.2452 1.6594 -#> 86: 93.5097 -6.2256 -0.1106 2.2701 -0.9928 1.5833 6.2468 0.4365 0.2749 0.1632 6.2007 1.7178 -#> 87: 93.5165 -6.3038 -0.1046 2.2731 -0.9877 1.5041 5.9345 0.4398 0.2667 0.1603 6.3928 1.7003 -#> 88: 93.3766 -6.2723 -0.1071 2.2771 -0.9881 1.4289 5.6378 0.4241 0.2538 0.1598 6.1043 1.6772 -#> 89: 93.4448 -6.0430 -0.1102 2.2781 -0.9725 1.3575 5.3559 0.4187 0.2915 0.1518 6.0153 1.7593 -#> 90: 93.2843 -6.1065 -0.1089 2.2866 -0.9705 1.5362 5.0881 0.4203 0.2844 0.1656 5.9235 1.6631 -#> 91: 93.4159 -6.0210 -0.1095 2.2879 -0.9798 2.1371 4.8337 0.4245 0.2857 0.1573 5.9182 1.7482 -#> 92: 93.3198 -6.2526 -0.1075 2.2919 -0.9791 2.0303 4.7352 0.4159 0.2918 0.1590 6.0853 1.6755 -#> 93: 93.3269 -6.1838 -0.1173 2.2809 -0.9999 1.9287 4.4985 0.4211 0.2893 0.1684 6.1189 1.6734 -#> 94: 93.2077 -6.1086 -0.1148 2.2890 -0.9918 2.1061 4.2736 0.4230 0.2802 0.1662 5.9328 1.7116 -#> 95: 93.0207 -6.1510 -0.1170 2.2665 -0.9791 2.1360 4.0630 0.4199 0.2937 0.1734 6.1415 1.6737 -#> 96: 93.2134 -6.1614 -0.1152 2.2861 -0.9711 2.5372 4.1579 0.4211 0.2790 0.1647 6.1575 1.6338 -#> 97: 93.1425 -6.2333 -0.1140 2.2912 -0.9665 2.4103 4.4551 0.4136 0.2835 0.1645 6.0790 1.6652 -#> 98: 92.9412 -6.2651 -0.1167 2.2847 -0.9738 2.2898 4.7233 0.4095 0.2882 0.1836 5.9305 1.6158 -#> 99: 92.9087 -6.1870 -0.1177 2.2833 -0.9744 2.1753 4.4872 0.4142 0.2913 0.1876 5.9838 1.7003 -#> 100: 92.7788 -6.2113 -0.1146 2.2928 -0.9939 2.0665 4.4195 0.4109 0.2945 0.1866 6.0195 1.7275 -#> 101: 92.8783 -6.0718 -0.1080 2.2959 -0.9968 1.9632 4.1985 0.4142 0.2966 0.1778 6.2542 1.6844 -#> 102: 93.0451 -6.3706 -0.1086 2.2894 -0.9974 1.8650 5.2121 0.4135 0.3030 0.1769 6.2204 1.6281 -#> 103: 93.2901 -6.4069 -0.1066 2.2943 -0.9896 1.7718 5.7453 0.4152 0.2879 0.1818 6.0239 1.7299 -#> 104: 93.3437 -6.3694 -0.1063 2.2769 -0.9914 1.6832 5.8903 0.4210 0.2884 0.1855 6.1116 1.7415 -#> 105: 93.4609 -6.2767 -0.1060 2.2751 -1.0157 1.5990 5.5958 0.4214 0.2865 0.1841 6.1287 1.7322 -#> 106: 93.5833 -6.2340 -0.1006 2.2879 -1.0084 1.8669 5.3160 0.4272 0.2982 0.1829 6.0211 1.6726 -#> 107: 93.7800 -6.1505 -0.0948 2.2685 -1.0219 1.7735 5.0502 0.4325 0.2841 0.1753 5.8556 1.7636 -#> 108: 93.8532 -6.3744 -0.0938 2.2650 -1.0210 2.0297 5.7080 0.4307 0.2836 0.1701 6.0669 1.6804 -#> 109: 93.8994 -6.3544 -0.0829 2.2862 -1.0287 1.9282 5.4226 0.4184 0.3113 0.1789 6.2343 1.6667 -#> 110: 94.0150 -6.5609 -0.0905 2.2821 -1.0088 2.1118 6.8121 0.4276 0.3275 0.1845 6.1640 1.6706 -#> 111: 93.7887 -6.0185 -0.0925 2.2831 -1.0097 2.0062 6.4715 0.4209 0.3255 0.1852 6.2823 1.6301 -#> 112: 93.9709 -6.0918 -0.0934 2.2857 -1.0067 2.2032 6.1479 0.4207 0.3285 0.1817 6.1718 1.6494 -#> 113: 93.8761 -6.3434 -0.0955 2.2919 -1.0223 2.5209 5.8405 0.4259 0.3293 0.1842 6.0377 1.6431 -#> 114: 93.6959 -6.2312 -0.0934 2.2782 -1.0154 2.3949 5.5485 0.4237 0.3460 0.1814 6.2225 1.6229 -#> 115: 93.5487 -6.0915 -0.0971 2.2836 -1.0083 2.2751 5.2711 0.4199 0.3557 0.1783 6.5929 1.6479 -#> 116: 93.5953 -6.1479 -0.1013 2.2760 -1.0018 2.1614 5.0075 0.4163 0.3399 0.1794 6.1822 1.6222 -#> 117: 93.3508 -6.1730 -0.1076 2.2632 -0.9953 2.0533 4.7571 0.4057 0.3303 0.1803 6.3444 1.7106 -#> 118: 93.4462 -5.9724 -0.1177 2.2557 -0.9963 2.0318 4.5193 0.3956 0.3349 0.1920 6.0439 1.7146 -#> 119: 93.5841 -6.0400 -0.1151 2.2480 -1.0035 1.9956 4.2933 0.3968 0.3448 0.1929 6.0754 1.6750 -#> 120: 93.4891 -6.0937 -0.1175 2.2499 -1.0006 1.8958 4.0786 0.3927 0.3392 0.1927 6.1654 1.6495 -#> 121: 93.4611 -6.1371 -0.1217 2.2538 -1.0067 1.8011 3.8747 0.3864 0.3549 0.1851 5.9558 1.6940 -#> 122: 93.4636 -6.1015 -0.1243 2.2564 -1.0002 1.7414 3.6810 0.3840 0.3557 0.1860 6.0583 1.6629 -#> 123: 93.2988 -5.9318 -0.1243 2.2601 -0.9989 2.2063 3.4969 0.3840 0.3543 0.1833 5.9686 1.5966 -#> 124: 93.4200 -5.9847 -0.1231 2.2594 -0.9991 2.0959 3.3221 0.3846 0.3544 0.1787 6.1292 1.5957 -#> 125: 93.3727 -6.1217 -0.1239 2.2584 -1.0082 1.9911 3.6395 0.3838 0.3577 0.1782 6.2794 1.6262 -#> 126: 93.4956 -6.0529 -0.1244 2.2482 -1.0096 1.8916 3.4576 0.3847 0.3505 0.1753 6.1181 1.6347 -#> 127: 93.6265 -5.9360 -0.1298 2.2342 -1.0075 1.7970 3.2847 0.3887 0.3367 0.1691 6.2315 1.7051 -#> 128: 93.4446 -6.0523 -0.1337 2.2453 -1.0079 1.7072 3.1205 0.3840 0.3302 0.1759 6.2082 1.6705 -#> 129: 93.4470 -6.0065 -0.1321 2.2321 -1.0015 1.6636 2.9644 0.3853 0.3303 0.1671 6.1479 1.6733 -#> 130: 93.3205 -5.9628 -0.1290 2.2252 -0.9954 2.0336 2.9210 0.3879 0.3284 0.1634 6.0582 1.6372 -#> 131: 93.3836 -5.8919 -0.1358 2.2375 -0.9930 2.1392 2.7749 0.3801 0.3202 0.1644 5.9972 1.6837 -#> 132: 93.1041 -5.9265 -0.1203 2.2552 -0.9929 2.0323 2.8741 0.3831 0.3353 0.1755 6.0648 1.5934 -#> 133: 93.1617 -6.0668 -0.1175 2.2538 -0.9963 1.9306 3.6825 0.3846 0.3187 0.1790 6.0732 1.5684 -#> 134: 93.1503 -6.1208 -0.1232 2.2644 -0.9851 2.3429 3.8026 0.3788 0.3296 0.1737 5.8807 1.5722 -#> 135: 92.8629 -5.9726 -0.1197 2.2650 -0.9761 2.2257 3.6124 0.3802 0.3407 0.1765 5.8408 1.5446 -#> 136: 93.1460 -6.0654 -0.1227 2.2661 -0.9736 2.1144 3.4583 0.3770 0.3434 0.1700 5.7690 1.5561 -#> 137: 93.1243 -6.2350 -0.1274 2.2472 -0.9811 2.0087 4.3526 0.3733 0.3670 0.1615 5.9377 1.5224 -#> 138: 93.1203 -6.1704 -0.1283 2.2472 -0.9891 1.9083 4.1557 0.3788 0.3671 0.1641 5.8765 1.5525 -#> 139: 93.2841 -6.0586 -0.1366 2.2404 -0.9894 1.8129 4.3184 0.3718 0.3693 0.1630 6.1854 1.6388 -#> 140: 93.4239 -6.2398 -0.1382 2.2459 -0.9713 1.7241 4.5903 0.3713 0.3627 0.1548 6.0737 1.5826 -#> 141: 93.4149 -6.1972 -0.1388 2.2605 -0.9686 2.2179 4.5557 0.3701 0.3675 0.1486 6.0793 1.5603 -#> 142: 93.4404 -5.8955 -0.1203 2.2682 -0.9706 2.1070 4.3279 0.3830 0.3719 0.1581 5.9534 1.6189 -#> 143: 93.3108 -5.8069 -0.1142 2.2835 -0.9672 2.0194 4.1115 0.3787 0.3924 0.1592 5.9410 1.5521 -#> 144: 93.3953 -5.7456 -0.1154 2.2891 -0.9553 2.2741 3.9059 0.3787 0.3849 0.1633 6.0163 1.5640 -#> 145: 93.3322 -5.8301 -0.1100 2.2926 -0.9595 2.1604 3.7106 0.3687 0.3754 0.1657 5.8968 1.5844 -#> 146: 93.0844 -5.8926 -0.1084 2.2870 -0.9605 2.0524 3.5251 0.3649 0.3713 0.1646 6.1960 1.5691 -#> 147: 93.2106 -6.0084 -0.1074 2.2931 -0.9654 1.9498 3.5341 0.3646 0.3669 0.1641 6.0548 1.5230 -#> 148: 93.2005 -6.1989 -0.1065 2.2924 -0.9740 1.8523 4.4855 0.3631 0.3660 0.1759 5.9600 1.5194 -#> 149: 93.0788 -6.2470 -0.1108 2.2861 -0.9836 2.1348 4.7630 0.3597 0.3815 0.1815 5.9584 1.5227 -#> 150: 93.2241 -6.2660 -0.1126 2.2847 -0.9912 2.1149 5.0574 0.3656 0.3788 0.1781 5.7213 1.5379 -#> 151: 93.0046 -6.5379 -0.1164 2.2757 -0.9845 2.0092 6.8660 0.3719 0.3827 0.1807 5.7612 1.5697 -#> 152: 93.2222 -6.4637 -0.1154 2.2737 -0.9950 1.6744 6.2289 0.3670 0.3881 0.1638 5.8514 1.5920 -#> 153: 93.1619 -6.3230 -0.1224 2.2638 -0.9924 1.7907 5.5429 0.3842 0.3946 0.1720 5.7562 1.5493 -#> 154: 93.0402 -6.4004 -0.1205 2.2633 -0.9868 1.7620 6.2494 0.3860 0.3891 0.1737 5.7577 1.5109 -#> 155: 93.1692 -6.4353 -0.1203 2.2696 -0.9761 1.8710 6.4519 0.3949 0.3962 0.1721 5.8348 1.4949 -#> 156: 93.2709 -6.2672 -0.1203 2.2663 -0.9708 2.1172 5.1692 0.3949 0.4187 0.1637 6.1251 1.5012 -#> 157: 93.1264 -6.1931 -0.1208 2.2728 -0.9669 1.9985 4.7739 0.3938 0.4031 0.1696 6.1014 1.5627 -#> 158: 93.1263 -6.1951 -0.1237 2.2826 -0.9729 1.7675 4.6131 0.3928 0.3904 0.1659 6.1582 1.5647 -#> 159: 92.9780 -6.2831 -0.1242 2.2726 -0.9770 1.8348 5.4674 0.3938 0.3887 0.1631 6.0622 1.5787 -#> 160: 93.1289 -6.4397 -0.1263 2.2651 -0.9675 2.4637 6.0560 0.3919 0.4017 0.1626 5.9486 1.5859 -#> 161: 93.2629 -6.3336 -0.1294 2.2670 -0.9666 2.9602 5.4966 0.3872 0.3988 0.1667 5.9034 1.5421 -#> 162: 93.1652 -6.3800 -0.1342 2.2518 -0.9754 2.8800 5.6206 0.3908 0.4158 0.1627 5.9332 1.5306 -#> 163: 93.2886 -6.4115 -0.1437 2.2330 -0.9685 1.9997 6.2760 0.4015 0.4076 0.1623 5.7905 1.5398 -#> 164: 93.4631 -6.7246 -0.1396 2.2358 -0.9854 1.8885 7.8014 0.3952 0.4028 0.1573 5.7052 1.5695 -#> 165: 93.4757 -6.8408 -0.1404 2.2346 -0.9825 2.4877 9.3632 0.3948 0.4019 0.1615 5.8406 1.5902 -#> 166: 93.9075 -6.7707 -0.1428 2.2331 -0.9848 1.9761 8.9292 0.3939 0.3909 0.1610 5.7600 1.5966 -#> 167: 93.8895 -7.1938 -0.1363 2.2449 -0.9870 2.0894 11.4058 0.3850 0.3899 0.1627 5.8501 1.5748 -#> 168: 93.5849 -6.8478 -0.1294 2.2466 -0.9888 2.3573 9.4037 0.3935 0.3808 0.1645 6.0206 1.6591 -#> 169: 93.4931 -6.4550 -0.1173 2.2727 -0.9990 2.1948 6.5738 0.3844 0.4029 0.1699 6.0990 1.6123 -#> 170: 93.7188 -6.4015 -0.1173 2.2715 -0.9981 1.8800 6.1745 0.3844 0.4001 0.1635 6.1990 1.5745 -#> 171: 93.5938 -6.4389 -0.1119 2.2663 -0.9893 2.5731 6.5397 0.3858 0.4044 0.1554 6.1636 1.5631 -#> 172: 93.4515 -6.2049 -0.1050 2.2937 -0.9701 2.6134 4.6813 0.3687 0.4017 0.1715 6.3875 1.5006 -#> 173: 93.2254 -6.2074 -0.1041 2.3111 -0.9661 2.5799 4.6939 0.3669 0.4016 0.1738 6.5633 1.5229 -#> 174: 93.4116 -6.1198 -0.1050 2.3075 -0.9711 3.0196 4.3080 0.3720 0.3988 0.1778 6.4856 1.5214 -#> 175: 93.4952 -6.0439 -0.1050 2.3008 -0.9714 3.1172 3.7728 0.3720 0.3979 0.1749 6.1918 1.4985 -#> 176: 93.6186 -6.0891 -0.1061 2.3033 -0.9794 2.1081 3.8909 0.3705 0.4029 0.1796 6.1064 1.4657 -#> 177: 93.6432 -5.9977 -0.1031 2.2953 -0.9950 1.9411 3.4156 0.3694 0.3970 0.1843 6.0473 1.4918 -#> 178: 93.5736 -6.0079 -0.0996 2.2986 -0.9809 1.7778 3.5107 0.3696 0.3909 0.1840 6.1243 1.4937 -#> 179: 93.6407 -6.0246 -0.0977 2.3042 -0.9770 2.0631 3.8144 0.3718 0.3885 0.1798 6.1851 1.5212 -#> 180: 93.6336 -5.8865 -0.0969 2.3217 -0.9871 2.2566 3.1377 0.3721 0.3715 0.1784 6.0747 1.5546 -#> 181: 93.5075 -5.8632 -0.0965 2.3140 -0.9764 2.5812 2.9771 0.3715 0.3728 0.1876 5.9833 1.5356 -#> 182: 93.4464 -5.8627 -0.0930 2.3211 -0.9713 2.5956 2.8054 0.3836 0.3759 0.1861 6.1293 1.6259 -#> 183: 93.2737 -5.8238 -0.0977 2.3127 -0.9642 2.8739 2.6277 0.3846 0.3743 0.1868 6.0451 1.6493 -#> 184: 93.2191 -5.9175 -0.0993 2.3107 -0.9592 2.3088 3.0689 0.3829 0.3515 0.1711 6.1487 1.6666 -#> 185: 93.3626 -5.8872 -0.1070 2.3112 -0.9413 2.2812 3.2719 0.3712 0.3555 0.1783 6.1295 1.6288 -#> 186: 93.1585 -5.8532 -0.1053 2.3140 -0.9665 2.7906 2.8415 0.3734 0.3531 0.1680 6.0294 1.6104 -#> 187: 93.3041 -5.6798 -0.0957 2.3158 -0.9608 3.1056 2.0850 0.3813 0.3484 0.1728 6.1191 1.5813 -#> 188: 93.2466 -5.6791 -0.0954 2.3172 -0.9446 3.8296 2.1956 0.3816 0.3439 0.1757 5.9670 1.5445 -#> 189: 93.3532 -5.6883 -0.0859 2.3335 -0.9594 2.8968 2.3125 0.3691 0.3512 0.1812 5.9467 1.6101 -#> 190: 93.5064 -5.6288 -0.0726 2.3548 -0.9562 2.8233 2.1930 0.3334 0.3700 0.1759 6.4036 1.5877 -#> 191: 93.4145 -5.6906 -0.0726 2.3467 -0.9624 2.8818 2.3581 0.3334 0.3771 0.1712 6.2046 1.4952 -#> 192: 93.2060 -5.7479 -0.0716 2.3433 -0.9618 2.5221 2.6613 0.3324 0.3909 0.1552 6.1651 1.4971 -#> 193: 93.2904 -5.7634 -0.0811 2.3327 -0.9585 2.6968 2.6324 0.3339 0.3856 0.1632 6.5621 1.5258 -#> 194: 93.5271 -5.7859 -0.0874 2.3419 -0.9580 2.8361 2.8424 0.3286 0.3784 0.1636 6.3714 1.5386 -#> 195: 93.3944 -5.9358 -0.0838 2.3407 -0.9718 3.4161 3.2427 0.3315 0.3787 0.1678 6.3722 1.5181 -#> 196: 93.2341 -5.9078 -0.0701 2.3492 -0.9816 3.1580 3.0586 0.3285 0.3666 0.1681 6.4633 1.5382 -#> 197: 93.2967 -6.0131 -0.0745 2.3426 -0.9991 3.7978 3.6459 0.3353 0.3491 0.1796 6.2264 1.5310 -#> 198: 93.2628 -5.7991 -0.0730 2.3434 -0.9819 2.3896 2.6695 0.3371 0.3431 0.1762 6.3141 1.5254 -#> 199: 93.2765 -5.9078 -0.0782 2.3553 -0.9864 2.2760 3.3883 0.3420 0.3459 0.1866 6.0192 1.4982 -#> 200: 93.0447 -5.9148 -0.0769 2.3543 -0.9759 2.1516 2.9675 0.3455 0.3476 0.1870 5.9079 1.4688 -#> 201: 93.1655 -5.8951 -0.0763 2.3493 -0.9707 1.8254 2.9481 0.3448 0.3526 0.1831 6.0676 1.5097 -#> 202: 93.1082 -5.8916 -0.0768 2.3499 -0.9673 1.8503 2.9562 0.3447 0.3574 0.1821 6.1282 1.5026 -#> 203: 93.0728 -5.9316 -0.0774 2.3506 -0.9650 2.0210 3.2306 0.3441 0.3563 0.1827 6.1253 1.4974 -#> 204: 93.0846 -5.9347 -0.0773 2.3494 -0.9648 2.1463 3.2567 0.3453 0.3563 0.1824 6.1301 1.4911 -#> 205: 93.0929 -5.9439 -0.0781 2.3491 -0.9659 2.2204 3.3165 0.3453 0.3572 0.1823 6.1098 1.4941 -#> 206: 93.1795 -5.9401 -0.0795 2.3481 -0.9681 2.2588 3.2940 0.3470 0.3568 0.1829 6.1132 1.4996 -#> 207: 93.2303 -5.9158 -0.0805 2.3467 -0.9703 2.3439 3.1823 0.3484 0.3571 0.1845 6.1021 1.5059 -#> 208: 93.2161 -5.8969 -0.0825 2.3440 -0.9700 2.3306 3.0999 0.3496 0.3563 0.1848 6.0998 1.5177 -#> 209: 93.2077 -5.8842 -0.0848 2.3413 -0.9681 2.3580 3.0406 0.3499 0.3553 0.1841 6.0829 1.5199 -#> 210: 93.1951 -5.8661 -0.0867 2.3383 -0.9656 2.4170 2.9578 0.3501 0.3543 0.1833 6.0562 1.5261 -#> 211: 93.1870 -5.8543 -0.0892 2.3347 -0.9645 2.4650 2.9307 0.3502 0.3548 0.1831 6.0286 1.5289 -#> 212: 93.2077 -5.8506 -0.0915 2.3316 -0.9626 2.4909 2.9544 0.3504 0.3555 0.1835 6.0079 1.5300 -#> 213: 93.2104 -5.8492 -0.0938 2.3283 -0.9612 2.4695 2.9635 0.3503 0.3548 0.1841 5.9859 1.5341 -#> 214: 93.2059 -5.8537 -0.0959 2.3255 -0.9615 2.4264 3.0084 0.3499 0.3540 0.1835 5.9698 1.5370 -#> 215: 93.2051 -5.8569 -0.0977 2.3227 -0.9608 2.4277 3.0541 0.3495 0.3534 0.1830 5.9586 1.5374 -#> 216: 93.1879 -5.8596 -0.0993 2.3199 -0.9600 2.4347 3.0802 0.3493 0.3534 0.1828 5.9465 1.5380 -#> 217: 93.1834 -5.8621 -0.1008 2.3173 -0.9594 2.4479 3.0998 0.3491 0.3535 0.1827 5.9369 1.5402 -#> 218: 93.1796 -5.8657 -0.1021 2.3152 -0.9593 2.4234 3.1238 0.3492 0.3534 0.1835 5.9184 1.5441 -#> 219: 93.1680 -5.8721 -0.1032 2.3132 -0.9588 2.4640 3.1464 0.3494 0.3531 0.1839 5.8929 1.5493 -#> 220: 93.1579 -5.8839 -0.1044 2.3118 -0.9586 2.5707 3.1909 0.3495 0.3531 0.1847 5.8754 1.5496 -#> 221: 93.1557 -5.8882 -0.1058 2.3100 -0.9583 2.6662 3.2052 0.3492 0.3533 0.1854 5.8662 1.5518 -#> 222: 93.1624 -5.8832 -0.1074 2.3075 -0.9578 2.7993 3.1736 0.3490 0.3542 0.1861 5.8489 1.5546 -#> 223: 93.1699 -5.8771 -0.1086 2.3052 -0.9583 2.9085 3.1456 0.3488 0.3558 0.1871 5.8436 1.5610 -#> 224: 93.1870 -5.8751 -0.1097 2.3037 -0.9583 2.9988 3.1279 0.3487 0.3570 0.1878 5.8390 1.5628 -#> 225: 93.2094 -5.8719 -0.1110 2.3012 -0.9583 3.0581 3.1018 0.3485 0.3574 0.1885 5.8214 1.5656 -#> 226: 93.2352 -5.8683 -0.1122 2.2988 -0.9587 3.1297 3.0761 0.3482 0.3584 0.1895 5.8105 1.5680 -#> 227: 93.2611 -5.8653 -0.1132 2.2964 -0.9589 3.1563 3.0610 0.3476 0.3594 0.1904 5.8038 1.5701 -#> 228: 93.2741 -5.8593 -0.1140 2.2943 -0.9591 3.1641 3.0356 0.3470 0.3603 0.1911 5.7984 1.5730 -#> 229: 93.2899 -5.8593 -0.1151 2.2919 -0.9595 3.1626 3.0313 0.3466 0.3613 0.1918 5.7999 1.5745 -#> 230: 93.3048 -5.8650 -0.1164 2.2899 -0.9593 3.1743 3.0542 0.3460 0.3624 0.1921 5.7990 1.5753 -#> 231: 93.3159 -5.8638 -0.1177 2.2875 -0.9592 3.1930 3.0524 0.3454 0.3631 0.1924 5.7956 1.5748 -#> 232: 93.3209 -5.8611 -0.1189 2.2852 -0.9590 3.1872 3.0420 0.3450 0.3639 0.1926 5.7921 1.5755 -#> 233: 93.3196 -5.8556 -0.1200 2.2833 -0.9589 3.1861 3.0209 0.3445 0.3644 0.1926 5.7852 1.5779 -#> 234: 93.3245 -5.8530 -0.1210 2.2813 -0.9591 3.1890 3.0115 0.3441 0.3651 0.1922 5.7781 1.5786 -#> 235: 93.3219 -5.8522 -0.1218 2.2800 -0.9593 3.1573 3.0042 0.3437 0.3659 0.1917 5.7813 1.5797 -#> 236: 93.3155 -5.8524 -0.1227 2.2789 -0.9595 3.1542 3.0035 0.3433 0.3669 0.1913 5.7834 1.5800 -#> 237: 93.3060 -5.8556 -0.1235 2.2779 -0.9599 3.1308 3.0158 0.3430 0.3678 0.1910 5.7833 1.5809 -#> 238: 93.3111 -5.8563 -0.1242 2.2772 -0.9602 3.1194 3.0099 0.3427 0.3683 0.1907 5.7842 1.5809 -#> 239: 93.3177 -5.8580 -0.1248 2.2764 -0.9605 3.0944 3.0130 0.3423 0.3686 0.1904 5.7840 1.5815 -#> 240: 93.3222 -5.8606 -0.1255 2.2754 -0.9608 3.0739 3.0140 0.3420 0.3686 0.1902 5.7843 1.5825 -#> 241: 93.3289 -5.8627 -0.1262 2.2740 -0.9611 3.0848 3.0167 0.3417 0.3688 0.1900 5.7836 1.5840 -#> 242: 93.3366 -5.8627 -0.1270 2.2727 -0.9612 3.1273 3.0103 0.3415 0.3691 0.1898 5.7855 1.5850 -#> 243: 93.3441 -5.8646 -0.1277 2.2714 -0.9614 3.1530 3.0218 0.3414 0.3692 0.1896 5.7829 1.5856 -#> 244: 93.3499 -5.8645 -0.1285 2.2700 -0.9618 3.1705 3.0265 0.3412 0.3694 0.1894 5.7778 1.5874 -#> 245: 93.3619 -5.8673 -0.1294 2.2686 -0.9622 3.1863 3.0397 0.3412 0.3694 0.1892 5.7752 1.5889 -#> 246: 93.3745 -5.8698 -0.1301 2.2671 -0.9627 3.2105 3.0484 0.3412 0.3693 0.1890 5.7716 1.5905 -#> 247: 93.3838 -5.8757 -0.1307 2.2659 -0.9632 3.2158 3.0715 0.3412 0.3693 0.1889 5.7688 1.5922 -#> 248: 93.3914 -5.8799 -0.1314 2.2650 -0.9640 3.2268 3.0851 0.3413 0.3690 0.1889 5.7648 1.5934 -#> 249: 93.3983 -5.8844 -0.1319 2.2640 -0.9648 3.2471 3.0990 0.3415 0.3691 0.1889 5.7641 1.5944 -#> 250: 93.4032 -5.8898 -0.1324 2.2629 -0.9655 3.2828 3.1197 0.3414 0.3694 0.1887 5.7623 1.5965 -#> 251: 93.4053 -5.8939 -0.1329 2.2621 -0.9657 3.3074 3.1303 0.3414 0.3698 0.1887 5.7611 1.5978 -#> 252: 93.4095 -5.8950 -0.1334 2.2613 -0.9658 3.3479 3.1281 0.3414 0.3701 0.1887 5.7578 1.5986 -#> 253: 93.4132 -5.8956 -0.1340 2.2606 -0.9660 3.3486 3.1283 0.3413 0.3703 0.1887 5.7559 1.5999 -#> 254: 93.4201 -5.8966 -0.1345 2.2597 -0.9660 3.3502 3.1298 0.3413 0.3706 0.1888 5.7593 1.5997 -#> 255: 93.4235 -5.8953 -0.1349 2.2590 -0.9656 3.3332 3.1220 0.3412 0.3706 0.1887 5.7571 1.6012 -#> 256: 93.4231 -5.8926 -0.1353 2.2585 -0.9651 3.3255 3.1104 0.3411 0.3706 0.1886 5.7569 1.6018 -#> 257: 93.4247 -5.8874 -0.1356 2.2582 -0.9646 3.3164 3.0917 0.3410 0.3705 0.1885 5.7585 1.6030 -#> 258: 93.4198 -5.8857 -0.1359 2.2580 -0.9641 3.3086 3.0828 0.3409 0.3702 0.1885 5.7608 1.6026 -#> 259: 93.4125 -5.8833 -0.1362 2.2576 -0.9638 3.2926 3.0726 0.3408 0.3701 0.1885 5.7651 1.6023 -#> 260: 93.4073 -5.8847 -0.1365 2.2572 -0.9640 3.2737 3.0759 0.3406 0.3703 0.1885 5.7687 1.6030 -#> 261: 93.4049 -5.8885 -0.1368 2.2571 -0.9642 3.2510 3.0904 0.3402 0.3702 0.1882 5.7742 1.6028 -#> 262: 93.4036 -5.8931 -0.1371 2.2566 -0.9645 3.2279 3.1104 0.3397 0.3699 0.1880 5.7766 1.6033 -#> 263: 93.4026 -5.8964 -0.1375 2.2562 -0.9647 3.2024 3.1313 0.3395 0.3696 0.1877 5.7786 1.6029 -#> 264: 93.3990 -5.9003 -0.1377 2.2559 -0.9649 3.1808 3.1545 0.3393 0.3694 0.1874 5.7778 1.6022 -#> 265: 93.4005 -5.9013 -0.1380 2.2555 -0.9650 3.1664 3.1680 0.3390 0.3693 0.1871 5.7765 1.6021 -#> 266: 93.4005 -5.9011 -0.1382 2.2552 -0.9653 3.1530 3.1708 0.3387 0.3692 0.1869 5.7763 1.6020 -#> 267: 93.4006 -5.9035 -0.1384 2.2549 -0.9654 3.1384 3.1902 0.3384 0.3690 0.1866 5.7768 1.6014 -#> 268: 93.3972 -5.9086 -0.1385 2.2547 -0.9653 3.1224 3.2331 0.3380 0.3688 0.1863 5.7778 1.6008 -#> 269: 93.3936 -5.9113 -0.1386 2.2547 -0.9654 3.0959 3.2552 0.3377 0.3688 0.1861 5.7782 1.6001 -#> 270: 93.3867 -5.9139 -0.1387 2.2547 -0.9653 3.0853 3.2756 0.3372 0.3687 0.1859 5.7787 1.5989 -#> 271: 93.3836 -5.9154 -0.1389 2.2545 -0.9654 3.0824 3.2889 0.3367 0.3686 0.1858 5.7761 1.5980 -#> 272: 93.3812 -5.9160 -0.1390 2.2543 -0.9653 3.0741 3.2919 0.3362 0.3686 0.1857 5.7729 1.5977 -#> 273: 93.3767 -5.9174 -0.1390 2.2542 -0.9652 3.0663 3.2992 0.3358 0.3687 0.1856 5.7699 1.5970 -#> 274: 93.3696 -5.9171 -0.1391 2.2543 -0.9652 3.0604 3.2940 0.3355 0.3687 0.1855 5.7688 1.5958 -#> 275: 93.3658 -5.9177 -0.1393 2.2544 -0.9651 3.0605 3.2961 0.3353 0.3687 0.1853 5.7675 1.5952 -#> 276: 93.3621 -5.9185 -0.1395 2.2543 -0.9649 3.0508 3.2992 0.3351 0.3686 0.1852 5.7672 1.5940 -#> 277: 93.3602 -5.9206 -0.1397 2.2542 -0.9649 3.0453 3.3087 0.3349 0.3685 0.1851 5.7679 1.5935 -#> 278: 93.3565 -5.9213 -0.1400 2.2539 -0.9648 3.0366 3.3117 0.3347 0.3683 0.1852 5.7695 1.5931 -#> 279: 93.3548 -5.9222 -0.1403 2.2535 -0.9647 3.0284 3.3179 0.3345 0.3682 0.1854 5.7703 1.5928 -#> 280: 93.3544 -5.9215 -0.1407 2.2528 -0.9647 3.0193 3.3141 0.3344 0.3683 0.1854 5.7714 1.5927 -#> 281: 93.3533 -5.9205 -0.1410 2.2522 -0.9647 3.0130 3.3090 0.3341 0.3685 0.1855 5.7706 1.5927 -#> 282: 93.3564 -5.9189 -0.1414 2.2514 -0.9648 3.0025 3.3019 0.3339 0.3686 0.1856 5.7682 1.5930 -#> 283: 93.3571 -5.9164 -0.1417 2.2508 -0.9646 2.9990 3.2926 0.3337 0.3686 0.1858 5.7642 1.5943 -#> 284: 93.3576 -5.9154 -0.1421 2.2501 -0.9644 2.9976 3.2895 0.3336 0.3686 0.1860 5.7625 1.5942 -#> 285: 93.3584 -5.9142 -0.1425 2.2496 -0.9644 2.9906 3.2835 0.3334 0.3684 0.1861 5.7591 1.5939 -#> 286: 93.3609 -5.9137 -0.1429 2.2491 -0.9642 2.9852 3.2817 0.3332 0.3682 0.1863 5.7572 1.5939 -#> 287: 93.3641 -5.9131 -0.1433 2.2485 -0.9641 2.9732 3.2785 0.3331 0.3680 0.1863 5.7547 1.5944 -#> 288: 93.3671 -5.9128 -0.1436 2.2480 -0.9641 2.9673 3.2767 0.3330 0.3679 0.1864 5.7540 1.5939 -#> 289: 93.3676 -5.9125 -0.1440 2.2474 -0.9639 2.9663 3.2765 0.3329 0.3678 0.1865 5.7536 1.5939 -#> 290: 93.3659 -5.9126 -0.1443 2.2469 -0.9637 2.9570 3.2776 0.3328 0.3678 0.1866 5.7523 1.5941 -#> 291: 93.3620 -5.9109 -0.1447 2.2466 -0.9634 2.9472 3.2713 0.3327 0.3676 0.1866 5.7527 1.5943 -#> 292: 93.3601 -5.9096 -0.1450 2.2462 -0.9632 2.9359 3.2664 0.3326 0.3675 0.1866 5.7517 1.5944 -#> 293: 93.3582 -5.9077 -0.1453 2.2457 -0.9629 2.9295 3.2586 0.3326 0.3675 0.1866 5.7514 1.5945 -#> 294: 93.3583 -5.9054 -0.1456 2.2454 -0.9626 2.9203 3.2478 0.3326 0.3676 0.1867 5.7508 1.5942 -#> 295: 93.3577 -5.9037 -0.1459 2.2449 -0.9624 2.9216 3.2406 0.3325 0.3678 0.1867 5.7493 1.5934 -#> 296: 93.3570 -5.9016 -0.1462 2.2445 -0.9623 2.9304 3.2334 0.3323 0.3680 0.1868 5.7502 1.5933 -#> 297: 93.3538 -5.8988 -0.1462 2.2441 -0.9621 2.9429 3.2217 0.3321 0.3681 0.1870 5.7539 1.5939 -#> 298: 93.3525 -5.8966 -0.1463 2.2438 -0.9620 2.9662 3.2118 0.3319 0.3683 0.1870 5.7555 1.5942 -#> 299: 93.3526 -5.8957 -0.1465 2.2437 -0.9619 2.9812 3.2056 0.3318 0.3685 0.1870 5.7582 1.5938 -#> 300: 93.3504 -5.8953 -0.1467 2.2436 -0.9616 2.9982 3.2029 0.3316 0.3688 0.1873 5.7609 1.5937 -#> 301: 93.3469 -5.8941 -0.1469 2.2434 -0.9612 3.0124 3.1993 0.3315 0.3690 0.1875 5.7641 1.5933 -#> 302: 93.3442 -5.8944 -0.1472 2.2434 -0.9609 3.0353 3.2015 0.3313 0.3692 0.1876 5.7660 1.5937 -#> 303: 93.3428 -5.8970 -0.1474 2.2432 -0.9607 3.0454 3.2160 0.3312 0.3692 0.1876 5.7654 1.5938 -#> 304: 93.3407 -5.9012 -0.1475 2.2430 -0.9607 3.0626 3.2409 0.3310 0.3693 0.1877 5.7649 1.5932 -#> 305: 93.3395 -5.9051 -0.1476 2.2429 -0.9607 3.0756 3.2632 0.3308 0.3693 0.1879 5.7650 1.5924 -#> 306: 93.3398 -5.9099 -0.1478 2.2429 -0.9607 3.0881 3.2952 0.3306 0.3694 0.1880 5.7655 1.5920 -#> 307: 93.3406 -5.9128 -0.1479 2.2427 -0.9608 3.0995 3.3163 0.3305 0.3695 0.1880 5.7666 1.5921 -#> 308: 93.3418 -5.9165 -0.1480 2.2426 -0.9610 3.1060 3.3420 0.3303 0.3696 0.1881 5.7674 1.5914 -#> 309: 93.3437 -5.9205 -0.1481 2.2424 -0.9610 3.1185 3.3703 0.3301 0.3697 0.1882 5.7665 1.5908 -#> 310: 93.3442 -5.9236 -0.1482 2.2422 -0.9612 3.1270 3.3902 0.3299 0.3698 0.1882 5.7650 1.5904 -#> 311: 93.3482 -5.9268 -0.1482 2.2421 -0.9614 3.1333 3.4086 0.3296 0.3698 0.1882 5.7636 1.5900 -#> 312: 93.3529 -5.9286 -0.1482 2.2420 -0.9615 3.1348 3.4186 0.3294 0.3699 0.1882 5.7622 1.5895 -#> 313: 93.3573 -5.9290 -0.1481 2.2419 -0.9617 3.1332 3.4199 0.3291 0.3699 0.1882 5.7621 1.5891 -#> 314: 93.3630 -5.9293 -0.1482 2.2418 -0.9619 3.1398 3.4211 0.3289 0.3700 0.1883 5.7594 1.5888 -#> 315: 93.3669 -5.9284 -0.1483 2.2416 -0.9622 3.1464 3.4155 0.3286 0.3702 0.1885 5.7586 1.5889 -#> 316: 93.3724 -5.9279 -0.1485 2.2412 -0.9624 3.1426 3.4124 0.3283 0.3704 0.1887 5.7581 1.5887 -#> 317: 93.3763 -5.9281 -0.1487 2.2409 -0.9626 3.1335 3.4108 0.3281 0.3706 0.1888 5.7573 1.5880 -#> 318: 93.3786 -5.9275 -0.1488 2.2405 -0.9627 3.1262 3.4057 0.3279 0.3709 0.1888 5.7579 1.5876 -#> 319: 93.3821 -5.9275 -0.1490 2.2402 -0.9628 3.1273 3.4032 0.3276 0.3711 0.1889 5.7570 1.5870 -#> 320: 93.3856 -5.9272 -0.1491 2.2401 -0.9629 3.1337 3.3989 0.3273 0.3715 0.1888 5.7563 1.5861 -#> 321: 93.3902 -5.9263 -0.1492 2.2399 -0.9631 3.1388 3.3931 0.3269 0.3718 0.1887 5.7555 1.5852 -#> 322: 93.3951 -5.9251 -0.1493 2.2397 -0.9631 3.1415 3.3856 0.3266 0.3721 0.1886 5.7552 1.5846 -#> 323: 93.3988 -5.9251 -0.1493 2.2395 -0.9632 3.1377 3.3824 0.3262 0.3724 0.1885 5.7556 1.5841 -#> 324: 93.4030 -5.9236 -0.1494 2.2394 -0.9633 3.1355 3.3738 0.3259 0.3727 0.1885 5.7562 1.5837 -#> 325: 93.4047 -5.9219 -0.1495 2.2393 -0.9633 3.1415 3.3647 0.3256 0.3731 0.1884 5.7553 1.5831 -#> 326: 93.4077 -5.9204 -0.1495 2.2391 -0.9634 3.1489 3.3564 0.3254 0.3735 0.1884 5.7562 1.5829 -#> 327: 93.4121 -5.9185 -0.1496 2.2390 -0.9635 3.1503 3.3472 0.3250 0.3739 0.1884 5.7562 1.5825 -#> 328: 93.4157 -5.9182 -0.1496 2.2389 -0.9636 3.1564 3.3432 0.3246 0.3743 0.1884 5.7559 1.5823 -#> 329: 93.4181 -5.9169 -0.1496 2.2388 -0.9638 3.1666 3.3361 0.3243 0.3746 0.1884 5.7544 1.5822 -#> 330: 93.4206 -5.9171 -0.1497 2.2386 -0.9640 3.1726 3.3349 0.3239 0.3748 0.1885 5.7538 1.5824 -#> 331: 93.4214 -5.9172 -0.1497 2.2385 -0.9642 3.1764 3.3332 0.3236 0.3750 0.1886 5.7540 1.5824 -#> 332: 93.4226 -5.9171 -0.1497 2.2385 -0.9645 3.1787 3.3303 0.3232 0.3752 0.1887 5.7539 1.5826 -#> 333: 93.4242 -5.9168 -0.1497 2.2384 -0.9645 3.1757 3.3287 0.3229 0.3755 0.1886 5.7545 1.5823 -#> 334: 93.4273 -5.9167 -0.1497 2.2383 -0.9645 3.1832 3.3290 0.3226 0.3758 0.1887 5.7540 1.5818 -#> 335: 93.4306 -5.9170 -0.1498 2.2384 -0.9644 3.1910 3.3318 0.3223 0.3760 0.1887 5.7548 1.5814 -#> 336: 93.4315 -5.9177 -0.1498 2.2384 -0.9644 3.1999 3.3355 0.3219 0.3762 0.1887 5.7558 1.5811 -#> 337: 93.4332 -5.9181 -0.1499 2.2384 -0.9643 3.2145 3.3360 0.3216 0.3764 0.1887 5.7581 1.5805 -#> 338: 93.4352 -5.9169 -0.1498 2.2384 -0.9643 3.2221 3.3307 0.3213 0.3767 0.1887 5.7592 1.5802 -#> 339: 93.4385 -5.9152 -0.1498 2.2384 -0.9643 3.2356 3.3242 0.3210 0.3770 0.1887 5.7605 1.5797 -#> 340: 93.4417 -5.9130 -0.1498 2.2384 -0.9643 3.2506 3.3167 0.3207 0.3773 0.1888 5.7599 1.5794 -#> 341: 93.4452 -5.9102 -0.1497 2.2382 -0.9641 3.2568 3.3064 0.3205 0.3772 0.1888 5.7590 1.5799 -#> 342: 93.4487 -5.9077 -0.1497 2.2381 -0.9641 3.2628 3.2970 0.3203 0.3772 0.1889 5.7587 1.5802 -#> 343: 93.4519 -5.9055 -0.1497 2.2380 -0.9642 3.2685 3.2892 0.3201 0.3772 0.1889 5.7585 1.5810 -#> 344: 93.4556 -5.9048 -0.1497 2.2379 -0.9643 3.2690 3.2847 0.3200 0.3771 0.1891 5.7573 1.5812 -#> 345: 93.4588 -5.9041 -0.1498 2.2377 -0.9645 3.2704 3.2807 0.3199 0.3771 0.1893 5.7567 1.5811 -#> 346: 93.4605 -5.9033 -0.1498 2.2376 -0.9647 3.2655 3.2747 0.3198 0.3770 0.1893 5.7557 1.5808 -#> 347: 93.4638 -5.9027 -0.1498 2.2375 -0.9648 3.2725 3.2701 0.3198 0.3768 0.1894 5.7532 1.5808 -#> 348: 93.4643 -5.9028 -0.1498 2.2373 -0.9649 3.2764 3.2676 0.3197 0.3768 0.1893 5.7523 1.5807 -#> 349: 93.4664 -5.9023 -0.1497 2.2372 -0.9650 3.2806 3.2638 0.3197 0.3767 0.1893 5.7527 1.5815 -#> 350: 93.4700 -5.9014 -0.1497 2.2370 -0.9651 3.2817 3.2585 0.3196 0.3767 0.1892 5.7534 1.5817 -#> 351: 93.4724 -5.9001 -0.1497 2.2369 -0.9652 3.2825 3.2522 0.3196 0.3768 0.1892 5.7541 1.5818 -#> 352: 93.4744 -5.8986 -0.1497 2.2369 -0.9653 3.2875 3.2460 0.3195 0.3768 0.1891 5.7546 1.5819 -#> 353: 93.4738 -5.8975 -0.1496 2.2369 -0.9653 3.2891 3.2407 0.3195 0.3769 0.1889 5.7560 1.5822 -#> 354: 93.4733 -5.8960 -0.1496 2.2369 -0.9652 3.2856 3.2333 0.3194 0.3768 0.1889 5.7579 1.5824 -#> 355: 93.4731 -5.8944 -0.1496 2.2370 -0.9652 3.2893 3.2259 0.3194 0.3767 0.1888 5.7599 1.5826 -#> 356: 93.4724 -5.8933 -0.1495 2.2373 -0.9652 3.2924 3.2197 0.3194 0.3767 0.1888 5.7608 1.5832 -#> 357: 93.4723 -5.8929 -0.1493 2.2376 -0.9654 3.2907 3.2164 0.3194 0.3767 0.1887 5.7605 1.5833 -#> 358: 93.4723 -5.8923 -0.1491 2.2378 -0.9654 3.2875 3.2120 0.3194 0.3766 0.1886 5.7608 1.5837 -#> 359: 93.4705 -5.8931 -0.1490 2.2379 -0.9656 3.2875 3.2121 0.3194 0.3764 0.1886 5.7606 1.5843 -#> 360: 93.4699 -5.8938 -0.1488 2.2382 -0.9658 3.2837 3.2133 0.3195 0.3763 0.1886 5.7606 1.5848 -#> 361: 93.4693 -5.8951 -0.1487 2.2383 -0.9659 3.2822 3.2164 0.3195 0.3763 0.1886 5.7600 1.5852 -#> 362: 93.4691 -5.8963 -0.1486 2.2385 -0.9660 3.2770 3.2196 0.3195 0.3763 0.1884 5.7618 1.5856 -#> 363: 93.4681 -5.8970 -0.1485 2.2387 -0.9660 3.2706 3.2208 0.3195 0.3762 0.1883 5.7639 1.5857 -#> 364: 93.4674 -5.8970 -0.1484 2.2389 -0.9660 3.2593 3.2189 0.3195 0.3760 0.1881 5.7659 1.5855 -#> 365: 93.4680 -5.8968 -0.1482 2.2391 -0.9659 3.2513 3.2174 0.3196 0.3758 0.1881 5.7686 1.5857 -#> 366: 93.4672 -5.8962 -0.1480 2.2393 -0.9658 3.2493 3.2161 0.3196 0.3755 0.1880 5.7714 1.5861 -#> 367: 93.4656 -5.8953 -0.1479 2.2396 -0.9657 3.2462 3.2121 0.3195 0.3753 0.1881 5.7721 1.5862 -#> 368: 93.4645 -5.8946 -0.1478 2.2398 -0.9657 3.2469 3.2083 0.3194 0.3750 0.1882 5.7724 1.5860 -#> 369: 93.4638 -5.8946 -0.1476 2.2401 -0.9657 3.2544 3.2068 0.3194 0.3749 0.1882 5.7713 1.5856 -#> 370: 93.4639 -5.8946 -0.1475 2.2404 -0.9657 3.2547 3.2066 0.3194 0.3748 0.1882 5.7719 1.5853 -#> 371: 93.4646 -5.8959 -0.1474 2.2407 -0.9657 3.2584 3.2129 0.3194 0.3746 0.1883 5.7725 1.5847 -#> 372: 93.4648 -5.8964 -0.1473 2.2409 -0.9658 3.2649 3.2172 0.3193 0.3745 0.1883 5.7730 1.5843 -#> 373: 93.4658 -5.8958 -0.1471 2.2411 -0.9659 3.2744 3.2135 0.3193 0.3743 0.1884 5.7730 1.5843 -#> 374: 93.4678 -5.8953 -0.1470 2.2412 -0.9662 3.2855 3.2100 0.3192 0.3742 0.1885 5.7727 1.5847 -#> 375: 93.4697 -5.8955 -0.1470 2.2413 -0.9663 3.2917 3.2087 0.3190 0.3742 0.1885 5.7733 1.5845 -#> 376: 93.4707 -5.8960 -0.1469 2.2414 -0.9664 3.2997 3.2095 0.3189 0.3741 0.1885 5.7726 1.5841 -#> 377: 93.4712 -5.8965 -0.1468 2.2415 -0.9665 3.3016 3.2100 0.3188 0.3741 0.1885 5.7724 1.5836 -#> 378: 93.4706 -5.8971 -0.1468 2.2416 -0.9665 3.2958 3.2113 0.3187 0.3741 0.1884 5.7733 1.5829 -#> 379: 93.4699 -5.8983 -0.1467 2.2418 -0.9666 3.2940 3.2174 0.3186 0.3741 0.1883 5.7732 1.5827 -#> 380: 93.4709 -5.8993 -0.1467 2.2418 -0.9667 3.2907 3.2225 0.3185 0.3739 0.1882 5.7726 1.5826 -#> 381: 93.4730 -5.9009 -0.1467 2.2418 -0.9667 3.2861 3.2325 0.3185 0.3737 0.1881 5.7709 1.5825 -#> 382: 93.4746 -5.9018 -0.1467 2.2418 -0.9667 3.2841 3.2407 0.3184 0.3734 0.1880 5.7692 1.5822 -#> 383: 93.4744 -5.9033 -0.1468 2.2418 -0.9667 3.2847 3.2537 0.3184 0.3732 0.1878 5.7672 1.5819 -#> 384: 93.4747 -5.9049 -0.1468 2.2418 -0.9667 3.2854 3.2640 0.3184 0.3729 0.1878 5.7657 1.5816 -#> 385: 93.4751 -5.9062 -0.1468 2.2418 -0.9666 3.2917 3.2702 0.3184 0.3727 0.1877 5.7642 1.5813 -#> 386: 93.4756 -5.9074 -0.1468 2.2418 -0.9666 3.2971 3.2753 0.3185 0.3725 0.1876 5.7625 1.5810 -#> 387: 93.4761 -5.9084 -0.1469 2.2417 -0.9666 3.2988 3.2789 0.3185 0.3723 0.1875 5.7613 1.5804 -#> 388: 93.4777 -5.9092 -0.1469 2.2417 -0.9666 3.3055 3.2811 0.3185 0.3721 0.1875 5.7599 1.5803 -#> 389: 93.4805 -5.9092 -0.1468 2.2417 -0.9667 3.3138 3.2802 0.3185 0.3719 0.1874 5.7588 1.5803 -#> 390: 93.4828 -5.9089 -0.1468 2.2417 -0.9667 3.3164 3.2782 0.3186 0.3718 0.1873 5.7576 1.5806 -#> 391: 93.4854 -5.9094 -0.1467 2.2416 -0.9668 3.3265 3.2800 0.3186 0.3716 0.1873 5.7556 1.5804 -#> 392: 93.4877 -5.9103 -0.1467 2.2416 -0.9669 3.3327 3.2836 0.3187 0.3715 0.1873 5.7535 1.5803 -#> 393: 93.4899 -5.9110 -0.1467 2.2416 -0.9669 3.3419 3.2876 0.3187 0.3715 0.1873 5.7517 1.5803 -#> 394: 93.4925 -5.9117 -0.1467 2.2416 -0.9669 3.3494 3.2903 0.3187 0.3714 0.1873 5.7508 1.5801 -#> 395: 93.4945 -5.9121 -0.1467 2.2416 -0.9670 3.3536 3.2912 0.3187 0.3714 0.1873 5.7497 1.5796 -#> 396: 93.4951 -5.9124 -0.1467 2.2416 -0.9670 3.3590 3.2918 0.3187 0.3715 0.1873 5.7476 1.5793 -#> 397: 93.4955 -5.9123 -0.1467 2.2416 -0.9669 3.3626 3.2904 0.3186 0.3715 0.1873 5.7456 1.5788 -#> 398: 93.4971 -5.9120 -0.1467 2.2416 -0.9669 3.3735 3.2887 0.3186 0.3716 0.1873 5.7433 1.5786 -#> 399: 93.4995 -5.9116 -0.1467 2.2415 -0.9669 3.3854 3.2866 0.3186 0.3716 0.1873 5.7422 1.5785 -#> 400: 93.5007 -5.9116 -0.1466 2.2415 -0.9669 3.3923 3.2856 0.3186 0.3717 0.1873 5.7416 1.5786 -#> 401: 93.5028 -5.9109 -0.1467 2.2415 -0.9669 3.4020 3.2820 0.3186 0.3718 0.1873 5.7412 1.5787 -#> 402: 93.5042 -5.9099 -0.1467 2.2414 -0.9669 3.4114 3.2781 0.3186 0.3719 0.1874 5.7406 1.5788 -#> 403: 93.5054 -5.9090 -0.1467 2.2413 -0.9670 3.4179 3.2735 0.3186 0.3720 0.1874 5.7401 1.5785 -#> 404: 93.5071 -5.9093 -0.1468 2.2412 -0.9670 3.4190 3.2726 0.3186 0.3720 0.1875 5.7392 1.5779 -#> 405: 93.5087 -5.9087 -0.1468 2.2411 -0.9671 3.4186 3.2689 0.3186 0.3721 0.1876 5.7386 1.5776 -#> 406: 93.5091 -5.9087 -0.1469 2.2411 -0.9671 3.4228 3.2688 0.3186 0.3721 0.1876 5.7377 1.5774 -#> 407: 93.5094 -5.9091 -0.1470 2.2411 -0.9672 3.4285 3.2698 0.3186 0.3720 0.1877 5.7368 1.5770 -#> 408: 93.5108 -5.9081 -0.1470 2.2410 -0.9672 3.4378 3.2648 0.3187 0.3719 0.1877 5.7358 1.5766 -#> 409: 93.5113 -5.9082 -0.1470 2.2410 -0.9672 3.4444 3.2643 0.3187 0.3719 0.1878 5.7357 1.5763 -#> 410: 93.5102 -5.9099 -0.1470 2.2410 -0.9672 3.4502 3.2731 0.3188 0.3719 0.1878 5.7359 1.5756 -#> 411: 93.5097 -5.9109 -0.1469 2.2410 -0.9673 3.4534 3.2793 0.3188 0.3718 0.1878 5.7348 1.5753 -#> 412: 93.5102 -5.9114 -0.1469 2.2410 -0.9673 3.4522 3.2836 0.3189 0.3717 0.1878 5.7330 1.5753 -#> 413: 93.5110 -5.9120 -0.1469 2.2410 -0.9675 3.4534 3.2885 0.3189 0.3716 0.1878 5.7320 1.5756 -#> 414: 93.5126 -5.9130 -0.1469 2.2410 -0.9675 3.4550 3.2943 0.3190 0.3716 0.1878 5.7314 1.5753 -#> 415: 93.5144 -5.9140 -0.1469 2.2409 -0.9676 3.4574 3.3003 0.3190 0.3715 0.1878 5.7304 1.5751 -#> 416: 93.5147 -5.9149 -0.1469 2.2409 -0.9676 3.4632 3.3059 0.3191 0.3714 0.1878 5.7292 1.5750 -#> 417: 93.5132 -5.9156 -0.1468 2.2410 -0.9677 3.4675 3.3090 0.3192 0.3713 0.1878 5.7292 1.5747 -#> 418: 93.5131 -5.9165 -0.1468 2.2410 -0.9678 3.4680 3.3130 0.3192 0.3712 0.1878 5.7296 1.5747 -#> 419: 93.5142 -5.9166 -0.1467 2.2411 -0.9678 3.4663 3.3143 0.3193 0.3712 0.1879 5.7302 1.5744 -#> 420: 93.5150 -5.9164 -0.1466 2.2412 -0.9679 3.4626 3.3130 0.3193 0.3712 0.1879 5.7303 1.5744 -#> 421: 93.5162 -5.9169 -0.1465 2.2413 -0.9681 3.4596 3.3158 0.3194 0.3713 0.1880 5.7315 1.5743 -#> 422: 93.5173 -5.9172 -0.1465 2.2414 -0.9682 3.4567 3.3165 0.3194 0.3714 0.1881 5.7332 1.5740 -#> 423: 93.5174 -5.9178 -0.1464 2.2415 -0.9684 3.4550 3.3185 0.3194 0.3715 0.1882 5.7348 1.5741 -#> 424: 93.5174 -5.9189 -0.1464 2.2417 -0.9685 3.4531 3.3225 0.3193 0.3716 0.1882 5.7360 1.5737 -#> 425: 93.5171 -5.9184 -0.1463 2.2418 -0.9685 3.4508 3.3186 0.3192 0.3718 0.1882 5.7372 1.5738 -#> 426: 93.5167 -5.9177 -0.1462 2.2419 -0.9686 3.4566 3.3143 0.3192 0.3720 0.1882 5.7385 1.5735 -#> 427: 93.5185 -5.9174 -0.1462 2.2420 -0.9687 3.4561 3.3114 0.3191 0.3721 0.1881 5.7389 1.5734 -#> 428: 93.5192 -5.9177 -0.1461 2.2421 -0.9688 3.4574 3.3112 0.3191 0.3722 0.1880 5.7398 1.5731 -#> 429: 93.5184 -5.9179 -0.1460 2.2421 -0.9689 3.4558 3.3102 0.3190 0.3723 0.1879 5.7405 1.5729 -#> 430: 93.5170 -5.9187 -0.1460 2.2421 -0.9690 3.4575 3.3132 0.3190 0.3724 0.1879 5.7404 1.5727 -#> 431: 93.5156 -5.9192 -0.1460 2.2422 -0.9691 3.4556 3.3150 0.3190 0.3724 0.1879 5.7405 1.5726 -#> 432: 93.5148 -5.9203 -0.1459 2.2422 -0.9692 3.4557 3.3201 0.3190 0.3725 0.1878 5.7409 1.5727 -#> 433: 93.5134 -5.9215 -0.1459 2.2422 -0.9692 3.4569 3.3263 0.3190 0.3726 0.1878 5.7415 1.5731 -#> 434: 93.5128 -5.9222 -0.1459 2.2423 -0.9691 3.4623 3.3304 0.3190 0.3726 0.1877 5.7422 1.5728 -#> 435: 93.5116 -5.9231 -0.1459 2.2424 -0.9691 3.4672 3.3376 0.3191 0.3727 0.1877 5.7424 1.5726 -#> 436: 93.5111 -5.9228 -0.1459 2.2425 -0.9692 3.4658 3.3352 0.3190 0.3727 0.1876 5.7429 1.5725 -#> 437: 93.5100 -5.9227 -0.1459 2.2425 -0.9692 3.4651 3.3328 0.3190 0.3727 0.1876 5.7430 1.5725 -#> 438: 93.5071 -5.9230 -0.1459 2.2425 -0.9692 3.4614 3.3329 0.3190 0.3728 0.1876 5.7437 1.5725 -#> 439: 93.5035 -5.9225 -0.1459 2.2426 -0.9691 3.4555 3.3298 0.3190 0.3728 0.1875 5.7449 1.5725 -#> 440: 93.5006 -5.9222 -0.1459 2.2426 -0.9690 3.4503 3.3286 0.3190 0.3728 0.1874 5.7461 1.5723 -#> 441: 93.4988 -5.9220 -0.1459 2.2427 -0.9689 3.4445 3.3272 0.3190 0.3728 0.1874 5.7466 1.5721 -#> 442: 93.4971 -5.9216 -0.1459 2.2428 -0.9688 3.4392 3.3265 0.3190 0.3728 0.1874 5.7475 1.5721 -#> 443: 93.4957 -5.9214 -0.1458 2.2429 -0.9688 3.4338 3.3256 0.3190 0.3729 0.1874 5.7487 1.5723 -#> 444: 93.4949 -5.9210 -0.1458 2.2430 -0.9688 3.4288 3.3236 0.3189 0.3729 0.1874 5.7502 1.5721 -#> 445: 93.4932 -5.9210 -0.1458 2.2430 -0.9687 3.4283 3.3237 0.3189 0.3731 0.1874 5.7516 1.5719 -#> 446: 93.4922 -5.9205 -0.1458 2.2430 -0.9687 3.4253 3.3215 0.3188 0.3733 0.1873 5.7524 1.5717 -#> 447: 93.4917 -5.9205 -0.1458 2.2430 -0.9686 3.4257 3.3213 0.3187 0.3736 0.1873 5.7528 1.5715 -#> 448: 93.4924 -5.9205 -0.1458 2.2430 -0.9685 3.4296 3.3209 0.3186 0.3737 0.1872 5.7532 1.5717 -#> 449: 93.4920 -5.9203 -0.1459 2.2430 -0.9684 3.4302 3.3194 0.3185 0.3739 0.1872 5.7542 1.5717 -#> 450: 93.4915 -5.9207 -0.1459 2.2430 -0.9684 3.4314 3.3217 0.3184 0.3741 0.1871 5.7551 1.5715 -#> 451: 93.4915 -5.9214 -0.1459 2.2430 -0.9684 3.4371 3.3253 0.3183 0.3743 0.1871 5.7562 1.5717 -#> 452: 93.4926 -5.9212 -0.1458 2.2430 -0.9683 3.4417 3.3242 0.3182 0.3745 0.1870 5.7567 1.5717 -#> 453: 93.4935 -5.9211 -0.1459 2.2430 -0.9683 3.4413 3.3232 0.3182 0.3746 0.1870 5.7574 1.5714 -#> 454: 93.4941 -5.9209 -0.1459 2.2429 -0.9683 3.4406 3.3222 0.3182 0.3748 0.1870 5.7580 1.5713 -#> 455: 93.4947 -5.9212 -0.1459 2.2429 -0.9684 3.4450 3.3232 0.3181 0.3750 0.1870 5.7580 1.5710 -#> 456: 93.4950 -5.9214 -0.1459 2.2429 -0.9684 3.4481 3.3236 0.3181 0.3751 0.1870 5.7585 1.5708 -#> 457: 93.4961 -5.9220 -0.1459 2.2429 -0.9685 3.4516 3.3266 0.3180 0.3752 0.1869 5.7590 1.5707 -#> 458: 93.4965 -5.9218 -0.1459 2.2428 -0.9685 3.4553 3.3257 0.3179 0.3753 0.1869 5.7589 1.5707 -#> 459: 93.4959 -5.9212 -0.1459 2.2428 -0.9685 3.4572 3.3229 0.3178 0.3754 0.1868 5.7596 1.5705 -#> 460: 93.4960 -5.9209 -0.1459 2.2428 -0.9685 3.4573 3.3209 0.3178 0.3755 0.1868 5.7598 1.5704 -#> 461: 93.4944 -5.9211 -0.1459 2.2428 -0.9685 3.4592 3.3202 0.3177 0.3757 0.1868 5.7609 1.5701 -#> 462: 93.4941 -5.9214 -0.1459 2.2428 -0.9686 3.4630 3.3206 0.3176 0.3759 0.1868 5.7617 1.5700 -#> 463: 93.4932 -5.9215 -0.1459 2.2429 -0.9686 3.4708 3.3197 0.3175 0.3761 0.1868 5.7622 1.5699 -#> 464: 93.4933 -5.9209 -0.1459 2.2429 -0.9685 3.4759 3.3162 0.3175 0.3762 0.1869 5.7628 1.5696 -#> 465: 93.4928 -5.9204 -0.1459 2.2428 -0.9685 3.4794 3.3133 0.3174 0.3764 0.1870 5.7642 1.5693 -#> 466: 93.4934 -5.9197 -0.1460 2.2428 -0.9685 3.4838 3.3105 0.3173 0.3766 0.1870 5.7659 1.5693 -#> 467: 93.4931 -5.9197 -0.1460 2.2428 -0.9685 3.4866 3.3094 0.3172 0.3768 0.1871 5.7667 1.5691 -#> 468: 93.4933 -5.9198 -0.1460 2.2428 -0.9685 3.4916 3.3099 0.3172 0.3769 0.1871 5.7672 1.5690 -#> 469: 93.4936 -5.9200 -0.1461 2.2427 -0.9685 3.4929 3.3119 0.3171 0.3771 0.1871 5.7681 1.5689 -#> 470: 93.4938 -5.9200 -0.1461 2.2427 -0.9685 3.4931 3.3111 0.3171 0.3773 0.1871 5.7685 1.5687 -#> 471: 93.4943 -5.9198 -0.1461 2.2427 -0.9685 3.4932 3.3097 0.3170 0.3776 0.1871 5.7681 1.5686 -#> 472: 93.4931 -5.9197 -0.1461 2.2427 -0.9684 3.4923 3.3092 0.3170 0.3778 0.1870 5.7683 1.5686 -#> 473: 93.4928 -5.9193 -0.1461 2.2426 -0.9684 3.4918 3.3068 0.3169 0.3781 0.1870 5.7690 1.5685 -#> 474: 93.4920 -5.9193 -0.1462 2.2426 -0.9683 3.4878 3.3075 0.3169 0.3781 0.1870 5.7687 1.5688 -#> 475: 93.4909 -5.9191 -0.1463 2.2425 -0.9683 3.4868 3.3069 0.3169 0.3782 0.1869 5.7681 1.5692 -#> 476: 93.4887 -5.9190 -0.1464 2.2424 -0.9682 3.4881 3.3072 0.3169 0.3783 0.1869 5.7673 1.5694 -#> 477: 93.4875 -5.9185 -0.1465 2.2423 -0.9681 3.4847 3.3059 0.3169 0.3784 0.1868 5.7667 1.5696 -#> 478: 93.4867 -5.9182 -0.1466 2.2421 -0.9681 3.4804 3.3056 0.3170 0.3784 0.1867 5.7661 1.5700 -#> 479: 93.4865 -5.9178 -0.1468 2.2419 -0.9681 3.4768 3.3043 0.3171 0.3784 0.1867 5.7657 1.5702 -#> 480: 93.4863 -5.9181 -0.1469 2.2417 -0.9680 3.4733 3.3057 0.3172 0.3784 0.1866 5.7656 1.5702 -#> 481: 93.4865 -5.9182 -0.1470 2.2415 -0.9680 3.4694 3.3069 0.3173 0.3784 0.1866 5.7648 1.5705 -#> 482: 93.4871 -5.9187 -0.1472 2.2412 -0.9681 3.4667 3.3089 0.3173 0.3784 0.1865 5.7631 1.5709 -#> 483: 93.4860 -5.9192 -0.1473 2.2410 -0.9681 3.4668 3.3107 0.3174 0.3785 0.1865 5.7624 1.5709 -#> 484: 93.4858 -5.9193 -0.1474 2.2408 -0.9681 3.4681 3.3111 0.3174 0.3786 0.1864 5.7615 1.5713 -#> 485: 93.4858 -5.9195 -0.1476 2.2406 -0.9681 3.4643 3.3110 0.3174 0.3787 0.1864 5.7612 1.5717 -#> 486: 93.4853 -5.9198 -0.1477 2.2404 -0.9682 3.4665 3.3115 0.3174 0.3788 0.1864 5.7612 1.5717 -#> 487: 93.4856 -5.9201 -0.1478 2.2402 -0.9682 3.4687 3.3143 0.3173 0.3790 0.1864 5.7612 1.5719 -#> 488: 93.4858 -5.9209 -0.1479 2.2401 -0.9683 3.4688 3.3186 0.3173 0.3792 0.1864 5.7626 1.5722 -#> 489: 93.4870 -5.9211 -0.1480 2.2399 -0.9684 3.4681 3.3198 0.3174 0.3794 0.1863 5.7640 1.5725 -#> 490: 93.4881 -5.9213 -0.1481 2.2398 -0.9684 3.4694 3.3211 0.3174 0.3797 0.1864 5.7650 1.5728 -#> 491: 93.4892 -5.9210 -0.1482 2.2395 -0.9685 3.4716 3.3193 0.3173 0.3799 0.1864 5.7650 1.5732 -#> 492: 93.4907 -5.9211 -0.1483 2.2393 -0.9686 3.4754 3.3179 0.3173 0.3801 0.1865 5.7648 1.5736 -#> 493: 93.4928 -5.9215 -0.1484 2.2390 -0.9686 3.4858 3.3185 0.3173 0.3803 0.1865 5.7640 1.5738 -#> 494: 93.4937 -5.9217 -0.1485 2.2388 -0.9687 3.4940 3.3182 0.3172 0.3805 0.1865 5.7639 1.5740 -#> 495: 93.4945 -5.9213 -0.1485 2.2386 -0.9688 3.4998 3.3151 0.3172 0.3808 0.1866 5.7638 1.5742 -#> 496: 93.4953 -5.9208 -0.1486 2.2384 -0.9688 3.5036 3.3123 0.3172 0.3810 0.1867 5.7635 1.5745 -#> 497: 93.4969 -5.9205 -0.1487 2.2382 -0.9689 3.5064 3.3109 0.3172 0.3813 0.1868 5.7637 1.5747 -#> 498: 93.4980 -5.9205 -0.1488 2.2379 -0.9690 3.5057 3.3104 0.3171 0.3815 0.1868 5.7639 1.5752 -#> 499: 93.4999 -5.9205 -0.1488 2.2377 -0.9691 3.5095 3.3102 0.3171 0.3817 0.1869 5.7639 1.5756 -#> 500: 93.5013 -5.9210 -0.1489 2.2376 -0.9691 3.5093 3.3135 0.3171 0.3818 0.1869 5.7644 1.5758
                                                                #> Calculating covariance matrix
                                                                #>
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_A1 |f_parent_qlogis | log_alpha | -#> |.....................| log_beta |sigma_parent | sigma_A1 | o1 | -#> |.....................| o2 | o3 | o4 | o5 | -#> | 1| 470.09130 | 1.000 | -1.000 | -0.9119 | -0.8960 | -#> |.....................| -0.8494 | -0.8528 | -0.8683 | -0.8768 | -#> |.....................| -0.8744 | -0.8681 | -0.8700 | -0.8694 | -#> | U| 470.0913 | 94.11 | -5.371 | -0.9909 | -0.1965 | -#> |.....................| 2.121 | 1.952 | 1.178 | 0.7545 | -#> |.....................| 0.8769 | 1.189 | 1.095 | 1.127 | -#> | X| 470.0913 | 94.11 | 0.004648 | 0.2707 | 0.8216 | -#> |.....................| 8.339 | 1.952 | 1.178 | 0.7545 | -#> |.....................| 0.8769 | 1.189 | 1.095 | 1.127 | -#> | G| Gill Diff. | 72.01 | 2.213 | -0.2476 | -0.3163 | -#> |.....................| -0.8532 | -32.82 | -13.44 | 9.552 | -#> |.....................| 11.72 | -12.16 | -9.599 | -9.049 | -#> | 2| 5180.4321 | 0.1393 | -1.026 | -0.9090 | -0.8922 | -#> |.....................| -0.8392 | -0.4605 | -0.7077 | -0.9910 | -#> |.....................| -1.014 | -0.7228 | -0.7553 | -0.7612 | -#> | U| 5180.4321 | 13.11 | -5.398 | -0.9880 | -0.1927 | -#> |.....................| 2.131 | 2.334 | 1.272 | 0.6684 | -#> |.....................| 0.7541 | 1.362 | 1.220 | 1.248 | -#> | X| 5180.4321 | 13.11 | 0.004526 | 0.2713 | 0.8247 | -#> |.....................| 8.424 | 2.334 | 1.272 | 0.6684 | -#> |.....................| 0.7541 | 1.362 | 1.220 | 1.248 | -#> | 3| 529.93288 | 0.9139 | -1.003 | -0.9116 | -0.8956 | -#> |.....................| -0.8484 | -0.8135 | -0.8523 | -0.8883 | -#> |.....................| -0.8884 | -0.8536 | -0.8585 | -0.8585 | -#> | U| 529.93288 | 86.01 | -5.374 | -0.9906 | -0.1961 | -#> |.....................| 2.122 | 1.990 | 1.187 | 0.7459 | -#> |.....................| 0.8647 | 1.206 | 1.107 | 1.139 | -#> | X| 529.93288 | 86.01 | 0.004635 | 0.2708 | 0.8219 | -#> |.....................| 8.347 | 1.990 | 1.187 | 0.7459 | -#> |.....................| 0.8647 | 1.206 | 1.107 | 1.139 | -#> | 4| 469.96296 | 0.9914 | -1.000 | -0.9119 | -0.8959 | -#> |.....................| -0.8493 | -0.8489 | -0.8667 | -0.8780 | -#> |.....................| -0.8758 | -0.8667 | -0.8689 | -0.8683 | -#> | U| 469.96296 | 93.30 | -5.372 | -0.9909 | -0.1965 | -#> |.....................| 2.121 | 1.955 | 1.179 | 0.7536 | -#> |.....................| 0.8757 | 1.191 | 1.096 | 1.128 | -#> | X| 469.96296 | 93.30 | 0.004646 | 0.2707 | 0.8216 | -#> |.....................| 8.339 | 1.955 | 1.179 | 0.7536 | -#> |.....................| 0.8757 | 1.191 | 1.096 | 1.128 | -#> | F| Forward Diff. | -91.63 | 2.121 | -0.4143 | -0.3985 | -#> |.....................| -1.124 | -34.23 | -12.87 | 9.567 | -#> |.....................| 8.592 | -11.79 | -9.469 | -8.518 | -#> | 5| 469.41305 | 0.9973 | -1.001 | -0.9118 | -0.8959 | -#> |.....................| -0.8491 | -0.8424 | -0.8642 | -0.8798 | -#> |.....................| -0.8776 | -0.8644 | -0.8670 | -0.8666 | -#> | U| 469.41305 | 93.85 | -5.372 | -0.9908 | -0.1964 | -#> |.....................| 2.121 | 1.962 | 1.180 | 0.7523 | -#> |.....................| 0.8741 | 1.193 | 1.098 | 1.130 | -#> | X| 469.41305 | 93.85 | 0.004644 | 0.2707 | 0.8217 | -#> |.....................| 8.341 | 1.962 | 1.180 | 0.7523 | -#> |.....................| 0.8741 | 1.193 | 1.098 | 1.130 | -#> | F| Forward Diff. | 19.88 | 2.163 | -0.2989 | -0.3449 | -#> |.....................| -0.9473 | -32.84 | -13.22 | 8.952 | -#> |.....................| 11.37 | -11.75 | -9.421 | -8.530 | -#> | 6| 469.13124 | 0.9930 | -1.001 | -0.9118 | -0.8958 | -#> |.....................| -0.8489 | -0.8354 | -0.8614 | -0.8817 | -#> |.....................| -0.8801 | -0.8619 | -0.8650 | -0.8648 | -#> | U| 469.13124 | 93.45 | -5.373 | -0.9908 | -0.1963 | -#> |.....................| 2.121 | 1.969 | 1.182 | 0.7508 | -#> |.....................| 0.8719 | 1.196 | 1.100 | 1.132 | -#> | X| 469.13124 | 93.45 | 0.004642 | 0.2708 | 0.8218 | -#> |.....................| 8.343 | 1.969 | 1.182 | 0.7508 | -#> |.....................| 0.8719 | 1.196 | 1.100 | 1.132 | -#> | F| Forward Diff. | -60.06 | 2.108 | -0.3845 | -0.3876 | -#> |.....................| -1.088 | -32.82 | -12.89 | 8.720 | -#> |.....................| 9.663 | -11.60 | -9.301 | -8.348 | -#> | 7| 468.71336 | 0.9979 | -1.002 | -0.9117 | -0.8957 | -#> |.....................| -0.8487 | -0.8285 | -0.8586 | -0.8835 | -#> |.....................| -0.8823 | -0.8594 | -0.8631 | -0.8630 | -#> | U| 468.71336 | 93.91 | -5.373 | -0.9907 | -0.1962 | -#> |.....................| 2.122 | 1.975 | 1.183 | 0.7495 | -#> |.....................| 0.8700 | 1.199 | 1.102 | 1.134 | -#> | X| 468.71336 | 93.91 | 0.004640 | 0.2708 | 0.8218 | -#> |.....................| 8.345 | 1.975 | 1.183 | 0.7495 | -#> |.....................| 0.8700 | 1.199 | 1.102 | 1.134 | -#> | F| Forward Diff. | 31.80 | 2.131 | -0.3007 | -0.3556 | -#> |.....................| -0.9543 | -30.66 | -12.35 | 8.979 | -#> |.....................| 9.681 | -11.54 | -9.231 | -8.330 | -#> | 8| 468.42878 | 0.9931 | -1.002 | -0.9116 | -0.8956 | -#> |.....................| -0.8484 | -0.8217 | -0.8559 | -0.8855 | -#> |.....................| -0.8845 | -0.8568 | -0.8610 | -0.8612 | -#> | U| 468.42878 | 93.46 | -5.373 | -0.9906 | -0.1962 | -#> |.....................| 2.122 | 1.982 | 1.185 | 0.7480 | -#> |.....................| 0.8681 | 1.202 | 1.105 | 1.136 | -#> | X| 468.42878 | 93.46 | 0.004638 | 0.2708 | 0.8219 | -#> |.....................| 8.346 | 1.982 | 1.185 | 0.7480 | -#> |.....................| 0.8681 | 1.202 | 1.105 | 1.136 | -#> | F| Forward Diff. | -55.97 | 2.081 | -0.3855 | -0.3928 | -#> |.....................| -1.100 | -30.89 | -12.11 | 8.596 | -#> |.....................| 9.353 | -11.36 | -9.087 | -8.137 | -#> | 9| 468.02528 | 0.9977 | -1.003 | -0.9115 | -0.8955 | -#> |.....................| -0.8482 | -0.8148 | -0.8531 | -0.8875 | -#> |.....................| -0.8866 | -0.8542 | -0.8589 | -0.8593 | -#> | U| 468.02528 | 93.90 | -5.374 | -0.9905 | -0.1961 | -#> |.....................| 2.122 | 1.989 | 1.187 | 0.7465 | -#> |.....................| 0.8662 | 1.206 | 1.107 | 1.138 | -#> | X| 468.02528 | 93.90 | 0.004636 | 0.2708 | 0.8220 | -#> |.....................| 8.348 | 1.989 | 1.187 | 0.7465 | -#> |.....................| 0.8662 | 1.206 | 1.107 | 1.138 | -#> | F| Forward Diff. | 28.40 | 2.101 | -0.3066 | -0.3612 | -#> |.....................| -0.9721 | -29.21 | -11.91 | 8.561 | -#> |.....................| 9.360 | -11.31 | -9.026 | -8.108 | -#> | 10| 467.76129 | 0.9930 | -1.003 | -0.9115 | -0.8954 | -#> |.....................| -0.8479 | -0.8081 | -0.8503 | -0.8895 | -#> |.....................| -0.8888 | -0.8515 | -0.8567 | -0.8574 | -#> | U| 467.76129 | 93.46 | -5.374 | -0.9905 | -0.1960 | -#> |.....................| 2.122 | 1.995 | 1.188 | 0.7450 | -#> |.....................| 0.8643 | 1.209 | 1.109 | 1.140 | -#> | X| 467.76129 | 93.46 | 0.004633 | 0.2708 | 0.8220 | -#> |.....................| 8.351 | 1.995 | 1.188 | 0.7450 | -#> |.....................| 0.8643 | 1.209 | 1.109 | 1.140 | -#> | F| Forward Diff. | -56.33 | 2.052 | -0.3905 | -0.3944 | -#> |.....................| -1.108 | -29.62 | -11.80 | 8.124 | -#> |.....................| 9.000 | -11.14 | -8.878 | -7.912 | -#> | 11| 467.36507 | 0.9976 | -1.004 | -0.9114 | -0.8953 | -#> |.....................| -0.8477 | -0.8013 | -0.8475 | -0.8914 | -#> |.....................| -0.8910 | -0.8487 | -0.8545 | -0.8554 | -#> | U| 467.36507 | 93.88 | -5.375 | -0.9904 | -0.1959 | -#> |.....................| 2.123 | 2.002 | 1.190 | 0.7435 | -#> |.....................| 0.8624 | 1.212 | 1.112 | 1.142 | -#> | X| 467.36507 | 93.88 | 0.004631 | 0.2708 | 0.8221 | -#> |.....................| 8.353 | 2.002 | 1.190 | 0.7435 | -#> |.....................| 0.8624 | 1.212 | 1.112 | 1.142 | -#> | F| Forward Diff. | 25.62 | 2.072 | -0.2964 | -0.3658 | -#> |.....................| -0.9890 | -26.78 | -10.91 | 8.547 | -#> |.....................| 9.002 | -11.08 | -8.799 | -7.879 | -#> | 12| 467.13453 | 0.9928 | -1.004 | -0.9113 | -0.8952 | -#> |.....................| -0.8474 | -0.7947 | -0.8448 | -0.8935 | -#> |.....................| -0.8932 | -0.8459 | -0.8523 | -0.8534 | -#> | U| 467.13453 | 93.43 | -5.376 | -0.9903 | -0.1958 | -#> |.....................| 2.123 | 2.008 | 1.191 | 0.7419 | -#> |.....................| 0.8604 | 1.215 | 1.114 | 1.145 | -#> | X| 467.13453 | 93.43 | 0.004628 | 0.2709 | 0.8222 | -#> |.....................| 8.355 | 2.008 | 1.191 | 0.7419 | -#> |.....................| 0.8604 | 1.215 | 1.114 | 1.145 | -#> | F| Forward Diff. | -59.86 | 2.021 | -0.3893 | -0.4093 | -#> |.....................| -1.140 | -28.00 | -11.13 | 7.926 | -#> |.....................| 9.918 | -10.90 | -8.684 | -7.680 | -#> | 13| 466.72836 | 0.9971 | -1.005 | -0.9112 | -0.8951 | -#> |.....................| -0.8471 | -0.7882 | -0.8421 | -0.8957 | -#> |.....................| -0.8959 | -0.8428 | -0.8499 | -0.8513 | -#> | U| 466.72836 | 93.84 | -5.376 | -0.9902 | -0.1956 | -#> |.....................| 2.123 | 2.015 | 1.193 | 0.7403 | -#> |.....................| 0.8581 | 1.219 | 1.117 | 1.147 | -#> | X| 466.72836 | 93.84 | 0.004626 | 0.2709 | 0.8223 | -#> |.....................| 8.358 | 2.015 | 1.193 | 0.7403 | -#> |.....................| 0.8581 | 1.219 | 1.117 | 1.147 | -#> | F| Forward Diff. | 18.13 | 2.039 | -0.3145 | -0.3694 | -#> |.....................| -1.015 | -26.10 | -10.63 | 8.044 | -#> |.....................| 8.616 | -10.80 | -8.580 | -7.637 | -#> | 14| 466.53378 | 0.9925 | -1.005 | -0.9111 | -0.8950 | -#> |.....................| -0.8468 | -0.7815 | -0.8394 | -0.8978 | -#> |.....................| -0.8981 | -0.8400 | -0.8477 | -0.8494 | -#> | U| 466.53378 | 93.40 | -5.377 | -0.9901 | -0.1956 | -#> |.....................| 2.123 | 2.021 | 1.195 | 0.7387 | -#> |.....................| 0.8562 | 1.222 | 1.119 | 1.149 | -#> | X| 466.53378 | 93.40 | 0.004623 | 0.2709 | 0.8224 | -#> |.....................| 8.360 | 2.021 | 1.195 | 0.7387 | -#> |.....................| 0.8562 | 1.222 | 1.119 | 1.149 | -#> | F| Forward Diff. | -63.81 | 1.989 | -0.4067 | -0.4178 | -#> |.....................| -1.167 | -26.39 | -10.45 | 7.924 | -#> |.....................| 8.221 | -10.62 | -8.445 | -7.432 | -#> | 15| 466.13347 | 0.9972 | -1.006 | -0.9110 | -0.8949 | -#> |.....................| -0.8464 | -0.7752 | -0.8368 | -0.9000 | -#> |.....................| -0.9002 | -0.8369 | -0.8452 | -0.8472 | -#> | U| 466.13347 | 93.85 | -5.377 | -0.9900 | -0.1954 | -#> |.....................| 2.124 | 2.027 | 1.196 | 0.7370 | -#> |.....................| 0.8543 | 1.226 | 1.122 | 1.152 | -#> | X| 466.13347 | 93.85 | 0.004620 | 0.2709 | 0.8225 | -#> |.....................| 8.363 | 2.027 | 1.196 | 0.7370 | -#> |.....................| 0.8543 | 1.226 | 1.122 | 1.152 | -#> | F| Forward Diff. | 18.92 | 2.012 | -0.3108 | -0.3757 | -#> |.....................| -1.021 | -25.52 | -10.81 | 7.279 | -#> |.....................| 9.661 | -10.54 | -8.331 | -7.395 | -#> | 16| 465.94504 | 0.9925 | -1.006 | -0.9109 | -0.8948 | -#> |.....................| -0.8461 | -0.7686 | -0.8339 | -0.9019 | -#> |.....................| -0.9028 | -0.8341 | -0.8430 | -0.8453 | -#> | U| 465.94504 | 93.41 | -5.378 | -0.9899 | -0.1953 | -#> |.....................| 2.124 | 2.034 | 1.198 | 0.7356 | -#> |.....................| 0.8521 | 1.229 | 1.124 | 1.154 | -#> | X| 465.94504 | 93.41 | 0.004618 | 0.2709 | 0.8226 | -#> |.....................| 8.366 | 2.034 | 1.198 | 0.7356 | -#> |.....................| 0.8521 | 1.229 | 1.124 | 1.154 | -#> | F| Forward Diff. | -61.65 | 1.961 | -0.4097 | -0.4254 | -#> |.....................| -1.181 | -25.22 | -10.13 | 7.338 | -#> |.....................| 9.206 | -10.38 | -8.223 | -7.205 | -#> | 17| 465.56754 | 0.9973 | -1.007 | -0.9108 | -0.8946 | -#> |.....................| -0.8457 | -0.7626 | -0.8312 | -0.9037 | -#> |.....................| -0.9058 | -0.8309 | -0.8405 | -0.8432 | -#> | U| 465.56754 | 93.86 | -5.378 | -0.9898 | -0.1952 | -#> |.....................| 2.125 | 2.040 | 1.199 | 0.7342 | -#> |.....................| 0.8494 | 1.233 | 1.127 | 1.156 | -#> | X| 465.56754 | 93.86 | 0.004615 | 0.2710 | 0.8227 | -#> |.....................| 8.369 | 2.040 | 1.199 | 0.7342 | -#> |.....................| 0.8494 | 1.233 | 1.127 | 1.156 | -#> | F| Forward Diff. | 20.78 | 1.982 | -0.3060 | -0.3796 | -#> |.....................| -1.026 | -23.61 | -9.859 | 7.282 | -#> |.....................| 6.603 | -10.29 | -8.096 | -7.167 | -#> | 18| 465.36858 | 0.9928 | -1.008 | -0.9107 | -0.8945 | -#> |.....................| -0.8454 | -0.7560 | -0.8284 | -0.9059 | -#> |.....................| -0.9077 | -0.8278 | -0.8381 | -0.8410 | -#> | U| 465.36858 | 93.44 | -5.379 | -0.9897 | -0.1950 | -#> |.....................| 2.125 | 2.046 | 1.201 | 0.7326 | -#> |.....................| 0.8477 | 1.237 | 1.130 | 1.159 | -#> | X| 465.36858 | 93.44 | 0.004612 | 0.2710 | 0.8228 | -#> |.....................| 8.372 | 2.046 | 1.201 | 0.7326 | -#> |.....................| 0.8477 | 1.237 | 1.130 | 1.159 | -#> | F| Forward Diff. | -55.43 | 1.935 | -0.4028 | -0.4254 | -#> |.....................| -1.182 | -23.34 | -9.189 | 7.305 | -#> |.....................| 7.555 | -10.07 | -7.946 | -6.960 | -#> | 19| 465.01863 | 0.9972 | -1.008 | -0.9105 | -0.8943 | -#> |.....................| -0.8449 | -0.7499 | -0.8257 | -0.9082 | -#> |.....................| -0.9092 | -0.8240 | -0.8352 | -0.8386 | -#> | U| 465.01863 | 93.84 | -5.380 | -0.9895 | -0.1948 | -#> |.....................| 2.125 | 2.052 | 1.203 | 0.7308 | -#> |.....................| 0.8464 | 1.241 | 1.133 | 1.161 | -#> | X| 465.01863 | 93.84 | 0.004609 | 0.2710 | 0.8230 | -#> |.....................| 8.376 | 2.052 | 1.203 | 0.7308 | -#> |.....................| 0.8464 | 1.241 | 1.133 | 1.161 | -#> | F| Forward Diff. | 18.74 | 1.956 | -0.3105 | -0.3857 | -#> |.....................| -1.041 | -22.36 | -9.386 | 7.151 | -#> |.....................| 7.639 | -9.969 | -7.832 | -6.900 | -#> | 20| 464.81883 | 0.9930 | -1.009 | -0.9104 | -0.8942 | -#> |.....................| -0.8445 | -0.7435 | -0.8230 | -0.9105 | -#> |.....................| -0.9115 | -0.8207 | -0.8326 | -0.8363 | -#> | U| 464.81883 | 93.45 | -5.381 | -0.9894 | -0.1947 | -#> |.....................| 2.126 | 2.058 | 1.204 | 0.7291 | -#> |.....................| 0.8444 | 1.245 | 1.136 | 1.164 | -#> | X| 464.81883 | 93.45 | 0.004605 | 0.2710 | 0.8231 | -#> |.....................| 8.380 | 2.058 | 1.204 | 0.7291 | -#> |.....................| 0.8444 | 1.245 | 1.136 | 1.164 | -#> | F| Forward Diff. | -51.40 | 1.910 | -0.3971 | -0.4173 | -#> |.....................| -1.192 | -21.85 | -8.569 | 7.088 | -#> |.....................| 7.257 | -9.784 | -7.694 | -6.698 | -#> | 21| 464.49434 | 0.9973 | -1.010 | -0.9102 | -0.8940 | -#> |.....................| -0.8439 | -0.7380 | -0.8206 | -0.9131 | -#> |.....................| -0.9139 | -0.8168 | -0.8296 | -0.8338 | -#> | U| 464.49434 | 93.85 | -5.381 | -0.9892 | -0.1945 | -#> |.....................| 2.126 | 2.064 | 1.206 | 0.7271 | -#> |.....................| 0.8423 | 1.250 | 1.139 | 1.167 | -#> | X| 464.49434 | 93.85 | 0.004602 | 0.2711 | 0.8233 | -#> |.....................| 8.385 | 2.064 | 1.206 | 0.7271 | -#> |.....................| 0.8423 | 1.250 | 1.139 | 1.167 | -#> | F| Forward Diff. | 20.43 | 1.927 | -0.3065 | -0.3887 | -#> |.....................| -1.043 | -20.85 | -8.676 | 6.819 | -#> |.....................| 7.291 | -9.652 | -7.555 | -6.636 | -#> | 22| 464.27900 | 0.9935 | -1.011 | -0.9101 | -0.8938 | -#> |.....................| -0.8433 | -0.7319 | -0.8180 | -0.9156 | -#> |.....................| -0.9164 | -0.8129 | -0.8266 | -0.8314 | -#> | U| 464.279 | 93.50 | -5.382 | -0.9891 | -0.1943 | -#> |.....................| 2.127 | 2.070 | 1.207 | 0.7252 | -#> |.....................| 0.8401 | 1.255 | 1.142 | 1.169 | -#> | X| 464.279 | 93.50 | 0.004598 | 0.2711 | 0.8234 | -#> |.....................| 8.389 | 2.070 | 1.207 | 0.7252 | -#> |.....................| 0.8401 | 1.255 | 1.142 | 1.169 | -#> | F| Forward Diff. | -42.65 | 1.884 | -0.3905 | -0.4168 | -#> |.....................| -1.174 | -21.12 | -8.566 | 6.431 | -#> |.....................| 8.301 | -9.439 | -7.399 | -6.436 | -#> | 23| 463.98221 | 0.9971 | -1.012 | -0.9099 | -0.8935 | -#> |.....................| -0.8426 | -0.7266 | -0.8156 | -0.9179 | -#> |.....................| -0.9200 | -0.8088 | -0.8235 | -0.8288 | -#> | U| 463.98221 | 93.84 | -5.383 | -0.9889 | -0.1940 | -#> |.....................| 2.128 | 2.075 | 1.209 | 0.7235 | -#> |.....................| 0.8370 | 1.260 | 1.146 | 1.172 | -#> | X| 463.98221 | 93.84 | 0.004593 | 0.2711 | 0.8236 | -#> |.....................| 8.395 | 2.075 | 1.209 | 0.7235 | -#> |.....................| 0.8370 | 1.260 | 1.146 | 1.172 | -#> | F| Forward Diff. | 17.69 | 1.891 | -0.3039 | -0.3774 | -#> |.....................| -1.038 | -20.36 | -8.704 | 6.334 | -#> |.....................| 6.886 | -9.291 | -7.246 | -6.355 | -#> | 24| 463.80345 | 0.9930 | -1.013 | -0.9097 | -0.8933 | -#> |.....................| -0.8421 | -0.7205 | -0.8127 | -0.9199 | -#> |.....................| -0.9227 | -0.8053 | -0.8209 | -0.8265 | -#> | U| 463.80345 | 93.45 | -5.384 | -0.9887 | -0.1939 | -#> |.....................| 2.128 | 2.081 | 1.210 | 0.7220 | -#> |.....................| 0.8346 | 1.264 | 1.148 | 1.175 | -#> | X| 463.80345 | 93.45 | 0.004590 | 0.2712 | 0.8238 | -#> |.....................| 8.399 | 2.081 | 1.210 | 0.7220 | -#> |.....................| 0.8346 | 1.264 | 1.148 | 1.175 | -#> | F| Forward Diff. | -49.16 | 1.846 | -0.3979 | -0.4233 | -#> |.....................| -1.191 | -20.11 | -8.128 | 6.150 | -#> |.....................| 7.842 | -9.114 | -7.113 | -6.163 | -#> | 25| 463.50095 | 0.9970 | -1.014 | -0.9095 | -0.8930 | -#> |.....................| -0.8413 | -0.7152 | -0.8100 | -0.9219 | -#> |.....................| -0.9258 | -0.8011 | -0.8178 | -0.8240 | -#> | U| 463.50095 | 93.83 | -5.385 | -0.9885 | -0.1936 | -#> |.....................| 2.129 | 2.086 | 1.212 | 0.7205 | -#> |.....................| 0.8318 | 1.269 | 1.152 | 1.178 | -#> | X| 463.50095 | 93.83 | 0.004585 | 0.2712 | 0.8240 | -#> |.....................| 8.406 | 2.086 | 1.212 | 0.7205 | -#> |.....................| 0.8318 | 1.269 | 1.152 | 1.178 | -#> | F| Forward Diff. | 15.76 | 1.857 | -0.2989 | -0.3817 | -#> |.....................| -1.050 | -19.47 | -8.354 | 5.597 | -#> |.....................| 5.177 | -8.956 | -6.950 | -6.091 | -#> | 26| 463.33971 | 0.9930 | -1.014 | -0.9093 | -0.8928 | -#> |.....................| -0.8408 | -0.7088 | -0.8070 | -0.9237 | -#> |.....................| -0.9274 | -0.7974 | -0.8150 | -0.8217 | -#> | U| 463.33971 | 93.45 | -5.386 | -0.9883 | -0.1934 | -#> |.....................| 2.129 | 2.092 | 1.214 | 0.7192 | -#> |.....................| 0.8304 | 1.273 | 1.155 | 1.180 | -#> | X| 463.33971 | 93.45 | 0.004581 | 0.2712 | 0.8242 | -#> |.....................| 8.411 | 2.092 | 1.214 | 0.7192 | -#> |.....................| 0.8304 | 1.273 | 1.155 | 1.180 | -#> | F| Forward Diff. | -49.38 | 1.817 | -0.3945 | -0.4254 | -#> |.....................| -1.192 | -18.49 | -7.219 | 6.140 | -#> |.....................| 6.147 | -8.752 | -6.775 | -5.892 | -#> | 27| 463.06378 | 0.9971 | -1.016 | -0.9091 | -0.8925 | -#> |.....................| -0.8398 | -0.7035 | -0.8044 | -0.9255 | -#> |.....................| -0.9274 | -0.7927 | -0.8116 | -0.8189 | -#> | U| 463.06378 | 93.84 | -5.387 | -0.9881 | -0.1930 | -#> |.....................| 2.130 | 2.097 | 1.215 | 0.7178 | -#> |.....................| 0.8305 | 1.279 | 1.159 | 1.184 | -#> | X| 463.06378 | 93.84 | 0.004575 | 0.2713 | 0.8245 | -#> |.....................| 8.419 | 2.097 | 1.215 | 0.7178 | -#> |.....................| 0.8305 | 1.279 | 1.159 | 1.184 | -#> | F| Forward Diff. | 17.15 | 1.839 | -0.2941 | -0.3829 | -#> |.....................| -1.046 | -18.21 | -7.786 | 5.595 | -#> |.....................| 7.714 | -8.592 | -6.652 | -5.814 | -#> | 28| 462.87224 | 0.9938 | -1.017 | -0.9088 | -0.8922 | -#> |.....................| -0.8390 | -0.6982 | -0.8019 | -0.9277 | -#> |.....................| -0.9311 | -0.7885 | -0.8085 | -0.8163 | -#> | U| 462.87224 | 93.52 | -5.388 | -0.9879 | -0.1927 | -#> |.....................| 2.131 | 2.102 | 1.217 | 0.7161 | -#> |.....................| 0.8272 | 1.284 | 1.162 | 1.186 | -#> | X| 462.87224 | 93.52 | 0.004570 | 0.2713 | 0.8247 | -#> |.....................| 8.425 | 2.102 | 1.217 | 0.7161 | -#> |.....................| 0.8272 | 1.284 | 1.162 | 1.186 | -#> | F| Forward Diff. | -35.81 | 1.797 | -0.3699 | -0.4180 | -#> |.....................| -1.164 | -17.54 | -6.949 | 5.683 | -#> |.....................| 5.938 | -8.368 | -6.484 | -5.617 | -#> | 29| 462.64279 | 0.9976 | -1.018 | -0.9085 | -0.8918 | -#> |.....................| -0.8379 | -0.6938 | -0.7998 | -0.9297 | -#> |.....................| -0.9347 | -0.7837 | -0.8051 | -0.8136 | -#> | U| 462.64279 | 93.88 | -5.390 | -0.9876 | -0.1923 | -#> |.....................| 2.132 | 2.107 | 1.218 | 0.7146 | -#> |.....................| 0.8240 | 1.289 | 1.166 | 1.189 | -#> | X| 462.64279 | 93.88 | 0.004563 | 0.2714 | 0.8250 | -#> |.....................| 8.435 | 2.107 | 1.218 | 0.7146 | -#> |.....................| 0.8240 | 1.289 | 1.166 | 1.189 | -#> | F| Forward Diff. | 23.89 | 1.802 | -0.2695 | -0.3764 | -#> |.....................| -1.014 | -17.48 | -7.590 | 5.234 | -#> |.....................| 7.275 | -8.199 | -6.306 | -5.540 | -#> | 30| 462.43086 | 0.9946 | -1.020 | -0.9083 | -0.8914 | -#> |.....................| -0.8367 | -0.6890 | -0.7974 | -0.9317 | -#> |.....................| -0.9381 | -0.7789 | -0.8017 | -0.8108 | -#> | U| 462.43086 | 93.61 | -5.391 | -0.9873 | -0.1919 | -#> |.....................| 2.134 | 2.111 | 1.219 | 0.7131 | -#> |.....................| 0.8211 | 1.295 | 1.169 | 1.193 | -#> | X| 462.43086 | 93.61 | 0.004556 | 0.2715 | 0.8254 | -#> |.....................| 8.445 | 2.111 | 1.219 | 0.7131 | -#> |.....................| 0.8211 | 1.295 | 1.169 | 1.193 | -#> | F| Forward Diff. | -22.12 | 1.763 | -0.3409 | -0.4033 | -#> |.....................| -1.105 | -16.76 | -6.743 | 5.132 | -#> |.....................| 5.573 | -7.935 | -6.123 | -5.337 | -#> | 31| 462.24769 | 0.9981 | -1.021 | -0.9079 | -0.8909 | -#> |.....................| -0.8355 | -0.6838 | -0.7950 | -0.9332 | -#> |.....................| -0.9404 | -0.7741 | -0.7984 | -0.8080 | -#> | U| 462.24769 | 93.94 | -5.393 | -0.9870 | -0.1915 | -#> |.....................| 2.135 | 2.117 | 1.221 | 0.7120 | -#> |.....................| 0.8190 | 1.301 | 1.173 | 1.196 | -#> | X| 462.24769 | 93.94 | 0.004549 | 0.2715 | 0.8258 | -#> |.....................| 8.455 | 2.117 | 1.221 | 0.7120 | -#> |.....................| 0.8190 | 1.301 | 1.173 | 1.196 | -#> | F| Forward Diff. | 32.76 | 1.771 | -0.2440 | -0.3645 | -#> |.....................| -0.9678 | -16.08 | -6.874 | 5.077 | -#> |.....................| 5.606 | -7.758 | -5.959 | -5.256 | -#> | 32| 462.04894 | 0.9949 | -1.023 | -0.9076 | -0.8904 | -#> |.....................| -0.8341 | -0.6790 | -0.7932 | -0.9353 | -#> |.....................| -0.9395 | -0.7687 | -0.7947 | -0.8049 | -#> | U| 462.04894 | 93.63 | -5.395 | -0.9866 | -0.1909 | -#> |.....................| 2.136 | 2.121 | 1.222 | 0.7104 | -#> |.....................| 0.8198 | 1.307 | 1.177 | 1.199 | -#> | X| 462.04894 | 93.63 | 0.004540 | 0.2716 | 0.8262 | -#> |.....................| 8.467 | 2.121 | 1.222 | 0.7104 | -#> |.....................| 0.8198 | 1.307 | 1.177 | 1.199 | -#> | F| Forward Diff. | -16.92 | 1.743 | -0.3189 | -0.3951 | -#> |.....................| -1.072 | -15.84 | -6.430 | 4.847 | -#> |.....................| 5.467 | -7.483 | -5.756 | -5.023 | -#> | 33| 461.88553 | 0.9980 | -1.025 | -0.9073 | -0.8898 | -#> |.....................| -0.8327 | -0.6736 | -0.7912 | -0.9375 | -#> |.....................| -0.9397 | -0.7637 | -0.7912 | -0.8019 | -#> | U| 461.88553 | 93.92 | -5.397 | -0.9863 | -0.1904 | -#> |.....................| 2.138 | 2.126 | 1.223 | 0.7088 | -#> |.....................| 0.8197 | 1.313 | 1.181 | 1.203 | -#> | X| 461.88553 | 93.92 | 0.004531 | 0.2716 | 0.8266 | -#> |.....................| 8.479 | 2.126 | 1.223 | 0.7088 | -#> |.....................| 0.8197 | 1.313 | 1.181 | 1.203 | -#> | F| Forward Diff. | 30.55 | 1.755 | -0.2327 | -0.3563 | -#> |.....................| -0.9551 | -15.13 | -6.434 | 4.973 | -#> |.....................| 5.515 | -7.304 | -5.584 | -4.904 | -#> | 34| 461.69674 | 0.9949 | -1.028 | -0.9069 | -0.8892 | -#> |.....................| -0.8309 | -0.6692 | -0.7896 | -0.9402 | -#> |.....................| -0.9399 | -0.7583 | -0.7876 | -0.7990 | -#> | U| 461.69674 | 93.63 | -5.400 | -0.9859 | -0.1897 | -#> |.....................| 2.139 | 2.131 | 1.224 | 0.7067 | -#> |.....................| 0.8195 | 1.320 | 1.185 | 1.206 | -#> | X| 461.69674 | 93.63 | 0.004519 | 0.2717 | 0.8272 | -#> |.....................| 8.494 | 2.131 | 1.224 | 0.7067 | -#> |.....................| 0.8195 | 1.320 | 1.185 | 1.206 | -#> | F| Forward Diff. | -16.57 | 1.720 | -0.3086 | -0.3856 | -#> |.....................| -1.039 | -14.73 | -5.908 | 4.823 | -#> |.....................| 5.359 | -7.008 | -5.393 | -4.695 | -#> | 35| 461.54208 | 0.9978 | -1.031 | -0.9065 | -0.8885 | -#> |.....................| -0.8293 | -0.6648 | -0.7883 | -0.9440 | -#> |.....................| -0.9414 | -0.7533 | -0.7842 | -0.7963 | -#> | U| 461.54208 | 93.91 | -5.402 | -0.9855 | -0.1891 | -#> |.....................| 2.141 | 2.135 | 1.225 | 0.7038 | -#> |.....................| 0.8182 | 1.325 | 1.189 | 1.209 | -#> | X| 461.54208 | 93.91 | 0.004507 | 0.2718 | 0.8277 | -#> |.....................| 8.508 | 2.135 | 1.225 | 0.7038 | -#> |.....................| 0.8182 | 1.325 | 1.189 | 1.209 | -#> | F| Forward Diff. | 27.49 | 1.722 | -0.2172 | -0.3438 | -#> |.....................| -0.9069 | -13.76 | -5.979 | 4.702 | -#> |.....................| 5.353 | -6.828 | -5.231 | -4.587 | -#> | 36| 461.38014 | 0.9949 | -1.034 | -0.9061 | -0.8878 | -#> |.....................| -0.8274 | -0.6624 | -0.7872 | -0.9482 | -#> |.....................| -0.9437 | -0.7482 | -0.7807 | -0.7935 | -#> | U| 461.38014 | 93.63 | -5.405 | -0.9851 | -0.1883 | -#> |.....................| 2.143 | 2.137 | 1.225 | 0.7007 | -#> |.....................| 0.8162 | 1.332 | 1.192 | 1.212 | -#> | X| 461.38014 | 93.63 | 0.004492 | 0.2719 | 0.8283 | -#> |.....................| 8.524 | 2.137 | 1.225 | 0.7007 | -#> |.....................| 0.8162 | 1.332 | 1.192 | 1.212 | -#> | F| Forward Diff. | -16.54 | 1.681 | -0.2967 | -0.3702 | -#> |.....................| -1.003 | -14.15 | -5.693 | 4.358 | -#> |.....................| 5.078 | -6.560 | -5.051 | -4.397 | -#> | 37| 461.22820 | 0.9976 | -1.038 | -0.9057 | -0.8870 | -#> |.....................| -0.8255 | -0.6585 | -0.7854 | -0.9513 | -#> |.....................| -0.9460 | -0.7433 | -0.7774 | -0.7908 | -#> | U| 461.2282 | 93.88 | -5.409 | -0.9847 | -0.1876 | -#> |.....................| 2.145 | 2.141 | 1.226 | 0.6983 | -#> |.....................| 0.8141 | 1.337 | 1.196 | 1.215 | -#> | X| 461.2282 | 93.88 | 0.004476 | 0.2720 | 0.8290 | -#> |.....................| 8.540 | 2.141 | 1.226 | 0.6983 | -#> |.....................| 0.8141 | 1.337 | 1.196 | 1.215 | -#> | F| Forward Diff. | 22.68 | 1.675 | -0.2117 | -0.3293 | -#> |.....................| -0.8651 | -13.27 | -5.458 | 4.237 | -#> |.....................| 3.708 | -6.326 | -4.874 | -4.289 | -#> | 38| 461.10880 | 0.9948 | -1.041 | -0.9053 | -0.8864 | -#> |.....................| -0.8238 | -0.6532 | -0.7845 | -0.9533 | -#> |.....................| -0.9419 | -0.7394 | -0.7747 | -0.7885 | -#> | U| 461.1088 | 93.62 | -5.412 | -0.9844 | -0.1869 | -#> |.....................| 2.146 | 2.146 | 1.227 | 0.6968 | -#> |.....................| 0.8177 | 1.342 | 1.199 | 1.218 | -#> | X| 461.1088 | 93.62 | 0.004461 | 0.2720 | 0.8295 | -#> |.....................| 8.555 | 2.146 | 1.227 | 0.6968 | -#> |.....................| 0.8177 | 1.342 | 1.199 | 1.218 | -#> | F| Forward Diff. | -17.23 | 1.655 | -0.2888 | -0.3567 | -#> |.....................| -0.9524 | -13.71 | -5.652 | 3.877 | -#> |.....................| 5.125 | -6.149 | -4.743 | -4.110 | -#> | 39| 460.99174 | 0.9974 | -1.045 | -0.9049 | -0.8856 | -#> |.....................| -0.8221 | -0.6468 | -0.7824 | -0.9536 | -#> |.....................| -0.9388 | -0.7360 | -0.7723 | -0.7867 | -#> | U| 460.99174 | 93.87 | -5.416 | -0.9840 | -0.1862 | -#> |.....................| 2.148 | 2.153 | 1.228 | 0.6966 | -#> |.....................| 0.8204 | 1.346 | 1.202 | 1.220 | -#> | X| 460.99174 | 93.87 | 0.004444 | 0.2721 | 0.8301 | -#> |.....................| 8.569 | 2.153 | 1.228 | 0.6966 | -#> |.....................| 0.8204 | 1.346 | 1.202 | 1.220 | -#> | F| Forward Diff. | 21.44 | 1.663 | -0.2166 | -0.3206 | -#> |.....................| -0.8444 | -13.00 | -5.647 | 3.881 | -#> |.....................| 5.370 | -6.036 | -4.631 | -4.039 | -#> | 40| 460.85317 | 0.9948 | -1.049 | -0.9044 | -0.8849 | -#> |.....................| -0.8203 | -0.6417 | -0.7791 | -0.9516 | -#> |.....................| -0.9438 | -0.7341 | -0.7712 | -0.7862 | -#> | U| 460.85317 | 93.62 | -5.420 | -0.9835 | -0.1854 | -#> |.....................| 2.150 | 2.158 | 1.230 | 0.6981 | -#> |.....................| 0.8161 | 1.348 | 1.203 | 1.220 | -#> | X| 460.85317 | 93.62 | 0.004425 | 0.2722 | 0.8308 | -#> |.....................| 8.585 | 2.158 | 1.230 | 0.6981 | -#> |.....................| 0.8161 | 1.348 | 1.203 | 1.220 | -#> | F| Forward Diff. | -17.08 | 1.613 | -0.2650 | -0.3380 | -#> |.....................| -0.8994 | -12.83 | -5.261 | 3.879 | -#> |.....................| 3.650 | -5.911 | -4.518 | -3.985 | -#> | 41| 460.73362 | 0.9974 | -1.054 | -0.9040 | -0.8841 | -#> |.....................| -0.8184 | -0.6359 | -0.7754 | -0.9517 | -#> |.....................| -0.9423 | -0.7308 | -0.7693 | -0.7845 | -#> | U| 460.73362 | 93.86 | -5.425 | -0.9831 | -0.1846 | -#> |.....................| 2.152 | 2.163 | 1.232 | 0.6980 | -#> |.....................| 0.8173 | 1.352 | 1.205 | 1.222 | -#> | X| 460.73362 | 93.86 | 0.004404 | 0.2723 | 0.8314 | -#> |.....................| 8.601 | 2.163 | 1.232 | 0.6980 | -#> |.....................| 0.8173 | 1.352 | 1.205 | 1.222 | -#> | F| Forward Diff. | 20.68 | 1.612 | -0.1811 | -0.2966 | -#> |.....................| -0.7710 | -11.91 | -4.976 | 4.011 | -#> |.....................| 3.788 | -5.788 | -4.468 | -3.936 | -#> | 42| 460.64877 | 0.9948 | -1.058 | -0.9038 | -0.8835 | -#> |.....................| -0.8171 | -0.6318 | -0.7737 | -0.9543 | -#> |.....................| -0.9372 | -0.7272 | -0.7669 | -0.7822 | -#> | U| 460.64877 | 93.62 | -5.429 | -0.9829 | -0.1841 | -#> |.....................| 2.153 | 2.167 | 1.233 | 0.6961 | -#> |.....................| 0.8219 | 1.357 | 1.208 | 1.225 | -#> | X| 460.64877 | 93.62 | 0.004387 | 0.2723 | 0.8319 | -#> |.....................| 8.612 | 2.167 | 1.233 | 0.6961 | -#> |.....................| 0.8219 | 1.357 | 1.208 | 1.225 | -#> | F| Forward Diff. | -16.17 | 1.594 | -0.2646 | -0.3254 | -#> |.....................| -0.8335 | -11.77 | -4.666 | 3.810 | -#> |.....................| 5.289 | -5.625 | -4.348 | -3.754 | -#> | 43| 460.54180 | 0.9972 | -1.063 | -0.9035 | -0.8829 | -#> |.....................| -0.8158 | -0.6297 | -0.7745 | -0.9584 | -#> |.....................| -0.9393 | -0.7227 | -0.7634 | -0.7794 | -#> | U| 460.5418 | 93.85 | -5.434 | -0.9826 | -0.1834 | -#> |.....................| 2.154 | 2.169 | 1.233 | 0.6929 | -#> |.....................| 0.8200 | 1.362 | 1.211 | 1.228 | -#> | X| 460.5418 | 93.85 | 0.004366 | 0.2724 | 0.8324 | -#> |.....................| 8.623 | 2.169 | 1.233 | 0.6929 | -#> |.....................| 0.8200 | 1.362 | 1.211 | 1.228 | -#> | F| Forward Diff. | 18.48 | 1.582 | -0.1851 | -0.2851 | -#> |.....................| -0.7462 | -11.38 | -4.808 | 3.651 | -#> |.....................| 5.261 | -5.402 | -4.159 | -3.623 | -#> | 44| 460.43711 | 0.9948 | -1.067 | -0.9032 | -0.8823 | -#> |.....................| -0.8147 | -0.6284 | -0.7753 | -0.9609 | -#> |.....................| -0.9464 | -0.7199 | -0.7613 | -0.7778 | -#> | U| 460.43711 | 93.63 | -5.438 | -0.9823 | -0.1829 | -#> |.....................| 2.156 | 2.171 | 1.232 | 0.6911 | -#> |.....................| 0.8138 | 1.365 | 1.214 | 1.230 | -#> | X| 460.43711 | 93.63 | 0.004347 | 0.2724 | 0.8329 | -#> |.....................| 8.632 | 2.171 | 1.232 | 0.6911 | -#> |.....................| 0.8138 | 1.365 | 1.214 | 1.230 | -#> | 45| 460.35910 | 0.9948 | -1.072 | -0.9029 | -0.8817 | -#> |.....................| -0.8135 | -0.6285 | -0.7770 | -0.9633 | -#> |.....................| -0.9542 | -0.7172 | -0.7594 | -0.7765 | -#> | U| 460.3591 | 93.63 | -5.443 | -0.9820 | -0.1822 | -#> |.....................| 2.157 | 2.170 | 1.231 | 0.6893 | -#> |.....................| 0.8069 | 1.368 | 1.216 | 1.231 | -#> | X| 460.3591 | 93.63 | 0.004325 | 0.2725 | 0.8334 | -#> |.....................| 8.643 | 2.170 | 1.231 | 0.6893 | -#> |.....................| 0.8069 | 1.368 | 1.216 | 1.231 | -#> | 46| 460.06586 | 0.9948 | -1.095 | -0.9016 | -0.8789 | -#> |.....................| -0.8080 | -0.6294 | -0.7850 | -0.9744 | -#> |.....................| -0.9902 | -0.7052 | -0.7507 | -0.7704 | -#> | U| 460.06586 | 93.63 | -5.466 | -0.9807 | -0.1794 | -#> |.....................| 2.162 | 2.170 | 1.227 | 0.6809 | -#> |.....................| 0.7753 | 1.383 | 1.225 | 1.238 | -#> | X| 460.06586 | 93.63 | 0.004227 | 0.2728 | 0.8358 | -#> |.....................| 8.691 | 2.170 | 1.227 | 0.6809 | -#> |.....................| 0.7753 | 1.383 | 1.225 | 1.238 | -#> | 47| 459.86897 | 0.9949 | -1.169 | -0.8972 | -0.8697 | -#> |.....................| -0.7899 | -0.6321 | -0.8109 | -1.010 | -#> |.....................| -1.107 | -0.6662 | -0.7224 | -0.7508 | -#> | U| 459.86897 | 93.63 | -5.541 | -0.9763 | -0.1702 | -#> |.....................| 2.180 | 2.167 | 1.211 | 0.6537 | -#> |.....................| 0.6731 | 1.429 | 1.256 | 1.260 | -#> | X| 459.86897 | 93.63 | 0.003924 | 0.2736 | 0.8435 | -#> |.....................| 8.849 | 2.167 | 1.211 | 0.6537 | -#> |.....................| 0.6731 | 1.429 | 1.256 | 1.260 | -#> | F| Forward Diff. | -18.09 | 0.8663 | 0.2544 | 0.003114 | -#> |.....................| -0.1212 | -11.64 | -7.047 | 0.1395 | -#> |.....................| -6.727 | -2.881 | -1.866 | -2.263 | -#> | 48| 458.58262 | 0.9946 | -1.323 | -0.9067 | -0.8597 | -#> |.....................| -0.7710 | -0.5295 | -0.7001 | -0.9650 | -#> |.....................| -1.113 | -0.6398 | -0.7228 | -0.7390 | -#> | U| 458.58262 | 93.60 | -5.695 | -0.9858 | -0.1602 | -#> |.....................| 2.199 | 2.267 | 1.277 | 0.6880 | -#> |.....................| 0.6674 | 1.460 | 1.256 | 1.273 | -#> | X| 458.58262 | 93.60 | 0.003363 | 0.2717 | 0.8520 | -#> |.....................| 9.019 | 2.267 | 1.277 | 0.6880 | -#> |.....................| 0.6674 | 1.460 | 1.256 | 1.273 | -#> | F| Forward Diff. | -24.91 | 0.5848 | -0.03458 | 0.2475 | -#> |.....................| 0.3762 | -4.573 | -0.04388 | 1.648 | -#> |.....................| -5.878 | -2.073 | -1.935 | -2.146 | -#> | 49| 460.44377 | 0.9922 | -1.558 | -0.9059 | -0.8818 | -#> |.....................| -0.8081 | -0.3861 | -0.8607 | -1.070 | -#> |.....................| -0.9432 | -0.5131 | -0.5915 | -0.5851 | -#> | U| 460.44377 | 93.38 | -5.929 | -0.9849 | -0.1824 | -#> |.....................| 2.162 | 2.407 | 1.182 | 0.6086 | -#> |.....................| 0.8166 | 1.611 | 1.400 | 1.447 | -#> | X| 460.44377 | 93.38 | 0.002660 | 0.2719 | 0.8333 | -#> |.....................| 8.690 | 2.407 | 1.182 | 0.6086 | -#> |.....................| 0.8166 | 1.611 | 1.400 | 1.447 | -#> | 50| 458.18867 | 0.9958 | -1.393 | -0.9065 | -0.8663 | -#> |.....................| -0.7821 | -0.4865 | -0.7479 | -0.9965 | -#> |.....................| -1.062 | -0.6019 | -0.6835 | -0.6930 | -#> | U| 458.18867 | 93.71 | -5.765 | -0.9855 | -0.1668 | -#> |.....................| 2.188 | 2.309 | 1.248 | 0.6642 | -#> |.....................| 0.7122 | 1.506 | 1.299 | 1.325 | -#> | X| 458.18867 | 93.71 | 0.003136 | 0.2718 | 0.8463 | -#> |.....................| 8.919 | 2.309 | 1.248 | 0.6642 | -#> |.....................| 0.7122 | 1.506 | 1.299 | 1.325 | -#> | F| Forward Diff. | -3.049 | 0.4396 | -0.1330 | 0.02964 | -#> |.....................| -0.08039 | -2.599 | -3.012 | -0.1957 | -#> |.....................| -2.463 | -0.6721 | 0.3494 | 0.7476 | -#> | 51| 458.45407 | 0.9980 | -1.449 | -0.8787 | -0.8738 | -#> |.....................| -0.7836 | -0.4935 | -0.7244 | -1.061 | -#> |.....................| -1.034 | -0.5419 | -0.6952 | -0.7610 | -#> | U| 458.45407 | 93.92 | -5.821 | -0.9579 | -0.1743 | -#> |.....................| 2.187 | 2.302 | 1.262 | 0.6155 | -#> |.....................| 0.7366 | 1.577 | 1.286 | 1.249 | -#> | X| 458.45407 | 93.92 | 0.002965 | 0.2773 | 0.8400 | -#> |.....................| 8.906 | 2.302 | 1.262 | 0.6155 | -#> |.....................| 0.7366 | 1.577 | 1.286 | 1.249 | -#> | 52| 458.19883 | 0.9985 | -1.406 | -0.9001 | -0.8680 | -#> |.....................| -0.7823 | -0.4861 | -0.7404 | -1.011 | -#> |.....................| -1.054 | -0.5879 | -0.6864 | -0.7089 | -#> | U| 458.19883 | 93.97 | -5.778 | -0.9792 | -0.1685 | -#> |.....................| 2.188 | 2.309 | 1.253 | 0.6534 | -#> |.....................| 0.7193 | 1.522 | 1.296 | 1.307 | -#> | X| 458.19883 | 93.97 | 0.003096 | 0.2731 | 0.8449 | -#> |.....................| 8.917 | 2.309 | 1.253 | 0.6534 | -#> |.....................| 0.7193 | 1.522 | 1.296 | 1.307 | -#> | 53| 458.20478 | 0.9986 | -1.399 | -0.9039 | -0.8670 | -#> |.....................| -0.7821 | -0.4848 | -0.7433 | -1.002 | -#> |.....................| -1.058 | -0.5961 | -0.6848 | -0.6996 | -#> | U| 458.20478 | 93.98 | -5.770 | -0.9830 | -0.1675 | -#> |.....................| 2.188 | 2.311 | 1.251 | 0.6601 | -#> |.....................| 0.7162 | 1.512 | 1.297 | 1.318 | -#> | X| 458.20478 | 93.98 | 0.003120 | 0.2723 | 0.8458 | -#> |.....................| 8.919 | 2.311 | 1.251 | 0.6601 | -#> |.....................| 0.7162 | 1.512 | 1.297 | 1.318 | -#> | 54| 458.21371 | 0.9986 | -1.394 | -0.9063 | -0.8663 | -#> |.....................| -0.7820 | -0.4840 | -0.7451 | -0.9963 | -#> |.....................| -1.060 | -0.6013 | -0.6838 | -0.6937 | -#> | U| 458.21371 | 93.98 | -5.765 | -0.9854 | -0.1669 | -#> |.....................| 2.188 | 2.311 | 1.250 | 0.6644 | -#> |.....................| 0.7142 | 1.506 | 1.298 | 1.325 | -#> | X| 458.21371 | 93.98 | 0.003135 | 0.2718 | 0.8463 | -#> |.....................| 8.920 | 2.311 | 1.250 | 0.6644 | -#> |.....................| 0.7142 | 1.506 | 1.298 | 1.325 | -#> | 55| 458.18572 | 0.9965 | -1.393 | -0.9064 | -0.8663 | -#> |.....................| -0.7820 | -0.4858 | -0.7472 | -0.9964 | -#> |.....................| -1.062 | -0.6017 | -0.6836 | -0.6932 | -#> | U| 458.18572 | 93.79 | -5.765 | -0.9855 | -0.1668 | -#> |.....................| 2.188 | 2.310 | 1.249 | 0.6643 | -#> |.....................| 0.7128 | 1.506 | 1.299 | 1.325 | -#> | X| 458.18572 | 93.79 | 0.003136 | 0.2718 | 0.8463 | -#> |.....................| 8.919 | 2.310 | 1.249 | 0.6643 | -#> |.....................| 0.7128 | 1.506 | 1.299 | 1.325 | -#> | F| Forward Diff. | 5.905 | 0.4355 | -0.1157 | 0.02634 | -#> |.....................| -0.05151 | -1.735 | -2.785 | -0.07657 | -#> |.....................| -2.587 | -0.1320 | 0.06282 | 0.8041 | -#> | 56| 458.18221 | 0.9957 | -1.394 | -0.9063 | -0.8663 | -#> |.....................| -0.7820 | -0.4856 | -0.7465 | -0.9968 | -#> |.....................| -1.061 | -0.6016 | -0.6835 | -0.6937 | -#> | U| 458.18221 | 93.70 | -5.765 | -0.9853 | -0.1669 | -#> |.....................| 2.188 | 2.310 | 1.249 | 0.6640 | -#> |.....................| 0.7132 | 1.506 | 1.299 | 1.325 | -#> | X| 458.18221 | 93.70 | 0.003135 | 0.2718 | 0.8463 | -#> |.....................| 8.920 | 2.310 | 1.249 | 0.6640 | -#> |.....................| 0.7132 | 1.506 | 1.299 | 1.325 | -#> | F| Forward Diff. | -4.339 | 0.4378 | -0.1282 | 0.03581 | -#> |.....................| -0.09329 | -1.978 | -2.551 | -0.01933 | -#> |.....................| -3.951 | -0.1424 | 0.01723 | 0.8408 | -#> | 57| 458.17882 | 0.9963 | -1.394 | -0.9061 | -0.8663 | -#> |.....................| -0.7819 | -0.4855 | -0.7459 | -0.9972 | -#> |.....................| -1.060 | -0.6016 | -0.6832 | -0.6941 | -#> | U| 458.17882 | 93.76 | -5.766 | -0.9852 | -0.1669 | -#> |.....................| 2.188 | 2.310 | 1.250 | 0.6637 | -#> |.....................| 0.7139 | 1.506 | 1.299 | 1.324 | -#> | X| 458.17882 | 93.76 | 0.003134 | 0.2719 | 0.8463 | -#> |.....................| 8.920 | 2.310 | 1.250 | 0.6637 | -#> |.....................| 0.7139 | 1.506 | 1.299 | 1.324 | -#> | F| Forward Diff. | 2.737 | 0.4289 | -0.1193 | 0.04099 | -#> |.....................| -0.07175 | -2.104 | -2.655 | -0.1084 | -#> |.....................| -2.489 | -0.08715 | 0.1037 | 0.7775 | -#> | 58| 458.17628 | 0.9955 | -1.394 | -0.9061 | -0.8663 | -#> |.....................| -0.7819 | -0.4849 | -0.7451 | -0.9972 | -#> |.....................| -1.060 | -0.6016 | -0.6832 | -0.6943 | -#> | U| 458.17628 | 93.69 | -5.766 | -0.9851 | -0.1669 | -#> |.....................| 2.188 | 2.311 | 1.250 | 0.6637 | -#> |.....................| 0.7145 | 1.506 | 1.299 | 1.324 | -#> | X| 458.17628 | 93.69 | 0.003133 | 0.2719 | 0.8463 | -#> |.....................| 8.920 | 2.311 | 1.250 | 0.6637 | -#> |.....................| 0.7145 | 1.506 | 1.299 | 1.324 | -#> | F| Forward Diff. | -5.829 | 0.4364 | -0.1238 | 0.03009 | -#> |.....................| -0.09450 | -1.871 | -2.366 | 0.01771 | -#> |.....................| -2.486 | -0.08743 | 0.03350 | 0.7982 | -#> | 59| 458.17323 | 0.9963 | -1.395 | -0.9059 | -0.8664 | -#> |.....................| -0.7819 | -0.4846 | -0.7446 | -0.9977 | -#> |.....................| -1.059 | -0.6018 | -0.6829 | -0.6949 | -#> | U| 458.17323 | 93.77 | -5.766 | -0.9850 | -0.1669 | -#> |.....................| 2.188 | 2.311 | 1.250 | 0.6633 | -#> |.....................| 0.7149 | 1.506 | 1.299 | 1.323 | -#> | X| 458.17323 | 93.77 | 0.003132 | 0.2719 | 0.8463 | -#> |.....................| 8.921 | 2.311 | 1.250 | 0.6633 | -#> |.....................| 0.7149 | 1.506 | 1.299 | 1.323 | -#> | F| Forward Diff. | 3.135 | 0.4259 | -0.1111 | 0.03860 | -#> |.....................| -0.07150 | -1.713 | -2.294 | -0.1635 | -#> |.....................| -3.755 | -0.1071 | 0.1242 | 0.7274 | -#> | 60| 458.17055 | 0.9957 | -1.395 | -0.9058 | -0.8664 | -#> |.....................| -0.7818 | -0.4843 | -0.7440 | -0.9980 | -#> |.....................| -1.058 | -0.6018 | -0.6828 | -0.6953 | -#> | U| 458.17055 | 93.70 | -5.766 | -0.9848 | -0.1669 | -#> |.....................| 2.188 | 2.311 | 1.251 | 0.6631 | -#> |.....................| 0.7157 | 1.506 | 1.300 | 1.323 | -#> | X| 458.17055 | 93.70 | 0.003131 | 0.2719 | 0.8463 | -#> |.....................| 8.921 | 2.311 | 1.251 | 0.6631 | -#> |.....................| 0.7157 | 1.506 | 1.300 | 1.323 | -#> | F| Forward Diff. | -3.767 | 0.4346 | -0.1027 | 0.03296 | -#> |.....................| -0.07232 | -2.503 | -3.089 | -0.1630 | -#> |.....................| -2.382 | -0.08570 | 0.1151 | 0.7161 | -#> | 61| 458.16819 | 0.9965 | -1.395 | -0.9058 | -0.8664 | -#> |.....................| -0.7818 | -0.4837 | -0.7432 | -0.9981 | -#> |.....................| -1.058 | -0.6018 | -0.6828 | -0.6955 | -#> | U| 458.16819 | 93.79 | -5.767 | -0.9848 | -0.1669 | -#> |.....................| 2.188 | 2.312 | 1.251 | 0.6630 | -#> |.....................| 0.7162 | 1.506 | 1.300 | 1.322 | -#> | X| 458.16819 | 93.79 | 0.003130 | 0.2719 | 0.8462 | -#> |.....................| 8.921 | 2.312 | 1.251 | 0.6630 | -#> |.....................| 0.7162 | 1.506 | 1.300 | 1.322 | -#> | F| Forward Diff. | 6.568 | 0.4333 | -0.07429 | 0.03599 | -#> |.....................| -0.03802 | -2.553 | -3.191 | -0.5393 | -#> |.....................| -0.9714 | -0.8035 | 0.1031 | 0.6902 | -#> | 62| 458.16513 | 0.9957 | -1.396 | -0.9056 | -0.8666 | -#> |.....................| -0.7821 | -0.4835 | -0.7425 | -0.9983 | -#> |.....................| -1.057 | -0.6019 | -0.6824 | -0.6959 | -#> | U| 458.16513 | 93.70 | -5.767 | -0.9847 | -0.1672 | -#> |.....................| 2.188 | 2.312 | 1.252 | 0.6629 | -#> |.....................| 0.7164 | 1.506 | 1.300 | 1.322 | -#> | X| 458.16513 | 93.70 | 0.003129 | 0.2720 | 0.8461 | -#> |.....................| 8.919 | 2.312 | 1.252 | 0.6629 | -#> |.....................| 0.7164 | 1.506 | 1.300 | 1.322 | -#> | F| Forward Diff. | -3.933 | 0.4306 | -0.09800 | 0.02413 | -#> |.....................| -0.09225 | -1.469 | -2.000 | -0.05194 | -#> |.....................| -3.675 | -0.07209 | 0.09082 | 0.7196 | -#> | 63| 458.16261 | 0.9962 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4834 | -0.7420 | -0.9986 | -#> |.....................| -1.057 | -0.6017 | -0.6820 | -0.6964 | -#> | U| 458.16261 | 93.76 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.312 | 1.252 | 0.6626 | -#> |.....................| 0.7170 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16261 | 93.76 | 0.003127 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.312 | 1.252 | 0.6626 | -#> |.....................| 0.7170 | 1.506 | 1.300 | 1.321 | -#> | F| Forward Diff. | 2.233 | 0.4197 | -0.09277 | 0.03004 | -#> |.....................| -0.08165 | -1.772 | -2.245 | -0.08206 | -#> |.....................| -2.339 | -0.1510 | 0.07888 | 0.6887 | -#> | 64| 458.16062 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16062 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16062 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | M| Mixed Diff. | -6.515 | 0.4169 | -0.1028 |-1.670e+05 | -#> |.....................| -0.1097 | -2.956 | -2.997 | -0.5657 | -#> |.....................| -4.153 | -0.6659 | -0.7853 | 0.1256 | -#> | 65| 458.16519 | 0.9948 | -1.397 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4822 | -0.7405 | -0.9986 | -#> |.....................| -1.055 | -0.6016 | -0.6821 | -0.6969 | -#> | U| 458.16519 | 93.62 | -5.768 | -0.9844 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.253 | 0.6627 | -#> |.....................| 0.7183 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16519 | 93.62 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.253 | 0.6627 | -#> |.....................| 0.7183 | 1.506 | 1.300 | 1.321 | -#> | 66| 458.16209 | 0.9951 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4825 | -0.7409 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6968 | -#> | U| 458.16209 | 93.65 | -5.768 | -0.9844 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.253 | 0.6626 | -#> |.....................| 0.7180 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16209 | 93.65 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.253 | 0.6626 | -#> |.....................| 0.7180 | 1.506 | 1.300 | 1.321 | -#> | 67| 458.16115 | 0.9953 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4827 | -0.7410 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16115 | 93.67 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.253 | 0.6626 | -#> |.....................| 0.7178 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16115 | 93.67 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.253 | 0.6626 | -#> |.....................| 0.7178 | 1.506 | 1.300 | 1.321 | -#> | 68| 458.16084 | 0.9954 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4827 | -0.7411 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16084 | 93.68 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16084 | 93.68 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | -#> | 69| 458.16072 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16072 | 93.68 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16072 | 93.68 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | -#> | 70| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7177 | 1.506 | 1.300 | 1.321 | -#> | 71| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 72| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 73| 458.16072 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16072 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16072 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 74| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 75| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 76| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 77| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 78| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 79| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 80| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 81| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 82| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 83| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 84| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 85| 458.16068 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16068 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16068 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 86| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 87| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 88| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 89| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 90| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 91| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | 92| 458.16067 | 0.9955 | -1.396 | -0.9054 | -0.8667 | -#> |.....................| -0.7822 | -0.4828 | -0.7412 | -0.9986 | -#> |.....................| -1.056 | -0.6017 | -0.6821 | -0.6967 | -#> | U| 458.16067 | 93.69 | -5.768 | -0.9845 | -0.1673 | -#> |.....................| 2.188 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> | X| 458.16067 | 93.69 | 0.003126 | 0.2720 | 0.8460 | -#> |.....................| 8.918 | 2.313 | 1.252 | 0.6626 | -#> |.....................| 0.7176 | 1.506 | 1.300 | 1.321 | -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1: 93.5791 -5.6199 -2.0817 -3.9984 -1.2037 0.1481 4.5359 1.6042 1.1515 2.4545 0.4989 0.5230 19.1822 10.0277 -#> 2: 93.5157 -5.6781 -1.9742 -4.0546 -1.1333 0.1109 4.4678 1.5240 1.0939 2.3318 0.4740 0.6338 12.8885 7.4711 -#> 3: 93.2898 -5.7047 -1.8559 -4.1328 -1.0939 0.0438 5.0096 1.4478 1.1939 2.2152 0.4503 0.6021 11.0381 5.1444 -#> 4: 93.0426 -5.7814 -1.8501 -4.1839 -1.0410 0.0594 6.3802 1.4778 1.2229 2.1556 0.4278 0.5972 10.2381 4.4049 -#> 5: 92.9134 -5.8482 -1.8162 -4.2071 -1.0582 0.0732 6.9858 1.8242 1.1718 2.3151 0.4064 0.5822 9.8642 4.4088 -#> 6: 92.7655 -5.8047 -1.8535 -4.2041 -0.9870 0.0611 6.6365 1.7739 1.1619 2.2910 0.3861 0.5531 8.6374 4.0594 -#> 7: 93.0259 -5.8252 -1.9173 -4.2093 -0.9549 0.0995 6.3047 2.2731 1.1038 2.1765 0.3668 0.5255 9.0819 3.0678 -#> 8: 93.1406 -5.7510 -1.9019 -4.2213 -0.9559 0.1508 5.9894 2.5908 1.0919 2.0948 0.3484 0.4992 8.3332 2.3703 -#> 9: 93.3980 -5.5162 -1.9512 -4.2707 -0.9026 0.1570 5.6900 2.4612 1.0373 2.3579 0.3310 0.4742 7.7762 2.1692 -#> 10: 93.5148 -5.4966 -1.9184 -4.2482 -0.9045 0.1396 5.4055 2.3382 0.9855 2.2400 0.3145 0.4652 7.5796 1.9233 -#> 11: 93.1833 -5.5679 -1.9315 -4.2869 -0.9148 0.1713 5.1352 2.2213 0.9362 2.1465 0.2987 0.4622 7.5181 1.8003 -#> 12: 92.9902 -5.7249 -1.9741 -4.3054 -0.9148 0.1927 4.8784 2.9298 0.8975 2.3858 0.2838 0.5005 7.3638 1.7074 -#> 13: 92.5821 -5.7143 -1.9662 -4.3403 -0.8940 0.1595 4.6345 2.8035 0.9305 2.5370 0.2696 0.4755 7.1732 1.6333 -#> 14: 92.1385 -5.5571 -1.9874 -4.2935 -0.8815 0.1762 5.5000 2.6634 0.9011 2.4102 0.2561 0.5012 7.1920 1.7020 -#> 15: 92.1244 -5.5198 -1.9701 -4.3134 -0.8984 0.1704 5.2250 2.5302 0.9705 2.4401 0.2433 0.4839 7.4072 1.6160 -#> 16: 92.6306 -5.4666 -1.9776 -4.3023 -0.8906 0.1737 4.9638 2.4037 1.0278 2.3181 0.2312 0.5183 7.5105 1.6033 -#> 17: 92.5769 -5.4886 -2.0034 -4.3892 -0.8863 0.1967 5.6659 2.2835 1.0796 2.7700 0.2196 0.5138 7.6495 1.4656 -#> 18: 92.0321 -5.5257 -2.0086 -4.3651 -0.8914 0.1869 6.5345 2.1693 1.0771 2.6315 0.2086 0.4906 7.8248 1.4297 -#> 19: 92.5497 -5.5509 -1.9892 -4.3590 -0.8947 0.2148 6.2078 2.0936 1.0629 2.4999 0.1992 0.4847 7.8809 1.4881 -#> 20: 92.3638 -5.5322 -1.9943 -4.3507 -0.9153 0.1787 6.2176 2.1784 1.0242 2.5190 0.1923 0.4604 7.7900 1.5147 -#> 21: 92.3946 -5.5963 -1.9984 -4.3234 -0.9031 0.1961 5.9067 2.4305 0.9962 2.3930 0.1827 0.4374 7.6671 1.5182 -#> 22: 92.3389 -5.7757 -1.9686 -4.3485 -0.9054 0.1677 5.6113 3.2010 0.9650 2.4493 0.1907 0.4220 7.1305 1.5425 -#> 23: 92.5054 -5.7766 -1.9947 -4.3613 -0.9069 0.1781 5.3308 3.2506 0.9932 2.5478 0.1868 0.4217 7.6690 1.4526 -#> 24: 92.5865 -5.8597 -1.9691 -4.4676 -0.8950 0.1755 5.0642 4.0954 0.9471 3.5482 0.1891 0.4438 7.2397 1.6349 -#> 25: 92.3775 -5.8727 -1.9577 -4.4964 -0.8955 0.1477 4.8354 3.8906 0.9557 3.5054 0.2003 0.4216 6.7966 1.5576 -#> 26: 92.2427 -5.9696 -1.9672 -4.4384 -0.9063 0.1733 4.5937 4.0917 0.9924 3.3326 0.1918 0.4341 6.9377 1.5723 -#> 27: 92.7312 -5.8434 -1.9590 -4.3655 -0.9095 0.1669 4.4448 3.8871 1.0032 3.1660 0.2006 0.4320 7.1970 1.5118 -#> 28: 92.7033 -5.8759 -1.9827 -4.3776 -0.9145 0.1844 4.5885 3.6928 0.9750 3.0077 0.2093 0.4104 6.8745 1.4865 -#> 29: 92.5242 -5.8627 -1.9806 -4.4623 -0.9142 0.2069 5.2823 3.5081 0.9748 3.5849 0.2098 0.4120 6.9735 1.5115 -#> 30: 92.2312 -5.8332 -1.9739 -4.3699 -0.9100 0.1624 5.0182 3.5473 0.9553 3.4056 0.2102 0.3914 6.8547 1.5172 -#> 31: 92.1659 -5.7898 -1.9642 -4.3956 -0.9105 0.1625 4.7672 3.3700 0.9442 3.2571 0.2071 0.3795 6.5191 1.5452 -#> 32: 92.5436 -5.7968 -1.9642 -4.3987 -0.9179 0.1110 4.5289 3.2015 0.9382 3.0943 0.2024 0.3605 6.5921 1.5105 -#> 33: 92.7837 -5.8155 -1.9539 -4.3145 -0.9157 0.1398 4.3024 3.3616 0.9119 2.9395 0.1981 0.3494 6.2870 1.6036 -#> 34: 93.0500 -5.8853 -1.9587 -4.2507 -0.9146 0.1455 4.0873 4.3592 0.9129 2.7926 0.1961 0.3319 6.3493 1.6059 -#> 35: 93.1208 -5.8581 -1.9614 -4.2722 -0.9127 0.1255 4.0645 4.1413 0.9262 2.6529 0.1964 0.3157 6.1337 1.6010 -#> 36: 93.1002 -5.8598 -1.9886 -4.2092 -0.9076 0.1192 4.2392 3.9342 0.9566 2.5203 0.2015 0.3222 6.5326 1.4847 -#> 37: 92.8242 -5.6228 -1.9655 -4.2054 -0.9099 0.1010 6.8190 3.7375 0.9087 2.3943 0.1942 0.3141 6.2613 1.6015 -#> 38: 93.1512 -5.5747 -1.9736 -4.2054 -0.9115 0.0887 6.4781 3.5506 0.8904 2.2746 0.1930 0.3298 6.4960 1.5750 -#> 39: 92.9998 -5.5416 -1.9750 -4.2124 -0.9101 0.0953 6.1542 3.3731 0.9013 2.1608 0.1858 0.3204 6.6470 1.5705 -#> 40: 93.2158 -5.7057 -1.9587 -4.2101 -0.9122 0.0630 5.8464 3.2044 0.9350 2.1357 0.1851 0.3044 6.6842 1.5069 -#> 41: 93.0585 -5.5453 -1.9306 -4.2101 -0.9021 0.0531 5.5541 3.0442 0.9458 2.1673 0.1851 0.2892 6.3923 1.5949 -#> 42: 93.0958 -5.4512 -1.9484 -4.2227 -0.8959 0.0649 5.2764 2.8920 0.9571 2.1930 0.1829 0.2747 6.3082 1.5985 -#> 43: 93.2333 -5.5398 -1.9391 -4.2400 -0.8972 0.0870 5.0126 2.7474 0.9913 2.2830 0.1984 0.2720 6.0810 1.6131 -#> 44: 92.9479 -5.5648 -1.9227 -4.2468 -0.9104 0.0963 4.7620 2.6100 0.9682 2.2976 0.2038 0.2648 5.8461 1.6955 -#> 45: 93.0244 -5.6247 -1.9379 -4.2588 -0.9093 0.0865 5.2997 2.4894 0.9837 2.3100 0.2039 0.2844 5.9439 1.6121 -#> 46: 92.5959 -5.6240 -1.9513 -4.2588 -0.9172 0.0923 5.3111 2.5081 1.0158 2.3100 0.2050 0.2702 6.0141 1.6189 -#> 47: 92.8483 -5.5823 -1.9529 -4.2684 -0.9194 0.0770 6.2469 2.3827 1.0328 2.3567 0.2104 0.2567 6.0472 1.5858 -#> 48: 92.6210 -5.6336 -1.9379 -4.3049 -0.9054 0.0747 7.5721 2.3177 1.0379 2.5427 0.2103 0.2439 6.0431 1.5860 -#> 49: 92.6337 -5.6723 -1.9486 -4.2879 -0.8985 0.0773 7.1935 2.6572 1.0181 2.4515 0.2056 0.2559 6.0895 1.5217 -#> 50: 92.2413 -5.7138 -1.9587 -4.2804 -0.8926 0.0774 8.1551 2.9779 1.0282 2.4807 0.2090 0.2510 6.2355 1.5223 -#> 51: 92.2223 -5.6765 -1.9496 -4.2971 -0.8840 0.1034 7.7638 3.0625 1.0017 2.6024 0.2075 0.2384 6.3495 1.6621 -#> 52: 92.4242 -5.6573 -1.9408 -4.2943 -0.8993 0.1136 8.3190 2.9093 1.0044 2.4822 0.2163 0.2411 6.0611 1.5241 -#> 53: 92.6070 -5.5921 -1.9397 -4.2873 -0.9046 0.0904 10.3681 2.7639 1.0098 2.4895 0.2194 0.2393 6.1728 1.5264 -#> 54: 92.9339 -5.6194 -1.9292 -4.2950 -0.9006 0.1010 9.9150 2.6257 1.0088 2.4268 0.2346 0.2492 5.9203 1.5693 -#> 55: 93.4640 -5.5851 -1.8969 -4.2614 -0.9065 0.1058 10.3986 2.4944 1.0204 2.3055 0.2257 0.2403 5.7030 1.5717 -#> 56: 93.3646 -5.5851 -1.9127 -4.3130 -0.9196 0.1077 9.8787 2.3697 1.0067 2.6259 0.2261 0.2370 5.7389 1.5053 -#> 57: 93.5408 -5.4962 -1.9150 -4.3285 -0.9148 0.0880 9.3848 2.2512 0.9903 2.6118 0.2160 0.2494 5.7530 1.5780 -#> 58: 93.5195 -5.4358 -1.9459 -4.3041 -0.9076 0.1022 8.9155 2.1386 1.0220 2.5253 0.2220 0.2578 6.0138 1.4494 -#> 59: 93.5906 -5.4624 -1.9507 -4.3065 -0.9124 0.1374 8.4698 2.0317 1.0230 2.5539 0.2212 0.2449 5.7538 1.6021 -#> 60: 93.3308 -5.3784 -1.9540 -4.2417 -0.9173 0.1337 8.0463 1.9301 1.0298 2.4262 0.2173 0.2327 5.8841 1.4634 -#> 61: 93.3506 -5.4000 -1.9688 -4.2389 -0.9130 0.0942 7.6440 1.8336 1.0437 2.3049 0.2216 0.2210 6.0098 1.4243 -#> 62: 93.6969 -5.4175 -1.9467 -4.2389 -0.9135 0.1315 7.2618 1.7419 1.0213 2.2519 0.2250 0.2149 5.6278 1.4755 -#> 63: 93.6188 -5.3860 -1.9295 -4.2637 -0.9222 0.1196 7.8033 1.6548 1.0340 2.2699 0.2282 0.2282 5.6763 1.4755 -#> 64: 93.6782 -5.4118 -1.9518 -4.2655 -0.9298 0.1055 8.3519 1.5721 1.0227 2.4426 0.2317 0.2560 5.8006 1.4724 -#> 65: 93.5253 -5.4313 -1.9314 -4.2538 -0.9245 0.0919 7.9343 1.4980 1.0771 2.3486 0.2249 0.2635 5.8752 1.4850 -#> 66: 93.3192 -5.5672 -1.9715 -4.2575 -0.9224 0.1404 8.2293 1.9722 1.0233 2.3758 0.2365 0.2546 5.9462 1.5148 -#> 67: 93.0765 -5.4861 -1.9673 -4.2472 -0.9103 0.0935 8.3227 1.8736 0.9889 2.3305 0.2493 0.2419 5.7836 1.4946 -#> 68: 93.2666 -5.4963 -1.9635 -4.2435 -0.9093 0.0940 9.2911 1.7800 1.0050 2.3179 0.2495 0.2298 5.7104 1.4797 -#> 69: 93.3894 -5.5666 -1.9342 -4.2325 -0.9227 0.0957 9.0211 2.0287 1.0012 2.3052 0.2483 0.2348 5.8939 1.5158 -#> 70: 93.2671 -5.5710 -1.9486 -4.2723 -0.9323 0.1062 8.5700 2.1251 0.9714 2.3266 0.2498 0.2466 6.1562 1.5041 -#> 71: 92.9975 -5.5829 -1.9507 -4.2632 -0.9317 0.1166 8.1415 2.0322 0.9403 2.3654 0.2373 0.2454 5.8668 1.5122 -#> 72: 92.6364 -5.5255 -1.9888 -4.2605 -0.9255 0.1062 8.8866 1.9306 0.9680 2.4488 0.2314 0.2438 6.2101 1.5098 -#> 73: 92.4442 -5.5679 -1.9880 -4.3501 -0.9070 0.0972 9.1986 1.9203 0.9597 3.1091 0.2369 0.2412 6.1257 1.5029 -#> 74: 92.3866 -5.5447 -1.9895 -4.3137 -0.9004 0.0898 10.2222 1.8961 0.9573 2.9536 0.2494 0.2361 6.0474 1.4875 -#> 75: 92.2491 -5.6481 -1.9591 -4.3587 -0.8991 0.1028 9.7111 2.2694 1.0140 2.9121 0.2524 0.2243 6.0995 1.4780 -#> 76: 92.4656 -5.6014 -1.9860 -4.3538 -0.9015 0.0978 11.3121 2.1560 0.9861 2.9372 0.2489 0.2314 6.0996 1.4464 -#> 77: 92.5076 -5.5929 -1.9560 -4.3624 -0.9051 0.1008 12.0483 2.0482 1.0212 3.0132 0.2551 0.2378 5.9595 1.5081 -#> 78: 92.5987 -5.7000 -1.9592 -4.3611 -0.9131 0.0958 11.4458 2.3873 1.0062 2.9848 0.2549 0.2372 6.0385 1.4666 -#> 79: 92.4883 -5.7675 -1.9900 -4.4226 -0.9163 0.1153 10.8735 2.7867 0.9616 3.4984 0.2546 0.2309 5.9441 1.4722 -#> 80: 92.1716 -5.7782 -1.9810 -4.4398 -0.9122 0.1193 10.3299 3.0280 0.9642 3.6766 0.2520 0.2291 6.3013 1.4698 -#> 81: 92.1145 -5.8494 -1.9836 -4.3634 -0.9196 0.1013 9.8134 3.1850 0.9160 3.4927 0.2562 0.2409 6.2458 1.4664 -#> 82: 92.3761 -5.9668 -1.9722 -4.3888 -0.9240 0.1139 9.9738 3.9484 0.8923 3.3519 0.2434 0.2318 6.0987 1.4847 -#> 83: 92.7805 -6.1135 -1.9335 -4.3600 -0.9273 0.1027 11.2060 4.7684 0.8932 3.1843 0.2454 0.2202 5.9824 1.4920 -#> 84: 92.9601 -6.2190 -1.9374 -4.3187 -0.9376 0.1140 10.6457 5.6632 0.9077 3.0250 0.2464 0.2188 5.9979 1.5152 -#> 85: 92.4579 -6.1486 -1.9398 -4.3269 -0.9417 0.0979 10.1134 5.3800 0.9011 2.8738 0.2446 0.2330 5.7007 1.5648 -#> 86: 92.3580 -6.2177 -1.9549 -4.3287 -0.9510 0.1073 9.6077 5.1608 0.9318 2.7301 0.2497 0.2214 5.9916 1.5305 -#> 87: 92.8919 -6.3309 -1.9480 -4.3285 -0.9647 0.1009 9.1273 6.4577 0.9494 2.7023 0.2408 0.2126 5.9053 1.4313 -#> 88: 93.0621 -6.1220 -1.9623 -4.3341 -0.9624 0.1300 8.6710 6.1349 0.9563 2.6593 0.2404 0.2130 6.1925 1.4510 -#> 89: 92.7711 -6.2636 -1.9545 -4.3520 -0.9496 0.1227 8.2374 6.2143 0.9791 2.5862 0.2346 0.2333 5.9772 1.4523 -#> 90: 92.9148 -6.5481 -1.9586 -4.3275 -0.9496 0.1096 7.8255 8.2617 0.9787 2.4647 0.2346 0.2216 5.9136 1.4247 -#> 91: 92.8129 -6.4655 -1.9753 -4.3287 -0.9435 0.1210 9.1893 7.8487 0.9642 2.5304 0.2354 0.2268 5.9129 1.4229 -#> 92: 93.1090 -6.4752 -1.9841 -4.3533 -0.9428 0.1509 10.1133 7.7232 0.9160 2.6037 0.2457 0.2265 5.8601 1.4646 -#> 93: 93.4781 -6.3780 -1.9909 -4.3713 -0.9450 0.1544 9.6076 7.3370 0.9153 2.7656 0.2485 0.2499 5.9150 1.5180 -#> 94: 93.2125 -6.3021 -1.9798 -4.3459 -0.9470 0.1520 9.6738 6.9702 0.9314 2.6273 0.2428 0.2519 5.8752 1.4456 -#> 95: 93.0091 -5.9727 -1.9828 -4.3777 -0.9447 0.1370 9.6411 6.6217 0.9107 2.7137 0.2428 0.2556 5.8302 1.4477 -#> 96: 92.8731 -5.7813 -1.9952 -4.3343 -0.9352 0.1505 9.1590 6.2906 0.9011 2.5780 0.2366 0.2546 6.0545 1.4887 -#> 97: 92.7834 -5.8119 -1.9975 -4.3303 -0.9258 0.1231 8.8022 5.9760 0.9005 2.5331 0.2392 0.2419 5.9522 1.4754 -#> 98: 92.8447 -5.9773 -1.9940 -4.3353 -0.9301 0.1409 8.3621 5.6772 0.9244 2.4828 0.2426 0.2490 6.1027 1.4129 -#> 99: 93.1697 -5.8958 -1.9964 -4.3325 -0.9248 0.1411 7.9440 5.3934 0.9586 2.6138 0.2378 0.2545 6.2793 1.3719 -#> 100: 93.2536 -5.8481 -2.0009 -4.3408 -0.9304 0.1718 8.7965 5.1237 0.9290 2.6161 0.2398 0.2418 6.0908 1.4534 -#> 101: 93.2942 -5.8684 -1.9650 -4.3096 -0.9305 0.1496 9.7633 4.8675 0.9166 2.4853 0.2372 0.2565 5.9079 1.4948 -#> 102: 93.2636 -6.1363 -1.9517 -4.2653 -0.9235 0.1175 10.7772 5.1927 0.8944 2.3610 0.2448 0.2812 5.7748 1.5533 -#> 103: 92.6954 -5.9371 -1.9524 -4.2792 -0.9045 0.1288 10.2383 4.9331 0.8876 2.2429 0.2406 0.2720 5.5496 1.5601 -#> 104: 92.6149 -6.0650 -1.9532 -4.2752 -0.9048 0.0973 10.9914 4.6864 0.8845 2.1875 0.2475 0.2584 5.5593 1.4897 -#> 105: 92.8231 -5.9779 -1.9650 -4.2939 -0.9013 0.1112 10.4712 4.4521 0.9193 2.1985 0.2416 0.2455 5.4420 1.4910 -#> 106: 92.7599 -5.9602 -1.9594 -4.3018 -0.9026 0.1273 10.1396 4.2295 0.9308 2.1700 0.2453 0.2625 5.5458 1.4429 -#> 107: 93.1433 -5.9509 -1.9638 -4.2715 -0.9324 0.1385 9.6327 4.0415 0.9271 2.1026 0.2415 0.2626 5.4762 1.4286 -#> 108: 93.1354 -5.7359 -1.9691 -4.2962 -0.9256 0.1346 10.2794 3.8394 0.9387 2.1671 0.2412 0.2627 5.5107 1.4200 -#> 109: 92.9608 -5.8252 -1.9780 -4.3149 -0.9125 0.1564 9.7654 4.0619 0.9380 2.1731 0.2325 0.2657 5.8118 1.4379 -#> 110: 93.1043 -5.7632 -1.9874 -4.2868 -0.9113 0.1178 9.2771 3.8588 0.9420 2.1477 0.2214 0.2524 5.9352 1.4377 -#> 111: 92.8879 -5.7965 -1.9781 -4.2851 -0.9147 0.1107 8.8133 3.6659 0.9526 2.1891 0.2130 0.2398 5.6360 1.4461 -#> 112: 92.9347 -5.7484 -1.9460 -4.2825 -0.9195 0.1078 8.3726 3.4826 0.9710 2.2687 0.2051 0.2278 5.5771 1.5123 -#> 113: 92.7217 -5.7193 -1.9328 -4.2721 -0.9252 0.1021 7.9540 3.3085 1.0056 2.2848 0.2244 0.2164 5.7135 1.5082 -#> 114: 92.9944 -5.7382 -1.9414 -4.2835 -0.9210 0.1210 7.5563 3.1430 1.0184 2.2457 0.2260 0.2182 5.6799 1.4751 -#> 115: 93.1261 -5.8876 -1.9290 -4.2753 -0.9382 0.0960 9.7696 3.4406 1.0140 2.2745 0.2171 0.2073 5.3919 1.4919 -#> 116: 92.7669 -5.9842 -1.9484 -4.2828 -0.9504 0.1122 9.2811 4.1332 1.0202 2.2835 0.2160 0.2136 5.3651 1.5337 -#> 117: 92.9804 -5.9847 -1.9584 -4.2879 -0.9474 0.1234 9.2911 3.9265 0.9692 2.3115 0.2135 0.2163 5.1053 1.4774 -#> 118: 93.2853 -5.8443 -1.9494 -4.2700 -0.9400 0.1105 9.8572 3.7302 0.9736 2.2489 0.2192 0.2223 5.2416 1.4668 -#> 119: 93.2776 -5.8592 -1.9458 -4.2600 -0.9394 0.1072 9.3643 3.5437 0.9789 2.1964 0.2176 0.2205 5.2942 1.4847 -#> 120: 93.0335 -5.8156 -1.9453 -4.2623 -0.9437 0.1139 8.8961 3.3665 0.9698 2.2380 0.2206 0.2231 5.4427 1.4470 -#> 121: 93.0115 -5.8402 -1.9355 -4.2596 -0.9291 0.1138 8.4513 3.3018 0.9743 2.1463 0.2096 0.2120 5.1537 1.4487 -#> 122: 93.6277 -5.8852 -1.9276 -4.2787 -0.9419 0.1388 8.0287 3.4114 0.9438 2.1410 0.2072 0.2104 5.1198 1.5201 -#> 123: 93.4952 -6.0977 -1.9332 -4.2847 -0.9431 0.1412 7.6273 4.8225 0.9472 2.1335 0.2081 0.2129 5.2003 1.6193 -#> 124: 93.7207 -6.2280 -1.9105 -4.2692 -0.9551 0.1422 7.2459 5.4835 0.9657 2.0896 0.2148 0.2272 5.2901 1.5482 -#> 125: 93.6041 -6.0808 -1.9356 -4.2748 -0.9531 0.1184 7.0201 5.2094 0.9591 2.0421 0.2089 0.2158 5.3848 1.4896 -#> 126: 93.5193 -6.0164 -1.9296 -4.2890 -0.9600 0.1351 7.6848 4.9489 0.9931 2.1387 0.1989 0.2129 5.1988 1.4492 -#> 127: 93.7135 -5.9340 -1.9448 -4.2883 -0.9633 0.1428 8.3411 4.7014 0.9820 2.1192 0.1985 0.2046 5.3953 1.4985 -#> 128: 94.2312 -5.8849 -1.9404 -4.2754 -0.9633 0.1495 7.9240 4.4664 0.9884 2.0587 0.1902 0.2171 5.7113 1.4987 -#> 129: 94.0390 -5.8674 -1.9229 -4.3309 -0.9614 0.1472 8.5108 4.2430 1.0319 2.1023 0.1909 0.2154 5.5654 1.4294 -#> 130: 93.4178 -6.0458 -1.9224 -4.3364 -0.9560 0.1570 8.0852 4.4639 1.0184 2.2804 0.1869 0.2182 5.6585 1.4443 -#> 131: 93.5483 -6.2682 -1.9258 -4.3654 -0.9554 0.1449 7.6810 5.6020 1.0254 2.3477 0.1857 0.2230 5.4266 1.4324 -#> 132: 93.5180 -6.3297 -1.9204 -4.3577 -0.9640 0.1365 7.2969 5.5672 1.0354 2.3257 0.1788 0.2118 5.4913 1.4859 -#> 133: 93.4707 -6.0990 -1.9415 -4.3315 -0.9775 0.1232 6.9321 5.2888 1.0686 2.3421 0.1851 0.2012 5.8429 1.4618 -#> 134: 93.1012 -6.1236 -1.9308 -4.3409 -0.9654 0.1225 7.6471 5.0244 1.0517 2.4652 0.1947 0.2008 5.6902 1.5432 -#> 135: 93.2545 -6.1070 -1.9408 -4.3415 -0.9553 0.1228 9.2701 4.7732 1.0160 2.3607 0.1919 0.1907 5.5154 1.5317 -#> 136: 93.3338 -6.0321 -1.9336 -4.3074 -0.9598 0.1120 8.8066 4.5345 0.9652 2.2427 0.1999 0.2249 5.3667 1.6036 -#> 137: 93.5910 -6.0627 -1.9339 -4.3074 -0.9529 0.1407 8.3663 4.3078 0.9538 2.2128 0.1966 0.2195 5.2959 1.6015 -#> 138: 93.6338 -5.9702 -1.9252 -4.3105 -0.9615 0.1373 7.9480 4.0924 0.9875 2.2635 0.1964 0.2218 5.4532 1.5261 -#> 139: 93.6403 -5.8913 -1.9237 -4.2962 -0.9582 0.1165 8.0749 3.8878 0.9746 2.2457 0.1972 0.2125 5.9356 1.5173 -#> 140: 92.8503 -5.8314 -1.9452 -4.3180 -0.9487 0.1142 8.6356 3.6934 0.9933 2.2044 0.1961 0.2019 5.7908 1.5138 -#> 141: 93.1249 -6.0584 -1.9448 -4.3139 -0.9367 0.0950 8.9231 4.4196 1.0220 2.2246 0.2079 0.2077 6.0233 1.4339 -#> 142: 93.1846 -6.3026 -1.9152 -4.3093 -0.9392 0.0866 10.1508 5.9592 1.0562 2.3325 0.2082 0.2133 5.5285 1.4832 -#> 143: 92.4682 -6.1485 -1.9146 -4.2812 -0.9376 0.0260 9.6433 5.6613 1.0618 2.3594 0.2000 0.2027 6.0573 1.4428 -#> 144: 92.7792 -6.1108 -1.8939 -4.2740 -0.9341 0.0765 9.1611 5.3782 1.0917 2.3074 0.2057 0.2240 6.2141 1.4953 -#> 145: 93.1314 -6.2086 -1.8939 -4.3580 -0.9341 0.0741 8.7031 5.1093 1.0931 2.7164 0.2105 0.2229 5.8543 1.4855 -#> 146: 93.2254 -6.2170 -1.8998 -4.3724 -0.9311 0.0677 8.2679 5.0506 1.0811 2.8434 0.2049 0.2118 5.5455 1.4763 -#> 147: 93.3264 -6.0136 -1.8998 -4.3853 -0.9328 0.0817 9.4673 4.7980 1.0668 2.8512 0.2009 0.2114 5.5518 1.5225 -#> 148: 93.2298 -5.9143 -1.8921 -4.5001 -0.9296 0.1057 8.9939 4.5581 1.0563 3.8266 0.1982 0.2043 5.5242 1.5614 -#> 149: 93.3604 -5.9894 -1.8832 -4.5223 -0.9338 0.0858 8.5442 4.3302 1.0544 4.3930 0.1986 0.2003 5.4353 1.4957 -#> 150: 93.4715 -5.9630 -1.8833 -4.4796 -0.9335 0.0827 8.1170 4.1137 1.0912 4.1733 0.1984 0.1903 5.7477 1.4554 -#> 151: 93.3385 -5.8026 -1.9052 -4.4507 -0.9368 0.0684 8.7726 3.9080 1.1249 3.9647 0.2074 0.1808 5.7693 1.4400 -#> 152: 93.1682 -5.8529 -1.9441 -4.3545 -0.9309 0.0752 8.8042 3.1783 1.0496 3.0168 0.2069 0.1688 5.9161 1.4565 -#> 153: 93.0559 -6.0261 -1.9425 -4.3431 -0.9327 0.1016 9.1435 3.9939 1.0120 2.8470 0.1894 0.1509 5.4435 1.5486 -#> 154: 92.8582 -6.0887 -1.9278 -4.3094 -0.9352 0.1064 8.4316 4.2991 0.9819 2.6257 0.1907 0.1609 5.4587 1.5208 -#> 155: 93.3200 -5.8480 -1.9149 -4.3363 -0.9294 0.1143 9.6700 3.1734 0.9942 2.6441 0.1824 0.1906 5.5193 1.6410 -#> 156: 93.3199 -5.9053 -1.9213 -4.3163 -0.9369 0.1291 7.5899 3.5902 0.9823 2.4648 0.1770 0.1956 5.3816 1.5356 -#> 157: 93.2434 -5.8763 -1.9161 -4.3035 -0.9549 0.1075 8.4137 3.2576 0.9935 2.5007 0.1795 0.1852 5.4053 1.5706 -#> 158: 93.1494 -5.9243 -1.8929 -4.3162 -0.9680 0.1296 8.2959 3.3262 1.0029 2.4943 0.1866 0.1921 5.4369 1.5510 -#> 159: 93.5683 -6.0335 -1.9127 -4.3040 -0.9675 0.1271 7.7222 4.0079 0.9768 2.5765 0.1869 0.2028 5.7165 1.4968 -#> 160: 93.9417 -6.0018 -1.9085 -4.2818 -0.9611 0.1161 5.8791 4.4991 0.9658 2.4933 0.1878 0.1986 6.0579 1.5272 -#> 161: 94.1252 -5.9264 -1.8943 -4.2805 -0.9645 0.0860 4.9517 3.6307 0.9754 2.4988 0.1934 0.1785 5.7457 1.5878 -#> 162: 93.9389 -5.7613 -1.8946 -4.2410 -0.9752 0.0898 6.7269 2.5865 1.0184 2.4379 0.1933 0.1908 5.9052 1.5215 -#> 163: 93.5890 -5.7243 -1.8992 -4.2636 -0.9722 0.0759 8.4484 2.5137 1.0151 2.3869 0.1928 0.1889 5.4694 1.5048 -#> 164: 93.9751 -5.7314 -1.8786 -4.3271 -0.9702 0.1020 6.6884 2.5136 1.0133 2.8395 0.1907 0.1998 5.4625 1.4854 -#> 165: 93.9708 -5.7409 -1.8856 -4.3129 -0.9616 0.1094 5.8809 2.4589 1.0401 2.6662 0.1912 0.1998 5.4339 1.4549 -#> 166: 93.9265 -5.6937 -1.9134 -4.3080 -0.9702 0.1151 5.6940 2.4086 1.0065 2.6864 0.1983 0.1987 5.6907 1.4857 -#> 167: 93.4157 -5.7312 -1.9163 -4.3286 -0.9638 0.1216 5.1230 2.5468 1.0487 2.5930 0.1917 0.1940 5.5938 1.4267 -#> 168: 93.3701 -5.8757 -1.9196 -4.3493 -0.9579 0.1134 6.0802 3.3929 1.0517 2.6981 0.1888 0.2063 5.4125 1.4365 -#> 169: 93.4342 -6.0262 -1.9041 -4.3347 -0.9526 0.0997 6.0780 3.6349 1.0623 2.7344 0.1946 0.1978 5.4930 1.4594 -#> 170: 93.3751 -6.1195 -1.9093 -4.3541 -0.9872 0.0834 6.8972 4.0337 1.0763 2.8428 0.2077 0.2005 5.6759 1.4455 -#> 171: 93.3603 -6.0360 -1.9196 -4.4632 -0.9763 0.0866 7.4236 3.6025 1.0684 3.7611 0.2046 0.1894 5.6282 1.4414 -#> 172: 93.2776 -5.9538 -1.9031 -4.4815 -0.9779 0.1024 5.4751 3.2802 1.0599 3.9487 0.2115 0.1990 5.6116 1.4230 -#> 173: 93.4470 -5.8580 -1.9193 -4.4170 -0.9641 0.0957 5.6416 2.8005 1.0440 3.4509 0.2066 0.1863 5.5804 1.4485 -#> 174: 93.2952 -5.8590 -1.9010 -4.3600 -0.9640 0.0789 6.4314 2.9503 1.0808 2.9773 0.2045 0.1969 5.4423 1.4421 -#> 175: 93.3756 -5.7733 -1.8959 -4.3621 -0.9504 0.0609 6.1723 2.5287 1.0950 3.0019 0.2127 0.2053 5.4338 1.4470 -#> 176: 93.1450 -5.8266 -1.9053 -4.3401 -0.9457 0.0633 6.5237 3.0522 1.0942 2.9464 0.2134 0.2021 5.6501 1.3664 -#> 177: 92.7723 -5.9978 -1.9231 -4.3529 -0.9524 0.0658 7.4519 4.2374 1.0640 3.0260 0.2158 0.2146 5.9180 1.4100 -#> 178: 92.7261 -5.9836 -1.9189 -4.3349 -0.9576 0.0768 5.5211 4.2557 1.0611 2.8827 0.2169 0.2088 5.8872 1.4206 -#> 179: 92.9599 -6.0071 -1.9259 -4.3081 -0.9581 0.0657 6.0953 3.8205 1.0816 2.6709 0.2122 0.2014 5.8221 1.4026 -#> 180: 93.0831 -6.1544 -1.9400 -4.3018 -0.9496 0.0411 4.2312 4.9005 1.1064 2.6542 0.2143 0.2221 6.3264 1.3820 -#> 181: 92.8840 -6.0889 -1.9364 -4.3200 -0.9566 0.0861 4.2186 4.4615 1.0930 2.7270 0.2142 0.2424 6.0486 1.4035 -#> 182: 93.1913 -6.1457 -1.9384 -4.3085 -0.9606 0.0733 6.2878 4.6026 1.0917 2.6393 0.2131 0.2151 5.7042 1.4952 -#> 183: 93.1218 -6.3114 -1.9355 -4.2883 -0.9742 0.0741 7.2675 5.1377 1.0914 2.5060 0.2220 0.2111 5.5099 1.4097 -#> 184: 93.1462 -6.3147 -1.9068 -4.2880 -0.9653 0.0893 7.6928 5.6510 1.0563 2.5066 0.2256 0.2201 5.4138 1.5319 -#> 185: 93.1825 -6.3608 -1.9265 -4.2815 -0.9549 0.0873 7.1340 5.9801 1.0363 2.4788 0.2177 0.1958 5.4202 1.4569 -#> 186: 93.6270 -6.1413 -1.9278 -4.2702 -0.9696 0.1185 6.7652 4.5535 1.0400 2.3673 0.2163 0.1932 5.3005 1.5012 -#> 187: 93.9922 -6.3364 -1.9269 -4.2702 -0.9729 0.1197 7.7694 6.1592 0.9948 2.3673 0.2196 0.2091 5.3075 1.5105 -#> 188: 93.8884 -6.0236 -1.9207 -4.2928 -0.9900 0.1343 7.8090 4.2847 0.9840 2.4238 0.2195 0.1966 5.2861 1.5607 -#> 189: 94.3110 -6.0809 -1.9145 -4.2826 -0.9840 0.1224 8.5580 4.0998 0.9800 2.4505 0.2294 0.1840 5.7107 1.5180 -#> 190: 94.0039 -6.0996 -1.9140 -4.2793 -0.9782 0.1429 10.6594 4.1655 0.9796 2.4415 0.2297 0.1960 5.7533 1.5720 -#> 191: 93.9692 -6.1129 -1.9362 -4.3261 -0.9705 0.1462 8.8201 4.3146 1.0124 2.4625 0.2287 0.2049 5.5670 1.5206 -#> 192: 93.3178 -5.9759 -1.9192 -4.3378 -0.9664 0.1434 8.8047 3.7150 1.0282 2.4137 0.2243 0.1977 5.3858 1.4599 -#> 193: 93.1427 -5.9388 -1.9391 -4.3211 -0.9650 0.1401 7.1862 3.2835 1.0218 2.3216 0.2163 0.1866 5.3930 1.5017 -#> 194: 93.0588 -6.0605 -1.9361 -4.3350 -0.9462 0.1330 6.8930 4.0020 1.0166 2.3186 0.2057 0.1818 5.2535 1.5075 -#> 195: 93.1820 -6.1201 -1.9579 -4.3034 -0.9534 0.1557 8.1300 4.4218 0.9932 2.1873 0.2099 0.1834 5.4862 1.4698 -#> 196: 93.2230 -5.8879 -1.9725 -4.2965 -0.9584 0.1390 8.1307 3.0777 1.0051 2.1597 0.2089 0.1683 5.7058 1.3970 -#> 197: 93.3504 -5.8829 -1.9677 -4.3075 -0.9577 0.1638 6.7115 3.0660 1.0050 2.1377 0.2024 0.1642 5.4691 1.5016 -#> 198: 93.3016 -5.8771 -1.9885 -4.3241 -0.9605 0.1562 6.4722 3.0381 0.9727 2.2053 0.1975 0.1683 5.3434 1.4885 -#> 199: 93.2464 -5.8787 -1.9871 -4.3430 -0.9528 0.1751 4.5894 3.0445 0.9748 2.2247 0.1886 0.1780 5.4469 1.4405 -#> 200: 93.3474 -5.7995 -1.9767 -4.3298 -0.9480 0.1947 4.7024 2.8535 0.9895 2.2234 0.1951 0.2012 5.5130 1.4641 -#> 201: 93.3231 -5.8169 -1.9737 -4.3268 -0.9510 0.1804 4.4248 2.8913 0.9738 2.2141 0.1955 0.2057 5.5422 1.4843 -#> 202: 93.3484 -5.8009 -1.9732 -4.3240 -0.9519 0.1674 4.4068 2.8084 0.9736 2.2040 0.1959 0.2033 5.5843 1.4744 -#> 203: 93.2617 -5.7915 -1.9678 -4.3211 -0.9535 0.1629 4.5333 2.7678 0.9877 2.1980 0.1961 0.2023 5.6265 1.4811 -#> 204: 93.2210 -5.8071 -1.9647 -4.3220 -0.9504 0.1629 4.6144 2.8347 0.9922 2.1938 0.1937 0.2013 5.5745 1.4988 -#> 205: 93.1914 -5.8104 -1.9667 -4.3225 -0.9484 0.1593 4.5880 2.8639 0.9931 2.1952 0.1916 0.1979 5.5960 1.5057 -#> 206: 93.1827 -5.8348 -1.9697 -4.3236 -0.9498 0.1587 4.7189 3.0353 0.9929 2.2016 0.1922 0.1947 5.6096 1.5136 -#> 207: 93.2017 -5.8760 -1.9714 -4.3239 -0.9518 0.1592 4.8171 3.2659 0.9947 2.2042 0.1927 0.1910 5.6413 1.5078 -#> 208: 93.2226 -5.8819 -1.9736 -4.3261 -0.9532 0.1610 4.8241 3.2964 0.9957 2.2122 0.1938 0.1878 5.6704 1.5031 -#> 209: 93.2158 -5.8786 -1.9743 -4.3278 -0.9538 0.1595 4.6275 3.2763 0.9963 2.2279 0.1950 0.1848 5.6758 1.5038 -#> 210: 93.2216 -5.8798 -1.9746 -4.3286 -0.9535 0.1589 4.5667 3.2857 0.9974 2.2473 0.1948 0.1834 5.6707 1.5054 -#> 211: 93.2238 -5.8847 -1.9763 -4.3302 -0.9530 0.1591 4.5745 3.2932 0.9956 2.2576 0.1948 0.1823 5.6691 1.4990 -#> 212: 93.2242 -5.8893 -1.9777 -4.3323 -0.9532 0.1600 4.6203 3.2955 0.9938 2.2704 0.1958 0.1814 5.6732 1.4994 -#> 213: 93.2246 -5.8950 -1.9756 -4.3345 -0.9532 0.1588 4.7363 3.3106 0.9894 2.2864 0.1960 0.1791 5.6401 1.5015 -#> 214: 93.2056 -5.9070 -1.9740 -4.3368 -0.9532 0.1586 4.7814 3.3538 0.9888 2.3047 0.1960 0.1761 5.6265 1.5008 -#> 215: 93.2126 -5.9157 -1.9720 -4.3405 -0.9533 0.1580 4.9117 3.3916 0.9890 2.3191 0.1959 0.1742 5.6054 1.5015 -#> 216: 93.2161 -5.9242 -1.9716 -4.3423 -0.9533 0.1594 5.0163 3.4425 0.9897 2.3291 0.1959 0.1739 5.5975 1.5005 -#> 217: 93.2193 -5.9351 -1.9715 -4.3445 -0.9537 0.1614 4.9927 3.5085 0.9905 2.3309 0.1957 0.1739 5.5905 1.5024 -#> 218: 93.1973 -5.9314 -1.9725 -4.3479 -0.9548 0.1640 5.0502 3.4902 0.9918 2.3344 0.1952 0.1740 5.5909 1.5046 -#> 219: 93.1938 -5.9312 -1.9729 -4.3508 -0.9539 0.1664 5.0446 3.4901 0.9922 2.3365 0.1949 0.1746 5.5808 1.5046 -#> 220: 93.1994 -5.9424 -1.9734 -4.3531 -0.9536 0.1683 5.0462 3.5593 0.9917 2.3370 0.1945 0.1754 5.5831 1.5055 -#> 221: 93.2015 -5.9511 -1.9746 -4.3550 -0.9537 0.1702 5.1062 3.6002 0.9899 2.3368 0.1945 0.1762 5.5731 1.5043 -#> 222: 93.2057 -5.9653 -1.9756 -4.3571 -0.9541 0.1718 5.1727 3.6876 0.9886 2.3364 0.1943 0.1776 5.5813 1.5047 -#> 223: 93.1998 -5.9723 -1.9761 -4.3592 -0.9540 0.1726 5.1866 3.7239 0.9871 2.3428 0.1940 0.1791 5.5702 1.5047 -#> 224: 93.2042 -5.9799 -1.9768 -4.3615 -0.9540 0.1734 5.1516 3.7613 0.9849 2.3531 0.1934 0.1809 5.5705 1.5039 -#> 225: 93.1974 -5.9813 -1.9776 -4.3648 -0.9540 0.1740 5.1225 3.7676 0.9840 2.3663 0.1929 0.1834 5.5698 1.5030 -#> 226: 93.1963 -5.9807 -1.9777 -4.3679 -0.9535 0.1751 5.1632 3.7694 0.9839 2.3785 0.1927 0.1850 5.5676 1.5069 -#> 227: 93.1912 -5.9740 -1.9783 -4.3707 -0.9533 0.1768 5.1987 3.7421 0.9835 2.3931 0.1922 0.1855 5.5597 1.5091 -#> 228: 93.1902 -5.9799 -1.9792 -4.3745 -0.9533 0.1784 5.2070 3.7641 0.9825 2.4134 0.1917 0.1861 5.5502 1.5086 -#> 229: 93.1903 -5.9894 -1.9805 -4.3792 -0.9533 0.1796 5.2398 3.8109 0.9812 2.4382 0.1910 0.1870 5.5486 1.5075 -#> 230: 93.1833 -5.9946 -1.9816 -4.3836 -0.9530 0.1814 5.2357 3.8346 0.9800 2.4614 0.1904 0.1883 5.5515 1.5065 -#> 231: 93.1740 -6.0001 -1.9834 -4.3871 -0.9528 0.1833 5.2848 3.8635 0.9783 2.4814 0.1898 0.1893 5.5526 1.5057 -#> 232: 93.1581 -6.0071 -1.9852 -4.3904 -0.9523 0.1857 5.3056 3.8967 0.9766 2.5002 0.1891 0.1904 5.5571 1.5057 -#> 233: 93.1417 -6.0131 -1.9865 -4.3933 -0.9517 0.1869 5.3290 3.9227 0.9745 2.5129 0.1885 0.1909 5.5609 1.5069 -#> 234: 93.1245 -6.0198 -1.9878 -4.3961 -0.9514 0.1880 5.3062 3.9567 0.9731 2.5269 0.1886 0.1916 5.5645 1.5074 -#> 235: 93.1084 -6.0269 -1.9885 -4.3985 -0.9514 0.1892 5.3213 3.9969 0.9729 2.5390 0.1887 0.1931 5.5722 1.5065 -#> 236: 93.1037 -6.0382 -1.9897 -4.4009 -0.9517 0.1899 5.3601 4.0674 0.9744 2.5501 0.1886 0.1949 5.5811 1.5066 -#> 237: 93.0989 -6.0432 -1.9906 -4.4031 -0.9518 0.1909 5.3744 4.0877 0.9755 2.5623 0.1885 0.1964 5.5890 1.5051 -#> 238: 93.0932 -6.0433 -1.9912 -4.4041 -0.9521 0.1915 5.4192 4.0775 0.9772 2.5698 0.1886 0.1980 5.5980 1.5029 -#> 239: 93.0943 -6.0475 -1.9913 -4.4056 -0.9520 0.1921 5.4483 4.0960 0.9792 2.5785 0.1888 0.1997 5.5999 1.5011 -#> 240: 93.0904 -6.0498 -1.9909 -4.4070 -0.9520 0.1925 5.4921 4.1095 0.9814 2.5867 0.1887 0.2011 5.5974 1.5013 -#> 241: 93.0883 -6.0508 -1.9910 -4.4086 -0.9520 0.1931 5.5503 4.1140 0.9827 2.5966 0.1887 0.2023 5.6049 1.4997 -#> 242: 93.0884 -6.0487 -1.9916 -4.4102 -0.9517 0.1940 5.5634 4.1021 0.9831 2.6059 0.1886 0.2039 5.6116 1.5005 -#> 243: 93.0836 -6.0466 -1.9920 -4.4123 -0.9517 0.1950 5.5786 4.0878 0.9837 2.6204 0.1887 0.2054 5.6217 1.5000 -#> 244: 93.0756 -6.0477 -1.9926 -4.4149 -0.9517 0.1956 5.5827 4.0904 0.9843 2.6385 0.1887 0.2070 5.6306 1.4995 -#> 245: 93.0664 -6.0533 -1.9930 -4.4174 -0.9514 0.1963 5.6228 4.1208 0.9857 2.6549 0.1888 0.2086 5.6346 1.4996 -#> 246: 93.0643 -6.0543 -1.9931 -4.4200 -0.9511 0.1969 5.6236 4.1257 0.9872 2.6735 0.1886 0.2096 5.6381 1.4989 -#> 247: 93.0631 -6.0568 -1.9929 -4.4227 -0.9511 0.1974 5.6045 4.1389 0.9889 2.6910 0.1886 0.2107 5.6408 1.4984 -#> 248: 93.0636 -6.0567 -1.9924 -4.4264 -0.9513 0.1974 5.6016 4.1412 0.9906 2.7225 0.1886 0.2117 5.6424 1.4992 -#> 249: 93.0727 -6.0560 -1.9920 -4.4302 -0.9514 0.1973 5.6088 4.1383 0.9922 2.7584 0.1885 0.2125 5.6441 1.4992 -#> 250: 93.0865 -6.0551 -1.9915 -4.4337 -0.9512 0.1973 5.6127 4.1386 0.9941 2.7852 0.1884 0.2135 5.6522 1.4977 -#> 251: 93.0887 -6.0551 -1.9910 -4.4364 -0.9511 0.1967 5.5869 4.1455 0.9964 2.8060 0.1883 0.2146 5.6561 1.4968 -#> 252: 93.0877 -6.0522 -1.9904 -4.4376 -0.9511 0.1964 5.5778 4.1346 0.9987 2.8151 0.1883 0.2155 5.6583 1.4964 -#> 253: 93.0843 -6.0518 -1.9897 -4.4391 -0.9512 0.1961 5.5948 4.1323 1.0011 2.8253 0.1884 0.2164 5.6588 1.4972 -#> 254: 93.0818 -6.0518 -1.9896 -4.4399 -0.9512 0.1957 5.6122 4.1352 1.0016 2.8319 0.1882 0.2169 5.6573 1.4991 -#> 255: 93.0838 -6.0524 -1.9895 -4.4401 -0.9514 0.1954 5.6310 4.1366 1.0025 2.8408 0.1880 0.2174 5.6584 1.4996 -#> 256: 93.0850 -6.0579 -1.9892 -4.4400 -0.9515 0.1948 5.6526 4.1752 1.0043 2.8482 0.1879 0.2181 5.6611 1.4979 -#> 257: 93.0868 -6.0600 -1.9890 -4.4391 -0.9517 0.1940 5.6742 4.1941 1.0055 2.8499 0.1878 0.2189 5.6649 1.4985 -#> 258: 93.0873 -6.0606 -1.9888 -4.4391 -0.9518 0.1932 5.7088 4.2037 1.0066 2.8552 0.1877 0.2196 5.6668 1.4983 -#> 259: 93.0912 -6.0650 -1.9882 -4.4377 -0.9519 0.1925 5.7494 4.2300 1.0080 2.8537 0.1877 0.2204 5.6729 1.4977 -#> 260: 93.0964 -6.0699 -1.9874 -4.4362 -0.9519 0.1918 5.7609 4.2588 1.0100 2.8513 0.1877 0.2212 5.6792 1.4974 -#> 261: 93.1014 -6.0737 -1.9866 -4.4350 -0.9522 0.1913 5.7971 4.2807 1.0115 2.8496 0.1877 0.2220 5.6812 1.4969 -#> 262: 93.1064 -6.0734 -1.9859 -4.4346 -0.9526 0.1909 5.7936 4.2719 1.0129 2.8505 0.1877 0.2228 5.6824 1.4958 -#> 263: 93.1092 -6.0783 -1.9850 -4.4344 -0.9530 0.1906 5.8078 4.2973 1.0141 2.8525 0.1879 0.2233 5.6815 1.4954 -#> 264: 93.1128 -6.0830 -1.9842 -4.4338 -0.9535 0.1901 5.8245 4.3273 1.0146 2.8527 0.1880 0.2237 5.6768 1.4958 -#> 265: 93.1198 -6.0874 -1.9834 -4.4331 -0.9541 0.1895 5.8467 4.3490 1.0149 2.8522 0.1880 0.2238 5.6693 1.4965 -#> 266: 93.1284 -6.0890 -1.9828 -4.4327 -0.9546 0.1888 5.8350 4.3488 1.0149 2.8513 0.1881 0.2239 5.6650 1.4970 -#> 267: 93.1380 -6.0926 -1.9819 -4.4326 -0.9549 0.1883 5.8440 4.3677 1.0156 2.8526 0.1883 0.2240 5.6609 1.4974 -#> 268: 93.1480 -6.0915 -1.9810 -4.4321 -0.9552 0.1873 5.8565 4.3552 1.0163 2.8522 0.1886 0.2238 5.6537 1.4990 -#> 269: 93.1539 -6.0910 -1.9803 -4.4314 -0.9556 0.1866 5.8709 4.3438 1.0179 2.8503 0.1888 0.2237 5.6495 1.4989 -#> 270: 93.1620 -6.0898 -1.9798 -4.4311 -0.9561 0.1861 5.8678 4.3301 1.0197 2.8507 0.1890 0.2235 5.6466 1.4984 -#> 271: 93.1668 -6.0881 -1.9792 -4.4305 -0.9565 0.1857 5.8508 4.3147 1.0209 2.8487 0.1891 0.2234 5.6487 1.4997 -#> 272: 93.1725 -6.0848 -1.9787 -4.4300 -0.9569 0.1855 5.8431 4.2948 1.0217 2.8474 0.1894 0.2233 5.6488 1.5000 -#> 273: 93.1770 -6.0809 -1.9783 -4.4297 -0.9572 0.1850 5.8432 4.2739 1.0227 2.8470 0.1897 0.2235 5.6497 1.5000 -#> 274: 93.1797 -6.0774 -1.9776 -4.4299 -0.9574 0.1846 5.8549 4.2532 1.0243 2.8494 0.1901 0.2235 5.6511 1.5003 -#> 275: 93.1829 -6.0759 -1.9774 -4.4303 -0.9578 0.1845 5.8633 4.2387 1.0255 2.8514 0.1906 0.2234 5.6561 1.5010 -#> 276: 93.1846 -6.0764 -1.9771 -4.4303 -0.9581 0.1845 5.8738 4.2322 1.0267 2.8523 0.1911 0.2232 5.6554 1.5020 -#> 277: 93.1880 -6.0792 -1.9768 -4.4305 -0.9584 0.1844 5.8980 4.2423 1.0278 2.8541 0.1915 0.2229 5.6586 1.5019 -#> 278: 93.1920 -6.0791 -1.9766 -4.4307 -0.9586 0.1841 5.9368 4.2391 1.0289 2.8559 0.1919 0.2226 5.6600 1.5024 -#> 279: 93.1892 -6.0786 -1.9766 -4.4310 -0.9586 0.1839 5.9822 4.2309 1.0300 2.8584 0.1925 0.2226 5.6642 1.5015 -#> 280: 93.1868 -6.0782 -1.9765 -4.4311 -0.9587 0.1836 6.0381 4.2253 1.0311 2.8616 0.1930 0.2227 5.6686 1.5008 -#> 281: 93.1805 -6.0781 -1.9764 -4.4309 -0.9586 0.1832 6.0718 4.2228 1.0325 2.8626 0.1936 0.2227 5.6741 1.5002 -#> 282: 93.1780 -6.0768 -1.9762 -4.4318 -0.9585 0.1829 6.0867 4.2160 1.0341 2.8701 0.1941 0.2228 5.6740 1.4998 -#> 283: 93.1777 -6.0736 -1.9760 -4.4325 -0.9583 0.1825 6.1250 4.2003 1.0355 2.8768 0.1946 0.2228 5.6761 1.5010 -#> 284: 93.1745 -6.0726 -1.9757 -4.4337 -0.9582 0.1823 6.1509 4.1975 1.0370 2.8843 0.1951 0.2227 5.6764 1.5009 -#> 285: 93.1742 -6.0719 -1.9755 -4.4348 -0.9579 0.1820 6.1652 4.1936 1.0381 2.8910 0.1954 0.2225 5.6773 1.5011 -#> 286: 93.1706 -6.0698 -1.9754 -4.4356 -0.9576 0.1818 6.1840 4.1844 1.0394 2.8966 0.1958 0.2224 5.6780 1.5011 -#> 287: 93.1672 -6.0678 -1.9752 -4.4370 -0.9573 0.1816 6.2123 4.1767 1.0400 2.9079 0.1963 0.2224 5.6757 1.5015 -#> 288: 93.1628 -6.0658 -1.9753 -4.4379 -0.9572 0.1815 6.2355 4.1700 1.0407 2.9150 0.1967 0.2223 5.6742 1.5013 -#> 289: 93.1588 -6.0628 -1.9753 -4.4389 -0.9569 0.1818 6.2435 4.1565 1.0416 2.9217 0.1969 0.2218 5.6777 1.5007 -#> 290: 93.1560 -6.0590 -1.9754 -4.4399 -0.9565 0.1820 6.2564 4.1394 1.0425 2.9291 0.1971 0.2214 5.6778 1.5006 -#> 291: 93.1552 -6.0555 -1.9754 -4.4409 -0.9562 0.1821 6.2753 4.1246 1.0435 2.9375 0.1973 0.2210 5.6779 1.5009 -#> 292: 93.1546 -6.0541 -1.9754 -4.4415 -0.9558 0.1820 6.2881 4.1183 1.0444 2.9414 0.1975 0.2205 5.6762 1.5006 -#> 293: 93.1506 -6.0535 -1.9756 -4.4424 -0.9555 0.1821 6.2856 4.1182 1.0454 2.9474 0.1976 0.2200 5.6770 1.4994 -#> 294: 93.1453 -6.0520 -1.9758 -4.4424 -0.9553 0.1819 6.2733 4.1124 1.0463 2.9487 0.1979 0.2195 5.6792 1.4985 -#> 295: 93.1431 -6.0487 -1.9760 -4.4421 -0.9551 0.1820 6.2655 4.1009 1.0469 2.9498 0.1982 0.2190 5.6797 1.4989 -#> 296: 93.1425 -6.0460 -1.9760 -4.4432 -0.9548 0.1818 6.2801 4.0912 1.0478 2.9566 0.1984 0.2185 5.6795 1.4989 -#> 297: 93.1403 -6.0442 -1.9761 -4.4440 -0.9545 0.1818 6.2979 4.0836 1.0485 2.9626 0.1987 0.2182 5.6783 1.4978 -#> 298: 93.1400 -6.0438 -1.9763 -4.4440 -0.9543 0.1817 6.3069 4.0842 1.0492 2.9646 0.1989 0.2178 5.6783 1.4968 -#> 299: 93.1373 -6.0426 -1.9764 -4.4445 -0.9540 0.1813 6.3134 4.0790 1.0505 2.9694 0.1991 0.2175 5.6800 1.4953 -#> 300: 93.1340 -6.0412 -1.9764 -4.4450 -0.9538 0.1811 6.3192 4.0731 1.0516 2.9744 0.1993 0.2171 5.6782 1.4938 -#> 301: 93.1330 -6.0402 -1.9766 -4.4455 -0.9535 0.1808 6.3278 4.0685 1.0531 2.9784 0.1996 0.2167 5.6819 1.4925 -#> 302: 93.1308 -6.0402 -1.9768 -4.4457 -0.9534 0.1806 6.3417 4.0684 1.0549 2.9813 0.1998 0.2163 5.6824 1.4905 -#> 303: 93.1294 -6.0373 -1.9769 -4.4459 -0.9532 0.1804 6.3489 4.0538 1.0565 2.9838 0.2000 0.2159 5.6841 1.4890 -#> 304: 93.1304 -6.0345 -1.9771 -4.4461 -0.9530 0.1801 6.3543 4.0409 1.0581 2.9859 0.2002 0.2155 5.6869 1.4875 -#> 305: 93.1287 -6.0319 -1.9772 -4.4463 -0.9528 0.1800 6.3496 4.0293 1.0597 2.9882 0.2003 0.2151 5.6902 1.4867 -#> 306: 93.1261 -6.0301 -1.9775 -4.4474 -0.9527 0.1802 6.3479 4.0231 1.0614 2.9989 0.2003 0.2145 5.6963 1.4856 -#> 307: 93.1232 -6.0284 -1.9777 -4.4479 -0.9526 0.1802 6.3507 4.0135 1.0629 3.0036 0.2004 0.2141 5.6987 1.4849 -#> 308: 93.1192 -6.0264 -1.9779 -4.4483 -0.9524 0.1802 6.3641 4.0019 1.0644 3.0084 0.2004 0.2135 5.6991 1.4837 -#> 309: 93.1137 -6.0253 -1.9783 -4.4487 -0.9522 0.1803 6.3579 3.9953 1.0658 3.0133 0.2004 0.2130 5.7035 1.4826 -#> 310: 93.1100 -6.0223 -1.9787 -4.4489 -0.9520 0.1804 6.3423 3.9800 1.0665 3.0171 0.2005 0.2126 5.7061 1.4822 -#> 311: 93.1044 -6.0215 -1.9791 -4.4496 -0.9517 0.1804 6.3365 3.9744 1.0675 3.0251 0.2005 0.2121 5.7092 1.4816 -#> 312: 93.1006 -6.0206 -1.9795 -4.4501 -0.9516 0.1806 6.3317 3.9681 1.0688 3.0321 0.2006 0.2115 5.7128 1.4805 -#> 313: 93.0951 -6.0194 -1.9797 -4.4499 -0.9516 0.1805 6.3297 3.9609 1.0702 3.0333 0.2008 0.2109 5.7137 1.4805 -#> 314: 93.0922 -6.0192 -1.9800 -4.4497 -0.9515 0.1804 6.3486 3.9570 1.0715 3.0345 0.2009 0.2104 5.7144 1.4800 -#> 315: 93.0883 -6.0186 -1.9804 -4.4495 -0.9515 0.1803 6.3712 3.9528 1.0726 3.0351 0.2011 0.2100 5.7156 1.4794 -#> 316: 93.0808 -6.0182 -1.9808 -4.4492 -0.9514 0.1802 6.3979 3.9483 1.0738 3.0345 0.2013 0.2097 5.7164 1.4792 -#> 317: 93.0758 -6.0174 -1.9813 -4.4487 -0.9513 0.1801 6.4377 3.9428 1.0747 3.0327 0.2015 0.2094 5.7175 1.4787 -#> 318: 93.0713 -6.0166 -1.9816 -4.4484 -0.9513 0.1801 6.4856 3.9375 1.0757 3.0316 0.2017 0.2091 5.7197 1.4778 -#> 319: 93.0659 -6.0176 -1.9819 -4.4482 -0.9511 0.1800 6.5263 3.9425 1.0768 3.0313 0.2018 0.2088 5.7218 1.4772 -#> 320: 93.0607 -6.0165 -1.9822 -4.4484 -0.9510 0.1798 6.5554 3.9372 1.0777 3.0329 0.2019 0.2087 5.7236 1.4771 -#> 321: 93.0551 -6.0145 -1.9825 -4.4487 -0.9509 0.1797 6.5844 3.9275 1.0787 3.0368 0.2021 0.2085 5.7256 1.4766 -#> 322: 93.0531 -6.0130 -1.9827 -4.4491 -0.9507 0.1797 6.6073 3.9201 1.0797 3.0400 0.2021 0.2082 5.7250 1.4759 -#> 323: 93.0477 -6.0123 -1.9828 -4.4493 -0.9506 0.1794 6.6255 3.9149 1.0804 3.0420 0.2021 0.2080 5.7249 1.4756 -#> 324: 93.0425 -6.0107 -1.9829 -4.4498 -0.9504 0.1792 6.6282 3.9060 1.0813 3.0457 0.2022 0.2078 5.7250 1.4754 -#> 325: 93.0389 -6.0090 -1.9830 -4.4504 -0.9503 0.1792 6.6252 3.8965 1.0819 3.0496 0.2022 0.2077 5.7246 1.4749 -#> 326: 93.0411 -6.0093 -1.9832 -4.4509 -0.9503 0.1795 6.6358 3.8976 1.0827 3.0516 0.2022 0.2076 5.7248 1.4738 -#> 327: 93.0418 -6.0095 -1.9834 -4.4514 -0.9503 0.1797 6.6415 3.8962 1.0834 3.0533 0.2022 0.2075 5.7237 1.4737 -#> 328: 93.0434 -6.0093 -1.9835 -4.4520 -0.9503 0.1798 6.6621 3.8957 1.0841 3.0550 0.2022 0.2074 5.7247 1.4731 -#> 329: 93.0446 -6.0109 -1.9836 -4.4522 -0.9503 0.1798 6.6763 3.9048 1.0847 3.0543 0.2022 0.2072 5.7259 1.4725 -#> 330: 93.0451 -6.0133 -1.9838 -4.4518 -0.9503 0.1799 6.6859 3.9192 1.0852 3.0521 0.2022 0.2070 5.7252 1.4719 -#> 331: 93.0456 -6.0136 -1.9838 -4.4516 -0.9503 0.1799 6.6773 3.9217 1.0858 3.0505 0.2022 0.2067 5.7250 1.4715 -#> 332: 93.0463 -6.0133 -1.9839 -4.4515 -0.9504 0.1799 6.6560 3.9195 1.0863 3.0494 0.2022 0.2063 5.7255 1.4710 -#> 333: 93.0496 -6.0122 -1.9839 -4.4513 -0.9505 0.1800 6.6484 3.9134 1.0869 3.0474 0.2022 0.2060 5.7253 1.4705 -#> 334: 93.0520 -6.0105 -1.9838 -4.4513 -0.9505 0.1801 6.6314 3.9035 1.0877 3.0462 0.2022 0.2056 5.7259 1.4702 -#> 335: 93.0550 -6.0088 -1.9836 -4.4510 -0.9507 0.1800 6.6194 3.8941 1.0887 3.0451 0.2022 0.2051 5.7263 1.4702 -#> 336: 93.0554 -6.0081 -1.9834 -4.4509 -0.9508 0.1800 6.6100 3.8896 1.0896 3.0444 0.2022 0.2048 5.7266 1.4705 -#> 337: 93.0582 -6.0067 -1.9832 -4.4507 -0.9509 0.1800 6.6089 3.8805 1.0904 3.0445 0.2021 0.2044 5.7260 1.4706 -#> 338: 93.0631 -6.0073 -1.9831 -4.4507 -0.9511 0.1801 6.5993 3.8798 1.0908 3.0443 0.2021 0.2040 5.7250 1.4711 -#> 339: 93.0689 -6.0071 -1.9831 -4.4508 -0.9513 0.1803 6.5976 3.8749 1.0911 3.0442 0.2021 0.2037 5.7240 1.4714 -#> 340: 93.0694 -6.0085 -1.9831 -4.4507 -0.9516 0.1804 6.5915 3.8779 1.0914 3.0436 0.2022 0.2032 5.7227 1.4711 -#> 341: 93.0709 -6.0097 -1.9830 -4.4508 -0.9518 0.1804 6.5862 3.8803 1.0915 3.0429 0.2023 0.2026 5.7213 1.4715 -#> 342: 93.0741 -6.0104 -1.9829 -4.4507 -0.9521 0.1804 6.5894 3.8812 1.0918 3.0417 0.2024 0.2022 5.7204 1.4714 -#> 343: 93.0781 -6.0122 -1.9829 -4.4505 -0.9523 0.1804 6.5907 3.8870 1.0921 3.0410 0.2024 0.2016 5.7202 1.4712 -#> 344: 93.0818 -6.0134 -1.9829 -4.4503 -0.9525 0.1804 6.5908 3.8895 1.0926 3.0400 0.2025 0.2011 5.7182 1.4712 -#> 345: 93.0850 -6.0148 -1.9829 -4.4500 -0.9528 0.1806 6.5984 3.8931 1.0926 3.0387 0.2026 0.2006 5.7169 1.4712 -#> 346: 93.0849 -6.0155 -1.9831 -4.4502 -0.9529 0.1807 6.6079 3.8986 1.0931 3.0401 0.2028 0.2002 5.7172 1.4716 -#> 347: 93.0859 -6.0161 -1.9832 -4.4503 -0.9530 0.1809 6.6307 3.9028 1.0941 3.0404 0.2029 0.1998 5.7170 1.4712 -#> 348: 93.0885 -6.0173 -1.9833 -4.4503 -0.9532 0.1809 6.6470 3.9096 1.0951 3.0404 0.2030 0.1993 5.7174 1.4708 -#> 349: 93.0894 -6.0189 -1.9835 -4.4503 -0.9534 0.1810 6.6443 3.9190 1.0955 3.0410 0.2031 0.1989 5.7175 1.4707 -#> 350: 93.0924 -6.0196 -1.9836 -4.4502 -0.9535 0.1813 6.6543 3.9218 1.0957 3.0409 0.2032 0.1983 5.7182 1.4705 -#> 351: 93.0938 -6.0203 -1.9838 -4.4503 -0.9536 0.1814 6.6630 3.9233 1.0963 3.0417 0.2032 0.1977 5.7189 1.4703 -#> 352: 93.0946 -6.0210 -1.9838 -4.4505 -0.9537 0.1816 6.6698 3.9263 1.0968 3.0432 0.2033 0.1973 5.7196 1.4701 -#> 353: 93.0969 -6.0214 -1.9839 -4.4505 -0.9538 0.1818 6.6837 3.9270 1.0973 3.0442 0.2034 0.1968 5.7199 1.4701 -#> 354: 93.1014 -6.0199 -1.9839 -4.4504 -0.9539 0.1817 6.7040 3.9204 1.0978 3.0438 0.2034 0.1962 5.7191 1.4703 -#> 355: 93.1035 -6.0197 -1.9838 -4.4502 -0.9539 0.1816 6.7119 3.9222 1.0983 3.0433 0.2034 0.1957 5.7194 1.4706 -#> 356: 93.1055 -6.0198 -1.9839 -4.4496 -0.9539 0.1815 6.7302 3.9277 1.0989 3.0409 0.2035 0.1952 5.7206 1.4707 -#> 357: 93.1080 -6.0188 -1.9837 -4.4490 -0.9540 0.1813 6.7558 3.9243 1.0997 3.0386 0.2035 0.1948 5.7217 1.4706 -#> 358: 93.1111 -6.0182 -1.9835 -4.4484 -0.9541 0.1812 6.7733 3.9204 1.1005 3.0365 0.2035 0.1944 5.7209 1.4700 -#> 359: 93.1148 -6.0175 -1.9834 -4.4481 -0.9542 0.1811 6.7997 3.9151 1.1012 3.0355 0.2035 0.1940 5.7191 1.4696 -#> 360: 93.1157 -6.0176 -1.9832 -4.4478 -0.9543 0.1810 6.8133 3.9155 1.1017 3.0340 0.2035 0.1937 5.7158 1.4691 -#> 361: 93.1169 -6.0185 -1.9830 -4.4476 -0.9544 0.1808 6.8098 3.9232 1.1022 3.0328 0.2035 0.1934 5.7143 1.4690 -#> 362: 93.1173 -6.0205 -1.9829 -4.4472 -0.9545 0.1805 6.8125 3.9361 1.1024 3.0319 0.2035 0.1931 5.7137 1.4693 -#> 363: 93.1162 -6.0230 -1.9828 -4.4467 -0.9545 0.1801 6.8240 3.9524 1.1025 3.0312 0.2035 0.1928 5.7125 1.4695 -#> 364: 93.1173 -6.0240 -1.9826 -4.4464 -0.9546 0.1799 6.8341 3.9575 1.1027 3.0307 0.2035 0.1924 5.7092 1.4695 -#> 365: 93.1199 -6.0259 -1.9824 -4.4462 -0.9547 0.1796 6.8476 3.9687 1.1028 3.0316 0.2036 0.1920 5.7073 1.4695 -#> 366: 93.1220 -6.0277 -1.9821 -4.4461 -0.9548 0.1793 6.8542 3.9777 1.1032 3.0319 0.2037 0.1916 5.7060 1.4694 -#> 367: 93.1230 -6.0287 -1.9819 -4.4460 -0.9548 0.1791 6.8633 3.9829 1.1038 3.0331 0.2038 0.1914 5.7056 1.4693 -#> 368: 93.1255 -6.0276 -1.9816 -4.4459 -0.9549 0.1789 6.8734 3.9764 1.1038 3.0341 0.2038 0.1912 5.7050 1.4695 -#> 369: 93.1258 -6.0263 -1.9814 -4.4461 -0.9549 0.1787 6.8756 3.9698 1.1039 3.0357 0.2039 0.1910 5.7031 1.4697 -#> 370: 93.1288 -6.0252 -1.9811 -4.4463 -0.9548 0.1785 6.8892 3.9639 1.1039 3.0375 0.2040 0.1909 5.7029 1.4701 -#> 371: 93.1317 -6.0245 -1.9810 -4.4467 -0.9548 0.1784 6.8974 3.9601 1.1037 3.0391 0.2040 0.1907 5.7037 1.4700 -#> 372: 93.1346 -6.0233 -1.9811 -4.4465 -0.9548 0.1781 6.9042 3.9536 1.1035 3.0386 0.2040 0.1905 5.7038 1.4700 -#> 373: 93.1340 -6.0234 -1.9810 -4.4461 -0.9547 0.1778 6.9034 3.9548 1.1034 3.0371 0.2039 0.1903 5.7040 1.4698 -#> 374: 93.1324 -6.0230 -1.9811 -4.4456 -0.9547 0.1775 6.9080 3.9527 1.1034 3.0349 0.2038 0.1901 5.7055 1.4691 -#> 375: 93.1309 -6.0226 -1.9812 -4.4451 -0.9546 0.1773 6.9093 3.9493 1.1034 3.0334 0.2037 0.1899 5.7063 1.4683 -#> 376: 93.1298 -6.0215 -1.9811 -4.4447 -0.9546 0.1770 6.9039 3.9432 1.1035 3.0319 0.2036 0.1897 5.7064 1.4678 -#> 377: 93.1296 -6.0209 -1.9811 -4.4443 -0.9546 0.1768 6.8932 3.9390 1.1036 3.0305 0.2035 0.1895 5.7056 1.4672 -#> 378: 93.1292 -6.0200 -1.9810 -4.4438 -0.9545 0.1764 6.8850 3.9349 1.1037 3.0288 0.2034 0.1892 5.7068 1.4667 -#> 379: 93.1284 -6.0196 -1.9808 -4.4432 -0.9544 0.1760 6.8766 3.9318 1.1038 3.0266 0.2033 0.1890 5.7072 1.4665 -#> 380: 93.1304 -6.0182 -1.9806 -4.4425 -0.9543 0.1756 6.8737 3.9249 1.1040 3.0236 0.2033 0.1888 5.7074 1.4662 -#> 381: 93.1315 -6.0169 -1.9804 -4.4417 -0.9542 0.1754 6.8707 3.9193 1.1040 3.0210 0.2032 0.1886 5.7066 1.4661 -#> 382: 93.1331 -6.0160 -1.9801 -4.4409 -0.9542 0.1750 6.8645 3.9150 1.1040 3.0187 0.2032 0.1885 5.7063 1.4664 -#> 383: 93.1334 -6.0153 -1.9800 -4.4403 -0.9542 0.1746 6.8599 3.9123 1.1037 3.0167 0.2032 0.1882 5.7074 1.4665 -#> 384: 93.1328 -6.0140 -1.9801 -4.4397 -0.9540 0.1742 6.8600 3.9074 1.1034 3.0149 0.2031 0.1879 5.7072 1.4667 -#> 385: 93.1306 -6.0137 -1.9801 -4.4392 -0.9539 0.1739 6.8449 3.9073 1.1031 3.0137 0.2030 0.1876 5.7084 1.4665 -#> 386: 93.1281 -6.0134 -1.9801 -4.4388 -0.9539 0.1735 6.8356 3.9088 1.1028 3.0123 0.2029 0.1872 5.7087 1.4667 -#> 387: 93.1267 -6.0141 -1.9801 -4.4384 -0.9537 0.1732 6.8364 3.9150 1.1025 3.0110 0.2028 0.1869 5.7101 1.4669 -#> 388: 93.1252 -6.0142 -1.9801 -4.4380 -0.9536 0.1730 6.8374 3.9192 1.1022 3.0097 0.2028 0.1866 5.7110 1.4670 -#> 389: 93.1223 -6.0140 -1.9801 -4.4375 -0.9535 0.1728 6.8334 3.9209 1.1019 3.0083 0.2028 0.1862 5.7105 1.4674 -#> 390: 93.1221 -6.0144 -1.9800 -4.4371 -0.9534 0.1726 6.8248 3.9256 1.1014 3.0068 0.2028 0.1859 5.7098 1.4675 -#> 391: 93.1210 -6.0149 -1.9799 -4.4365 -0.9533 0.1725 6.8339 3.9293 1.1011 3.0054 0.2028 0.1856 5.7109 1.4678 -#> 392: 93.1193 -6.0145 -1.9799 -4.4360 -0.9532 0.1724 6.8360 3.9279 1.1009 3.0040 0.2028 0.1852 5.7107 1.4678 -#> 393: 93.1200 -6.0149 -1.9799 -4.4357 -0.9532 0.1723 6.8461 3.9287 1.1005 3.0019 0.2028 0.1849 5.7100 1.4678 -#> 394: 93.1202 -6.0138 -1.9799 -4.4355 -0.9532 0.1723 6.8520 3.9229 1.1006 3.0003 0.2028 0.1846 5.7085 1.4679 -#> 395: 93.1203 -6.0134 -1.9800 -4.4354 -0.9532 0.1723 6.8583 3.9200 1.1005 2.9987 0.2027 0.1844 5.7072 1.4680 -#> 396: 93.1195 -6.0131 -1.9800 -4.4353 -0.9532 0.1724 6.8593 3.9169 1.1004 2.9969 0.2027 0.1842 5.7062 1.4676 -#> 397: 93.1195 -6.0130 -1.9801 -4.4352 -0.9532 0.1724 6.8591 3.9143 1.1004 2.9958 0.2027 0.1839 5.7046 1.4675 -#> 398: 93.1200 -6.0128 -1.9801 -4.4352 -0.9532 0.1725 6.8522 3.9125 1.1004 2.9945 0.2028 0.1836 5.7032 1.4675 -#> 399: 93.1200 -6.0135 -1.9803 -4.4351 -0.9531 0.1726 6.8471 3.9166 1.1003 2.9933 0.2028 0.1833 5.7032 1.4673 -#> 400: 93.1204 -6.0139 -1.9803 -4.4351 -0.9531 0.1727 6.8438 3.9191 1.1003 2.9918 0.2027 0.1832 5.7026 1.4671 -#> 401: 93.1198 -6.0139 -1.9804 -4.4351 -0.9530 0.1728 6.8373 3.9186 1.1004 2.9901 0.2027 0.1831 5.7015 1.4670 -#> 402: 93.1199 -6.0141 -1.9804 -4.4351 -0.9530 0.1729 6.8357 3.9194 1.1005 2.9882 0.2027 0.1830 5.7003 1.4671 -#> 403: 93.1196 -6.0155 -1.9804 -4.4350 -0.9530 0.1730 6.8285 3.9255 1.1007 2.9863 0.2026 0.1829 5.7001 1.4671 -#> 404: 93.1183 -6.0164 -1.9805 -4.4350 -0.9531 0.1732 6.8204 3.9308 1.1009 2.9843 0.2026 0.1829 5.7008 1.4670 -#> 405: 93.1178 -6.0161 -1.9805 -4.4350 -0.9532 0.1733 6.8205 3.9286 1.1012 2.9823 0.2025 0.1829 5.7013 1.4669 -#> 406: 93.1176 -6.0171 -1.9806 -4.4348 -0.9533 0.1735 6.8253 3.9319 1.1013 2.9801 0.2025 0.1828 5.7026 1.4666 -#> 407: 93.1168 -6.0185 -1.9807 -4.4348 -0.9533 0.1736 6.8290 3.9373 1.1015 2.9788 0.2024 0.1830 5.7033 1.4664 -#> 408: 93.1165 -6.0198 -1.9808 -4.4349 -0.9534 0.1738 6.8217 3.9428 1.1017 2.9773 0.2023 0.1830 5.7047 1.4663 -#> 409: 93.1165 -6.0210 -1.9809 -4.4350 -0.9534 0.1741 6.8208 3.9505 1.1019 2.9761 0.2021 0.1830 5.7055 1.4661 -#> 410: 93.1169 -6.0230 -1.9810 -4.4351 -0.9535 0.1745 6.8239 3.9617 1.1020 2.9751 0.2020 0.1829 5.7052 1.4658 -#> 411: 93.1166 -6.0237 -1.9811 -4.4353 -0.9536 0.1748 6.8234 3.9664 1.1020 2.9741 0.2019 0.1829 5.7043 1.4657 -#> 412: 93.1164 -6.0235 -1.9812 -4.4355 -0.9536 0.1751 6.8205 3.9643 1.1020 2.9735 0.2017 0.1827 5.7053 1.4654 -#> 413: 93.1182 -6.0232 -1.9814 -4.4356 -0.9537 0.1755 6.8133 3.9615 1.1020 2.9726 0.2016 0.1825 5.7070 1.4650 -#> 414: 93.1190 -6.0226 -1.9815 -4.4360 -0.9537 0.1760 6.8113 3.9578 1.1021 2.9726 0.2015 0.1825 5.7081 1.4648 -#> 415: 93.1183 -6.0226 -1.9817 -4.4364 -0.9538 0.1765 6.8081 3.9557 1.1021 2.9725 0.2014 0.1824 5.7085 1.4646 -#> 416: 93.1185 -6.0238 -1.9818 -4.4369 -0.9538 0.1768 6.8134 3.9617 1.1020 2.9734 0.2013 0.1822 5.7103 1.4645 -#> 417: 93.1190 -6.0245 -1.9819 -4.4373 -0.9540 0.1770 6.8164 3.9664 1.1022 2.9743 0.2012 0.1819 5.7102 1.4650 -#> 418: 93.1219 -6.0256 -1.9818 -4.4376 -0.9542 0.1773 6.8206 3.9710 1.1026 2.9745 0.2011 0.1816 5.7110 1.4655 -#> 419: 93.1255 -6.0261 -1.9817 -4.4381 -0.9543 0.1776 6.8183 3.9714 1.1030 2.9759 0.2010 0.1814 5.7134 1.4659 -#> 420: 93.1294 -6.0262 -1.9816 -4.4385 -0.9546 0.1779 6.8113 3.9704 1.1033 2.9768 0.2009 0.1810 5.7156 1.4666 -#> 421: 93.1319 -6.0259 -1.9815 -4.4392 -0.9547 0.1781 6.7989 3.9685 1.1036 2.9786 0.2008 0.1808 5.7171 1.4676 -#> 422: 93.1338 -6.0263 -1.9814 -4.4398 -0.9548 0.1783 6.7922 3.9681 1.1038 2.9806 0.2006 0.1808 5.7179 1.4681 -#> 423: 93.1353 -6.0266 -1.9813 -4.4406 -0.9550 0.1786 6.7868 3.9674 1.1040 2.9837 0.2006 0.1808 5.7181 1.4687 -#> 424: 93.1374 -6.0270 -1.9811 -4.4414 -0.9550 0.1787 6.7758 3.9674 1.1043 2.9866 0.2004 0.1807 5.7198 1.4693 -#> 425: 93.1383 -6.0270 -1.9811 -4.4420 -0.9551 0.1787 6.7547 3.9674 1.1042 2.9887 0.2003 0.1806 5.7211 1.4702 -#> 426: 93.1400 -6.0268 -1.9811 -4.4427 -0.9551 0.1789 6.7376 3.9654 1.1043 2.9917 0.2002 0.1805 5.7241 1.4706 -#> 427: 93.1391 -6.0268 -1.9811 -4.4433 -0.9552 0.1790 6.7196 3.9634 1.1045 2.9951 0.2001 0.1805 5.7271 1.4710 -#> 428: 93.1404 -6.0268 -1.9810 -4.4442 -0.9552 0.1792 6.7104 3.9628 1.1044 2.9999 0.2000 0.1803 5.7282 1.4712 -#> 429: 93.1431 -6.0265 -1.9810 -4.4450 -0.9553 0.1793 6.7029 3.9612 1.1045 3.0043 0.1999 0.1803 5.7293 1.4716 -#> 430: 93.1464 -6.0263 -1.9809 -4.4457 -0.9554 0.1795 6.6962 3.9606 1.1046 3.0074 0.1999 0.1802 5.7291 1.4724 -#> 431: 93.1485 -6.0267 -1.9809 -4.4460 -0.9555 0.1797 6.6865 3.9623 1.1046 3.0082 0.1998 0.1802 5.7287 1.4726 -#> 432: 93.1509 -6.0277 -1.9808 -4.4462 -0.9556 0.1798 6.6843 3.9658 1.1047 3.0086 0.1998 0.1801 5.7280 1.4727 -#> 433: 93.1528 -6.0289 -1.9806 -4.4464 -0.9557 0.1798 6.6840 3.9714 1.1049 3.0087 0.1998 0.1801 5.7282 1.4729 -#> 434: 93.1555 -6.0286 -1.9804 -4.4467 -0.9557 0.1798 6.6870 3.9693 1.1052 3.0094 0.1997 0.1800 5.7277 1.4729 -#> 435: 93.1574 -6.0290 -1.9803 -4.4467 -0.9558 0.1798 6.6893 3.9712 1.1055 3.0095 0.1996 0.1800 5.7278 1.4727 -#> 436: 93.1594 -6.0299 -1.9802 -4.4468 -0.9558 0.1798 6.6934 3.9749 1.1059 3.0103 0.1996 0.1801 5.7271 1.4727 -#> 437: 93.1600 -6.0311 -1.9800 -4.4469 -0.9558 0.1797 6.7010 3.9812 1.1065 3.0110 0.1996 0.1801 5.7275 1.4727 -#> 438: 93.1617 -6.0318 -1.9799 -4.4471 -0.9559 0.1796 6.7120 3.9865 1.1069 3.0121 0.1995 0.1801 5.7271 1.4727 -#> 439: 93.1634 -6.0329 -1.9798 -4.4472 -0.9559 0.1795 6.7279 3.9930 1.1075 3.0127 0.1995 0.1802 5.7268 1.4727 -#> 440: 93.1644 -6.0332 -1.9797 -4.4473 -0.9559 0.1794 6.7338 3.9962 1.1080 3.0136 0.1994 0.1803 5.7270 1.4726 -#> 441: 93.1654 -6.0335 -1.9795 -4.4477 -0.9558 0.1794 6.7435 3.9988 1.1085 3.0155 0.1994 0.1805 5.7274 1.4728 -#> 442: 93.1670 -6.0340 -1.9792 -4.4480 -0.9558 0.1794 6.7493 4.0028 1.1091 3.0173 0.1993 0.1808 5.7282 1.4729 -#> 443: 93.1685 -6.0346 -1.9790 -4.4485 -0.9558 0.1793 6.7577 4.0073 1.1092 3.0202 0.1992 0.1811 5.7267 1.4732 -#> 444: 93.1671 -6.0346 -1.9789 -4.4491 -0.9558 0.1792 6.7559 4.0069 1.1093 3.0238 0.1992 0.1813 5.7258 1.4733 -#> 445: 93.1655 -6.0355 -1.9789 -4.4497 -0.9557 0.1790 6.7552 4.0127 1.1094 3.0276 0.1992 0.1814 5.7262 1.4733 -#> 446: 93.1641 -6.0361 -1.9787 -4.4501 -0.9557 0.1789 6.7579 4.0169 1.1096 3.0306 0.1991 0.1816 5.7262 1.4732 -#> 447: 93.1628 -6.0363 -1.9786 -4.4503 -0.9556 0.1787 6.7680 4.0196 1.1099 3.0318 0.1991 0.1818 5.7258 1.4729 -#> 448: 93.1629 -6.0371 -1.9787 -4.4509 -0.9556 0.1786 6.7705 4.0248 1.1100 3.0358 0.1990 0.1820 5.7267 1.4725 -#> 449: 93.1626 -6.0381 -1.9785 -4.4510 -0.9556 0.1784 6.7800 4.0298 1.1101 3.0368 0.1989 0.1822 5.7266 1.4722 -#> 450: 93.1614 -6.0386 -1.9782 -4.4514 -0.9556 0.1782 6.7796 4.0316 1.1103 3.0392 0.1989 0.1824 5.7260 1.4720 -#> 451: 93.1603 -6.0397 -1.9779 -4.4518 -0.9556 0.1780 6.7799 4.0381 1.1107 3.0416 0.1988 0.1827 5.7264 1.4720 -#> 452: 93.1610 -6.0406 -1.9775 -4.4522 -0.9556 0.1777 6.7813 4.0424 1.1111 3.0443 0.1988 0.1828 5.7268 1.4719 -#> 453: 93.1618 -6.0414 -1.9771 -4.4523 -0.9556 0.1774 6.7814 4.0490 1.1115 3.0456 0.1987 0.1830 5.7262 1.4721 -#> 454: 93.1625 -6.0415 -1.9767 -4.4525 -0.9555 0.1771 6.7799 4.0499 1.1118 3.0473 0.1986 0.1831 5.7260 1.4723 -#> 455: 93.1636 -6.0412 -1.9765 -4.4528 -0.9555 0.1769 6.7778 4.0489 1.1123 3.0496 0.1985 0.1832 5.7268 1.4722 -#> 456: 93.1653 -6.0401 -1.9762 -4.4532 -0.9554 0.1768 6.7703 4.0441 1.1127 3.0517 0.1983 0.1834 5.7282 1.4725 -#> 457: 93.1672 -6.0396 -1.9760 -4.4535 -0.9554 0.1766 6.7683 4.0427 1.1129 3.0539 0.1982 0.1835 5.7281 1.4727 -#> 458: 93.1692 -6.0398 -1.9757 -4.4539 -0.9554 0.1765 6.7627 4.0450 1.1132 3.0570 0.1981 0.1835 5.7294 1.4729 -#> 459: 93.1708 -6.0402 -1.9756 -4.4542 -0.9554 0.1763 6.7615 4.0483 1.1133 3.0596 0.1980 0.1836 5.7320 1.4728 -#> 460: 93.1710 -6.0401 -1.9755 -4.4544 -0.9553 0.1762 6.7629 4.0487 1.1135 3.0615 0.1979 0.1835 5.7323 1.4730 -#> 461: 93.1708 -6.0403 -1.9755 -4.4546 -0.9552 0.1762 6.7639 4.0492 1.1136 3.0631 0.1978 0.1834 5.7321 1.4729 -#> 462: 93.1707 -6.0405 -1.9755 -4.4548 -0.9552 0.1760 6.7657 4.0506 1.1136 3.0647 0.1977 0.1833 5.7323 1.4727 -#> 463: 93.1690 -6.0403 -1.9755 -4.4548 -0.9551 0.1759 6.7607 4.0494 1.1136 3.0651 0.1976 0.1832 5.7332 1.4726 -#> 464: 93.1673 -6.0400 -1.9755 -4.4548 -0.9551 0.1758 6.7588 4.0480 1.1138 3.0652 0.1975 0.1832 5.7344 1.4724 -#> 465: 93.1657 -6.0399 -1.9755 -4.4548 -0.9550 0.1756 6.7601 4.0474 1.1138 3.0652 0.1974 0.1831 5.7350 1.4724 -#> 466: 93.1656 -6.0406 -1.9754 -4.4548 -0.9549 0.1755 6.7589 4.0514 1.1139 3.0658 0.1973 0.1831 5.7355 1.4723 -#> 467: 93.1657 -6.0408 -1.9753 -4.4548 -0.9549 0.1754 6.7558 4.0525 1.1139 3.0664 0.1972 0.1831 5.7358 1.4725 -#> 468: 93.1664 -6.0411 -1.9752 -4.4551 -0.9548 0.1753 6.7546 4.0551 1.1140 3.0679 0.1971 0.1832 5.7358 1.4723 -#> 469: 93.1667 -6.0412 -1.9751 -4.4552 -0.9547 0.1752 6.7547 4.0554 1.1141 3.0676 0.1970 0.1833 5.7354 1.4721 -#> 470: 93.1664 -6.0413 -1.9750 -4.4552 -0.9546 0.1751 6.7579 4.0564 1.1143 3.0676 0.1969 0.1833 5.7352 1.4718 -#> 471: 93.1656 -6.0411 -1.9750 -4.4553 -0.9545 0.1750 6.7611 4.0555 1.1142 3.0681 0.1968 0.1834 5.7354 1.4715 -#> 472: 93.1644 -6.0408 -1.9751 -4.4554 -0.9544 0.1749 6.7577 4.0542 1.1142 3.0686 0.1968 0.1834 5.7362 1.4712 -#> 473: 93.1632 -6.0405 -1.9751 -4.4554 -0.9543 0.1749 6.7527 4.0526 1.1141 3.0686 0.1967 0.1835 5.7363 1.4708 -#> 474: 93.1619 -6.0405 -1.9752 -4.4555 -0.9542 0.1748 6.7479 4.0521 1.1140 3.0689 0.1967 0.1835 5.7366 1.4705 -#> 475: 93.1609 -6.0413 -1.9753 -4.4557 -0.9542 0.1748 6.7469 4.0558 1.1139 3.0698 0.1967 0.1835 5.7379 1.4702 -#> 476: 93.1607 -6.0411 -1.9754 -4.4556 -0.9542 0.1747 6.7414 4.0549 1.1139 3.0697 0.1966 0.1835 5.7388 1.4698 -#> 477: 93.1597 -6.0413 -1.9754 -4.4560 -0.9542 0.1747 6.7321 4.0560 1.1137 3.0733 0.1966 0.1836 5.7392 1.4697 -#> 478: 93.1591 -6.0421 -1.9754 -4.4563 -0.9542 0.1745 6.7239 4.0608 1.1137 3.0765 0.1965 0.1836 5.7399 1.4697 -#> 479: 93.1589 -6.0438 -1.9754 -4.4564 -0.9542 0.1744 6.7150 4.0719 1.1136 3.0785 0.1964 0.1838 5.7421 1.4695 -#> 480: 93.1594 -6.0459 -1.9754 -4.4566 -0.9542 0.1742 6.7102 4.0895 1.1135 3.0807 0.1964 0.1839 5.7446 1.4695 -#> 481: 93.1604 -6.0472 -1.9754 -4.4570 -0.9542 0.1741 6.7104 4.1016 1.1135 3.0848 0.1964 0.1841 5.7456 1.4693 -#> 482: 93.1584 -6.0486 -1.9754 -4.4573 -0.9542 0.1739 6.7061 4.1152 1.1136 3.0877 0.1964 0.1842 5.7464 1.4690 -#> 483: 93.1561 -6.0501 -1.9754 -4.4576 -0.9541 0.1737 6.7067 4.1286 1.1135 3.0903 0.1963 0.1843 5.7475 1.4688 -#> 484: 93.1545 -6.0507 -1.9754 -4.4578 -0.9541 0.1737 6.7113 4.1362 1.1134 3.0918 0.1963 0.1845 5.7488 1.4687 -#> 485: 93.1524 -6.0507 -1.9754 -4.4583 -0.9540 0.1736 6.7094 4.1381 1.1134 3.0970 0.1964 0.1847 5.7496 1.4685 -#> 486: 93.1510 -6.0508 -1.9754 -4.4586 -0.9540 0.1735 6.7118 4.1405 1.1134 3.0996 0.1964 0.1847 5.7502 1.4682 -#> 487: 93.1495 -6.0507 -1.9755 -4.4591 -0.9539 0.1734 6.7128 4.1406 1.1134 3.1037 0.1965 0.1848 5.7510 1.4680 -#> 488: 93.1494 -6.0502 -1.9756 -4.4597 -0.9538 0.1734 6.7171 4.1384 1.1135 3.1081 0.1965 0.1848 5.7508 1.4677 -#> 489: 93.1497 -6.0497 -1.9756 -4.4604 -0.9538 0.1734 6.7188 4.1358 1.1135 3.1133 0.1966 0.1847 5.7499 1.4675 -#> 490: 93.1507 -6.0486 -1.9757 -4.4607 -0.9538 0.1735 6.7206 4.1319 1.1136 3.1157 0.1967 0.1847 5.7498 1.4672 -#> 491: 93.1507 -6.0476 -1.9757 -4.4612 -0.9537 0.1735 6.7141 4.1270 1.1136 3.1187 0.1968 0.1846 5.7503 1.4672 -#> 492: 93.1507 -6.0470 -1.9758 -4.4618 -0.9536 0.1735 6.7140 4.1238 1.1139 3.1218 0.1969 0.1846 5.7511 1.4669 -#> 493: 93.1513 -6.0468 -1.9758 -4.4623 -0.9535 0.1736 6.7214 4.1232 1.1141 3.1246 0.1970 0.1845 5.7514 1.4668 -#> 494: 93.1511 -6.0467 -1.9759 -4.4629 -0.9534 0.1737 6.7332 4.1232 1.1144 3.1278 0.1971 0.1845 5.7512 1.4664 -#> 495: 93.1511 -6.0464 -1.9761 -4.4635 -0.9533 0.1738 6.7377 4.1218 1.1145 3.1309 0.1972 0.1845 5.7515 1.4661 -#> 496: 93.1498 -6.0465 -1.9762 -4.4639 -0.9532 0.1739 6.7412 4.1241 1.1147 3.1325 0.1974 0.1845 5.7514 1.4657 -#> 497: 93.1482 -6.0467 -1.9764 -4.4644 -0.9532 0.1741 6.7506 4.1259 1.1149 3.1346 0.1975 0.1846 5.7513 1.4652 -#> 498: 93.1479 -6.0465 -1.9765 -4.4647 -0.9531 0.1743 6.7588 4.1263 1.1150 3.1357 0.1977 0.1846 5.7511 1.4648 -#> 499: 93.1462 -6.0455 -1.9766 -4.4651 -0.9530 0.1745 6.7659 4.1219 1.1152 3.1374 0.1978 0.1847 5.7515 1.4645 -#> 500: 93.1455 -6.0439 -1.9768 -4.4657 -0.9529 0.1747 6.7747 4.1151 1.1154 3.1404 0.1980 0.1848 5.7516 1.4641
                                                                #> Calculating covariance matrix
                                                                #>
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_A1 |f_parent_qlogis | log_k1 | -#> |.....................| log_k2 | g_qlogis |sigma_parent | sigma_A1 | -#> |.....................| o1 | o2 | o3 | o4 | -#> |.....................| o5 | o6 |...........|...........| -#> | 1| 488.12318 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 488.12318 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 488.12318 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | G| Gill Diff. | 52.24 | 2.364 | -0.1419 | 0.08101 | -#> |.....................| -0.5200 | 0.08781 | -28.20 | -16.37 | -#> |.....................| 14.83 | 13.24 | -12.01 | -2.482 | -#> |.....................| 5.466 | -10.09 |...........|...........| -#> | 2| 2642.5634 | 0.2192 | -1.035 | -0.9096 | -0.9332 | -#> |.....................| -0.9743 | -0.8898 | -0.4296 | -0.6255 | -#> |.....................| -1.099 | -1.073 | -0.6891 | -0.8357 | -#> |.....................| -0.9567 | -0.7180 |...........|...........| -#> | U| 2642.5634 | 20.48 | -5.348 | -0.9517 | -1.954 | -#> |.....................| -4.421 | 0.1928 | 2.469 | 1.224 | -#> |.....................| 0.5606 | 0.7036 | 1.386 | 1.005 | -#> |.....................| 0.7896 | 1.336 |...........|...........| -#> | X| 2642.5634 | 20.48 | 0.004759 | 0.2785 | 0.1417 | -#> |.....................| 0.01202 | 0.5480 | 2.469 | 1.224 | -#> |.....................| 0.5606 | 0.7036 | 1.386 | 1.005 | -#> |.....................| 0.7896 | 1.336 |...........|...........| -#> | 3| 546.98314 | 0.9219 | -1.004 | -0.9115 | -0.9321 | -#> |.....................| -0.9813 | -0.8886 | -0.8089 | -0.8458 | -#> |.....................| -0.9000 | -0.8944 | -0.8506 | -0.8691 | -#> |.....................| -0.8831 | -0.8538 |...........|...........| -#> | U| 546.98314 | 86.13 | -5.316 | -0.9535 | -1.953 | -#> |.....................| -4.428 | 0.1930 | 2.082 | 1.104 | -#> |.....................| 0.7044 | 0.8599 | 1.196 | 0.9723 | -#> |.....................| 0.8529 | 1.178 |...........|...........| -#> | X| 546.98314 | 86.13 | 0.004913 | 0.2782 | 0.1419 | -#> |.....................| 0.01193 | 0.5481 | 2.082 | 1.104 | -#> |.....................| 0.7044 | 0.8599 | 1.196 | 0.9723 | -#> |.....................| 0.8529 | 1.178 |...........|...........| -#> | 4| 506.37737 | 0.9922 | -1.000 | -0.9117 | -0.9320 | -#> |.....................| -0.9820 | -0.8885 | -0.8469 | -0.8679 | -#> |.....................| -0.8800 | -0.8766 | -0.8668 | -0.8724 | -#> |.....................| -0.8758 | -0.8674 |...........|...........| -#> | U| 506.37737 | 92.70 | -5.313 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.043 | 1.092 | -#> |.....................| 0.7187 | 0.8755 | 1.177 | 0.9691 | -#> |.....................| 0.8592 | 1.163 |...........|...........| -#> | X| 506.37737 | 92.70 | 0.004928 | 0.2781 | 0.1419 | -#> |.....................| 0.01193 | 0.5481 | 2.043 | 1.092 | -#> |.....................| 0.7187 | 0.8755 | 1.177 | 0.9691 | -#> |.....................| 0.8592 | 1.163 |...........|...........| -#> | 5| 506.42840 | 0.9992 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8507 | -0.8701 | -#> |.....................| -0.8780 | -0.8748 | -0.8684 | -0.8727 | -#> |.....................| -0.8751 | -0.8687 |...........|...........| -#> | U| 506.4284 | 93.35 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.091 | -#> |.....................| 0.7202 | 0.8771 | 1.175 | 0.9688 | -#> |.....................| 0.8598 | 1.161 |...........|...........| -#> | X| 506.4284 | 93.35 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.091 | -#> |.....................| 0.7202 | 0.8771 | 1.175 | 0.9688 | -#> |.....................| 0.8598 | 1.161 |...........|...........| -#> | 6| 506.47762 | 0.9999 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.47762 | 93.42 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.47762 | 93.42 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 7| 506.48298 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48298 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48298 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 8| 506.48363 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48363 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48363 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 9| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 10| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 11| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 12| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 13| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 14| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 15| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 16| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | 17| 506.48371 | 1.000 | -1.000 | -0.9117 | -0.9319 | -#> |.....................| -0.9821 | -0.8885 | -0.8511 | -0.8703 | -#> |.....................| -0.8778 | -0.8746 | -0.8686 | -0.8728 | -#> |.....................| -0.8750 | -0.8689 |...........|...........| -#> | U| 506.48371 | 93.43 | -5.312 | -0.9537 | -1.953 | -#> |.....................| -4.429 | 0.1930 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> | X| 506.48371 | 93.43 | 0.004930 | 0.2781 | 0.1419 | -#> |.....................| 0.01192 | 0.5481 | 2.039 | 1.090 | -#> |.....................| 0.7203 | 0.8772 | 1.175 | 0.9687 | -#> |.....................| 0.8599 | 1.161 |...........|...........| -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.22 0.089 1.31
                                                                f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[4]+THETA[4]; +#> rx_expr_8~ETA[5]+THETA[5]; +#> rx_expr_12~exp(-(rx_expr_8)); +#> rx_expr_14~t*rx_expr_12; +#> rx_expr_15~1+rx_expr_14; +#> rx_expr_17~rx_expr_7-(rx_expr_8); +#> rx_expr_19~exp(rx_expr_17); +#> d/dt(parent)=-rx_expr_19*parent/(rx_expr_15); +#> rx_expr_9~ETA[2]+THETA[2]; +#> rx_expr_11~exp(rx_expr_9); +#> d/dt(A1)=-rx_expr_11*A1+rx_expr_19*parent*f_parent_to_A1/(rx_expr_15); +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_13~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_13+rx_expr_3; +#> rx_hi_~rx_expr_13+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_10~parent*(rx_expr_2); +#> rx_expr_16~rx_expr_10*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1); +#> rx_r_=(rx_expr_0)*Rx_pow_di(THETA[7],2)+(rx_expr_2)*(rx_expr_1)*Rx_pow_di(THETA[6],2); +#> parent_0=THETA[1]; +#> log_k_A1=THETA[2]; +#> f_parent_qlogis=THETA[3]; +#> log_alpha=THETA[4]; +#> log_beta=THETA[5]; +#> sigma_parent=THETA[6]; +#> sigma_A1=THETA[7]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_A1=ETA[2]; +#> eta.f_parent_qlogis=ETA[3]; +#> eta.log_alpha=ETA[4]; +#> eta.log_beta=ETA[5]; +#> parent_0_model=rx_expr_6; +#> k_A1=rx_expr_11; +#> alpha=exp(rx_expr_7); +#> beta=exp(rx_expr_8); +#> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.784 0.418 7.2
                                                                f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.357 0.096 1.452
                                                                f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[4]+THETA[4]; +#> rx_expr_8~ETA[6]+THETA[6]; +#> rx_expr_9~ETA[5]+THETA[5]; +#> rx_expr_12~exp(rx_expr_7); +#> rx_expr_13~exp(rx_expr_9); +#> rx_expr_15~t*rx_expr_12; +#> rx_expr_16~t*rx_expr_13; +#> rx_expr_17~exp(-(rx_expr_8)); +#> rx_expr_19~1+rx_expr_17; +#> rx_expr_24~1/(rx_expr_19); +#> rx_expr_26~(rx_expr_24); +#> rx_expr_27~1-rx_expr_26; +#> d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27)); +#> rx_expr_10~ETA[2]+THETA[2]; +#> rx_expr_14~exp(rx_expr_10); +#> d/dt(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27)); +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_18~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_18+rx_expr_3; +#> rx_hi_~rx_expr_18+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_11~parent*(rx_expr_2); +#> rx_expr_22~rx_expr_11*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1); +#> rx_r_=(rx_expr_0)*Rx_pow_di(THETA[8],2)+(rx_expr_2)*(rx_expr_1)*Rx_pow_di(THETA[7],2); +#> parent_0=THETA[1]; +#> log_k_A1=THETA[2]; +#> f_parent_qlogis=THETA[3]; +#> log_k1=THETA[4]; +#> log_k2=THETA[5]; +#> g_qlogis=THETA[6]; +#> sigma_parent=THETA[7]; +#> sigma_A1=THETA[8]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_A1=ETA[2]; +#> eta.f_parent_qlogis=ETA[3]; +#> eta.log_k1=ETA[4]; +#> eta.log_k2=ETA[5]; +#> eta.g_qlogis=ETA[6]; +#> parent_0_model=rx_expr_6; +#> k_A1=rx_expr_14; +#> k1=rx_expr_12; +#> k2=rx_expr_13; +#> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); +#> g=1/(rx_expr_19); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 15.17 0.353 15.52
                                                                # Identical two-component error for all variables is only possible with # est = 'focei' in nlmixr f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_A1 |f_parent_qlogis | log_alpha | -#> |.....................| log_beta | sigma_low | rsd_high | o1 | -#> |.....................| o2 | o3 | o4 | o5 | -#> | 1| 504.82714 | 1.000 | -1.000 | -0.9114 | -0.8944 | -#> |.....................| -0.8457 | -0.8687 | -0.8916 | -0.8768 | -#> |.....................| -0.8745 | -0.8676 | -0.8705 | -0.8704 | -#> | U| 504.82714 | 93.12 | -5.303 | -0.9442 | -0.1065 | -#> |.....................| 2.291 | 1.160 | 0.03005 | 0.7578 | -#> |.....................| 0.8738 | 1.213 | 1.069 | 1.072 | -#> | X| 504.82714 | 93.12 | 0.004975 | 0.2801 | 0.8989 | -#> |.....................| 9.884 | 1.160 | 0.03005 | 0.7578 | -#> |.....................| 0.8738 | 1.213 | 1.069 | 1.072 | -#> | G| Gill Diff. | 73.79 | 2.406 | 0.05615 | 0.2285 | -#> |.....................| 0.009051 | -72.42 | -25.46 | 1.201 | -#> |.....................| 11.89 | -10.88 | -9.982 | -10.81 | -#> | 2| 4107.3121 | 0.3213 | -1.022 | -0.9119 | -0.8965 | -#> |.....................| -0.8458 | -0.2026 | -0.6574 | -0.8879 | -#> |.....................| -0.9839 | -0.7675 | -0.7787 | -0.7710 | -#> | U| 4107.3121 | 29.92 | -5.326 | -0.9447 | -0.1086 | -#> |.....................| 2.291 | 1.546 | 0.03357 | 0.7494 | -#> |.....................| 0.7782 | 1.335 | 1.167 | 1.179 | -#> | X| 4107.3121 | 29.92 | 0.004866 | 0.2800 | 0.8971 | -#> |.....................| 9.883 | 1.546 | 0.03357 | 0.7494 | -#> |.....................| 0.7782 | 1.335 | 1.167 | 1.179 | -#> | 3| 528.17103 | 0.9321 | -1.002 | -0.9115 | -0.8946 | -#> |.....................| -0.8457 | -0.8021 | -0.8682 | -0.8779 | -#> |.....................| -0.8854 | -0.8576 | -0.8613 | -0.8605 | -#> | U| 528.17103 | 86.80 | -5.306 | -0.9442 | -0.1067 | -#> |.....................| 2.291 | 1.198 | 0.03041 | 0.7570 | -#> |.....................| 0.8642 | 1.226 | 1.079 | 1.083 | -#> | X| 528.17103 | 86.80 | 0.004964 | 0.2800 | 0.8988 | -#> |.....................| 9.884 | 1.198 | 0.03041 | 0.7570 | -#> |.....................| 0.8642 | 1.226 | 1.079 | 1.083 | -#> | 4| 503.95550 | 0.9892 | -1.000 | -0.9114 | -0.8944 | -#> |.....................| -0.8457 | -0.8581 | -0.8879 | -0.8770 | -#> |.....................| -0.8762 | -0.8660 | -0.8691 | -0.8689 | -#> | U| 503.9555 | 92.11 | -5.304 | -0.9442 | -0.1066 | -#> |.....................| 2.291 | 1.166 | 0.03011 | 0.7577 | -#> |.....................| 0.8723 | 1.215 | 1.070 | 1.074 | -#> | X| 503.9555 | 92.11 | 0.004973 | 0.2801 | 0.8989 | -#> |.....................| 9.884 | 1.166 | 0.03011 | 0.7577 | -#> |.....................| 0.8723 | 1.215 | 1.070 | 1.074 | -#> | F| Forward Diff. | -82.12 | 2.266 | -0.2557 | 0.1457 | -#> |.....................| -0.3150 | -70.09 | -26.27 | 1.274 | -#> |.....................| 9.305 | -11.84 | -9.592 | -10.45 | -#> | 5| 503.06948 | 1.000 | -1.001 | -0.9114 | -0.8944 | -#> |.....................| -0.8456 | -0.8479 | -0.8841 | -0.8772 | -#> |.....................| -0.8776 | -0.8643 | -0.8677 | -0.8674 | -#> | U| 503.06948 | 93.16 | -5.304 | -0.9442 | -0.1066 | -#> |.....................| 2.291 | 1.172 | 0.03017 | 0.7575 | -#> |.....................| 0.8711 | 1.217 | 1.072 | 1.075 | -#> | X| 503.06948 | 93.16 | 0.004971 | 0.2801 | 0.8989 | -#> |.....................| 9.884 | 1.172 | 0.03017 | 0.7575 | -#> |.....................| 0.8711 | 1.217 | 1.072 | 1.075 | -#> | F| Forward Diff. | 78.20 | 2.380 | 0.07920 | 0.2489 | -#> |.....................| 0.04185 | -69.32 | -24.13 | 1.306 | -#> |.....................| 9.997 | -11.88 | -9.541 | -10.51 | -#> | 6| 502.21512 | 0.9895 | -1.001 | -0.9114 | -0.8945 | -#> |.....................| -0.8456 | -0.8375 | -0.8805 | -0.8774 | -#> |.....................| -0.8791 | -0.8625 | -0.8662 | -0.8658 | -#> | U| 502.21512 | 92.14 | -5.304 | -0.9442 | -0.1066 | -#> |.....................| 2.291 | 1.178 | 0.03022 | 0.7574 | -#> |.....................| 0.8698 | 1.220 | 1.073 | 1.077 | -#> | X| 502.21512 | 92.14 | 0.004969 | 0.2801 | 0.8989 | -#> |.....................| 9.884 | 1.178 | 0.03022 | 0.7574 | -#> |.....................| 0.8698 | 1.220 | 1.073 | 1.077 | -#> | F| Forward Diff. | -79.18 | 2.245 | -0.2400 | 0.1569 | -#> |.....................| -0.2882 | -67.02 | -25.09 | 1.000 | -#> |.....................| 9.365 | -11.67 | -9.440 | -10.32 | -#> | 7| 501.33312 | 1.000 | -1.001 | -0.9114 | -0.8945 | -#> |.....................| -0.8456 | -0.8270 | -0.8765 | -0.8775 | -#> |.....................| -0.8805 | -0.8607 | -0.8647 | -0.8642 | -#> | U| 501.33312 | 93.14 | -5.305 | -0.9441 | -0.1067 | -#> |.....................| 2.291 | 1.184 | 0.03028 | 0.7573 | -#> |.....................| 0.8685 | 1.222 | 1.075 | 1.079 | -#> | X| 501.33312 | 93.14 | 0.004968 | 0.2801 | 0.8988 | -#> |.....................| 9.884 | 1.184 | 0.03028 | 0.7573 | -#> |.....................| 0.8685 | 1.222 | 1.075 | 1.079 | -#> | F| Forward Diff. | 73.96 | 2.351 | 0.08380 | 0.2565 | -#> |.....................| 0.05289 | -66.42 | -23.08 | 0.9343 | -#> |.....................| 11.48 | -11.71 | -9.377 | -10.38 | -#> | 8| 500.50460 | 0.9897 | -1.002 | -0.9114 | -0.8946 | -#> |.....................| -0.8456 | -0.8163 | -0.8728 | -0.8777 | -#> |.....................| -0.8824 | -0.8588 | -0.8632 | -0.8625 | -#> | U| 500.5046 | 92.16 | -5.305 | -0.9442 | -0.1067 | -#> |.....................| 2.291 | 1.190 | 0.03034 | 0.7572 | -#> |.....................| 0.8669 | 1.224 | 1.077 | 1.081 | -#> | X| 500.5046 | 92.16 | 0.004966 | 0.2801 | 0.8988 | -#> |.....................| 9.884 | 1.190 | 0.03034 | 0.7572 | -#> |.....................| 0.8669 | 1.224 | 1.077 | 1.081 | -#> | F| Forward Diff. | -76.85 | 2.219 | -0.2273 | 0.1675 | -#> |.....................| -0.2752 | -63.09 | -23.56 | 1.068 | -#> |.....................| 8.794 | -11.52 | -9.279 | -10.19 | -#> | 9| 499.65692 | 1.000 | -1.002 | -0.9113 | -0.8946 | -#> |.....................| -0.8456 | -0.8056 | -0.8689 | -0.8779 | -#> |.....................| -0.8839 | -0.8568 | -0.8617 | -0.8608 | -#> | U| 499.65692 | 93.14 | -5.306 | -0.9441 | -0.1067 | -#> |.....................| 2.291 | 1.196 | 0.03040 | 0.7570 | -#> |.....................| 0.8655 | 1.226 | 1.078 | 1.082 | -#> | X| 499.65692 | 93.14 | 0.004964 | 0.2801 | 0.8988 | -#> |.....................| 9.885 | 1.196 | 0.03040 | 0.7570 | -#> |.....................| 0.8655 | 1.226 | 1.078 | 1.082 | -#> | F| Forward Diff. | 72.32 | 2.320 | 0.09176 | 0.2615 | -#> |.....................| 0.06934 | -62.36 | -21.54 | 1.140 | -#> |.....................| 9.404 | -11.56 | -9.216 | -10.24 | -#> | 10| 498.81870 | 0.9902 | -1.003 | -0.9114 | -0.8946 | -#> |.....................| -0.8456 | -0.7946 | -0.8650 | -0.8781 | -#> |.....................| -0.8856 | -0.8548 | -0.8600 | -0.8589 | -#> | U| 498.8187 | 92.21 | -5.306 | -0.9441 | -0.1068 | -#> |.....................| 2.291 | 1.203 | 0.03045 | 0.7569 | -#> |.....................| 0.8641 | 1.229 | 1.080 | 1.084 | -#> | X| 498.8187 | 92.21 | 0.004962 | 0.2801 | 0.8987 | -#> |.....................| 9.885 | 1.203 | 0.03045 | 0.7569 | -#> |.....................| 0.8641 | 1.229 | 1.080 | 1.084 | -#> | F| Forward Diff. | -70.56 | 2.198 | -0.2057 | 0.1798 | -#> |.....................| -0.2468 | -59.74 | -22.28 | 0.8150 | -#> |.....................| 7.180 | -11.33 | -9.109 | -10.05 | -#> | 11| 497.99655 | 1.000 | -1.003 | -0.9113 | -0.8947 | -#> |.....................| -0.8455 | -0.7835 | -0.8609 | -0.8782 | -#> |.....................| -0.8869 | -0.8527 | -0.8583 | -0.8571 | -#> | U| 497.99655 | 93.13 | -5.306 | -0.9441 | -0.1068 | -#> |.....................| 2.291 | 1.209 | 0.03052 | 0.7568 | -#> |.....................| 0.8629 | 1.231 | 1.082 | 1.086 | -#> | X| 497.99655 | 93.13 | 0.004960 | 0.2801 | 0.8987 | -#> |.....................| 9.885 | 1.209 | 0.03052 | 0.7568 | -#> |.....................| 0.8629 | 1.231 | 1.082 | 1.086 | -#> | F| Forward Diff. | 69.16 | 2.293 | 0.1087 | 0.2725 | -#> |.....................| 0.08752 | -59.63 | -20.54 | 0.7584 | -#> |.....................| 10.86 | -11.45 | -9.094 | -10.13 | -#> | 12| 497.16410 | 0.9907 | -1.003 | -0.9113 | -0.8947 | -#> |.....................| -0.8455 | -0.7720 | -0.8569 | -0.8784 | -#> |.....................| -0.8889 | -0.8505 | -0.8566 | -0.8551 | -#> | U| 497.1641 | 92.25 | -5.307 | -0.9441 | -0.1069 | -#> |.....................| 2.291 | 1.216 | 0.03058 | 0.7566 | -#> |.....................| 0.8612 | 1.234 | 1.084 | 1.088 | -#> | X| 497.1641 | 92.25 | 0.004958 | 0.2801 | 0.8987 | -#> |.....................| 9.885 | 1.216 | 0.03058 | 0.7566 | -#> |.....................| 0.8612 | 1.234 | 1.084 | 1.088 | -#> | F| Forward Diff. | -65.09 | 2.175 | -0.1829 | 0.1920 | -#> |.....................| -0.2233 | -56.76 | -21.02 | 0.6415 | -#> |.....................| 9.983 | -11.18 | -8.930 | -9.895 | -#> | 13| 496.40281 | 1.000 | -1.004 | -0.9113 | -0.8948 | -#> |.....................| -0.8455 | -0.7609 | -0.8528 | -0.8785 | -#> |.....................| -0.8909 | -0.8483 | -0.8548 | -0.8532 | -#> | U| 496.40281 | 93.15 | -5.307 | -0.9441 | -0.1069 | -#> |.....................| 2.291 | 1.222 | 0.03064 | 0.7566 | -#> |.....................| 0.8594 | 1.237 | 1.086 | 1.091 | -#> | X| 496.40281 | 93.15 | 0.004955 | 0.2801 | 0.8986 | -#> |.....................| 9.885 | 1.222 | 0.03064 | 0.7566 | -#> |.....................| 0.8594 | 1.237 | 1.086 | 1.091 | -#> | F| Forward Diff. | 70.05 | 2.265 | 0.1236 | 0.2851 | -#> |.....................| 0.1152 | -55.71 | -19.12 | 0.8701 | -#> |.....................| 7.394 | -11.22 | -8.890 | -9.949 | -#> | 14| 495.59236 | 0.9910 | -1.004 | -0.9113 | -0.8948 | -#> |.....................| -0.8455 | -0.7494 | -0.8488 | -0.8787 | -#> |.....................| -0.8926 | -0.8459 | -0.8530 | -0.8511 | -#> | U| 495.59236 | 92.28 | -5.308 | -0.9441 | -0.1070 | -#> |.....................| 2.291 | 1.229 | 0.03070 | 0.7564 | -#> |.....................| 0.8580 | 1.240 | 1.088 | 1.093 | -#> | X| 495.59236 | 92.28 | 0.004953 | 0.2801 | 0.8986 | -#> |.....................| 9.885 | 1.229 | 0.03070 | 0.7564 | -#> |.....................| 0.8580 | 1.240 | 1.088 | 1.093 | -#> | F| Forward Diff. | -61.97 | 2.150 | -0.1619 | 0.2028 | -#> |.....................| -0.2007 | -53.46 | -19.76 | 0.5341 | -#> |.....................| 9.715 | -10.96 | -8.745 | -9.729 | -#> | 15| 494.82198 | 1.000 | -1.005 | -0.9113 | -0.8949 | -#> |.....................| -0.8455 | -0.7378 | -0.8446 | -0.8788 | -#> |.....................| -0.8946 | -0.8435 | -0.8510 | -0.8489 | -#> | U| 494.82198 | 93.11 | -5.308 | -0.9441 | -0.1070 | -#> |.....................| 2.291 | 1.235 | 0.03076 | 0.7563 | -#> |.....................| 0.8562 | 1.243 | 1.090 | 1.095 | -#> | X| 494.82198 | 93.11 | 0.004951 | 0.2801 | 0.8985 | -#> |.....................| 9.886 | 1.235 | 0.03076 | 0.7563 | -#> |.....................| 0.8562 | 1.243 | 1.090 | 1.095 | -#> | F| Forward Diff. | 62.35 | 2.229 | 0.1203 | 0.2879 | -#> |.....................| 0.1180 | -52.16 | -17.88 | 0.7550 | -#> |.....................| 8.431 | -10.99 | -8.665 | -9.736 | -#> | 16| 494.07821 | 0.9910 | -1.005 | -0.9113 | -0.8949 | -#> |.....................| -0.8455 | -0.7261 | -0.8406 | -0.8789 | -#> |.....................| -0.8966 | -0.8410 | -0.8490 | -0.8467 | -#> | U| 494.07821 | 92.28 | -5.309 | -0.9441 | -0.1071 | -#> |.....................| 2.291 | 1.242 | 0.03082 | 0.7562 | -#> |.....................| 0.8544 | 1.246 | 1.092 | 1.098 | -#> | X| 494.07821 | 92.28 | 0.004948 | 0.2801 | 0.8985 | -#> |.....................| 9.885 | 1.242 | 0.03082 | 0.7562 | -#> |.....................| 0.8544 | 1.246 | 1.092 | 1.098 | -#> | F| Forward Diff. | -62.97 | 2.119 | -0.1628 | 0.2103 | -#> |.....................| -0.1835 | -49.97 | -18.50 | 0.4855 | -#> |.....................| 6.275 | -10.75 | -8.529 | -9.546 | -#> | 17| 493.31030 | 0.9997 | -1.006 | -0.9113 | -0.8950 | -#> |.....................| -0.8455 | -0.7143 | -0.8363 | -0.8790 | -#> |.....................| -0.8981 | -0.8383 | -0.8469 | -0.8443 | -#> | U| 493.3103 | 93.08 | -5.309 | -0.9441 | -0.1071 | -#> |.....................| 2.291 | 1.249 | 0.03089 | 0.7561 | -#> |.....................| 0.8531 | 1.249 | 1.094 | 1.100 | -#> | X| 493.3103 | 93.08 | 0.004946 | 0.2801 | 0.8984 | -#> |.....................| 9.886 | 1.249 | 0.03089 | 0.7561 | -#> |.....................| 0.8531 | 1.249 | 1.094 | 1.100 | -#> | F| Forward Diff. | 56.08 | 2.195 | 0.1067 | 0.2931 | -#> |.....................| 0.1254 | -49.64 | -16.98 | 0.3491 | -#> |.....................| 8.549 | -10.78 | -8.455 | -9.552 | -#> | 18| 492.59068 | 0.9914 | -1.006 | -0.9113 | -0.8951 | -#> |.....................| -0.8455 | -0.7023 | -0.8321 | -0.8791 | -#> |.....................| -0.9000 | -0.8355 | -0.8448 | -0.8419 | -#> | U| 492.59068 | 92.32 | -5.310 | -0.9441 | -0.1072 | -#> |.....................| 2.291 | 1.256 | 0.03095 | 0.7561 | -#> |.....................| 0.8514 | 1.252 | 1.096 | 1.103 | -#> | X| 492.59068 | 92.32 | 0.004943 | 0.2801 | 0.8983 | -#> |.....................| 9.885 | 1.256 | 0.03095 | 0.7561 | -#> |.....................| 0.8514 | 1.252 | 1.096 | 1.103 | -#> | F| Forward Diff. | -58.13 | 2.097 | -0.1289 | 0.2246 | -#> |.....................| -0.1582 | -47.13 | -17.33 | 0.3097 | -#> |.....................| 7.738 | -10.54 | -8.304 | -9.345 | -#> | 19| 491.88063 | 0.9998 | -1.007 | -0.9113 | -0.8951 | -#> |.....................| -0.8455 | -0.6905 | -0.8279 | -0.8791 | -#> |.....................| -0.9022 | -0.8327 | -0.8426 | -0.8394 | -#> | U| 491.88063 | 93.10 | -5.310 | -0.9441 | -0.1073 | -#> |.....................| 2.291 | 1.263 | 0.03101 | 0.7561 | -#> |.....................| 0.8496 | 1.256 | 1.099 | 1.105 | -#> | X| 491.88063 | 93.10 | 0.004940 | 0.2801 | 0.8983 | -#> |.....................| 9.886 | 1.263 | 0.03101 | 0.7561 | -#> |.....................| 0.8496 | 1.256 | 1.099 | 1.105 | -#> | F| Forward Diff. | 56.71 | 2.166 | 0.1292 | 0.3076 | -#> |.....................| 0.1542 | -45.57 | -15.60 | 0.4873 | -#> |.....................| 6.413 | -10.51 | -8.202 | -9.332 | -#> | 20| 491.19020 | 0.9917 | -1.008 | -0.9113 | -0.8952 | -#> |.....................| -0.8455 | -0.6785 | -0.8237 | -0.8792 | -#> |.....................| -0.9039 | -0.8296 | -0.8402 | -0.8366 | -#> | U| 491.1902 | 92.34 | -5.311 | -0.9441 | -0.1074 | -#> |.....................| 2.291 | 1.270 | 0.03107 | 0.7560 | -#> |.....................| 0.8481 | 1.259 | 1.101 | 1.108 | -#> | X| 491.1902 | 92.34 | 0.004937 | 0.2801 | 0.8982 | -#> |.....................| 9.885 | 1.270 | 0.03107 | 0.7560 | -#> |.....................| 0.8481 | 1.259 | 1.101 | 1.108 | -#> | F| Forward Diff. | -55.56 | 2.070 | -0.1130 | 0.2359 | -#> |.....................| -0.1346 | -44.07 | -16.23 | 0.1008 | -#> |.....................| 7.464 | -10.26 | -8.060 | -9.125 | -#> | 21| 490.47868 | 0.9993 | -1.008 | -0.9113 | -0.8953 | -#> |.....................| -0.8455 | -0.6665 | -0.8194 | -0.8791 | -#> |.....................| -0.9059 | -0.8264 | -0.8377 | -0.8337 | -#> | U| 490.47868 | 93.05 | -5.312 | -0.9441 | -0.1075 | -#> |.....................| 2.291 | 1.277 | 0.03114 | 0.7561 | -#> |.....................| 0.8463 | 1.263 | 1.104 | 1.111 | -#> | X| 490.47868 | 93.05 | 0.004934 | 0.2801 | 0.8981 | -#> |.....................| 9.885 | 1.277 | 0.03114 | 0.7561 | -#> |.....................| 0.8463 | 1.263 | 1.104 | 1.111 | -#> | F| Forward Diff. | 47.93 | 2.132 | 0.1269 | 0.3117 | -#> |.....................| 0.1562 | -43.27 | -14.78 | 0.06906 | -#> |.....................| 9.295 | -10.26 | -7.955 | -9.092 | -#> | 22| 489.84765 | 0.9918 | -1.009 | -0.9114 | -0.8954 | -#> |.....................| -0.8456 | -0.6545 | -0.8153 | -0.8790 | -#> |.....................| -0.9090 | -0.8231 | -0.8352 | -0.8308 | -#> | U| 489.84765 | 92.35 | -5.312 | -0.9441 | -0.1076 | -#> |.....................| 2.291 | 1.284 | 0.03120 | 0.7562 | -#> |.....................| 0.8436 | 1.267 | 1.107 | 1.115 | -#> | X| 489.84765 | 92.35 | 0.004930 | 0.2801 | 0.8980 | -#> |.....................| 9.885 | 1.284 | 0.03120 | 0.7562 | -#> |.....................| 0.8436 | 1.267 | 1.107 | 1.115 | -#> | F| Forward Diff. | -55.71 | 2.038 | -0.1283 | 0.2328 | -#> |.....................| -0.1164 | -41.15 | -15.14 | 0.009736 | -#> |.....................| 8.505 | -10.03 | -7.805 | -8.885 | -#> | 23| 489.17644 | 0.9994 | -1.010 | -0.9113 | -0.8955 | -#> |.....................| -0.8456 | -0.6429 | -0.8112 | -0.8788 | -#> |.....................| -0.9126 | -0.8197 | -0.8325 | -0.8278 | -#> | U| 489.17644 | 93.06 | -5.313 | -0.9441 | -0.1077 | -#> |.....................| 2.291 | 1.290 | 0.03126 | 0.7563 | -#> |.....................| 0.8405 | 1.272 | 1.109 | 1.118 | -#> | X| 489.17644 | 93.06 | 0.004927 | 0.2801 | 0.8979 | -#> |.....................| 9.885 | 1.290 | 0.03126 | 0.7563 | -#> |.....................| 0.8405 | 1.272 | 1.109 | 1.118 | -#> | F| Forward Diff. | 46.87 | 2.093 | 0.1493 | 0.3243 | -#> |.....................| 0.1838 | -40.03 | -13.57 | 0.1411 | -#> |.....................| 5.593 | -9.957 | -7.669 | -8.831 | -#> | 24| 488.58015 | 0.9920 | -1.011 | -0.9114 | -0.8957 | -#> |.....................| -0.8457 | -0.6309 | -0.8071 | -0.8787 | -#> |.....................| -0.9147 | -0.8159 | -0.8297 | -0.8244 | -#> | U| 488.58015 | 92.37 | -5.314 | -0.9442 | -0.1078 | -#> |.....................| 2.291 | 1.297 | 0.03132 | 0.7564 | -#> |.....................| 0.8386 | 1.276 | 1.112 | 1.121 | -#> | X| 488.58015 | 92.37 | 0.004923 | 0.2801 | 0.8978 | -#> |.....................| 9.884 | 1.297 | 0.03132 | 0.7564 | -#> |.....................| 0.8386 | 1.276 | 1.112 | 1.121 | -#> | F| Forward Diff. | -53.50 | 2.005 | -0.1078 | 0.2446 | -#> |.....................| -0.09190 | -37.89 | -13.87 | 0.05672 | -#> |.....................| 4.909 | -9.713 | -7.511 | -8.606 | -#> | 25| 487.93833 | 0.9991 | -1.011 | -0.9114 | -0.8958 | -#> |.....................| -0.8457 | -0.6190 | -0.8030 | -0.8785 | -#> |.....................| -0.9153 | -0.8117 | -0.8266 | -0.8207 | -#> | U| 487.93833 | 93.04 | -5.315 | -0.9442 | -0.1080 | -#> |.....................| 2.291 | 1.304 | 0.03139 | 0.7566 | -#> |.....................| 0.8381 | 1.281 | 1.116 | 1.125 | -#> | X| 487.93833 | 93.04 | 0.004918 | 0.2801 | 0.8977 | -#> |.....................| 9.883 | 1.304 | 0.03139 | 0.7566 | -#> |.....................| 0.8381 | 1.281 | 1.116 | 1.125 | -#> | F| Forward Diff. | 41.92 | 2.065 | 0.1569 | 0.3320 | -#> |.....................| 0.1961 | -37.34 | -12.63 | 0.01172 | -#> |.....................| 5.301 | -9.646 | -7.360 | -8.530 | -#> | 26| 487.37063 | 0.9925 | -1.012 | -0.9115 | -0.8960 | -#> |.....................| -0.8458 | -0.6069 | -0.7990 | -0.8783 | -#> |.....................| -0.9161 | -0.8073 | -0.8233 | -0.8168 | -#> | U| 487.37063 | 92.42 | -5.316 | -0.9443 | -0.1081 | -#> |.....................| 2.291 | 1.311 | 0.03145 | 0.7567 | -#> |.....................| 0.8374 | 1.287 | 1.119 | 1.130 | -#> | X| 487.37063 | 92.42 | 0.004913 | 0.2800 | 0.8975 | -#> |.....................| 9.882 | 1.311 | 0.03145 | 0.7567 | -#> |.....................| 0.8374 | 1.287 | 1.119 | 1.130 | -#> | F| Forward Diff. | -47.84 | 1.989 | -0.08553 | 0.2559 | -#> |.....................| -0.06263 | -35.59 | -12.91 | -0.09336 | -#> |.....................| 8.020 | -9.356 | -7.180 | -8.291 | -#> | 27| 486.76802 | 0.9991 | -1.014 | -0.9115 | -0.8962 | -#> |.....................| -0.8459 | -0.5954 | -0.7952 | -0.8779 | -#> |.....................| -0.9197 | -0.8027 | -0.8200 | -0.8127 | -#> | U| 486.76802 | 93.03 | -5.317 | -0.9443 | -0.1083 | -#> |.....................| 2.291 | 1.318 | 0.03150 | 0.7570 | -#> |.....................| 0.8342 | 1.292 | 1.123 | 1.134 | -#> | X| 486.76802 | 93.03 | 0.004908 | 0.2800 | 0.8973 | -#> |.....................| 9.881 | 1.318 | 0.03150 | 0.7570 | -#> |.....................| 0.8342 | 1.292 | 1.123 | 1.134 | -#> | F| Forward Diff. | 39.28 | 2.032 | 0.1697 | 0.3409 | -#> |.....................| 0.2161 | -34.26 | -11.60 | -0.04206 | -#> |.....................| 6.414 | -9.258 | -7.014 | -8.183 | -#> | 28| 486.25961 | 0.9924 | -1.015 | -0.9116 | -0.8964 | -#> |.....................| -0.8461 | -0.5843 | -0.7916 | -0.8775 | -#> |.....................| -0.9242 | -0.7980 | -0.8166 | -0.8086 | -#> | U| 486.25961 | 92.41 | -5.318 | -0.9444 | -0.1086 | -#> |.....................| 2.290 | 1.324 | 0.03156 | 0.7573 | -#> |.....................| 0.8303 | 1.298 | 1.126 | 1.138 | -#> | X| 486.25961 | 92.41 | 0.004902 | 0.2800 | 0.8971 | -#> |.....................| 9.880 | 1.324 | 0.03156 | 0.7573 | -#> |.....................| 0.8303 | 1.298 | 1.126 | 1.138 | -#> | F| Forward Diff. | -50.63 | 1.945 | -0.07307 | 0.2626 | -#> |.....................| -0.04930 | -33.11 | -12.03 | -0.1686 | -#> |.....................| 7.510 | -8.984 | -6.802 | -7.934 | -#> | 29| 485.66844 | 0.9985 | -1.016 | -0.9117 | -0.8967 | -#> |.....................| -0.8462 | -0.5738 | -0.7881 | -0.8769 | -#> |.....................| -0.9293 | -0.7927 | -0.8129 | -0.8039 | -#> | U| 485.66844 | 92.98 | -5.319 | -0.9445 | -0.1089 | -#> |.....................| 2.290 | 1.331 | 0.03161 | 0.7578 | -#> |.....................| 0.8259 | 1.304 | 1.130 | 1.143 | -#> | X| 485.66844 | 92.98 | 0.004895 | 0.2800 | 0.8969 | -#> |.....................| 9.878 | 1.331 | 0.03161 | 0.7578 | -#> |.....................| 0.8259 | 1.304 | 1.130 | 1.143 | -#> | F| Forward Diff. | 30.24 | 1.977 | 0.1746 | 0.3455 | -#> |.....................| 0.2218 | -32.22 | -10.87 | -0.2249 | -#> |.....................| 4.336 | -8.820 | -6.615 | -7.812 | -#> | 30| 485.23968 | 0.9921 | -1.017 | -0.9119 | -0.8970 | -#> |.....................| -0.8465 | -0.5622 | -0.7845 | -0.8762 | -#> |.....................| -0.9314 | -0.7876 | -0.8094 | -0.7994 | -#> | U| 485.23968 | 92.38 | -5.321 | -0.9447 | -0.1091 | -#> |.....................| 2.290 | 1.337 | 0.03166 | 0.7583 | -#> |.....................| 0.8240 | 1.310 | 1.134 | 1.148 | -#> | X| 485.23968 | 92.38 | 0.004889 | 0.2800 | 0.8966 | -#> |.....................| 9.876 | 1.337 | 0.03166 | 0.7583 | -#> |.....................| 0.8240 | 1.310 | 1.134 | 1.148 | -#> | F| Forward Diff. | -56.59 | 1.902 | -0.07536 | 0.2678 | -#> |.....................| -0.04797 | -30.46 | -11.14 | -0.09043 | -#> |.....................| 3.742 | -8.533 | -6.412 | -7.541 | -#> | 31| 484.69662 | 0.9984 | -1.019 | -0.9121 | -0.8974 | -#> |.....................| -0.8467 | -0.5517 | -0.7813 | -0.8754 | -#> |.....................| -0.9289 | -0.7816 | -0.8053 | -0.7941 | -#> | U| 484.69662 | 92.97 | -5.322 | -0.9448 | -0.1095 | -#> |.....................| 2.290 | 1.343 | 0.03171 | 0.7589 | -#> |.....................| 0.8262 | 1.318 | 1.138 | 1.154 | -#> | X| 484.69662 | 92.97 | 0.004881 | 0.2799 | 0.8963 | -#> |.....................| 9.873 | 1.343 | 0.03171 | 0.7589 | -#> |.....................| 0.8262 | 1.318 | 1.138 | 1.154 | -#> | F| Forward Diff. | 27.47 | 1.960 | 0.1737 | 0.3487 | -#> |.....................| 0.2320 | -29.84 | -10.04 | -0.2714 | -#> |.....................| 5.731 | -8.337 | -6.228 | -7.371 | -#> | 32| 484.27605 | 0.9928 | -1.021 | -0.9123 | -0.8978 | -#> |.....................| -0.8471 | -0.5404 | -0.7779 | -0.8746 | -#> |.....................| -0.9302 | -0.7757 | -0.8014 | -0.7889 | -#> | U| 484.27605 | 92.45 | -5.324 | -0.9451 | -0.1099 | -#> |.....................| 2.289 | 1.350 | 0.03176 | 0.7595 | -#> |.....................| 0.8251 | 1.325 | 1.143 | 1.159 | -#> | X| 484.27605 | 92.45 | 0.004872 | 0.2799 | 0.8959 | -#> |.....................| 9.870 | 1.350 | 0.03176 | 0.7595 | -#> |.....................| 0.8251 | 1.325 | 1.143 | 1.159 | -#> | F| Forward Diff. | -48.28 | 1.894 | -0.05804 | 0.2769 | -#> |.....................| -0.01457 | -28.21 | -10.24 | -0.1977 | -#> |.....................| 5.253 | -8.027 | -5.998 | -7.085 | -#> | 33| 483.77365 | 0.9986 | -1.023 | -0.9126 | -0.8983 | -#> |.....................| -0.8475 | -0.5309 | -0.7752 | -0.8734 | -#> |.....................| -0.9343 | -0.7690 | -0.7970 | -0.7831 | -#> | U| 483.77365 | 92.99 | -5.326 | -0.9453 | -0.1104 | -#> |.....................| 2.289 | 1.355 | 0.03180 | 0.7604 | -#> |.....................| 0.8215 | 1.333 | 1.147 | 1.166 | -#> | X| 483.77365 | 92.99 | 0.004861 | 0.2798 | 0.8954 | -#> |.....................| 9.866 | 1.355 | 0.03180 | 0.7604 | -#> |.....................| 0.8215 | 1.333 | 1.147 | 1.166 | -#> | F| Forward Diff. | 28.59 | 1.923 | 0.1952 | 0.3548 | -#> |.....................| 0.2608 | -27.76 | -9.333 | -0.3645 | -#> |.....................| 3.958 | -7.814 | -5.777 | -6.894 | -#> | 34| 483.37086 | 0.9934 | -1.025 | -0.9129 | -0.8989 | -#> |.....................| -0.8480 | -0.5203 | -0.7721 | -0.8720 | -#> |.....................| -0.9349 | -0.7624 | -0.7928 | -0.7774 | -#> | U| 483.37086 | 92.51 | -5.329 | -0.9456 | -0.1110 | -#> |.....................| 2.289 | 1.362 | 0.03185 | 0.7615 | -#> |.....................| 0.8209 | 1.341 | 1.152 | 1.172 | -#> | X| 483.37086 | 92.51 | 0.004850 | 0.2798 | 0.8949 | -#> |.....................| 9.861 | 1.362 | 0.03185 | 0.7615 | -#> |.....................| 0.8209 | 1.341 | 1.152 | 1.172 | -#> | F| Forward Diff. | -41.16 | 1.862 | -0.03265 | 0.2828 | -#> |.....................| 0.01951 | -26.43 | -9.488 | -0.2833 | -#> |.....................| 3.545 | -7.469 | -5.528 | -6.584 | -#> | 35| 482.96272 | 0.9987 | -1.028 | -0.9132 | -0.8995 | -#> |.....................| -0.8485 | -0.5103 | -0.7694 | -0.8702 | -#> |.....................| -0.9315 | -0.7558 | -0.7888 | -0.7716 | -#> | U| 482.96272 | 92.99 | -5.332 | -0.9459 | -0.1117 | -#> |.....................| 2.288 | 1.367 | 0.03189 | 0.7629 | -#> |.....................| 0.8240 | 1.349 | 1.156 | 1.178 | -#> | X| 482.96272 | 92.99 | 0.004836 | 0.2797 | 0.8943 | -#> |.....................| 9.856 | 1.367 | 0.03189 | 0.7629 | -#> |.....................| 0.8240 | 1.349 | 1.156 | 1.178 | -#> | F| Forward Diff. | 28.21 | 1.908 | 0.1917 | 0.3504 | -#> |.....................| 0.2712 | -25.82 | -8.599 | -0.3385 | -#> |.....................| 4.050 | -7.278 | -5.334 | -6.398 | -#> | 36| 482.60011 | 0.9939 | -1.032 | -0.9136 | -0.9003 | -#> |.....................| -0.8492 | -0.4998 | -0.7669 | -0.8684 | -#> |.....................| -0.9296 | -0.7490 | -0.7849 | -0.7659 | -#> | U| 482.60011 | 92.55 | -5.335 | -0.9462 | -0.1124 | -#> |.....................| 2.287 | 1.373 | 0.03193 | 0.7642 | -#> |.....................| 0.8256 | 1.357 | 1.160 | 1.184 | -#> | X| 482.60011 | 92.55 | 0.004820 | 0.2796 | 0.8937 | -#> |.....................| 9.849 | 1.373 | 0.03193 | 0.7642 | -#> |.....................| 0.8256 | 1.357 | 1.160 | 1.184 | -#> | F| Forward Diff. | -36.31 | 1.855 | -0.03781 | 0.2769 | -#> |.....................| 0.03076 | -24.99 | -8.890 | -0.4685 | -#> |.....................| 7.176 | -6.892 | -5.117 | -6.081 | -#> | 37| 482.21198 | 0.9982 | -1.035 | -0.9138 | -0.9009 | -#> |.....................| -0.8497 | -0.4920 | -0.7653 | -0.8661 | -#> |.....................| -0.9399 | -0.7441 | -0.7821 | -0.7617 | -#> | U| 482.21198 | 92.95 | -5.338 | -0.9465 | -0.1130 | -#> |.....................| 2.287 | 1.378 | 0.03195 | 0.7659 | -#> |.....................| 0.8166 | 1.363 | 1.163 | 1.189 | -#> | X| 482.21198 | 92.95 | 0.004805 | 0.2796 | 0.8931 | -#> |.....................| 9.844 | 1.378 | 0.03195 | 0.7659 | -#> |.....................| 0.8166 | 1.363 | 1.163 | 1.189 | -#> | F| Forward Diff. | 20.01 | 1.850 | 0.1852 | 0.3312 | -#> |.....................| 0.2616 | -23.95 | -7.997 | -0.3393 | -#> |.....................| 4.985 | -6.711 | -4.923 | -5.940 | -#> | 38| 481.96846 | 0.9924 | -1.037 | -0.9141 | -0.9014 | -#> |.....................| -0.8503 | -0.4828 | -0.7630 | -0.8646 | -#> |.....................| -0.9490 | -0.7399 | -0.7795 | -0.7579 | -#> | U| 481.96846 | 92.41 | -5.341 | -0.9468 | -0.1136 | -#> |.....................| 2.286 | 1.383 | 0.03199 | 0.7671 | -#> |.....................| 0.8087 | 1.368 | 1.166 | 1.193 | -#> | X| 481.96846 | 92.41 | 0.004793 | 0.2795 | 0.8927 | -#> |.....................| 9.838 | 1.383 | 0.03199 | 0.7671 | -#> |.....................| 0.8087 | 1.368 | 1.166 | 1.193 | -#> | F| Forward Diff. | -59.26 | 1.761 | -0.08116 | 0.2547 | -#> |.....................| -0.02692 | -22.78 | -8.366 | -0.2344 | -#> |.....................| 4.087 | -6.524 | -4.792 | -5.748 | -#> | 39| 481.52549 | 0.9980 | -1.042 | -0.9148 | -0.9024 | -#> |.....................| -0.8514 | -0.4755 | -0.7621 | -0.8625 | -#> |.....................| -0.9558 | -0.7333 | -0.7761 | -0.7520 | -#> | U| 481.52549 | 92.93 | -5.345 | -0.9474 | -0.1146 | -#> |.....................| 2.285 | 1.388 | 0.03200 | 0.7686 | -#> |.....................| 0.8027 | 1.376 | 1.170 | 1.199 | -#> | X| 481.52549 | 92.93 | 0.004770 | 0.2794 | 0.8917 | -#> |.....................| 9.827 | 1.388 | 0.03200 | 0.7686 | -#> |.....................| 0.8027 | 1.376 | 1.170 | 1.199 | -#> | F| Forward Diff. | 14.56 | 1.771 | 0.1903 | 0.3270 | -#> |.....................| 0.2641 | -22.44 | -7.508 | -0.4496 | -#> |.....................| 2.566 | -6.373 | -4.622 | -5.584 | -#> | 40| 481.26396 | 0.9932 | -1.045 | -0.9155 | -0.9032 | -#> |.....................| -0.8523 | -0.4642 | -0.7593 | -0.8605 | -#> |.....................| -0.9543 | -0.7272 | -0.7727 | -0.7469 | -#> | U| 481.26396 | 92.49 | -5.349 | -0.9480 | -0.1154 | -#> |.....................| 2.284 | 1.394 | 0.03204 | 0.7702 | -#> |.....................| 0.8040 | 1.384 | 1.173 | 1.205 | -#> | X| 481.26396 | 92.49 | 0.004753 | 0.2793 | 0.8910 | -#> |.....................| 9.818 | 1.394 | 0.03204 | 0.7702 | -#> |.....................| 0.8040 | 1.384 | 1.173 | 1.205 | -#> | F| Forward Diff. | -49.84 | 1.721 | -0.06329 | 0.2500 | -#> |.....................| 0.003387 | -21.58 | -7.808 | -0.4470 | -#> |.....................| 3.805 | -6.020 | -4.412 | -5.292 | -#> | 41| 480.91101 | 0.9981 | -1.051 | -0.9163 | -0.9044 | -#> |.....................| -0.8537 | -0.4552 | -0.7584 | -0.8559 | -#> |.....................| -0.9510 | -0.7207 | -0.7698 | -0.7416 | -#> | U| 480.91101 | 92.94 | -5.355 | -0.9488 | -0.1166 | -#> |.....................| 2.283 | 1.399 | 0.03206 | 0.7737 | -#> |.....................| 0.8069 | 1.392 | 1.176 | 1.210 | -#> | X| 480.91101 | 92.94 | 0.004727 | 0.2791 | 0.8900 | -#> |.....................| 9.804 | 1.399 | 0.03206 | 0.7737 | -#> |.....................| 0.8069 | 1.392 | 1.176 | 1.210 | -#> | F| Forward Diff. | 16.05 | 1.751 | 0.1631 | 0.3020 | -#> |.....................| 0.2540 | -20.90 | -6.928 | -0.3893 | -#> |.....................| 4.288 | -5.817 | -4.263 | -5.144 | -#> | 42| 480.64341 | 0.9941 | -1.056 | -0.9169 | -0.9053 | -#> |.....................| -0.8549 | -0.4456 | -0.7571 | -0.8527 | -#> |.....................| -0.9585 | -0.7158 | -0.7673 | -0.7373 | -#> | U| 480.64341 | 92.57 | -5.360 | -0.9493 | -0.1175 | -#> |.....................| 2.282 | 1.405 | 0.03208 | 0.7761 | -#> |.....................| 0.8004 | 1.398 | 1.179 | 1.215 | -#> | X| 480.64341 | 92.57 | 0.004703 | 0.2790 | 0.8892 | -#> |.....................| 9.793 | 1.405 | 0.03208 | 0.7761 | -#> |.....................| 0.8004 | 1.398 | 1.179 | 1.215 | -#> | F| Forward Diff. | -40.16 | 1.680 | -0.01378 | 0.2424 | -#> |.....................| 0.03021 | -20.27 | -7.228 | -0.4675 | -#> |.....................| 4.140 | -5.523 | -4.100 | -4.903 | -#> | 43| 480.34062 | 0.9982 | -1.062 | -0.9177 | -0.9064 | -#> |.....................| -0.8561 | -0.4387 | -0.7572 | -0.8486 | -#> |.....................| -0.9687 | -0.7122 | -0.7655 | -0.7338 | -#> | U| 480.34062 | 92.95 | -5.365 | -0.9501 | -0.1185 | -#> |.....................| 2.280 | 1.409 | 0.03207 | 0.7792 | -#> |.....................| 0.7914 | 1.402 | 1.181 | 1.219 | -#> | X| 480.34062 | 92.95 | 0.004675 | 0.2789 | 0.8883 | -#> |.....................| 9.781 | 1.409 | 0.03207 | 0.7792 | -#> |.....................| 0.7914 | 1.402 | 1.181 | 1.219 | -#> | 44| 480.11354 | 0.9982 | -1.069 | -0.9186 | -0.9075 | -#> |.....................| -0.8576 | -0.4327 | -0.7582 | -0.8437 | -#> |.....................| -0.9807 | -0.7086 | -0.7639 | -0.7301 | -#> | U| 480.11354 | 92.95 | -5.372 | -0.9510 | -0.1197 | -#> |.....................| 2.279 | 1.412 | 0.03206 | 0.7829 | -#> |.....................| 0.7810 | 1.406 | 1.183 | 1.223 | -#> | X| 480.11354 | 92.95 | 0.004643 | 0.2787 | 0.8872 | -#> |.....................| 9.767 | 1.412 | 0.03206 | 0.7829 | -#> |.....................| 0.7810 | 1.406 | 1.183 | 1.223 | -#> | 45| 479.24256 | 0.9982 | -1.100 | -0.9228 | -0.9129 | -#> |.....................| -0.8642 | -0.4061 | -0.7626 | -0.8221 | -#> |.....................| -1.034 | -0.6924 | -0.7565 | -0.7138 | -#> | U| 479.24256 | 92.95 | -5.404 | -0.9550 | -0.1250 | -#> |.....................| 2.272 | 1.428 | 0.03199 | 0.7993 | -#> |.....................| 0.7344 | 1.426 | 1.191 | 1.240 | -#> | X| 479.24256 | 92.95 | 0.004500 | 0.2779 | 0.8825 | -#> |.....................| 9.702 | 1.428 | 0.03199 | 0.7993 | -#> |.....................| 0.7344 | 1.426 | 1.191 | 1.240 | -#> | 46| 477.60836 | 1.003 | -1.228 | -0.9400 | -0.9346 | -#> |.....................| -0.8912 | -0.2901 | -0.7784 | -0.7332 | -#> |.....................| -1.206 | -0.6258 | -0.7257 | -0.6466 | -#> | U| 477.60836 | 93.40 | -5.531 | -0.9712 | -0.1467 | -#> |.....................| 2.245 | 1.495 | 0.03176 | 0.8667 | -#> |.....................| 0.5843 | 1.507 | 1.224 | 1.312 | -#> | X| 477.60836 | 93.40 | 0.003961 | 0.2746 | 0.8635 | -#> |.....................| 9.444 | 1.495 | 0.03176 | 0.8667 | -#> |.....................| 0.5843 | 1.507 | 1.224 | 1.312 | -#> | F| Forward Diff. | 50.81 | 0.8332 | 0.6263 | 0.04339 | -#> |.....................| 0.5543 | -9.740 | -2.969 | 0.1978 | -#> |.....................| -10.28 | -2.761 | -1.505 | -1.849 | -#> | 47| 476.77966 | 1.006 | -1.398 | -0.9862 | -0.9532 | -#> |.....................| -0.9413 | -0.07616 | -0.7687 | -0.6374 | -#> |.....................| -0.9573 | -0.5395 | -0.7103 | -0.5930 | -#> | U| 476.77966 | 93.71 | -5.701 | -1.015 | -0.1654 | -#> |.....................| 2.195 | 1.619 | 0.03190 | 0.9393 | -#> |.....................| 0.8014 | 1.612 | 1.240 | 1.369 | -#> | X| 476.77966 | 93.71 | 0.003342 | 0.2660 | 0.8476 | -#> |.....................| 8.982 | 1.619 | 0.03190 | 0.9393 | -#> |.....................| 0.8014 | 1.612 | 1.240 | 1.369 | -#> | F| Forward Diff. | 100.8 | 0.5681 | -2.148 | -0.2910 | -#> |.....................| -0.6169 | 0.8458 | 0.8586 | 0.3650 | -#> |.....................| 3.820 | 1.443 | -0.7364 | 0.2440 | -#> | 48| 478.65806 | 0.9952 | -1.512 | -0.6913 | -0.9031 | -#> |.....................| -0.8317 | -0.01918 | -0.7109 | -0.6555 | -#> |.....................| -0.9083 | -0.7021 | -0.6121 | -0.6260 | -#> | U| 478.65806 | 92.67 | -5.815 | -0.7363 | -0.1152 | -#> |.....................| 2.305 | 1.652 | 0.03277 | 0.9255 | -#> |.....................| 0.8442 | 1.414 | 1.345 | 1.334 | -#> | X| 478.65806 | 92.67 | 0.002982 | 0.3238 | 0.8912 | -#> |.....................| 10.02 | 1.652 | 0.03277 | 0.9255 | -#> |.....................| 0.8442 | 1.414 | 1.345 | 1.334 | -#> | 49| 476.83500 | 0.9931 | -1.426 | -0.9118 | -0.9406 | -#> |.....................| -0.9137 | -0.06192 | -0.7543 | -0.6420 | -#> |.....................| -0.9454 | -0.5805 | -0.6855 | -0.6013 | -#> | U| 476.835 | 92.48 | -5.730 | -0.9445 | -0.1527 | -#> |.....................| 2.223 | 1.627 | 0.03212 | 0.9358 | -#> |.....................| 0.8118 | 1.562 | 1.267 | 1.361 | -#> | X| 476.835 | 92.48 | 0.003247 | 0.2800 | 0.8584 | -#> |.....................| 9.234 | 1.627 | 0.03212 | 0.9358 | -#> |.....................| 0.8118 | 1.562 | 1.267 | 1.361 | -#> | 50| 476.86775 | 0.9928 | -1.411 | -0.9513 | -0.9473 | -#> |.....................| -0.9284 | -0.06958 | -0.7620 | -0.6396 | -#> |.....................| -0.9520 | -0.5587 | -0.6987 | -0.5969 | -#> | U| 476.86775 | 92.44 | -5.715 | -0.9819 | -0.1595 | -#> |.....................| 2.208 | 1.623 | 0.03200 | 0.9376 | -#> |.....................| 0.8060 | 1.588 | 1.252 | 1.365 | -#> | X| 476.86775 | 92.44 | 0.003297 | 0.2725 | 0.8526 | -#> |.....................| 9.099 | 1.623 | 0.03200 | 0.9376 | -#> |.....................| 0.8060 | 1.588 | 1.252 | 1.365 | -#> | 51| 476.94436 | 0.9926 | -1.403 | -0.9724 | -0.9509 | -#> |.....................| -0.9362 | -0.07366 | -0.7662 | -0.6383 | -#> |.....................| -0.9556 | -0.5471 | -0.7057 | -0.5945 | -#> | U| 476.94436 | 92.42 | -5.706 | -1.002 | -0.1630 | -#> |.....................| 2.200 | 1.621 | 0.03194 | 0.9386 | -#> |.....................| 0.8029 | 1.602 | 1.245 | 1.368 | -#> | X| 476.94436 | 92.42 | 0.003324 | 0.2686 | 0.8496 | -#> |.....................| 9.028 | 1.621 | 0.03194 | 0.9386 | -#> |.....................| 0.8029 | 1.602 | 1.245 | 1.368 | -#> | 52| 476.64580 | 0.9959 | -1.398 | -0.9860 | -0.9532 | -#> |.....................| -0.9413 | -0.07625 | -0.7688 | -0.6374 | -#> |.....................| -0.9577 | -0.5396 | -0.7102 | -0.5930 | -#> | U| 476.6458 | 92.74 | -5.701 | -1.015 | -0.1653 | -#> |.....................| 2.195 | 1.619 | 0.03190 | 0.9392 | -#> |.....................| 0.8011 | 1.611 | 1.240 | 1.369 | -#> | X| 476.6458 | 92.74 | 0.003342 | 0.2661 | 0.8476 | -#> |.....................| 8.983 | 1.619 | 0.03190 | 0.9392 | -#> |.....................| 0.8011 | 1.611 | 1.240 | 1.369 | -#> | F| Forward Diff. | -76.03 | 0.4748 | -3.401 | -0.5335 | -#> |.....................| -1.858 | 1.570 | -0.1336 | 0.2990 | -#> |.....................| 3.107 | 1.921 | -0.6340 | 0.6252 | -#> | 53| 476.45477 | 1.000 | -1.400 | -0.9787 | -0.9521 | -#> |.....................| -0.9380 | -0.07508 | -0.7683 | -0.6381 | -#> |.....................| -0.9567 | -0.5427 | -0.7079 | -0.5935 | -#> | U| 476.45477 | 93.14 | -5.704 | -1.008 | -0.1642 | -#> |.....................| 2.199 | 1.620 | 0.03191 | 0.9387 | -#> |.....................| 0.8019 | 1.608 | 1.243 | 1.369 | -#> | X| 476.45477 | 93.14 | 0.003334 | 0.2674 | 0.8486 | -#> |.....................| 9.012 | 1.620 | 0.03191 | 0.9387 | -#> |.....................| 0.8019 | 1.608 | 1.243 | 1.369 | -#> | F| Forward Diff. | 0.2803 | 0.4975 | -2.426 | -0.4122 | -#> |.....................| -1.237 | 1.245 | 0.3711 | 0.1250 | -#> |.....................| 4.601 | 1.480 | -0.5654 | 0.4236 | -#> | 54| 476.38303 | 0.9998 | -1.401 | -0.9743 | -0.9513 | -#> |.....................| -0.9358 | -0.07732 | -0.7690 | -0.6383 | -#> |.....................| -0.9650 | -0.5454 | -0.7069 | -0.5943 | -#> | U| 476.38303 | 93.10 | -5.704 | -1.004 | -0.1635 | -#> |.....................| 2.201 | 1.618 | 0.03190 | 0.9385 | -#> |.....................| 0.7947 | 1.604 | 1.244 | 1.368 | -#> | X| 476.38303 | 93.10 | 0.003331 | 0.2682 | 0.8492 | -#> |.....................| 9.032 | 1.618 | 0.03190 | 0.9385 | -#> |.....................| 0.7947 | 1.604 | 1.244 | 1.368 | -#> | 55| 476.22864 | 0.9983 | -1.404 | -0.9612 | -0.9491 | -#> |.....................| -0.9291 | -0.08404 | -0.7710 | -0.6390 | -#> |.....................| -0.9898 | -0.5533 | -0.7039 | -0.5966 | -#> | U| 476.22864 | 92.96 | -5.707 | -0.9912 | -0.1612 | -#> |.....................| 2.207 | 1.614 | 0.03187 | 0.9380 | -#> |.....................| 0.7730 | 1.595 | 1.247 | 1.366 | -#> | X| 476.22864 | 92.96 | 0.003322 | 0.2707 | 0.8511 | -#> |.....................| 9.093 | 1.614 | 0.03187 | 0.9380 | -#> |.....................| 0.7730 | 1.595 | 1.247 | 1.366 | -#> | 56| 476.57199 | 0.9958 | -1.445 | -0.8532 | -0.9271 | -#> |.....................| -0.8725 | -0.06353 | -0.7679 | -0.6421 | -#> |.....................| -0.9751 | -0.5970 | -0.6712 | -0.6082 | -#> | U| 476.57199 | 92.73 | -5.749 | -0.8892 | -0.1393 | -#> |.....................| 2.264 | 1.626 | 0.03191 | 0.9357 | -#> |.....................| 0.7859 | 1.542 | 1.282 | 1.353 | -#> | X| 476.57199 | 92.73 | 0.003186 | 0.2913 | 0.8700 | -#> |.....................| 9.623 | 1.626 | 0.03191 | 0.9357 | -#> |.....................| 0.7859 | 1.542 | 1.282 | 1.353 | -#> | F| Forward Diff. | -32.75 | 0.5399 | -1.515 | -0.3941 | -#> |.....................| -1.151 | 1.245 | 0.03890 | 0.2327 | -#> |.....................| 2.518 | 0.9004 | -0.2852 | 0.3306 | -#> | 57| 476.21990 | 0.9982 | -1.515 | -0.9538 | -0.8974 | -#> |.....................| -0.8289 | -0.1020 | -0.7526 | -0.6734 | -#> |.....................| -0.9899 | -0.5334 | -0.6863 | -0.5986 | -#> | U| 476.2199 | 92.95 | -5.819 | -0.9842 | -0.1096 | -#> |.....................| 2.308 | 1.604 | 0.03214 | 0.9120 | -#> |.....................| 0.7729 | 1.619 | 1.266 | 1.364 | -#> | X| 476.2199 | 92.95 | 0.002972 | 0.2721 | 0.8962 | -#> |.....................| 10.05 | 1.604 | 0.03214 | 0.9120 | -#> |.....................| 0.7729 | 1.619 | 1.266 | 1.364 | -#> | F| Forward Diff. | -17.29 | 0.1752 | -1.213 | 0.7541 | -#> |.....................| 1.907 | 0.8055 | -0.1948 | -0.02118 | -#> |.....................| 1.522 | 1.784 | 0.5826 | 0.3001 | -#> | 58| 476.15328 | 0.9997 | -1.587 | -0.9380 | -0.8926 | -#> |.....................| -0.8393 | -0.1057 | -0.7294 | -0.6920 | -#> |.....................| -0.9908 | -0.5546 | -0.6943 | -0.5998 | -#> | U| 476.15328 | 93.09 | -5.890 | -0.9693 | -0.1048 | -#> |.....................| 2.297 | 1.602 | 0.03249 | 0.8979 | -#> |.....................| 0.7721 | 1.593 | 1.257 | 1.362 | -#> | X| 476.15328 | 93.09 | 0.002766 | 0.2750 | 0.9005 | -#> |.....................| 9.947 | 1.602 | 0.03249 | 0.8979 | -#> |.....................| 0.7721 | 1.593 | 1.257 | 1.362 | -#> | F| Forward Diff. | 9.478 | -0.04668 | -0.07764 | 0.8847 | -#> |.....................| 1.686 | 1.059 | 0.2200 | -0.09397 | -#> |.....................| 3.078 | 0.7416 | 0.1570 | 0.2315 | -#> | 59| 476.01802 | 1.000 | -1.651 | -0.9570 | -0.8992 | -#> |.....................| -0.8607 | -0.1274 | -0.7088 | -0.7141 | -#> |.....................| -1.015 | -0.5543 | -0.6984 | -0.6027 | -#> | U| 476.01802 | 93.12 | -5.954 | -0.9872 | -0.1113 | -#> |.....................| 2.276 | 1.589 | 0.03280 | 0.8811 | -#> |.....................| 0.7512 | 1.594 | 1.253 | 1.359 | -#> | X| 476.01802 | 93.12 | 0.002594 | 0.2715 | 0.8947 | -#> |.....................| 9.736 | 1.589 | 0.03280 | 0.8811 | -#> |.....................| 0.7512 | 1.594 | 1.253 | 1.359 | -#> | 60| 476.22711 | 1.004 | -1.844 | -1.014 | -0.9185 | -#> |.....................| -0.9244 | -0.1921 | -0.6470 | -0.7805 | -#> |.....................| -1.085 | -0.5529 | -0.7106 | -0.6114 | -#> | U| 476.22711 | 93.52 | -6.147 | -1.041 | -0.1307 | -#> |.....................| 2.212 | 1.552 | 0.03373 | 0.8308 | -#> |.....................| 0.6895 | 1.595 | 1.240 | 1.350 | -#> | X| 476.22711 | 93.52 | 0.002140 | 0.2610 | 0.8775 | -#> |.....................| 9.136 | 1.552 | 0.03373 | 0.8308 | -#> |.....................| 0.6895 | 1.595 | 1.240 | 1.350 | -#> | F| Forward Diff. | 11.37 | -0.1053 | -1.010 | 0.7448 | -#> |.....................| 1.048 | 0.2820 | 0.2022 | -0.3140 | -#> |.....................| 0.8239 | 0.7199 | -0.08354 | 0.05077 | -#> | 61| 477.73164 | 0.9986 | -1.783 | -0.8482 | -1.092 | -#> |.....................| -0.9355 | -0.2068 | -0.7199 | -0.6608 | -#> |.....................| -1.022 | -0.4554 | -0.5612 | -0.5707 | -#> | U| 477.73164 | 92.99 | -6.086 | -0.8845 | -0.3044 | -#> |.....................| 2.201 | 1.543 | 0.03264 | 0.9215 | -#> |.....................| 0.7445 | 1.714 | 1.399 | 1.393 | -#> | X| 477.73164 | 92.99 | 0.002274 | 0.2922 | 0.7376 | -#> |.....................| 9.035 | 1.543 | 0.03264 | 0.9215 | -#> |.....................| 0.7445 | 1.714 | 1.399 | 1.393 | -#> | 62| 476.07192 | 0.9962 | -1.664 | -0.9459 | -0.9184 | -#> |.....................| -0.8684 | -0.1353 | -0.7100 | -0.7087 | -#> |.....................| -1.016 | -0.5448 | -0.6848 | -0.5995 | -#> | U| 476.07192 | 92.76 | -5.967 | -0.9768 | -0.1306 | -#> |.....................| 2.268 | 1.585 | 0.03278 | 0.8852 | -#> |.....................| 0.7503 | 1.605 | 1.267 | 1.362 | -#> | X| 476.07192 | 92.76 | 0.002561 | 0.2735 | 0.8776 | -#> |.....................| 9.662 | 1.585 | 0.03278 | 0.8852 | -#> |.....................| 0.7503 | 1.605 | 1.267 | 1.362 | -#> | 63| 476.10587 | 0.9957 | -1.654 | -0.9539 | -0.9043 | -#> |.....................| -0.8630 | -0.1295 | -0.7092 | -0.7126 | -#> |.....................| -1.015 | -0.5521 | -0.6949 | -0.6019 | -#> | U| 476.10587 | 92.72 | -5.958 | -0.9843 | -0.1164 | -#> |.....................| 2.274 | 1.588 | 0.03280 | 0.8822 | -#> |.....................| 0.7508 | 1.596 | 1.257 | 1.360 | -#> | X| 476.10587 | 92.72 | 0.002586 | 0.2720 | 0.8901 | -#> |.....................| 9.714 | 1.588 | 0.03280 | 0.8822 | -#> |.....................| 0.7508 | 1.596 | 1.257 | 1.360 | -#> | 64| 476.02413 | 0.9981 | -1.651 | -0.9568 | -0.8993 | -#> |.....................| -0.8609 | -0.1274 | -0.7088 | -0.7140 | -#> |.....................| -1.015 | -0.5544 | -0.6984 | -0.6027 | -#> | U| 476.02413 | 92.94 | -5.954 | -0.9870 | -0.1114 | -#> |.....................| 2.276 | 1.589 | 0.03280 | 0.8812 | -#> |.....................| 0.7511 | 1.593 | 1.253 | 1.359 | -#> | X| 476.02413 | 92.94 | 0.002594 | 0.2715 | 0.8946 | -#> |.....................| 9.735 | 1.589 | 0.03280 | 0.8812 | -#> |.....................| 0.7511 | 1.593 | 1.253 | 1.359 | -#> | 65| 476.01367 | 0.9993 | -1.651 | -0.9569 | -0.8992 | -#> |.....................| -0.8608 | -0.1274 | -0.7088 | -0.7141 | -#> |.....................| -1.015 | -0.5543 | -0.6984 | -0.6027 | -#> | U| 476.01367 | 93.05 | -5.954 | -0.9871 | -0.1114 | -#> |.....................| 2.276 | 1.589 | 0.03280 | 0.8812 | -#> |.....................| 0.7512 | 1.594 | 1.253 | 1.359 | -#> | X| 476.01367 | 93.05 | 0.002594 | 0.2715 | 0.8946 | -#> |.....................| 9.736 | 1.589 | 0.03280 | 0.8812 | -#> |.....................| 0.7512 | 1.594 | 1.253 | 1.359 | -#> | F| Forward Diff. | -0.2880 | -0.1104 | -1.088 | 0.7255 | -#> |.....................| 0.9655 | -0.09765 | 0.02713 | -0.4308 | -#> |.....................| 1.898 | 0.6709 | -0.08067 | 0.06084 | -#> | 66| 476.01068 | 0.9993 | -1.651 | -0.9566 | -0.8994 | -#> |.....................| -0.8610 | -0.1274 | -0.7088 | -0.7139 | -#> |.....................| -1.015 | -0.5545 | -0.6983 | -0.6027 | -#> | U| 476.01068 | 93.06 | -5.954 | -0.9868 | -0.1116 | -#> |.....................| 2.276 | 1.589 | 0.03280 | 0.8813 | -#> |.....................| 0.7507 | 1.593 | 1.253 | 1.359 | -#> | X| 476.01068 | 93.06 | 0.002595 | 0.2715 | 0.8944 | -#> |.....................| 9.733 | 1.589 | 0.03280 | 0.8813 | -#> |.....................| 0.7507 | 1.593 | 1.253 | 1.359 | -#> | 67| 476.00249 | 0.9996 | -1.651 | -0.9556 | -0.9000 | -#> |.....................| -0.8619 | -0.1273 | -0.7089 | -0.7136 | -#> |.....................| -1.017 | -0.5551 | -0.6983 | -0.6027 | -#> | U| 476.00249 | 93.08 | -5.954 | -0.9860 | -0.1122 | -#> |.....................| 2.275 | 1.589 | 0.03280 | 0.8815 | -#> |.....................| 0.7493 | 1.593 | 1.253 | 1.359 | -#> | X| 476.00249 | 93.08 | 0.002595 | 0.2717 | 0.8939 | -#> |.....................| 9.725 | 1.589 | 0.03280 | 0.8815 | -#> |.....................| 0.7493 | 1.593 | 1.253 | 1.359 | -#> | 68| 475.98648 | 0.9997 | -1.654 | -0.9518 | -0.9062 | -#> |.....................| -0.8643 | -0.1288 | -0.7101 | -0.7095 | -#> |.....................| -1.019 | -0.5521 | -0.6956 | -0.6031 | -#> | U| 475.98648 | 93.09 | -5.957 | -0.9823 | -0.1183 | -#> |.....................| 2.272 | 1.589 | 0.03278 | 0.8846 | -#> |.....................| 0.7477 | 1.596 | 1.256 | 1.359 | -#> | X| 475.98648 | 93.09 | 0.002587 | 0.2724 | 0.8884 | -#> |.....................| 9.702 | 1.589 | 0.03278 | 0.8846 | -#> |.....................| 0.7477 | 1.596 | 1.256 | 1.359 | -#> | 69| 475.97179 | 0.9994 | -1.666 | -0.9399 | -0.9282 | -#> |.....................| -0.8710 | -0.1347 | -0.7147 | -0.6948 | -#> |.....................| -1.020 | -0.5387 | -0.6854 | -0.6045 | -#> | U| 475.97179 | 93.06 | -5.969 | -0.9711 | -0.1404 | -#> |.....................| 2.266 | 1.585 | 0.03271 | 0.8957 | -#> |.....................| 0.7463 | 1.612 | 1.267 | 1.357 | -#> | X| 475.97179 | 93.06 | 0.002557 | 0.2747 | 0.8690 | -#> |.....................| 9.637 | 1.585 | 0.03271 | 0.8957 | -#> |.....................| 0.7463 | 1.612 | 1.267 | 1.357 | -#> | F| Forward Diff. | 1.543 | -0.1187 | -0.09427 | 0.04746 | -#> |.....................| 0.7019 | 0.1743 | 0.004057 | -0.1664 | -#> |.....................| 1.824 | 1.487 | 0.8060 | -0.1087 | -#> | 70| 475.93640 | 0.9984 | -1.664 | -0.9398 | -0.9470 | -#> |.....................| -0.8662 | -0.1315 | -0.7271 | -0.6595 | -#> |.....................| -1.030 | -0.5499 | -0.6986 | -0.5913 | -#> | U| 475.9364 | 92.96 | -5.967 | -0.9710 | -0.1592 | -#> |.....................| 2.270 | 1.587 | 0.03253 | 0.9225 | -#> |.....................| 0.7382 | 1.599 | 1.253 | 1.371 | -#> | X| 475.9364 | 92.96 | 0.002561 | 0.2747 | 0.8529 | -#> |.....................| 9.682 | 1.587 | 0.03253 | 0.9225 | -#> |.....................| 0.7382 | 1.599 | 1.253 | 1.371 | -#> | F| Forward Diff. | -18.02 | -0.07507 | -0.1675 | -0.4306 | -#> |.....................| 0.8222 | -0.4249 | -0.3576 | -0.06909 | -#> |.....................| -0.1553 | 0.7789 | -0.06902 | 0.4423 | -#> | 71| 475.93449 | 0.9995 | -1.655 | -0.9484 | -0.9330 | -#> |.....................| -0.8784 | -0.1258 | -0.7357 | -0.6330 | -#> |.....................| -1.033 | -0.5716 | -0.6758 | -0.5988 | -#> | U| 475.93449 | 93.07 | -5.959 | -0.9791 | -0.1451 | -#> |.....................| 2.258 | 1.590 | 0.03240 | 0.9426 | -#> |.....................| 0.7351 | 1.573 | 1.277 | 1.363 | -#> | X| 475.93449 | 93.07 | 0.002583 | 0.2731 | 0.8649 | -#> |.....................| 9.566 | 1.590 | 0.03240 | 0.9426 | -#> |.....................| 0.7351 | 1.573 | 1.277 | 1.363 | -#> | F| Forward Diff. | -1.432 | -0.03245 | -0.4539 | -0.04331 | -#> |.....................| 0.5695 | -0.03993 | -0.2223 | 0.1396 | -#> |.....................| -0.3709 | -0.08203 | 1.409 | 0.03273 | -#> | 72| 475.92305 | 1.001 | -1.648 | -0.9418 | -0.9189 | -#> |.....................| -0.8867 | -0.1240 | -0.7358 | -0.6284 | -#> |.....................| -1.035 | -0.5652 | -0.6857 | -0.6066 | -#> | U| 475.92305 | 93.18 | -5.952 | -0.9729 | -0.1311 | -#> |.....................| 2.250 | 1.591 | 0.03240 | 0.9461 | -#> |.....................| 0.7335 | 1.580 | 1.266 | 1.355 | -#> | X| 475.92305 | 93.18 | 0.002602 | 0.2743 | 0.8772 | -#> |.....................| 9.486 | 1.591 | 0.03240 | 0.9461 | -#> |.....................| 0.7335 | 1.580 | 1.266 | 1.355 | -#> | F| Forward Diff. | 18.31 | 0.001701 | 0.03033 | 0.3531 | -#> |.....................| 0.4204 | 0.05655 | -0.08057 | 0.1734 | -#> |.....................| -0.4632 | 0.1099 | 0.8178 | -0.3689 | -#> | 73| 475.91938 | 0.9986 | -1.638 | -0.9366 | -0.9070 | -#> |.....................| -0.8945 | -0.1236 | -0.7244 | -0.6267 | -#> |.....................| -1.037 | -0.5623 | -0.6914 | -0.6147 | -#> | U| 475.91938 | 92.99 | -5.941 | -0.9680 | -0.1192 | -#> |.....................| 2.242 | 1.592 | 0.03257 | 0.9474 | -#> |.....................| 0.7320 | 1.584 | 1.260 | 1.346 | -#> | X| 475.91938 | 92.99 | 0.002629 | 0.2753 | 0.8877 | -#> |.....................| 9.412 | 1.592 | 0.03257 | 0.9474 | -#> |.....................| 0.7320 | 1.584 | 1.260 | 1.346 | -#> | F| Forward Diff. | -15.99 | 0.01876 | 0.07238 | 0.5908 | -#> |.....................| -0.09055 | 0.2914 | -0.2119 | 0.1409 | -#> |.....................| 0.4365 | 0.1061 | 0.4376 | -0.5157 | -#> | 74| 475.91938 | 0.9986 | -1.638 | -0.9366 | -0.9070 | -#> |.....................| -0.8945 | -0.1236 | -0.7244 | -0.6267 | -#> |.....................| -1.037 | -0.5623 | -0.6914 | -0.6147 | -#> | U| 475.91938 | 92.99 | -5.941 | -0.9680 | -0.1192 | -#> |.....................| 2.242 | 1.592 | 0.03257 | 0.9474 | -#> |.....................| 0.7320 | 1.584 | 1.260 | 1.346 | -#> | X| 475.91938 | 92.99 | 0.002629 | 0.2753 | 0.8877 | -#> |.....................| 9.412 | 1.592 | 0.03257 | 0.9474 | -#> |.....................| 0.7320 | 1.584 | 1.260 | 1.346 | -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_A1 |f_parent_qlogis | log_k1 | -#> |.....................| log_k2 | g_qlogis | sigma_low | rsd_high | -#> |.....................| o1 | o2 | o3 | o4 | -#> |.....................| o5 | o6 |...........|...........| -#> | 1| 495.80376 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 495.80376 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 495.80376 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | G| Gill Diff. | 40.10 | 2.344 | -0.09792 | 0.01304 | -#> |.....................| -0.4854 | 0.6353 | -29.93 | -20.00 | -#> |.....................| 1.261 | 9.993 | -12.68 | -0.7774 | -#> |.....................| 8.106 | -12.55 |...........|...........| -#> | 2| 2936.2793 | 0.3119 | -1.040 | -0.9093 | -0.9382 | -#> |.....................| -0.9801 | -0.8941 | -0.3619 | -0.5483 | -#> |.....................| -0.8992 | -1.046 | -0.6506 | -0.8594 | -#> |.....................| -1.014 | -0.6521 |...........|...........| -#> | U| 2936.2793 | 28.54 | -5.229 | -0.8860 | -2.190 | -#> |.....................| -4.622 | 0.4539 | 1.041 | 0.06759 | -#> |.....................| 0.7138 | 0.7431 | 1.443 | 0.9756 | -#> |.....................| 0.7388 | 1.478 |...........|...........| -#> | X| 2936.2793 | 28.54 | 0.005360 | 0.2919 | 0.1119 | -#> |.....................| 0.009832 | 0.6116 | 1.041 | 0.06759 | -#> |.....................| 0.7138 | 0.7431 | 1.443 | 0.9756 | -#> |.....................| 0.7388 | 1.478 |...........|...........| -#> | 3| 515.54714 | 0.9312 | -1.004 | -0.9108 | -0.9380 | -#> |.....................| -0.9876 | -0.8843 | -0.8242 | -0.8571 | -#> |.....................| -0.8797 | -0.8912 | -0.8464 | -0.8714 | -#> |.....................| -0.8888 | -0.8460 |...........|...........| -#> | U| 515.54714 | 85.19 | -5.193 | -0.8873 | -2.190 | -#> |.....................| -4.630 | 0.4584 | 0.8493 | 0.05868 | -#> |.....................| 0.7280 | 0.8815 | 1.211 | 0.9641 | -#> |.....................| 0.8462 | 1.242 |...........|...........| -#> | X| 515.54714 | 85.19 | 0.005557 | 0.2917 | 0.1119 | -#> |.....................| 0.009758 | 0.6126 | 0.8493 | 0.05868 | -#> |.....................| 0.7280 | 0.8815 | 1.211 | 0.9641 | -#> |.....................| 0.8462 | 1.242 |...........|...........| -#> | 4| 501.46574 | 0.9922 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9884 | -0.8833 | -0.8697 | -0.8876 | -#> |.....................| -0.8778 | -0.8761 | -0.8657 | -0.8726 | -#> |.....................| -0.8765 | -0.8650 |...........|...........| -#> | U| 501.46574 | 90.77 | -5.189 | -0.8874 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8304 | 0.05781 | -#> |.....................| 0.7294 | 0.8952 | 1.188 | 0.9629 | -#> |.....................| 0.8568 | 1.219 |...........|...........| -#> | X| 501.46574 | 90.77 | 0.005577 | 0.2916 | 0.1119 | -#> |.....................| 0.009751 | 0.6127 | 0.8304 | 0.05781 | -#> |.....................| 0.7294 | 0.8952 | 1.188 | 0.9629 | -#> |.....................| 0.8568 | 1.219 |...........|...........| -#> | 5| 501.84206 | 0.9992 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9884 | -0.8832 | -0.8749 | -0.8911 | -#> |.....................| -0.8776 | -0.8743 | -0.8679 | -0.8727 | -#> |.....................| -0.8751 | -0.8673 |...........|...........| -#> | U| 501.84206 | 91.41 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8283 | 0.05771 | -#> |.....................| 0.7296 | 0.8967 | 1.185 | 0.9628 | -#> |.....................| 0.8580 | 1.216 |...........|...........| -#> | X| 501.84206 | 91.41 | 0.005579 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8283 | 0.05771 | -#> |.....................| 0.7296 | 0.8967 | 1.185 | 0.9628 | -#> |.....................| 0.8580 | 1.216 |...........|...........| -#> | 6| 501.90183 | 0.9999 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8914 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90183 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05770 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90183 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05770 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 7| 501.90808 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90808 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90808 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 8| 501.90873 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90873 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90873 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 9| 501.90880 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.9088 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.9088 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 10| 501.90881 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90881 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90881 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 11| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90882 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 12| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90882 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 13| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90882 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 14| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90882 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 15| 501.90882 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90882 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90882 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 16| 501.90883 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90883 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90883 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | 17| 501.90883 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8776 | -0.8741 | -0.8681 | -0.8727 | -#> |.....................| -0.8749 | -0.8675 |...........|...........| -#> | U| 501.90883 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> | X| 501.90883 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.7296 | 0.8969 | 1.185 | 0.9628 | -#> |.....................| 0.8582 | 1.216 |...........|...........| -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[4]+THETA[4]; +#> rx_expr_8~ETA[5]+THETA[5]; +#> rx_expr_14~exp(-(rx_expr_8)); +#> rx_expr_16~t*rx_expr_14; +#> rx_expr_17~1+rx_expr_16; +#> rx_expr_19~rx_expr_7-(rx_expr_8); +#> rx_expr_21~exp(rx_expr_19); +#> d/dt(parent)=-rx_expr_21*parent/(rx_expr_17); +#> rx_expr_9~ETA[2]+THETA[2]; +#> rx_expr_11~exp(rx_expr_9); +#> d/dt(A1)=-rx_expr_11*A1+rx_expr_21*parent*f_parent_to_A1/(rx_expr_17); +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_15~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_15+rx_expr_3; +#> rx_hi_~rx_expr_15+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_10~parent*(rx_expr_2); +#> rx_expr_18~rx_expr_10*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_18)*(rx_expr_0)+(rx_expr_4+rx_expr_18)*(rx_expr_2)*(rx_expr_1); +#> rx_expr_12~Rx_pow_di(THETA[7],2); +#> rx_expr_13~Rx_pow_di(THETA[6],2); +#> rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_18)*(rx_expr_0)+(rx_expr_4+rx_expr_18)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_12+rx_expr_13)*(rx_expr_0)+(rx_expr_12*Rx_pow_di(((rx_expr_4+rx_expr_18)*(rx_expr_1)),2)+rx_expr_13)*(rx_expr_2)*(rx_expr_1); +#> parent_0=THETA[1]; +#> log_k_A1=THETA[2]; +#> f_parent_qlogis=THETA[3]; +#> log_alpha=THETA[4]; +#> log_beta=THETA[5]; +#> sigma_low=THETA[6]; +#> rsd_high=THETA[7]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_A1=ETA[2]; +#> eta.f_parent_qlogis=ETA[3]; +#> eta.log_alpha=ETA[4]; +#> eta.log_beta=ETA[5]; +#> parent_0_model=rx_expr_6; +#> k_A1=rx_expr_11; +#> alpha=exp(rx_expr_7); +#> beta=exp(rx_expr_8); +#> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.708 0.429 9.135
                                                                f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[4]+THETA[4]; +#> rx_expr_8~ETA[6]+THETA[6]; +#> rx_expr_9~ETA[5]+THETA[5]; +#> rx_expr_12~exp(rx_expr_7); +#> rx_expr_13~exp(rx_expr_9); +#> rx_expr_15~t*rx_expr_12; +#> rx_expr_16~t*rx_expr_13; +#> rx_expr_19~exp(-(rx_expr_8)); +#> rx_expr_21~1+rx_expr_19; +#> rx_expr_26~1/(rx_expr_21); +#> rx_expr_28~(rx_expr_26); +#> rx_expr_29~1-rx_expr_28; +#> d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29)); +#> rx_expr_10~ETA[2]+THETA[2]; +#> rx_expr_14~exp(rx_expr_10); +#> d/dt(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29)); +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_20~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_20+rx_expr_3; +#> rx_hi_~rx_expr_20+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_11~parent*(rx_expr_2); +#> rx_expr_24~rx_expr_11*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1); +#> rx_expr_17~Rx_pow_di(THETA[8],2); +#> rx_expr_18~Rx_pow_di(THETA[7],2); +#> rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_0)+(rx_expr_17*Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_1)),2)+rx_expr_18)*(rx_expr_2)*(rx_expr_1); +#> parent_0=THETA[1]; +#> log_k_A1=THETA[2]; +#> f_parent_qlogis=THETA[3]; +#> log_k1=THETA[4]; +#> log_k2=THETA[5]; +#> g_qlogis=THETA[6]; +#> sigma_low=THETA[7]; +#> rsd_high=THETA[8]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_A1=ETA[2]; +#> eta.f_parent_qlogis=ETA[3]; +#> eta.log_k1=ETA[4]; +#> eta.log_k2=ETA[5]; +#> eta.g_qlogis=ETA[6]; +#> parent_0_model=rx_expr_6; +#> k_A1=rx_expr_14; +#> k1=rx_expr_12; +#> k2=rx_expr_13; +#> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); +#> g=1/(rx_expr_21); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 18.05 0.446 18.5
                                                                # Two-component error by variable is possible with both estimation methods # Variance by variable is supported by 'saem' and 'focei' f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem", error_model = "obs_tc") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> 1: 92.2740 -5.2361 0.2113 1.9393 -2.0029 2.8805 1.6298 0.7279 0.7192 0.4382 6.7264 0.4769 7.2363 0.6178 -#> 2: 93.1532 -5.3060 0.0602 2.0735 -2.0177 2.7365 1.5483 0.6915 0.8577 0.4163 7.5229 0.0003 8.5494 0.0006 -#> 3: 9.3232e+01 -5.5491e+00 5.1555e-02 2.4627e+00 -1.4981e+00 2.5997e+00 1.4709e+00 6.5697e-01 8.1480e-01 3.9549e-01 4.6581e+00 4.3492e-05 5.3112e+00 1.7818e-04 -#> 4: 9.3109e+01 -5.6749e+00 3.7928e-02 2.4274e+00 -1.3355e+00 2.4697e+00 1.3973e+00 6.2412e-01 7.7406e-01 3.7572e-01 3.5252e+00 9.5643e-05 4.0990e+00 4.6584e-05 -#> 5: 9.3327e+01 -5.8341e+00 -1.6798e-02 2.4024e+00 -1.2129e+00 2.3462e+00 1.3274e+00 5.9292e-01 7.3536e-01 3.5693e-01 3.3259e+00 1.6901e-05 3.5218e+00 4.0075e-05 -#> 6: 9.3449e+01 -6.0745e+00 -6.1031e-02 2.3458e+00 -1.2034e+00 2.2289e+00 1.8700e+00 5.6327e-01 6.9859e-01 3.3908e-01 2.9533e+00 6.5587e-07 3.1056e+00 2.1346e-02 -#> 7: 93.2519 -6.0564 -0.0590 2.3588 -1.1293 2.1174 1.8910 0.5351 0.6637 0.3221 2.8211 0.0082 2.8507 0.0251 -#> 8: 93.0343 -5.9362 -0.0851 2.2949 -1.0760 2.0116 1.7964 0.5084 0.6305 0.3060 2.5340 0.0181 2.6368 0.0243 -#> 9: 93.1444 -6.1910 -0.1199 2.2709 -1.1077 1.9110 1.8664 0.4829 0.5990 0.2907 2.3768 0.0191 2.3601 0.0284 -#> 10: 93.2748 -6.4970 -0.1598 2.2235 -1.1034 2.1024 3.1968 0.4588 0.5690 0.2762 2.1991 0.0255 2.2790 0.0316 -#> 11: 93.4141 -6.4463 -0.1698 2.1876 -1.0890 1.9973 3.0370 0.4358 0.5406 0.2624 2.1469 0.0266 2.1681 0.0325 -#> 12: 93.4935 -6.5467 -0.1715 2.1666 -1.0952 1.8974 3.7848 0.4141 0.5135 0.2493 1.9137 0.0292 2.0701 0.0331 -#> 13: 93.6730 -6.4173 -0.1752 2.1387 -1.0753 1.8026 3.7278 0.3934 0.4879 0.2368 1.9084 0.0272 2.0289 0.0369 -#> 14: 93.5721 -6.2146 -0.1738 2.1854 -1.0740 2.0902 3.5415 0.3737 0.4635 0.2250 1.9861 0.0239 2.0052 0.0347 -#> 15: 93.6638 -6.3103 -0.1693 2.1828 -1.0327 2.0702 3.3644 0.3720 0.4403 0.2137 1.8947 0.0247 1.9865 0.0375 -#> 16: 93.4156 -6.0957 -0.1666 2.1755 -1.0737 2.6391 3.1962 0.3691 0.4183 0.2030 1.9089 0.0241 2.0159 0.0360 -#> 17: 93.4257 -6.1494 -0.1705 2.1664 -1.0589 2.5072 3.0714 0.3697 0.3974 0.1929 1.8253 0.0268 2.0391 0.0301 -#> 18: 93.5593 -6.1696 -0.1780 2.1670 -1.0129 2.3818 3.7604 0.3725 0.3775 0.1832 1.8529 0.0304 1.8784 0.0298 -#> 19: 93.5027 -6.2960 -0.1791 2.1543 -1.0325 2.6052 4.5501 0.3942 0.3586 0.1741 1.8082 0.0328 1.8654 0.0335 -#> 20: 93.4480 -6.4389 -0.1776 2.1772 -1.0485 2.6607 5.1881 0.3894 0.3554 0.1654 1.8032 0.0322 1.9018 0.0312 -#> 21: 93.6411 -6.2893 -0.1750 2.1759 -1.0350 2.5276 4.9287 0.3817 0.3386 0.1605 1.8533 0.0264 1.9317 0.0301 -#> 22: 93.9320 -6.1469 -0.1750 2.1910 -1.0527 2.4013 4.6823 0.3720 0.3642 0.1525 1.8949 0.0273 1.8977 0.0310 -#> 23: 93.6074 -6.3097 -0.1502 2.2111 -1.0155 2.2812 4.6643 0.3832 0.4236 0.1449 1.7075 0.0340 1.7367 0.0337 -#> 24: 93.7425 -6.4598 -0.1446 2.2249 -1.0011 2.7056 6.0597 0.3949 0.4075 0.1479 1.7180 0.0360 1.7786 0.0302 -#> 25: 94.1822 -6.3674 -0.1496 2.1917 -1.0011 3.4724 5.7567 0.3897 0.4355 0.1465 1.6977 0.0356 1.8373 0.0328 -#> 26: 94.0446 -6.3235 -0.1496 2.2004 -1.0414 3.5912 5.4688 0.3897 0.4438 0.1405 1.6765 0.0344 1.8262 0.0355 -#> 27: 94.4454 -6.2148 -0.1370 2.2360 -1.0220 4.6238 5.1954 0.3702 0.4216 0.1335 1.7209 0.0349 1.7702 0.0336 -#> 28: 94.1837 -6.1301 -0.1376 2.2253 -1.0261 4.3926 4.9356 0.3644 0.4005 0.1345 1.6968 0.0290 1.8540 0.0316 -#> 29: 94.0681 -5.8726 -0.1440 2.2237 -1.0400 4.1730 4.6889 0.3750 0.4055 0.1464 1.7084 0.0329 1.7379 0.0407 -#> 30: 94.5866 -5.9141 -0.1416 2.2045 -1.0350 3.9896 4.4544 0.3770 0.3852 0.1769 1.6009 0.0326 1.8718 0.0350 -#> 31: 94.1640 -6.0370 -0.1382 2.2140 -1.0189 5.4942 4.2317 0.3759 0.3809 0.1680 1.5887 0.0386 1.8918 0.0286 -#> 32: 94.5952 -5.8349 -0.1373 2.2374 -1.0283 5.2195 4.0201 0.3745 0.3835 0.1636 1.6451 0.0375 1.7459 0.0382 -#> 33: 95.0936 -5.8145 -0.1356 2.2325 -1.0037 4.9634 3.8191 0.3614 0.3644 0.1677 1.6313 0.0414 1.6809 0.0399 -#> 34: 94.7033 -5.8916 -0.1208 2.2687 -0.9896 5.4935 3.6281 0.3741 0.3536 0.1701 1.5923 0.0376 1.2962 0.0644 -#> 35: 94.8127 -5.9839 -0.1122 2.2615 -0.9983 5.2188 3.7348 0.3817 0.3661 0.1712 1.5848 0.0313 1.1651 0.0752 -#> 36: 94.6798 -5.8938 -0.1203 2.2441 -1.0009 4.9578 3.5480 0.3835 0.3478 0.1708 1.5525 0.0313 1.1527 0.0712 -#> 37: 93.9759 -5.8017 -0.1274 2.2346 -1.0021 4.7100 3.3706 0.3868 0.3350 0.1622 1.6278 0.0256 1.7263 0.0372 -#> 38: 94.2013 -5.8617 -0.1206 2.2570 -1.0125 4.4745 3.2021 0.3754 0.3520 0.1574 1.5396 0.0290 1.0653 0.0746 -#> 39: 94.1314 -5.7645 -0.1261 2.2381 -1.0361 4.2507 3.0420 0.3804 0.3521 0.1543 1.6280 0.0267 1.1461 0.0755 -#> 40: 93.7934 -5.8654 -0.1206 2.2417 -1.0503 4.0382 2.8899 0.3624 0.3413 0.1747 1.6231 0.0239 1.5698 0.0513 -#> 41: 93.8756 -6.0150 -0.1171 2.2581 -1.0313 3.8363 3.3629 0.3809 0.3369 0.1944 1.6461 0.0217 1.7762 0.0345 -#> 42: 94.0644 -5.9723 -0.1136 2.2769 -1.0295 3.6445 3.2171 0.3702 0.3394 0.1920 1.5035 0.0416 1.5148 0.0475 -#> 43: 93.7394 -5.9927 -0.1233 2.2650 -1.0374 3.4622 3.0562 0.3735 0.3370 0.1824 1.6022 0.0379 1.5080 0.0468 -#> 44: 93.5428 -5.9784 -0.1187 2.2780 -1.0279 3.2891 2.9495 0.3732 0.3289 0.1742 1.5456 0.0471 1.4361 0.0517 -#> 45: 93.2885 -5.9836 -0.1273 2.2650 -1.0100 3.1247 3.2884 0.3768 0.3719 0.1655 1.6579 0.0336 1.4031 0.0585 -#> 46: 93.4080 -5.9261 -0.1371 2.2513 -1.0159 3.4180 3.1630 0.3709 0.3762 0.1711 1.7365 0.0269 1.4612 0.0530 -#> 47: 93.4548 -5.8101 -0.1372 2.2650 -1.0058 3.2471 3.0049 0.3703 0.3921 0.1797 1.7161 0.0300 1.4813 0.0524 -#> 48: 93.1829 -5.6877 -0.1391 2.2594 -1.0035 3.0848 2.8546 0.3690 0.3901 0.1707 1.7558 0.0292 1.5856 0.0487 -#> 49: 93.1860 -5.8153 -0.1349 2.2793 -0.9905 2.9305 2.7119 0.3619 0.3877 0.1690 1.7255 0.0299 1.6143 0.0465 -#> 50: 93.5597 -5.7551 -0.1334 2.2669 -0.9808 2.7840 2.5763 0.3652 0.3795 0.1716 1.6690 0.0290 1.4895 0.0536 -#> 51: 93.5952 -5.8089 -0.1358 2.2626 -1.0100 2.6448 2.4475 0.3640 0.4246 0.1630 1.5892 0.0344 1.3958 0.0604 -#> 52: 93.3111 -5.9181 -0.1323 2.2489 -0.9909 2.5126 2.8739 0.3695 0.4337 0.1549 1.5200 0.0329 1.2246 0.0685 -#> 53: 93.4921 -6.0837 -0.1307 2.2513 -1.0031 2.3869 3.6029 0.3678 0.4363 0.1682 1.4683 0.0336 1.2917 0.0665 -#> 54: 93.4808 -6.2019 -0.1488 2.2068 -1.0207 2.2676 4.1833 0.3952 0.4145 0.1598 1.6478 0.0325 1.2418 0.0659 -#> 55: 93.5453 -6.2747 -0.1411 2.2297 -1.0122 2.1542 4.5107 0.3941 0.4044 0.1556 1.5685 0.0358 1.3236 0.0654 -#> 56: 94.0212 -6.2713 -0.1355 2.2228 -1.0205 2.0465 5.1718 0.3901 0.4101 0.1516 1.5568 0.0341 1.1952 0.0736 -#> 57: 93.7155 -6.2511 -0.1574 2.1899 -1.0374 1.9442 4.9132 0.3991 0.3974 0.1442 1.5528 0.0364 1.5497 0.0485 -#> 58: 93.9064 -6.2021 -0.1543 2.1935 -1.0277 1.8470 4.6676 0.3935 0.3944 0.1458 1.5590 0.0354 1.3512 0.0613 -#> 59: 93.9059 -6.3971 -0.1550 2.1899 -1.0124 1.7546 5.8885 0.3925 0.3943 0.1446 1.5641 0.0373 1.4293 0.0550 -#> 60: 93.8600 -6.2474 -0.1552 2.1978 -0.9930 1.7661 5.5941 0.3905 0.4078 0.1532 1.5235 0.0364 1.5442 0.0477 -#> 61: 93.8936 -6.3077 -0.1568 2.2022 -1.0084 1.7122 5.3507 0.3946 0.4146 0.1455 1.5154 0.0342 1.3664 0.0587 -#> 62: 93.6133 -6.1446 -0.1473 2.2277 -1.0195 1.6266 5.0832 0.3794 0.4254 0.1383 1.5586 0.0330 1.1663 0.0705 -#> 63: 93.5549 -6.3005 -0.1437 2.2302 -1.0096 1.5452 5.0969 0.3651 0.4262 0.1349 1.5730 0.0323 1.2501 0.0668 -#> 64: 93.3212 -6.1190 -0.1428 2.2309 -1.0005 1.4826 4.8421 0.3661 0.4181 0.1443 1.6657 0.0259 1.3409 0.0627 -#> 65: 93.2534 -5.9614 -0.1492 2.2310 -0.9865 1.4084 4.6000 0.3735 0.4186 0.1695 1.6883 0.0235 1.4446 0.0563 -#> 66: 93.3429 -5.9786 -0.1401 2.2198 -0.9934 1.3380 4.3700 0.3807 0.4094 0.1610 1.6697 0.0270 1.1164 0.0778 -#> 67: 93.5657 -6.2158 -0.1405 2.2326 -0.9891 1.2711 4.4653 0.3827 0.4063 0.1530 1.5851 0.0316 1.3581 0.0590 -#> 68: 93.4898 -5.9763 -0.1375 2.2431 -0.9837 1.2076 4.2420 0.3771 0.4127 0.1453 1.6134 0.0325 1.1459 0.0744 -#> 69: 93.4995 -6.1375 -0.1412 2.2423 -1.0003 1.3178 4.3907 0.3746 0.4202 0.1403 1.6223 0.0304 1.3354 0.0608 -#> 70: 93.4369 -6.1690 -0.1395 2.2472 -1.0047 1.6239 4.5654 0.3793 0.4087 0.1400 1.6317 0.0349 1.4812 0.0494 -#> 71: 93.4041 -6.3637 -0.1489 2.2348 -1.0125 1.5427 5.3897 0.3603 0.3883 0.1330 1.5954 0.0303 1.3502 0.0612 -#> 72: 93.1755 -6.4067 -0.1441 2.2492 -0.9859 1.4656 6.3554 0.3423 0.3688 0.1388 1.6135 0.0287 1.6402 0.0435 -#> 73: 93.0023 -6.7319 -0.1526 2.2550 -0.9800 1.3923 7.6438 0.3341 0.3504 0.1462 1.5491 0.0312 1.3997 0.0554 -#> 74: 92.8952 -6.7189 -0.1530 2.2393 -0.9936 1.5478 7.2616 0.3344 0.3329 0.1503 1.5626 0.0326 1.3340 0.0634 -#> 75: 93.0812 -6.8015 -0.1546 2.2265 -0.9751 1.4704 8.9537 0.3501 0.3162 0.1438 1.6019 0.0268 1.1663 0.0715 -#> 76: 93.1080 -6.1728 -0.1515 2.2259 -1.0010 1.3969 8.5060 0.3407 0.3015 0.1398 1.6484 0.0279 1.3118 0.0637 -#> 77: 92.9248 -6.3432 -0.1573 2.2221 -0.9819 1.4456 8.0807 0.3506 0.3002 0.1442 1.5947 0.0294 1.6368 0.0407 -#> 78: 93.0194 -6.1448 -0.1611 2.2228 -0.9831 1.3733 7.6767 0.3487 0.3046 0.1369 1.6471 0.0254 1.4261 0.0529 -#> 79: 92.9378 -6.6970 -0.1593 2.2313 -0.9910 1.3046 10.0158 0.3460 0.2999 0.1386 1.6108 0.0267 1.5818 0.0420 -#> 80: 93.0293 -6.3275 -0.1579 2.2290 -0.9753 1.3191 9.5150 0.3543 0.2960 0.1490 1.6570 0.0259 1.5435 0.0431 -#> 81: 93.1417 -6.2258 -0.1607 2.2285 -0.9399 1.4131 9.0393 0.3514 0.3020 0.1415 1.6990 0.0236 1.6875 0.0364 -#> 82: 92.9115 -6.1764 -0.1555 2.2204 -0.9471 1.3424 8.5873 0.3502 0.2954 0.1540 1.6780 0.0216 1.2280 0.0687 -#> 83: 93.0528 -6.3505 -0.1559 2.2391 -0.9651 1.2753 8.1579 0.3499 0.2903 0.1706 1.6924 0.0242 1.6807 0.0465 -#> 84: 93.0032 -6.2300 -0.1596 2.2300 -0.9232 1.2115 7.9391 0.3470 0.2995 0.1858 1.7153 0.0259 1.7160 0.0406 -#> 85: 93.0518 -6.3704 -0.1434 2.2696 -0.9330 1.1510 8.3071 0.3504 0.2916 0.1765 1.7072 0.0275 1.5494 0.0490 -#> 86: 93.1344 -6.3566 -0.1424 2.2595 -0.9512 1.0934 9.2972 0.3520 0.2869 0.1677 1.6609 0.0253 1.5022 0.0508 -#> 87: 93.2468 -6.3860 -0.1449 2.2505 -0.9601 1.0387 8.8323 0.3474 0.3046 0.1593 1.6326 0.0262 1.3048 0.0626 -#> 88: 93.2286 -6.3886 -0.1466 2.2452 -0.9870 0.9868 8.3907 0.3474 0.2894 0.1513 1.6554 0.0245 1.6330 0.0376 -#> 89: 93.2892 -6.0277 -0.1469 2.2403 -0.9694 0.9375 7.9712 0.3451 0.2904 0.1438 1.6795 0.0251 1.6691 0.0365 -#> 90: 93.1766 -6.1076 -0.1460 2.2502 -0.9729 0.8906 7.5726 0.3458 0.2932 0.1481 1.6182 0.0331 1.5854 0.0401 -#> 91: 93.3300 -6.0932 -0.1559 2.2356 -0.9551 0.8461 7.1940 0.3771 0.2883 0.1512 1.6728 0.0272 1.6098 0.0401 -#> 92: 93.2470 -6.4839 -0.1592 2.2265 -1.0016 0.8038 6.8343 0.3813 0.2923 0.1597 1.7017 0.0300 1.6084 0.0423 -#> 93: 93.2272 -6.2819 -0.1612 2.2356 -1.0073 0.7636 6.4926 0.3849 0.2816 0.1722 1.5422 0.0420 1.4772 0.0493 -#> 94: 93.1441 -6.1805 -0.1571 2.2274 -1.0106 0.7254 6.1680 0.3878 0.2811 0.1636 1.5998 0.0403 1.4386 0.0535 -#> 95: 92.7747 -6.2274 -0.1709 2.2191 -1.0042 0.6891 5.8596 0.3909 0.2905 0.1591 1.7184 0.0282 1.6086 0.0519 -#> 96: 92.9830 -6.3291 -0.1603 2.2297 -1.0053 0.6547 5.5666 0.3774 0.2850 0.1512 1.7427 0.0284 1.7548 0.0384 -#> 97: 92.9302 -6.3943 -0.1608 2.2211 -0.9643 0.6219 5.2882 0.3817 0.2828 0.1589 1.7080 0.0295 1.7102 0.0398 -#> 98: 92.7704 -6.3554 -0.1679 2.1894 -0.9736 0.5908 5.4196 0.3864 0.2813 0.1560 1.7234 0.0240 1.2269 0.0685 -#> 99: 92.7596 -6.2138 -0.1687 2.2088 -0.9744 0.5613 5.1486 0.3939 0.2983 0.1482 1.6732 0.0250 1.5718 0.0497 -#> 100: 92.6608 -6.2662 -0.1687 2.2180 -1.0107 0.5332 5.1471 0.3939 0.2927 0.1408 1.8434 0.0232 1.7316 0.0413 -#> 101: 92.7024 -6.1288 -0.1643 2.2096 -1.0032 0.5066 4.8898 0.3934 0.2807 0.1349 1.7055 0.0253 1.5883 0.0439 -#> 102: 92.8885 -6.3175 -0.1697 2.2208 -0.9967 0.4812 4.9699 0.3888 0.2912 0.1371 1.7311 0.0284 1.6455 0.0402 -#> 103: 92.9487 -6.2493 -0.1677 2.1861 -0.9874 0.4572 4.9605 0.3907 0.2844 0.1626 1.6898 0.0279 1.6252 0.0409 -#> 104: 92.9633 -6.2534 -0.1731 2.1797 -0.9790 0.4343 4.8675 0.4015 0.2784 0.1758 1.6516 0.0268 1.6901 0.0360 -#> 105: 93.0513 -6.0656 -0.1748 2.1802 -0.9876 0.4126 4.6241 0.4041 0.2801 0.1670 1.6863 0.0269 1.6208 0.0366 -#> 106: 93.0600 -6.2162 -0.1860 2.1783 -0.9702 0.4570 4.5504 0.4451 0.2761 0.1586 1.6859 0.0274 1.5273 0.0437 -#> 107: 93.1856 -6.1826 -0.1801 2.1796 -0.9813 0.4341 4.7286 0.4517 0.2807 0.1575 1.6268 0.0341 1.2548 0.0630 -#> 108: 93.2401 -6.2943 -0.1783 2.1808 -0.9806 0.4124 5.3114 0.4502 0.2786 0.1496 1.6676 0.0291 1.4627 0.0484 -#> 109: 93.0988 -6.1669 -0.1655 2.2018 -0.9682 0.4036 5.0458 0.4302 0.3195 0.1435 1.6524 0.0295 1.5759 0.0447 -#> 110: 93.2129 -6.3104 -0.1748 2.1876 -0.9837 0.4825 5.6408 0.4430 0.3306 0.1595 1.6068 0.0326 1.6295 0.0388 -#> 111: 93.1292 -5.9096 -0.1740 2.1932 -0.9674 0.5262 5.3587 0.4444 0.3233 0.1646 1.5777 0.0334 1.6590 0.0374 -#> 112: 93.2723 -5.8153 -0.1706 2.1920 -0.9761 0.5109 5.0908 0.4486 0.3180 0.1634 1.6128 0.0321 1.6551 0.0396 -#> 113: 93.3171 -6.0458 -0.1666 2.1879 -0.9740 0.5530 4.8362 0.4508 0.3303 0.1607 1.5862 0.0325 1.2705 0.0643 -#> 114: 93.1717 -5.9615 -0.1655 2.1638 -0.9773 0.5254 4.5944 0.4472 0.3283 0.1657 1.6307 0.0287 1.2995 0.0677 -#> 115: 93.1917 -6.0856 -0.1592 2.1576 -1.0269 0.4991 4.3647 0.4349 0.3464 0.1574 1.6430 0.0354 1.2812 0.0714 -#> 116: 93.1287 -5.9635 -0.1609 2.1640 -0.9985 0.4741 4.1465 0.4237 0.3408 0.1495 1.6910 0.0269 1.2338 0.0738 -#> 117: 93.1184 -5.8768 -0.1603 2.1842 -0.9557 0.4504 3.9392 0.4211 0.3293 0.1420 1.6447 0.0257 1.2680 0.0705 -#> 118: 93.2207 -5.7436 -0.1654 2.1709 -0.9816 0.4279 3.7422 0.4158 0.3298 0.1349 1.6860 0.0238 1.1436 0.0780 -#> 119: 93.3064 -5.8397 -0.1713 2.1722 -1.0093 0.4065 3.5551 0.4100 0.3429 0.1384 1.6612 0.0262 1.6491 0.0458 -#> 120: 93.2749 -5.8221 -0.1737 2.1643 -1.0166 0.3862 3.3773 0.4044 0.3305 0.1527 1.6516 0.0232 1.7832 0.0410 -#> 121: 93.1620 -5.9756 -0.1579 2.2018 -1.0007 0.3818 3.2992 0.3841 0.3433 0.1620 1.6648 0.0251 1.3408 0.0665 -#> 122: 93.2070 -6.0164 -0.1540 2.2154 -1.0196 0.4217 3.5598 0.3649 0.3436 0.1539 1.6757 0.0287 1.3019 0.0652 -#> 123: 93.1588 -5.7424 -0.1581 2.2142 -0.9985 0.5270 3.3818 0.3491 0.3584 0.1655 1.6321 0.0237 1.3494 0.0644 -#> 124: 93.1496 -5.6257 -0.1463 2.2264 -0.9767 0.5914 3.2127 0.3347 0.3738 0.1573 1.6553 0.0226 1.5964 0.0544 -#> 125: 93.0224 -5.8536 -0.1742 2.1859 -0.9939 0.6381 3.0521 0.3840 0.3692 0.1664 1.6009 0.0246 1.4169 0.0652 -#> 126: 93.0788 -5.6973 -0.1778 2.1772 -0.9574 0.6062 2.8995 0.3710 0.3630 0.1839 1.5256 0.0312 1.5566 0.0518 -#> 127: 93.1613 -5.5833 -0.1729 2.1806 -0.9588 0.5759 2.7545 0.3532 0.3464 0.1878 1.5708 0.0307 1.6405 0.0476 -#> 128: 93.2043 -5.6742 -0.1746 2.1919 -0.9814 0.7099 2.6168 0.3569 0.3422 0.1848 1.6236 0.0312 1.5066 0.0517 -#> 129: 93.1963 -5.7026 -0.1770 2.1853 -0.9814 0.6744 2.4859 0.3544 0.3390 0.1774 1.6150 0.0293 1.5712 0.0479 -#> 130: 93.1669 -5.7260 -0.1826 2.1565 -0.9959 0.6407 2.3616 0.3750 0.3249 0.1685 1.6347 0.0215 1.5556 0.0535 -#> 131: 93.0792 -5.7201 -0.1971 2.1339 -1.0057 0.7376 2.2436 0.3901 0.3086 0.1616 1.7653 0.0206 1.6640 0.0458 -#> 132: 92.8580 -5.8266 -0.1877 2.1512 -0.9940 0.7008 2.3272 0.3895 0.3161 0.1863 1.6050 0.0231 1.5123 0.0558 -#> 133: 92.8479 -5.8397 -0.1834 2.1637 -0.9815 0.7195 2.4732 0.3875 0.3060 0.1877 1.6197 0.0217 1.4131 0.0617 -#> 134: 92.9218 -5.8317 -0.1903 2.1709 -0.9903 0.6835 2.5070 0.3808 0.3147 0.1857 1.7298 0.0225 1.5493 0.0521 -#> 135: 92.7533 -5.7287 -0.1909 2.1670 -0.9674 0.6493 2.3817 0.3792 0.3156 0.1981 1.7074 0.0222 1.2776 0.0718 -#> 136: 92.7255 -5.9071 -0.1787 2.1826 -0.9826 0.6169 2.8147 0.3603 0.3172 0.1882 1.6242 0.0288 1.2313 0.0682 -#> 137: 92.7882 -5.9574 -0.1847 2.1549 -0.9848 0.5860 3.0538 0.3651 0.3206 0.1787 1.5640 0.0277 1.1609 0.0716 -#> 138: 92.8155 -5.9445 -0.1719 2.1750 -0.9838 0.5567 3.3525 0.3568 0.3390 0.1698 1.5507 0.0259 1.0634 0.0816 -#> 139: 92.9393 -6.0638 -0.1726 2.1840 -0.9888 0.5289 4.1627 0.3562 0.3453 0.1613 1.5792 0.0259 1.5189 0.0533 -#> 140: 93.0330 -6.1823 -0.1726 2.1984 -0.9850 0.5024 4.3153 0.3562 0.3506 0.1533 1.6467 0.0248 1.5734 0.0459 -#> 141: 93.0651 -6.1847 -0.1702 2.2183 -0.9749 0.4773 4.1656 0.3604 0.3626 0.1527 1.5887 0.0272 1.5613 0.0433 -#> 142: 93.0350 -5.9581 -0.1641 2.2133 -0.9707 0.4535 3.9574 0.3642 0.3541 0.1662 1.5904 0.0246 1.4665 0.0556 -#> 143: 92.9215 -5.7798 -0.1642 2.2269 -0.9665 0.5015 3.7595 0.3665 0.3626 0.1667 1.6019 0.0275 1.3379 0.0563 -#> 144: 93.0132 -5.6752 -0.1629 2.2273 -0.9468 0.4764 3.5715 0.3648 0.3555 0.1648 1.5218 0.0320 1.1736 0.0695 -#> 145: 92.9596 -5.8104 -0.1449 2.2498 -0.9730 0.4526 3.3929 0.3465 0.3524 0.1670 1.5918 0.0284 1.3067 0.0630 -#> 146: 92.7925 -5.7223 -0.1458 2.2463 -0.9569 0.5591 3.2233 0.3443 0.3492 0.1587 1.6175 0.0260 1.0691 0.0729 -#> 147: 92.8399 -5.8322 -0.1478 2.2485 -0.9474 0.5312 3.2015 0.3422 0.3536 0.1507 1.6257 0.0255 1.2184 0.0622 -#> 148: 92.8390 -5.9554 -0.1498 2.2490 -0.9550 0.5046 3.6305 0.3387 0.3597 0.1615 1.5994 0.0263 1.2274 0.0638 -#> 149: 92.8158 -5.9697 -0.1511 2.2337 -0.9812 0.4794 3.8244 0.3386 0.3894 0.1559 1.5723 0.0255 1.0661 0.0760 -#> 150: 92.8379 -6.0841 -0.1532 2.2323 -0.9832 0.4554 4.3416 0.3340 0.3840 0.1575 1.5375 0.0272 1.1589 0.0677 -#> 151: 92.6741 -6.3268 -0.1572 2.2252 -0.9782 0.4327 5.9395 0.3389 0.3859 0.1584 1.5384 0.0252 1.2809 0.0638 -#> 152: 92.7165 -6.3594 -0.1527 2.2233 -1.0007 0.4210 5.8433 0.3384 0.3915 0.1324 1.5861 0.0254 1.0728 0.0756 -#> 153: 92.6823 -6.2114 -0.1640 2.2160 -0.9861 0.5285 5.4117 0.3473 0.3878 0.1376 1.6150 0.0255 1.2105 0.0659 -#> 154: 92.4787 -6.1829 -0.1622 2.2055 -0.9571 0.5031 5.7087 0.3490 0.3748 0.1345 1.5749 0.0250 1.0579 0.0741 -#> 155: 92.4780 -6.4925 -0.1675 2.2190 -0.9301 0.4020 7.4764 0.3587 0.3785 0.1287 1.5959 0.0258 1.1342 0.0709 -#> 156: 92.5151 -6.2825 -0.1673 2.2194 -0.9174 0.3603 5.6463 0.3589 0.3848 0.1202 1.5413 0.0301 1.1866 0.0674 -#> 157: 92.5140 -6.0058 -0.1644 2.2312 -0.9298 0.3857 4.2481 0.3610 0.3706 0.1281 1.5944 0.0292 1.2712 0.0631 -#> 158: 92.5669 -5.8692 -0.1673 2.2493 -0.9413 0.4751 3.7632 0.3600 0.3572 0.1383 1.6202 0.0323 1.4797 0.0499 -#> 159: 92.4844 -6.0078 -0.1540 2.2464 -0.9423 0.4626 4.6774 0.3587 0.3603 0.1450 1.6404 0.0280 1.3577 0.0587 -#> 160: 92.5182 -6.1231 -0.1504 2.2518 -0.9274 0.4153 5.0466 0.3616 0.3633 0.1373 1.5891 0.0297 1.2392 0.0653 -#> 161: 92.5665 -5.9062 -0.1569 2.2563 -0.9412 0.3989 4.3594 0.3541 0.3719 0.1433 1.6242 0.0314 1.2822 0.0627 -#> 162: 92.5749 -6.0936 -0.1507 2.2752 -0.9474 0.3140 4.4065 0.3438 0.3921 0.1320 1.5013 0.0378 1.1647 0.0662 -#> 163: 92.6248 -6.1392 -0.1565 2.2499 -0.9499 0.2129 4.6022 0.3512 0.3890 0.1425 1.4936 0.0336 1.4339 0.0494 -#> 164: 92.6486 -6.3898 -0.1590 2.2519 -0.9574 0.1948 5.7817 0.3564 0.3925 0.1308 1.5218 0.0326 1.2197 0.0630 -#> 165: 92.6600 -6.3261 -0.1606 2.2464 -0.9815 0.3054 5.9162 0.3611 0.3979 0.1433 1.5747 0.0316 1.2062 0.0632 -#> 166: 92.7951 -6.3068 -0.1630 2.2428 -0.9542 0.3144 5.7041 0.3597 0.3766 0.1612 1.5464 0.0317 1.2649 0.0617 -#> 167: 92.8541 -6.4919 -0.1642 2.2275 -0.9505 0.3509 6.3858 0.3639 0.3713 0.1581 1.5543 0.0315 1.3546 0.0574 -#> 168: 92.6848 -6.3299 -0.1618 2.2329 -0.9494 0.4645 5.7127 0.3700 0.3698 0.1544 1.5058 0.0340 1.1747 0.0685 -#> 169: 92.5817 -6.0236 -0.1572 2.2583 -0.9510 0.6725 3.9864 0.3672 0.3812 0.1763 1.4445 0.0386 1.3230 0.0583 -#> 170: 92.7223 -5.9170 -0.1609 2.2456 -0.9485 0.5137 3.7991 0.3712 0.3714 0.1601 1.5502 0.0385 1.3393 0.0547 -#> 171: 92.6532 -5.9417 -0.1544 2.2294 -0.9448 0.6206 3.9052 0.3789 0.3634 0.1487 1.5809 0.0314 1.1226 0.0711 -#> 172: 92.4803 -5.7302 -0.1414 2.2679 -0.9255 0.7853 2.7901 0.3598 0.3666 0.1508 1.5531 0.0341 1.1785 0.0667 -#> 173: 92.3172 -5.7462 -0.1405 2.2823 -0.9193 1.2505 2.9155 0.3579 0.3678 0.1480 1.4894 0.0434 1.2288 0.0618 -#> 174: 92.4674 -5.6638 -0.1415 2.2775 -0.9054 1.0653 2.8138 0.3623 0.3740 0.1371 1.5301 0.0393 1.0790 0.0669 -#> 175: 92.5581 -5.6388 -0.1338 2.2878 -0.9154 0.6617 2.5216 0.3471 0.3719 0.1546 1.5231 0.0361 1.0672 0.0723 -#> 176: 92.7218 -5.7548 -0.1249 2.3099 -0.9203 0.4464 2.8226 0.3570 0.3978 0.1570 1.4938 0.0354 1.1125 0.0655 -#> 177: 92.7655 -5.6769 -0.1232 2.3114 -0.9257 0.5291 2.5249 0.3571 0.4023 0.1657 1.4392 0.0386 1.1149 0.0663 -#> 178: 92.7966 -5.6766 -0.1219 2.3202 -0.9142 0.4897 2.3359 0.3605 0.3944 0.1720 1.4792 0.0401 1.1665 0.0637 -#> 179: 92.8304 -5.7678 -0.1133 2.3352 -0.9262 0.5428 2.8512 0.3552 0.4191 0.1716 1.4994 0.0410 1.0651 0.0701 -#> 180: 92.8413 -5.7485 -0.1124 2.3452 -0.9494 0.5179 2.6552 0.3555 0.4025 0.1778 1.5102 0.0383 1.1541 0.0670 -#> 181: 92.7078 -5.7437 -0.1145 2.3257 -0.9482 0.6237 2.5673 0.3564 0.3851 0.1897 1.5373 0.0335 1.1413 0.0698 -#> 182: 92.6278 -5.7965 -0.1115 2.3341 -0.9763 0.7558 2.7421 0.3541 0.3850 0.1625 1.5720 0.0309 1.1164 0.0758 -#> 183: 92.4359 -5.7826 -0.1211 2.3204 -0.9481 1.2089 3.0954 0.3598 0.3813 0.1384 1.6391 0.0333 1.2142 0.0646 -#> 184: 92.4840 -5.9143 -0.1218 2.2965 -0.9330 1.2610 4.0248 0.3752 0.3549 0.1597 1.6019 0.0292 1.0945 0.0767 -#> 185: 92.5659 -5.8333 -0.1223 2.2914 -0.9090 1.0578 3.9752 0.3706 0.3640 0.1769 1.5858 0.0287 1.7070 0.0404 -#> 186: 92.5157 -5.9540 -0.1274 2.2967 -0.9678 1.0199 3.7413 0.3625 0.3766 0.1354 1.5905 0.0321 1.2521 0.0660 -#> 187: 92.6988 -5.8607 -0.1193 2.2922 -0.9685 1.1721 2.9764 0.3511 0.3823 0.1347 1.5790 0.0352 1.1477 0.0746 -#> 188: 92.7427 -5.9073 -0.1166 2.3166 -0.9529 1.3606 2.9747 0.3487 0.3981 0.1322 1.5315 0.0344 1.3014 0.0594 -#> 189: 92.6288 -5.8326 -0.1075 2.3268 -0.9543 1.3459 3.2341 0.3388 0.3983 0.1622 1.5374 0.0334 1.5390 0.0504 -#> 190: 92.8047 -5.6198 -0.1064 2.3212 -0.9148 1.6280 2.5774 0.3319 0.4086 0.1656 1.5159 0.0321 1.5423 0.0515 -#> 191: 92.7642 -5.5780 -0.1105 2.3041 -0.9414 1.5723 2.6038 0.3402 0.4111 0.1612 1.5254 0.0321 1.1206 0.0792 -#> 192: 92.7137 -5.5650 -0.1087 2.3014 -0.9399 1.1968 2.0552 0.3412 0.4267 0.1418 1.4910 0.0332 0.9683 0.0834 -#> 193: 93.0503 -5.6414 -0.1060 2.3050 -0.9563 1.0067 2.2362 0.3434 0.4179 0.1371 1.5947 0.0279 1.0349 0.0813 -#> 194: 93.1071 -5.6349 -0.1048 2.3170 -0.9613 1.1495 2.6224 0.3451 0.4086 0.1419 1.6235 0.0276 1.0558 0.0792 -#> 195: 93.0741 -5.7863 -0.1052 2.3293 -0.9605 1.1597 3.0814 0.3440 0.4342 0.1394 1.5248 0.0348 1.0554 0.0771 -#> 196: 93.0768 -5.6986 -0.0911 2.3395 -0.9537 1.1388 2.7165 0.3463 0.4303 0.1467 1.5960 0.0324 1.1195 0.0755 -#> 197: 92.8638 -5.7840 -0.1009 2.3420 -0.9699 1.0231 2.8293 0.3625 0.4272 0.1849 1.5366 0.0360 1.3691 0.0602 -#> 198: 92.8979 -5.8328 -0.0905 2.3497 -0.9668 0.8847 2.7469 0.3509 0.4357 0.1842 1.5501 0.0361 1.1744 0.0715 -#> 199: 92.7817 -6.0173 -0.0946 2.3477 -0.9729 0.8131 3.4886 0.3517 0.4471 0.1906 1.4350 0.0393 1.2311 0.0693 -#> 200: 92.6353 -6.0362 -0.0924 2.3396 -0.9621 0.8259 3.3916 0.3556 0.4569 0.1867 1.4397 0.0350 1.0910 0.0793 -#> 201: 92.6908 -6.0423 -0.0917 2.3400 -0.9564 0.6766 3.6159 0.3552 0.4565 0.1735 1.4506 0.0362 1.0646 0.0794 -#> 202: 92.6302 -6.0238 -0.0919 2.3443 -0.9546 0.5824 3.6723 0.3555 0.4576 0.1716 1.4800 0.0363 1.0519 0.0791 -#> 203: 92.6040 -6.0387 -0.0944 2.3405 -0.9579 0.5710 3.9080 0.3583 0.4476 0.1752 1.4934 0.0373 1.0842 0.0762 -#> 204: 92.6042 -6.0088 -0.0965 2.3351 -0.9580 0.6145 3.8412 0.3608 0.4413 0.1720 1.5047 0.0374 1.0694 0.0760 -#> 205: 92.5887 -6.0107 -0.0968 2.3362 -0.9576 0.6432 3.8854 0.3606 0.4405 0.1711 1.4896 0.0380 1.0615 0.0750 -#> 206: 92.6452 -5.9990 -0.0992 2.3311 -0.9581 0.6728 3.8231 0.3636 0.4339 0.1683 1.4904 0.0379 1.0630 0.0747 -#> 207: 92.6867 -5.9760 -0.1012 2.3283 -0.9606 0.6907 3.6867 0.3665 0.4303 0.1665 1.4908 0.0376 1.0656 0.0739 -#> 208: 92.6867 -5.9652 -0.1033 2.3252 -0.9611 0.6656 3.6185 0.3680 0.4271 0.1656 1.4972 0.0369 1.0944 0.0724 -#> 209: 92.6807 -5.9535 -0.1051 2.3225 -0.9621 0.6532 3.5653 0.3669 0.4249 0.1641 1.4992 0.0366 1.1029 0.0721 -#> 210: 92.6772 -5.9392 -0.1067 2.3185 -0.9611 0.6492 3.4774 0.3661 0.4220 0.1620 1.5034 0.0360 1.0982 0.0723 -#> 211: 92.6803 -5.9099 -0.1089 2.3129 -0.9619 0.6462 3.3783 0.3656 0.4218 0.1622 1.5094 0.0354 1.1060 0.0725 -#> 212: 92.7033 -5.9046 -0.1110 2.3085 -0.9606 0.6467 3.3879 0.3653 0.4222 0.1602 1.5099 0.0350 1.1004 0.0726 -#> 213: 92.7143 -5.9026 -0.1135 2.3046 -0.9594 0.6326 3.3887 0.3646 0.4214 0.1585 1.5139 0.0347 1.1050 0.0722 -#> 214: 92.7156 -5.9151 -0.1157 2.3011 -0.9590 0.6186 3.4587 0.3637 0.4205 0.1571 1.5149 0.0344 1.1060 0.0720 -#> 215: 92.7185 -5.9240 -0.1177 2.2984 -0.9585 0.6226 3.5192 0.3630 0.4190 0.1564 1.5155 0.0342 1.1159 0.0713 -#> 216: 92.7133 -5.9331 -0.1197 2.2953 -0.9575 0.6253 3.5505 0.3630 0.4179 0.1552 1.5199 0.0338 1.1276 0.0708 -#> 217: 92.7111 -5.9341 -0.1215 2.2924 -0.9579 0.6200 3.5565 0.3627 0.4170 0.1542 1.5238 0.0337 1.1409 0.0702 -#> 218: 92.7142 -5.9390 -0.1226 2.2901 -0.9588 0.6110 3.5792 0.3623 0.4162 0.1541 1.5236 0.0335 1.1378 0.0704 -#> 219: 92.7121 -5.9351 -0.1233 2.2891 -0.9587 0.6083 3.5562 0.3617 0.4154 0.1535 1.5280 0.0335 1.1518 0.0697 -#> 220: 92.7133 -5.9467 -0.1244 2.2876 -0.9591 0.6158 3.6036 0.3614 0.4147 0.1542 1.5273 0.0334 1.1572 0.0693 -#> 221: 92.7206 -5.9543 -0.1253 2.2856 -0.9602 0.6252 3.6357 0.3610 0.4131 0.1540 1.5272 0.0335 1.1591 0.0692 -#> 222: 92.7267 -5.9436 -0.1262 2.2840 -0.9608 0.6377 3.5725 0.3608 0.4118 0.1540 1.5302 0.0334 1.1735 0.0683 -#> 223: 92.7364 -5.9346 -0.1268 2.2825 -0.9619 0.6430 3.5288 0.3606 0.4117 0.1542 1.5327 0.0332 1.1883 0.0676 -#> 224: 92.7464 -5.9269 -0.1274 2.2822 -0.9621 0.6394 3.4906 0.3604 0.4107 0.1541 1.5342 0.0334 1.2022 0.0667 -#> 225: 92.7572 -5.9244 -0.1278 2.2813 -0.9616 0.6340 3.4677 0.3603 0.4100 0.1535 1.5345 0.0334 1.2129 0.0661 -#> 226: 92.7662 -5.9237 -0.1282 2.2803 -0.9615 0.6336 3.4532 0.3603 0.4101 0.1532 1.5326 0.0334 1.2151 0.0661 -#> 227: 92.7778 -5.9193 -0.1286 2.2792 -0.9628 0.6280 3.4339 0.3604 0.4096 0.1527 1.5323 0.0334 1.2217 0.0658 -#> 228: 92.7824 -5.9112 -0.1289 2.2782 -0.9636 0.6217 3.3964 0.3607 0.4091 0.1525 1.5316 0.0335 1.2255 0.0658 -#> 229: 92.7895 -5.9077 -0.1291 2.2770 -0.9646 0.6178 3.3717 0.3607 0.4096 0.1521 1.5326 0.0334 1.2247 0.0660 -#> 230: 92.7987 -5.9153 -0.1297 2.2758 -0.9648 0.6177 3.4004 0.3603 0.4098 0.1517 1.5333 0.0334 1.2321 0.0656 -#> 231: 92.8081 -5.9176 -0.1308 2.2735 -0.9654 0.6185 3.4195 0.3596 0.4086 0.1513 1.5361 0.0331 1.2359 0.0656 -#> 232: 92.8119 -5.9161 -0.1318 2.2715 -0.9658 0.6140 3.4221 0.3590 0.4075 0.1513 1.5387 0.0330 1.2434 0.0653 -#> 233: 92.8117 -5.9111 -0.1329 2.2694 -0.9662 0.6096 3.4008 0.3586 0.4065 0.1511 1.5410 0.0328 1.2426 0.0654 -#> 234: 92.8132 -5.9040 -0.1339 2.2672 -0.9660 0.6097 3.3787 0.3583 0.4059 0.1506 1.5425 0.0325 1.2463 0.0654 -#> 235: 92.8117 -5.8978 -0.1347 2.2653 -0.9661 0.6020 3.3558 0.3579 0.4051 0.1502 1.5443 0.0324 1.2439 0.0657 -#> 236: 92.8050 -5.8967 -0.1355 2.2638 -0.9663 0.5963 3.3466 0.3575 0.4046 0.1495 1.5453 0.0322 1.2377 0.0661 -#> 237: 92.7975 -5.9004 -0.1362 2.2625 -0.9668 0.5891 3.3624 0.3571 0.4043 0.1491 1.5460 0.0321 1.2334 0.0664 -#> 238: 92.7965 -5.9036 -0.1371 2.2613 -0.9670 0.5828 3.3683 0.3569 0.4037 0.1488 1.5486 0.0320 1.2405 0.0662 -#> 239: 92.8006 -5.9067 -0.1376 2.2607 -0.9677 0.5767 3.3801 0.3568 0.4027 0.1490 1.5487 0.0319 1.2478 0.0658 -#> 240: 92.8061 -5.9102 -0.1382 2.2597 -0.9678 0.5697 3.3876 0.3566 0.4014 0.1489 1.5499 0.0319 1.2545 0.0654 -#> 241: 92.8111 -5.9132 -0.1388 2.2589 -0.9684 0.5647 3.3986 0.3567 0.4004 0.1489 1.5507 0.0319 1.2607 0.0651 -#> 242: 92.8157 -5.9119 -0.1395 2.2577 -0.9686 0.5610 3.3902 0.3568 0.3995 0.1490 1.5524 0.0319 1.2673 0.0647 -#> 243: 92.8204 -5.9142 -0.1401 2.2567 -0.9689 0.5597 3.3991 0.3570 0.3983 0.1492 1.5526 0.0319 1.2728 0.0646 -#> 244: 92.8272 -5.9129 -0.1408 2.2558 -0.9689 0.5598 3.3989 0.3574 0.3972 0.1493 1.5542 0.0319 1.2805 0.0642 -#> 245: 92.8361 -5.9152 -0.1414 2.2548 -0.9693 0.5617 3.4133 0.3580 0.3959 0.1500 1.5541 0.0318 1.2876 0.0638 -#> 246: 92.8432 -5.9122 -0.1420 2.2536 -0.9695 0.5627 3.4039 0.3584 0.3946 0.1507 1.5546 0.0318 1.2944 0.0633 -#> 247: 92.8481 -5.9125 -0.1426 2.2524 -0.9695 0.5574 3.4087 0.3588 0.3931 0.1515 1.5556 0.0318 1.3003 0.0629 -#> 248: 92.8486 -5.9123 -0.1433 2.2515 -0.9693 0.5545 3.4095 0.3594 0.3916 0.1519 1.5583 0.0317 1.3043 0.0626 -#> 249: 92.8515 -5.9123 -0.1439 2.2505 -0.9694 0.5547 3.4088 0.3600 0.3904 0.1523 1.5605 0.0316 1.3087 0.0623 -#> 250: 92.8521 -5.9139 -0.1443 2.2493 -0.9691 0.5589 3.4212 0.3604 0.3894 0.1525 1.5617 0.0316 1.3081 0.0624 -#> 251: 92.8530 -5.9118 -0.1450 2.2484 -0.9683 0.5562 3.4138 0.3612 0.3884 0.1528 1.5615 0.0316 1.3066 0.0625 -#> 252: 92.8568 -5.9075 -0.1457 2.2474 -0.9681 0.5506 3.3889 0.3619 0.3875 0.1531 1.5620 0.0315 1.3067 0.0625 -#> 253: 92.8603 -5.9070 -0.1464 2.2467 -0.9682 0.5476 3.3746 0.3622 0.3867 0.1539 1.5640 0.0314 1.3122 0.0622 -#> 254: 92.8653 -5.9077 -0.1470 2.2457 -0.9688 0.5448 3.3656 0.3626 0.3858 0.1546 1.5641 0.0314 1.3147 0.0620 -#> 255: 92.8686 -5.9059 -0.1477 2.2445 -0.9688 0.5406 3.3533 0.3630 0.3850 0.1549 1.5637 0.0314 1.3155 0.0619 -#> 256: 92.8706 -5.9011 -0.1483 2.2435 -0.9685 0.5384 3.3300 0.3634 0.3841 0.1550 1.5644 0.0313 1.3161 0.0617 -#> 257: 92.8721 -5.8957 -0.1488 2.2426 -0.9683 0.5398 3.3084 0.3638 0.3833 0.1552 1.5647 0.0313 1.3158 0.0617 -#> 258: 92.8725 -5.8928 -0.1493 2.2419 -0.9680 0.5392 3.2921 0.3641 0.3822 0.1552 1.5665 0.0312 1.3184 0.0614 -#> 259: 92.8718 -5.8915 -0.1498 2.2411 -0.9680 0.5367 3.2850 0.3644 0.3815 0.1553 1.5668 0.0312 1.3202 0.0613 -#> 260: 92.8701 -5.8928 -0.1499 2.2409 -0.9679 0.5339 3.2888 0.3652 0.3802 0.1552 1.5675 0.0312 1.3215 0.0612 -#> 261: 92.8700 -5.8961 -0.1499 2.2407 -0.9679 0.5302 3.2976 0.3659 0.3789 0.1551 1.5677 0.0312 1.3197 0.0613 -#> 262: 92.8683 -5.9013 -0.1500 2.2407 -0.9678 0.5282 3.3236 0.3666 0.3778 0.1549 1.5684 0.0312 1.3184 0.0613 -#> 263: 92.8662 -5.9021 -0.1498 2.2407 -0.9677 0.5271 3.3285 0.3670 0.3767 0.1547 1.5682 0.0313 1.3156 0.0615 -#> 264: 92.8631 -5.9059 -0.1495 2.2409 -0.9675 0.5244 3.3527 0.3673 0.3755 0.1547 1.5677 0.0313 1.3139 0.0616 -#> 265: 92.8635 -5.9042 -0.1492 2.2411 -0.9675 0.5220 3.3541 0.3675 0.3745 0.1545 1.5676 0.0313 1.3098 0.0618 -#> 266: 92.8636 -5.9033 -0.1490 2.2411 -0.9673 0.5208 3.3523 0.3680 0.3735 0.1546 1.5679 0.0312 1.3087 0.0619 -#> 267: 92.8639 -5.9035 -0.1489 2.2413 -0.9673 0.5208 3.3566 0.3685 0.3726 0.1546 1.5676 0.0312 1.3072 0.0621 -#> 268: 92.8620 -5.9065 -0.1487 2.2413 -0.9674 0.5191 3.3797 0.3689 0.3717 0.1545 1.5676 0.0312 1.3103 0.0620 -#> 269: 92.8593 -5.9073 -0.1486 2.2416 -0.9672 0.5192 3.3885 0.3693 0.3710 0.1545 1.5685 0.0312 1.3136 0.0618 -#> 270: 92.8549 -5.9087 -0.1487 2.2418 -0.9672 0.5209 3.4007 0.3695 0.3703 0.1544 1.5703 0.0312 1.3177 0.0615 -#> 271: 92.8519 -5.9089 -0.1487 2.2416 -0.9671 0.5227 3.4043 0.3696 0.3697 0.1545 1.5705 0.0312 1.3216 0.0613 -#> 272: 92.8493 -5.9084 -0.1488 2.2416 -0.9669 0.5223 3.3999 0.3698 0.3693 0.1543 1.5707 0.0311 1.3206 0.0614 -#> 273: 92.8479 -5.9090 -0.1486 2.2416 -0.9667 0.5230 3.3980 0.3701 0.3689 0.1544 1.5699 0.0311 1.3192 0.0615 -#> 274: 92.8456 -5.9108 -0.1485 2.2417 -0.9667 0.5249 3.4024 0.3705 0.3684 0.1544 1.5688 0.0311 1.3169 0.0617 -#> 275: 92.8440 -5.9131 -0.1483 2.2422 -0.9666 0.5253 3.4117 0.3707 0.3677 0.1542 1.5690 0.0311 1.3166 0.0616 -#> 276: 92.8425 -5.9132 -0.1482 2.2426 -0.9662 0.5241 3.4171 0.3709 0.3670 0.1540 1.5689 0.0311 1.3142 0.0617 -#> 277: 92.8412 -5.9139 -0.1481 2.2430 -0.9660 0.5214 3.4228 0.3711 0.3663 0.1540 1.5687 0.0311 1.3173 0.0615 -#> 278: 92.8398 -5.9139 -0.1479 2.2432 -0.9659 0.5184 3.4254 0.3712 0.3654 0.1540 1.5684 0.0311 1.3148 0.0617 -#> 279: 92.8386 -5.9156 -0.1478 2.2433 -0.9661 0.5157 3.4338 0.3713 0.3649 0.1539 1.5682 0.0311 1.3136 0.0618 -#> 280: 92.8378 -5.9173 -0.1478 2.2428 -0.9663 0.5127 3.4381 0.3714 0.3643 0.1537 1.5679 0.0311 1.3104 0.0621 -#> 281: 92.8364 -5.9188 -0.1479 2.2423 -0.9666 0.5089 3.4418 0.3716 0.3634 0.1533 1.5674 0.0311 1.3071 0.0623 -#> 282: 92.8377 -5.9179 -0.1481 2.2418 -0.9668 0.5045 3.4355 0.3717 0.3626 0.1530 1.5686 0.0311 1.3055 0.0624 -#> 283: 92.8385 -5.9157 -0.1485 2.2410 -0.9667 0.5014 3.4260 0.3720 0.3616 0.1527 1.5699 0.0311 1.3072 0.0622 -#> 284: 92.8388 -5.9156 -0.1489 2.2403 -0.9666 0.4977 3.4274 0.3723 0.3605 0.1525 1.5705 0.0310 1.3081 0.0621 -#> 285: 92.8374 -5.9156 -0.1492 2.2395 -0.9668 0.4944 3.4215 0.3727 0.3594 0.1525 1.5716 0.0310 1.3103 0.0619 -#> 286: 92.8376 -5.9168 -0.1496 2.2388 -0.9672 0.4915 3.4197 0.3731 0.3583 0.1526 1.5724 0.0310 1.3141 0.0617 -#> 287: 92.8393 -5.9176 -0.1498 2.2380 -0.9673 0.4886 3.4177 0.3735 0.3572 0.1523 1.5737 0.0309 1.3155 0.0615 -#> 288: 92.8400 -5.9206 -0.1502 2.2372 -0.9675 0.4873 3.4259 0.3739 0.3562 0.1523 1.5739 0.0309 1.3160 0.0614 -#> 289: 92.8404 -5.9217 -0.1506 2.2362 -0.9678 0.4845 3.4269 0.3744 0.3552 0.1524 1.5735 0.0309 1.3165 0.0614 -#> 290: 92.8395 -5.9255 -0.1510 2.2354 -0.9680 0.4830 3.4395 0.3748 0.3543 0.1521 1.5737 0.0308 1.3159 0.0615 -#> 291: 92.8384 -5.9274 -0.1513 2.2345 -0.9680 0.4841 3.4460 0.3752 0.3533 0.1518 1.5742 0.0309 1.3173 0.0613 -#> 292: 92.8384 -5.9276 -0.1515 2.2342 -0.9681 0.4865 3.4437 0.3755 0.3525 0.1516 1.5738 0.0309 1.3163 0.0614 -#> 293: 92.8385 -5.9281 -0.1517 2.2338 -0.9681 0.4882 3.4446 0.3757 0.3516 0.1513 1.5738 0.0308 1.3143 0.0614 -#> 294: 92.8400 -5.9277 -0.1519 2.2335 -0.9680 0.4871 3.4449 0.3758 0.3508 0.1512 1.5736 0.0308 1.3149 0.0614 -#> 295: 92.8414 -5.9279 -0.1520 2.2331 -0.9680 0.4842 3.4523 0.3760 0.3502 0.1510 1.5740 0.0308 1.3153 0.0614 -#> 296: 92.8424 -5.9282 -0.1521 2.2329 -0.9681 0.4835 3.4589 0.3760 0.3496 0.1509 1.5743 0.0307 1.3180 0.0613 -#> 297: 92.8409 -5.9281 -0.1522 2.2325 -0.9683 0.4827 3.4636 0.3760 0.3491 0.1509 1.5745 0.0307 1.3216 0.0611 -#> 298: 92.8395 -5.9276 -0.1522 2.2322 -0.9684 0.4819 3.4641 0.3761 0.3486 0.1508 1.5744 0.0307 1.3226 0.0612 -#> 299: 92.8388 -5.9305 -0.1524 2.2321 -0.9686 0.4800 3.4829 0.3761 0.3481 0.1507 1.5745 0.0307 1.3218 0.0612 -#> 300: 92.8375 -5.9329 -0.1524 2.2321 -0.9683 0.4792 3.4982 0.3761 0.3477 0.1505 1.5745 0.0307 1.3205 0.0613 -#> 301: 92.8359 -5.9337 -0.1524 2.2321 -0.9680 0.4788 3.5056 0.3762 0.3473 0.1503 1.5746 0.0306 1.3182 0.0614 -#> 302: 92.8346 -5.9360 -0.1524 2.2322 -0.9678 0.4800 3.5237 0.3763 0.3470 0.1500 1.5744 0.0306 1.3174 0.0614 -#> 303: 92.8338 -5.9387 -0.1524 2.2324 -0.9674 0.4795 3.5444 0.3764 0.3467 0.1501 1.5738 0.0307 1.3181 0.0613 -#> 304: 92.8318 -5.9436 -0.1524 2.2327 -0.9673 0.4787 3.5819 0.3766 0.3464 0.1502 1.5735 0.0307 1.3191 0.0612 -#> 305: 92.8300 -5.9486 -0.1524 2.2327 -0.9673 0.4794 3.6200 0.3766 0.3460 0.1502 1.5726 0.0308 1.3198 0.0611 -#> 306: 92.8294 -5.9540 -0.1524 2.2328 -0.9673 0.4788 3.6681 0.3766 0.3456 0.1502 1.5723 0.0309 1.3214 0.0610 -#> 307: 92.8287 -5.9579 -0.1525 2.2330 -0.9669 0.4779 3.7052 0.3766 0.3452 0.1498 1.5735 0.0309 1.3235 0.0609 -#> 308: 92.8290 -5.9624 -0.1524 2.2332 -0.9669 0.4775 3.7470 0.3766 0.3448 0.1500 1.5737 0.0309 1.3265 0.0607 -#> 309: 92.8293 -5.9653 -0.1524 2.2333 -0.9668 0.4774 3.7756 0.3766 0.3443 0.1499 1.5736 0.0309 1.3290 0.0605 -#> 310: 92.8289 -5.9672 -0.1523 2.2335 -0.9669 0.4762 3.7957 0.3767 0.3438 0.1499 1.5736 0.0309 1.3316 0.0603 -#> 311: 92.8301 -5.9702 -0.1521 2.2337 -0.9670 0.4755 3.8172 0.3767 0.3432 0.1498 1.5737 0.0309 1.3324 0.0603 -#> 312: 92.8322 -5.9715 -0.1520 2.2341 -0.9670 0.4742 3.8229 0.3767 0.3427 0.1496 1.5734 0.0309 1.3309 0.0603 -#> 313: 92.8338 -5.9713 -0.1517 2.2342 -0.9672 0.4737 3.8202 0.3766 0.3422 0.1494 1.5733 0.0309 1.3306 0.0604 -#> 314: 92.8360 -5.9711 -0.1515 2.2343 -0.9675 0.4725 3.8154 0.3767 0.3417 0.1493 1.5733 0.0309 1.3322 0.0603 -#> 315: 92.8378 -5.9694 -0.1514 2.2343 -0.9680 0.4714 3.8051 0.3767 0.3414 0.1494 1.5734 0.0309 1.3352 0.0601 -#> 316: 92.8400 -5.9683 -0.1514 2.2343 -0.9682 0.4705 3.7984 0.3767 0.3410 0.1495 1.5735 0.0309 1.3354 0.0602 -#> 317: 92.8422 -5.9689 -0.1513 2.2344 -0.9686 0.4695 3.7961 0.3768 0.3406 0.1497 1.5735 0.0309 1.3362 0.0602 -#> 318: 92.8440 -5.9696 -0.1510 2.2347 -0.9689 0.4681 3.7934 0.3769 0.3403 0.1499 1.5731 0.0309 1.3381 0.0601 -#> 319: 92.8458 -5.9710 -0.1508 2.2350 -0.9692 0.4668 3.7913 0.3769 0.3401 0.1500 1.5723 0.0309 1.3403 0.0599 -#> 320: 92.8474 -5.9719 -0.1506 2.2353 -0.9695 0.4667 3.7876 0.3769 0.3400 0.1502 1.5714 0.0309 1.3423 0.0598 -#> 321: 92.8494 -5.9710 -0.1503 2.2355 -0.9696 0.4673 3.7790 0.3769 0.3397 0.1503 1.5709 0.0309 1.3439 0.0597 -#> 322: 92.8511 -5.9693 -0.1501 2.2359 -0.9698 0.4690 3.7674 0.3769 0.3395 0.1503 1.5708 0.0309 1.3451 0.0596 -#> 323: 92.8528 -5.9700 -0.1498 2.2364 -0.9699 0.4696 3.7641 0.3768 0.3394 0.1504 1.5701 0.0310 1.3470 0.0594 -#> 324: 92.8547 -5.9695 -0.1495 2.2369 -0.9699 0.4703 3.7567 0.3767 0.3392 0.1505 1.5698 0.0310 1.3485 0.0593 -#> 325: 92.8563 -5.9678 -0.1490 2.2376 -0.9702 0.4701 3.7473 0.3769 0.3395 0.1505 1.5702 0.0311 1.3494 0.0592 -#> 326: 92.8582 -5.9676 -0.1486 2.2382 -0.9703 0.4709 3.7434 0.3771 0.3397 0.1506 1.5700 0.0311 1.3479 0.0593 -#> 327: 92.8603 -5.9665 -0.1481 2.2389 -0.9704 0.4716 3.7361 0.3769 0.3399 0.1507 1.5699 0.0311 1.3471 0.0594 -#> 328: 92.8622 -5.9671 -0.1477 2.2397 -0.9704 0.4726 3.7379 0.3767 0.3398 0.1507 1.5698 0.0311 1.3481 0.0593 -#> 329: 92.8639 -5.9667 -0.1473 2.2405 -0.9707 0.4735 3.7366 0.3766 0.3398 0.1506 1.5696 0.0311 1.3482 0.0593 -#> 330: 92.8663 -5.9673 -0.1469 2.2413 -0.9708 0.4736 3.7382 0.3765 0.3397 0.1506 1.5691 0.0312 1.3492 0.0592 -#> 331: 92.8674 -5.9670 -0.1464 2.2420 -0.9710 0.4740 3.7350 0.3763 0.3397 0.1507 1.5689 0.0312 1.3512 0.0591 -#> 332: 92.8681 -5.9664 -0.1460 2.2428 -0.9710 0.4737 3.7311 0.3762 0.3396 0.1509 1.5687 0.0312 1.3527 0.0590 -#> 333: 92.8683 -5.9649 -0.1456 2.2436 -0.9708 0.4727 3.7232 0.3760 0.3397 0.1509 1.5686 0.0312 1.3505 0.0591 -#> 334: 92.8690 -5.9642 -0.1452 2.2444 -0.9707 0.4723 3.7194 0.3758 0.3399 0.1511 1.5682 0.0312 1.3490 0.0592 -#> 335: 92.8698 -5.9656 -0.1447 2.2454 -0.9707 0.4722 3.7289 0.3756 0.3400 0.1512 1.5674 0.0313 1.3476 0.0592 -#> 336: 92.8691 -5.9664 -0.1443 2.2463 -0.9706 0.4724 3.7333 0.3753 0.3401 0.1511 1.5669 0.0313 1.3455 0.0593 -#> 337: 92.8687 -5.9670 -0.1440 2.2471 -0.9705 0.4742 3.7378 0.3749 0.3402 0.1510 1.5665 0.0314 1.3433 0.0594 -#> 338: 92.8683 -5.9663 -0.1435 2.2480 -0.9703 0.4747 3.7370 0.3746 0.3405 0.1510 1.5663 0.0313 1.3402 0.0595 -#> 339: 92.8682 -5.9650 -0.1431 2.2488 -0.9701 0.4760 3.7332 0.3743 0.3408 0.1509 1.5661 0.0313 1.3374 0.0597 -#> 340: 92.8684 -5.9639 -0.1427 2.2496 -0.9699 0.4774 3.7283 0.3739 0.3411 0.1510 1.5658 0.0313 1.3358 0.0597 -#> 341: 92.8685 -5.9610 -0.1423 2.2504 -0.9696 0.4782 3.7169 0.3735 0.3413 0.1510 1.5661 0.0313 1.3338 0.0598 -#> 342: 92.8681 -5.9581 -0.1419 2.2512 -0.9696 0.4802 3.7060 0.3731 0.3416 0.1511 1.5661 0.0313 1.3316 0.0599 -#> 343: 92.8671 -5.9557 -0.1414 2.2521 -0.9697 0.4821 3.6971 0.3726 0.3419 0.1510 1.5667 0.0313 1.3292 0.0601 -#> 344: 92.8662 -5.9550 -0.1409 2.2531 -0.9696 0.4825 3.6931 0.3722 0.3424 0.1509 1.5660 0.0314 1.3269 0.0602 -#> 345: 92.8651 -5.9542 -0.1405 2.2542 -0.9696 0.4825 3.6886 0.3717 0.3429 0.1511 1.5645 0.0315 1.3252 0.0602 -#> 346: 92.8636 -5.9534 -0.1401 2.2549 -0.9696 0.4822 3.6821 0.3714 0.3432 0.1510 1.5638 0.0315 1.3231 0.0603 -#> 347: 92.8622 -5.9532 -0.1397 2.2557 -0.9696 0.4815 3.6782 0.3712 0.3435 0.1509 1.5636 0.0315 1.3220 0.0604 -#> 348: 92.8593 -5.9538 -0.1394 2.2566 -0.9697 0.4813 3.6787 0.3709 0.3438 0.1508 1.5634 0.0315 1.3202 0.0605 -#> 349: 92.8574 -5.9532 -0.1389 2.2574 -0.9697 0.4808 3.6739 0.3706 0.3440 0.1506 1.5630 0.0316 1.3179 0.0606 -#> 350: 92.8561 -5.9528 -0.1385 2.2583 -0.9697 0.4801 3.6705 0.3703 0.3443 0.1505 1.5625 0.0316 1.3161 0.0607 -#> 351: 92.8541 -5.9518 -0.1381 2.2591 -0.9697 0.4804 3.6650 0.3700 0.3446 0.1505 1.5619 0.0316 1.3141 0.0608 -#> 352: 92.8528 -5.9516 -0.1377 2.2599 -0.9700 0.4818 3.6626 0.3698 0.3449 0.1504 1.5614 0.0316 1.3122 0.0609 -#> 353: 92.8506 -5.9518 -0.1373 2.2607 -0.9700 0.4836 3.6601 0.3697 0.3451 0.1506 1.5604 0.0317 1.3116 0.0610 -#> 354: 92.8482 -5.9507 -0.1369 2.2615 -0.9700 0.4852 3.6520 0.3696 0.3451 0.1506 1.5595 0.0317 1.3099 0.0611 -#> 355: 92.8459 -5.9500 -0.1365 2.2624 -0.9699 0.4873 3.6467 0.3695 0.3454 0.1505 1.5589 0.0318 1.3090 0.0611 -#> 356: 92.8441 -5.9494 -0.1361 2.2632 -0.9700 0.4893 3.6407 0.3696 0.3456 0.1505 1.5581 0.0319 1.3083 0.0612 -#> 357: 92.8425 -5.9492 -0.1356 2.2641 -0.9700 0.4906 3.6359 0.3696 0.3459 0.1506 1.5568 0.0320 1.3082 0.0612 -#> 358: 92.8414 -5.9487 -0.1351 2.2649 -0.9700 0.4914 3.6300 0.3697 0.3460 0.1506 1.5559 0.0321 1.3064 0.0613 -#> 359: 92.8395 -5.9487 -0.1346 2.2657 -0.9700 0.4923 3.6262 0.3699 0.3462 0.1507 1.5558 0.0321 1.3050 0.0614 -#> 360: 92.8373 -5.9478 -0.1341 2.2666 -0.9700 0.4922 3.6206 0.3700 0.3465 0.1509 1.5553 0.0322 1.3061 0.0614 -#> 361: 92.8353 -5.9475 -0.1337 2.2673 -0.9699 0.4912 3.6183 0.3700 0.3469 0.1510 1.5549 0.0322 1.3051 0.0614 -#> 362: 92.8339 -5.9474 -0.1333 2.2681 -0.9699 0.4896 3.6164 0.3700 0.3472 0.1510 1.5549 0.0322 1.3041 0.0616 -#> 363: 92.8318 -5.9470 -0.1328 2.2690 -0.9696 0.4882 3.6136 0.3700 0.3476 0.1510 1.5541 0.0323 1.3035 0.0616 -#> 364: 92.8305 -5.9460 -0.1325 2.2697 -0.9695 0.4863 3.6099 0.3701 0.3477 0.1510 1.5533 0.0324 1.3028 0.0616 -#> 365: 92.8300 -5.9451 -0.1320 2.2705 -0.9693 0.4851 3.6083 0.3703 0.3479 0.1511 1.5535 0.0324 1.3017 0.0617 -#> 366: 92.8290 -5.9444 -0.1317 2.2710 -0.9691 0.4841 3.6062 0.3707 0.3476 0.1512 1.5534 0.0325 1.3013 0.0617 -#> 367: 92.8279 -5.9438 -0.1313 2.2715 -0.9688 0.4829 3.6026 0.3711 0.3473 0.1513 1.5537 0.0325 1.2996 0.0618 -#> 368: 92.8270 -5.9437 -0.1310 2.2721 -0.9687 0.4824 3.6015 0.3715 0.3471 0.1513 1.5535 0.0325 1.2984 0.0619 -#> 369: 92.8268 -5.9444 -0.1306 2.2726 -0.9686 0.4829 3.6042 0.3718 0.3469 0.1514 1.5530 0.0325 1.2983 0.0619 -#> 370: 92.8268 -5.9455 -0.1303 2.2732 -0.9686 0.4833 3.6099 0.3721 0.3466 0.1513 1.5526 0.0326 1.2971 0.0619 -#> 371: 92.8269 -5.9462 -0.1300 2.2737 -0.9686 0.4842 3.6169 0.3723 0.3465 0.1512 1.5516 0.0326 1.2961 0.0619 -#> 372: 92.8272 -5.9465 -0.1297 2.2741 -0.9685 0.4852 3.6242 0.3726 0.3463 0.1512 1.5507 0.0327 1.2950 0.0620 -#> 373: 92.8275 -5.9456 -0.1294 2.2746 -0.9686 0.4861 3.6219 0.3729 0.3461 0.1511 1.5501 0.0328 1.2946 0.0620 -#> 374: 92.8278 -5.9445 -0.1291 2.2750 -0.9687 0.4867 3.6175 0.3730 0.3461 0.1509 1.5496 0.0328 1.2942 0.0620 -#> 375: 92.8285 -5.9438 -0.1289 2.2753 -0.9689 0.4874 3.6118 0.3731 0.3459 0.1509 1.5491 0.0329 1.2938 0.0620 -#> 376: 92.8286 -5.9439 -0.1287 2.2755 -0.9689 0.4876 3.6100 0.3733 0.3458 0.1508 1.5488 0.0329 1.2930 0.0621 -#> 377: 92.8289 -5.9431 -0.1285 2.2758 -0.9690 0.4870 3.6054 0.3735 0.3456 0.1508 1.5487 0.0329 1.2921 0.0621 -#> 378: 92.8293 -5.9428 -0.1284 2.2760 -0.9689 0.4865 3.6019 0.3737 0.3454 0.1508 1.5484 0.0329 1.2910 0.0622 -#> 379: 92.8294 -5.9441 -0.1282 2.2763 -0.9688 0.4857 3.6077 0.3739 0.3451 0.1507 1.5480 0.0329 1.2907 0.0622 -#> 380: 92.8296 -5.9448 -0.1281 2.2766 -0.9688 0.4844 3.6104 0.3741 0.3448 0.1506 1.5475 0.0329 1.2901 0.0622 -#> 381: 92.8301 -5.9461 -0.1280 2.2767 -0.9689 0.4833 3.6194 0.3743 0.3444 0.1505 1.5476 0.0329 1.2893 0.0622 -#> 382: 92.8312 -5.9464 -0.1278 2.2768 -0.9689 0.4823 3.6237 0.3745 0.3441 0.1505 1.5476 0.0329 1.2881 0.0622 -#> 383: 92.8317 -5.9459 -0.1277 2.2770 -0.9687 0.4817 3.6282 0.3747 0.3438 0.1504 1.5479 0.0329 1.2875 0.0622 -#> 384: 92.8325 -5.9458 -0.1276 2.2772 -0.9686 0.4818 3.6293 0.3749 0.3434 0.1503 1.5481 0.0329 1.2863 0.0623 -#> 385: 92.8337 -5.9449 -0.1275 2.2773 -0.9685 0.4832 3.6263 0.3751 0.3431 0.1503 1.5481 0.0330 1.2860 0.0622 -#> 386: 92.8346 -5.9455 -0.1274 2.2773 -0.9682 0.4834 3.6283 0.3754 0.3427 0.1501 1.5483 0.0330 1.2851 0.0623 -#> 387: 92.8353 -5.9460 -0.1273 2.2775 -0.9681 0.4831 3.6303 0.3756 0.3424 0.1499 1.5486 0.0330 1.2836 0.0623 -#> 388: 92.8365 -5.9462 -0.1272 2.2777 -0.9680 0.4831 3.6294 0.3759 0.3420 0.1498 1.5486 0.0330 1.2830 0.0624 -#> 389: 92.8378 -5.9456 -0.1271 2.2779 -0.9678 0.4830 3.6260 0.3762 0.3416 0.1497 1.5486 0.0330 1.2816 0.0624 -#> 390: 92.8397 -5.9454 -0.1270 2.2779 -0.9678 0.4835 3.6245 0.3765 0.3413 0.1496 1.5488 0.0330 1.2805 0.0625 -#> 391: 92.8416 -5.9461 -0.1269 2.2780 -0.9679 0.4841 3.6273 0.3768 0.3409 0.1497 1.5486 0.0330 1.2816 0.0624 -#> 392: 92.8430 -5.9471 -0.1269 2.2779 -0.9679 0.4844 3.6293 0.3771 0.3408 0.1498 1.5483 0.0330 1.2830 0.0623 -#> 393: 92.8444 -5.9478 -0.1269 2.2779 -0.9680 0.4841 3.6310 0.3774 0.3407 0.1500 1.5485 0.0330 1.2842 0.0623 -#> 394: 92.8458 -5.9492 -0.1268 2.2779 -0.9680 0.4839 3.6370 0.3775 0.3407 0.1502 1.5484 0.0330 1.2847 0.0622 -#> 395: 92.8474 -5.9501 -0.1268 2.2780 -0.9681 0.4830 3.6391 0.3777 0.3406 0.1503 1.5485 0.0330 1.2849 0.0622 -#> 396: 92.8484 -5.9500 -0.1267 2.2781 -0.9682 0.4820 3.6369 0.3778 0.3406 0.1504 1.5490 0.0330 1.2850 0.0622 -#> 397: 92.8497 -5.9490 -0.1267 2.2782 -0.9680 0.4813 3.6308 0.3779 0.3407 0.1504 1.5494 0.0330 1.2848 0.0622 -#> 398: 92.8511 -5.9478 -0.1267 2.2782 -0.9679 0.4811 3.6256 0.3780 0.3407 0.1505 1.5498 0.0330 1.2844 0.0622 -#> 399: 92.8531 -5.9467 -0.1266 2.2782 -0.9680 0.4804 3.6208 0.3781 0.3407 0.1505 1.5505 0.0330 1.2842 0.0623 -#> 400: 92.8545 -5.9465 -0.1266 2.2782 -0.9679 0.4793 3.6175 0.3783 0.3406 0.1505 1.5506 0.0329 1.2833 0.0623 -#> 401: 92.8558 -5.9458 -0.1266 2.2781 -0.9679 0.4787 3.6135 0.3784 0.3406 0.1506 1.5506 0.0329 1.2836 0.0623 -#> 402: 92.8571 -5.9454 -0.1266 2.2780 -0.9678 0.4788 3.6122 0.3786 0.3405 0.1506 1.5508 0.0329 1.2841 0.0623 -#> 403: 92.8583 -5.9454 -0.1267 2.2778 -0.9679 0.4794 3.6115 0.3790 0.3402 0.1507 1.5508 0.0330 1.2859 0.0622 -#> 404: 92.8593 -5.9466 -0.1268 2.2776 -0.9681 0.4787 3.6149 0.3793 0.3401 0.1508 1.5507 0.0330 1.2875 0.0621 -#> 405: 92.8598 -5.9475 -0.1269 2.2774 -0.9681 0.4781 3.6208 0.3796 0.3399 0.1509 1.5507 0.0330 1.2888 0.0620 -#> 406: 92.8596 -5.9480 -0.1269 2.2773 -0.9680 0.4776 3.6238 0.3798 0.3397 0.1509 1.5508 0.0330 1.2895 0.0619 -#> 407: 92.8588 -5.9487 -0.1270 2.2773 -0.9679 0.4773 3.6289 0.3801 0.3395 0.1508 1.5510 0.0331 1.2887 0.0619 -#> 408: 92.8587 -5.9489 -0.1271 2.2771 -0.9677 0.4777 3.6323 0.3804 0.3391 0.1508 1.5513 0.0331 1.2878 0.0620 -#> 409: 92.8585 -5.9498 -0.1272 2.2770 -0.9677 0.4791 3.6383 0.3806 0.3389 0.1506 1.5512 0.0331 1.2865 0.0621 -#> 410: 92.8574 -5.9522 -0.1272 2.2769 -0.9676 0.4810 3.6538 0.3809 0.3387 0.1507 1.5509 0.0331 1.2855 0.0621 -#> 411: 92.8568 -5.9532 -0.1272 2.2767 -0.9675 0.4817 3.6651 0.3811 0.3385 0.1507 1.5508 0.0332 1.2842 0.0622 -#> 412: 92.8562 -5.9535 -0.1273 2.2767 -0.9674 0.4819 3.6756 0.3812 0.3383 0.1507 1.5509 0.0332 1.2851 0.0621 -#> 413: 92.8559 -5.9542 -0.1274 2.2766 -0.9672 0.4824 3.6881 0.3814 0.3381 0.1507 1.5514 0.0332 1.2848 0.0621 -#> 414: 92.8556 -5.9550 -0.1274 2.2765 -0.9670 0.4835 3.6990 0.3815 0.3379 0.1507 1.5519 0.0332 1.2838 0.0622 -#> 415: 92.8551 -5.9566 -0.1274 2.2764 -0.9669 0.4838 3.7133 0.3816 0.3377 0.1506 1.5522 0.0332 1.2828 0.0623 -#> 416: 92.8547 -5.9581 -0.1275 2.2764 -0.9668 0.4848 3.7276 0.3818 0.3374 0.1504 1.5526 0.0332 1.2814 0.0623 -#> 417: 92.8538 -5.9581 -0.1274 2.2764 -0.9667 0.4856 3.7321 0.3818 0.3372 0.1503 1.5532 0.0332 1.2800 0.0624 -#> 418: 92.8527 -5.9590 -0.1273 2.2766 -0.9665 0.4869 3.7398 0.3817 0.3372 0.1502 1.5532 0.0332 1.2787 0.0625 -#> 419: 92.8524 -5.9596 -0.1272 2.2768 -0.9663 0.4869 3.7467 0.3817 0.3372 0.1501 1.5531 0.0332 1.2779 0.0625 -#> 420: 92.8520 -5.9598 -0.1271 2.2771 -0.9662 0.4863 3.7494 0.3817 0.3372 0.1501 1.5528 0.0332 1.2774 0.0625 -#> 421: 92.8516 -5.9601 -0.1270 2.2772 -0.9661 0.4855 3.7541 0.3817 0.3372 0.1500 1.5527 0.0333 1.2763 0.0625 -#> 422: 92.8509 -5.9602 -0.1270 2.2775 -0.9659 0.4855 3.7554 0.3818 0.3371 0.1499 1.5525 0.0333 1.2753 0.0626 -#> 423: 92.8497 -5.9608 -0.1269 2.2777 -0.9658 0.4855 3.7590 0.3819 0.3371 0.1499 1.5524 0.0334 1.2746 0.0626 -#> 424: 92.8490 -5.9620 -0.1269 2.2779 -0.9658 0.4852 3.7657 0.3820 0.3370 0.1498 1.5521 0.0334 1.2740 0.0626 -#> 425: 92.8481 -5.9615 -0.1268 2.2780 -0.9657 0.4852 3.7639 0.3819 0.3369 0.1497 1.5520 0.0334 1.2741 0.0625 -#> 426: 92.8471 -5.9611 -0.1267 2.2783 -0.9656 0.4859 3.7632 0.3819 0.3369 0.1495 1.5520 0.0335 1.2744 0.0625 -#> 427: 92.8470 -5.9605 -0.1266 2.2784 -0.9655 0.4856 3.7616 0.3819 0.3368 0.1494 1.5522 0.0335 1.2739 0.0625 -#> 428: 92.8464 -5.9602 -0.1266 2.2786 -0.9653 0.4851 3.7603 0.3820 0.3367 0.1493 1.5522 0.0335 1.2731 0.0625 -#> 429: 92.8450 -5.9593 -0.1265 2.2788 -0.9652 0.4852 3.7573 0.3820 0.3366 0.1493 1.5525 0.0335 1.2720 0.0626 -#> 430: 92.8440 -5.9590 -0.1264 2.2789 -0.9651 0.4862 3.7586 0.3821 0.3365 0.1493 1.5524 0.0335 1.2710 0.0627 -#> 431: 92.8428 -5.9583 -0.1263 2.2791 -0.9649 0.4868 3.7575 0.3821 0.3365 0.1493 1.5522 0.0335 1.2698 0.0627 -#> 432: 92.8417 -5.9583 -0.1262 2.2793 -0.9649 0.4881 3.7580 0.3821 0.3365 0.1493 1.5518 0.0335 1.2683 0.0628 -#> 433: 92.8404 -5.9589 -0.1261 2.2796 -0.9648 0.4888 3.7614 0.3821 0.3364 0.1494 1.5513 0.0335 1.2681 0.0628 -#> 434: 92.8392 -5.9585 -0.1260 2.2798 -0.9646 0.4900 3.7602 0.3821 0.3363 0.1494 1.5509 0.0336 1.2686 0.0627 -#> 435: 92.8376 -5.9587 -0.1260 2.2801 -0.9645 0.4913 3.7622 0.3822 0.3362 0.1494 1.5506 0.0336 1.2677 0.0627 -#> 436: 92.8367 -5.9581 -0.1259 2.2802 -0.9646 0.4912 3.7594 0.3821 0.3361 0.1494 1.5504 0.0336 1.2684 0.0627 -#> 437: 92.8352 -5.9588 -0.1259 2.2803 -0.9647 0.4910 3.7634 0.3821 0.3360 0.1494 1.5501 0.0337 1.2695 0.0626 -#> 438: 92.8332 -5.9592 -0.1259 2.2804 -0.9648 0.4913 3.7649 0.3821 0.3358 0.1494 1.5498 0.0337 1.2705 0.0625 -#> 439: 92.8310 -5.9589 -0.1258 2.2805 -0.9648 0.4916 3.7630 0.3821 0.3357 0.1494 1.5497 0.0337 1.2713 0.0625 -#> 440: 92.8292 -5.9590 -0.1258 2.2806 -0.9649 0.4915 3.7620 0.3821 0.3355 0.1493 1.5494 0.0338 1.2712 0.0625 -#> 441: 92.8276 -5.9590 -0.1258 2.2808 -0.9650 0.4915 3.7619 0.3822 0.3353 0.1493 1.5493 0.0338 1.2712 0.0625 -#> 442: 92.8258 -5.9587 -0.1257 2.2809 -0.9650 0.4927 3.7592 0.3822 0.3351 0.1493 1.5493 0.0338 1.2707 0.0625 -#> 443: 92.8241 -5.9586 -0.1256 2.2811 -0.9651 0.4941 3.7563 0.3822 0.3350 0.1493 1.5491 0.0338 1.2704 0.0625 -#> 444: 92.8228 -5.9591 -0.1256 2.2812 -0.9651 0.4954 3.7566 0.3822 0.3349 0.1493 1.5488 0.0339 1.2703 0.0625 -#> 445: 92.8210 -5.9596 -0.1256 2.2813 -0.9652 0.4972 3.7573 0.3821 0.3348 0.1493 1.5484 0.0339 1.2702 0.0625 -#> 446: 92.8193 -5.9595 -0.1255 2.2815 -0.9652 0.4989 3.7551 0.3821 0.3348 0.1494 1.5482 0.0339 1.2708 0.0624 -#> 447: 92.8183 -5.9598 -0.1255 2.2817 -0.9652 0.5002 3.7548 0.3820 0.3347 0.1494 1.5478 0.0339 1.2710 0.0624 -#> 448: 92.8177 -5.9607 -0.1255 2.2818 -0.9653 0.5019 3.7585 0.3819 0.3347 0.1495 1.5475 0.0340 1.2711 0.0624 -#> 449: 92.8171 -5.9613 -0.1254 2.2819 -0.9654 0.5040 3.7592 0.3819 0.3347 0.1495 1.5474 0.0340 1.2711 0.0624 -#> 450: 92.8164 -5.9621 -0.1253 2.2821 -0.9655 0.5060 3.7632 0.3818 0.3346 0.1495 1.5470 0.0340 1.2704 0.0624 -#> 451: 92.8157 -5.9628 -0.1253 2.2822 -0.9655 0.5082 3.7655 0.3816 0.3346 0.1495 1.5469 0.0340 1.2699 0.0625 -#> 452: 92.8157 -5.9633 -0.1252 2.2824 -0.9656 0.5092 3.7657 0.3815 0.3346 0.1495 1.5468 0.0340 1.2691 0.0625 -#> 453: 92.8155 -5.9631 -0.1252 2.2823 -0.9657 0.5099 3.7646 0.3815 0.3347 0.1494 1.5470 0.0340 1.2684 0.0625 -#> 454: 92.8149 -5.9627 -0.1252 2.2823 -0.9656 0.5110 3.7623 0.3815 0.3347 0.1495 1.5470 0.0340 1.2678 0.0626 -#> 455: 92.8147 -5.9626 -0.1253 2.2822 -0.9656 0.5118 3.7610 0.3816 0.3347 0.1495 1.5471 0.0340 1.2675 0.0626 -#> 456: 92.8146 -5.9631 -0.1253 2.2821 -0.9657 0.5124 3.7612 0.3817 0.3348 0.1495 1.5473 0.0340 1.2684 0.0625 -#> 457: 92.8146 -5.9639 -0.1253 2.2820 -0.9658 0.5131 3.7636 0.3817 0.3347 0.1494 1.5471 0.0340 1.2683 0.0625 -#> 458: 92.8142 -5.9641 -0.1254 2.2818 -0.9658 0.5143 3.7637 0.3817 0.3347 0.1493 1.5472 0.0340 1.2679 0.0626 -#> 459: 92.8129 -5.9636 -0.1254 2.2818 -0.9660 0.5155 3.7609 0.3817 0.3347 0.1493 1.5474 0.0340 1.2692 0.0625 -#> 460: 92.8118 -5.9630 -0.1254 2.2817 -0.9660 0.5155 3.7563 0.3818 0.3347 0.1493 1.5476 0.0340 1.2703 0.0624 -#> 461: 92.8102 -5.9625 -0.1255 2.2816 -0.9661 0.5159 3.7525 0.3818 0.3347 0.1493 1.5478 0.0340 1.2711 0.0624 -#> 462: 92.8090 -5.9628 -0.1255 2.2814 -0.9661 0.5163 3.7520 0.3819 0.3347 0.1492 1.5481 0.0340 1.2708 0.0624 -#> 463: 92.8075 -5.9633 -0.1256 2.2813 -0.9660 0.5180 3.7534 0.3819 0.3347 0.1491 1.5484 0.0340 1.2705 0.0624 -#> 464: 92.8066 -5.9628 -0.1256 2.2812 -0.9659 0.5194 3.7507 0.3820 0.3347 0.1490 1.5485 0.0340 1.2702 0.0624 -#> 465: 92.8058 -5.9627 -0.1257 2.2811 -0.9658 0.5212 3.7506 0.3820 0.3347 0.1490 1.5484 0.0340 1.2696 0.0625 -#> 466: 92.8055 -5.9624 -0.1258 2.2808 -0.9656 0.5227 3.7510 0.3821 0.3347 0.1489 1.5487 0.0340 1.2704 0.0624 -#> 467: 92.8052 -5.9624 -0.1260 2.2805 -0.9656 0.5242 3.7518 0.3822 0.3346 0.1488 1.5488 0.0340 1.2715 0.0623 -#> 468: 92.8054 -5.9623 -0.1261 2.2803 -0.9654 0.5260 3.7545 0.3823 0.3346 0.1487 1.5493 0.0340 1.2730 0.0623 -#> 469: 92.8052 -5.9629 -0.1262 2.2803 -0.9654 0.5278 3.7617 0.3824 0.3346 0.1486 1.5495 0.0340 1.2737 0.0622 -#> 470: 92.8055 -5.9638 -0.1263 2.2802 -0.9653 0.5290 3.7667 0.3825 0.3347 0.1486 1.5494 0.0341 1.2729 0.0623 -#> 471: 92.8061 -5.9645 -0.1263 2.2801 -0.9653 0.5293 3.7702 0.3825 0.3347 0.1485 1.5494 0.0341 1.2724 0.0623 -#> 472: 92.8057 -5.9645 -0.1264 2.2800 -0.9653 0.5288 3.7699 0.3826 0.3347 0.1484 1.5495 0.0341 1.2728 0.0623 -#> 473: 92.8053 -5.9643 -0.1265 2.2799 -0.9652 0.5282 3.7701 0.3827 0.3347 0.1483 1.5494 0.0341 1.2721 0.0623 -#> 474: 92.8049 -5.9638 -0.1266 2.2798 -0.9653 0.5273 3.7676 0.3828 0.3347 0.1483 1.5495 0.0341 1.2722 0.0623 -#> 475: 92.8041 -5.9639 -0.1267 2.2796 -0.9654 0.5269 3.7668 0.3829 0.3347 0.1482 1.5495 0.0341 1.2721 0.0623 -#> 476: 92.8032 -5.9641 -0.1269 2.2794 -0.9653 0.5260 3.7681 0.3830 0.3347 0.1481 1.5496 0.0341 1.2716 0.0623 -#> 477: 92.8026 -5.9634 -0.1270 2.2792 -0.9653 0.5249 3.7647 0.3831 0.3347 0.1480 1.5500 0.0341 1.2716 0.0623 -#> 478: 92.8021 -5.9627 -0.1271 2.2789 -0.9653 0.5241 3.7606 0.3832 0.3346 0.1480 1.5500 0.0341 1.2718 0.0623 -#> 479: 92.8019 -5.9623 -0.1272 2.2787 -0.9654 0.5241 3.7581 0.3833 0.3345 0.1480 1.5502 0.0342 1.2714 0.0624 -#> 480: 92.8017 -5.9631 -0.1274 2.2784 -0.9654 0.5241 3.7606 0.3835 0.3344 0.1479 1.5503 0.0342 1.2711 0.0624 -#> 481: 92.8020 -5.9638 -0.1275 2.2781 -0.9654 0.5237 3.7659 0.3837 0.3343 0.1478 1.5508 0.0342 1.2720 0.0624 -#> 482: 92.8024 -5.9640 -0.1278 2.2777 -0.9654 0.5228 3.7668 0.3838 0.3342 0.1478 1.5512 0.0342 1.2729 0.0623 -#> 483: 92.8017 -5.9645 -0.1280 2.2773 -0.9654 0.5224 3.7676 0.3840 0.3341 0.1478 1.5515 0.0342 1.2741 0.0622 -#> 484: 92.8012 -5.9642 -0.1281 2.2771 -0.9653 0.5221 3.7649 0.3841 0.3340 0.1478 1.5521 0.0341 1.2747 0.0622 -#> 485: 92.8009 -5.9642 -0.1283 2.2769 -0.9653 0.5214 3.7635 0.3842 0.3339 0.1479 1.5523 0.0341 1.2752 0.0622 -#> 486: 92.8002 -5.9639 -0.1284 2.2767 -0.9652 0.5213 3.7609 0.3842 0.3339 0.1480 1.5523 0.0341 1.2760 0.0621 -#> 487: 92.7998 -5.9636 -0.1285 2.2767 -0.9652 0.5212 3.7603 0.3842 0.3339 0.1480 1.5525 0.0341 1.2762 0.0621 -#> 488: 92.7995 -5.9634 -0.1285 2.2766 -0.9652 0.5218 3.7592 0.3841 0.3339 0.1480 1.5530 0.0341 1.2773 0.0621 -#> 489: 92.7996 -5.9630 -0.1286 2.2765 -0.9653 0.5220 3.7578 0.3841 0.3339 0.1480 1.5532 0.0341 1.2778 0.0621 -#> 490: 92.8001 -5.9629 -0.1287 2.2764 -0.9652 0.5226 3.7573 0.3841 0.3339 0.1479 1.5533 0.0341 1.2788 0.0620 -#> 491: 92.8001 -5.9629 -0.1287 2.2762 -0.9651 0.5225 3.7568 0.3841 0.3338 0.1479 1.5533 0.0341 1.2790 0.0620 -#> 492: 92.8005 -5.9625 -0.1288 2.2761 -0.9651 0.5228 3.7544 0.3840 0.3339 0.1479 1.5536 0.0341 1.2797 0.0619 -#> 493: 92.8010 -5.9626 -0.1289 2.2759 -0.9651 0.5228 3.7544 0.3840 0.3339 0.1479 1.5537 0.0340 1.2795 0.0620 -#> 494: 92.8014 -5.9623 -0.1290 2.2757 -0.9651 0.5239 3.7523 0.3839 0.3340 0.1479 1.5540 0.0340 1.2790 0.0620 -#> 495: 92.8017 -5.9617 -0.1291 2.2755 -0.9652 0.5244 3.7491 0.3838 0.3341 0.1480 1.5540 0.0340 1.2787 0.0621 -#> 496: 92.8019 -5.9613 -0.1291 2.2754 -0.9652 0.5246 3.7459 0.3837 0.3341 0.1481 1.5539 0.0340 1.2802 0.0620 -#> 497: 92.8023 -5.9611 -0.1292 2.2753 -0.9653 0.5252 3.7447 0.3836 0.3340 0.1482 1.5539 0.0340 1.2814 0.0620 -#> 498: 92.8025 -5.9615 -0.1292 2.2752 -0.9653 0.5254 3.7446 0.3836 0.3339 0.1483 1.5539 0.0340 1.2825 0.0619 -#> 499: 92.8033 -5.9616 -0.1292 2.2751 -0.9654 0.5254 3.7447 0.3836 0.3338 0.1483 1.5538 0.0340 1.2834 0.0619 -#> 500: 92.8041 -5.9630 -0.1292 2.2752 -0.9655 0.5248 3.7529 0.3836 0.3337 0.1484 1.5538 0.0340 1.2841 0.0619
                                                                #> Calculating covariance matrix
                                                                #>
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.763 0.036 0.799
                                                                f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", error_model = "obs_tc") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_A1 |f_parent_qlogis | log_alpha | -#> |.....................| log_beta |sigma_low_parent |rsd_high_parent |sigma_low_A1 | -#> |.....................|rsd_high_A1 | o1 | o2 | o3 | -#> |.....................| o4 | o5 |...........|...........| -#> | 1| 504.82714 | 1.000 | -1.000 | -0.9114 | -0.8944 | -#> |.....................| -0.8457 | -0.8687 | -0.8916 | -0.8687 | -#> |.....................| -0.8916 | -0.8768 | -0.8745 | -0.8676 | -#> |.....................| -0.8705 | -0.8704 |...........|...........| -#> | U| 504.82714 | 93.12 | -5.303 | -0.9442 | -0.1065 | -#> |.....................| 2.291 | 1.160 | 0.03005 | 1.160 | -#> |.....................| 0.03005 | 0.7578 | 0.8738 | 1.213 | -#> |.....................| 1.069 | 1.072 |...........|...........| -#> | X| 504.82714 | 93.12 | 0.004975 | 0.2801 | 0.8989 | -#> |.....................| 9.884 | 1.160 | 0.03005 | 1.160 | -#> |.....................| 0.03005 | 0.7578 | 0.8738 | 1.213 | -#> |.....................| 1.069 | 1.072 |...........|...........| -#> | G| Gill Diff. | 73.79 | 2.406 | 0.05615 | 0.2285 | -#> |.....................| 0.009051 | -73.50 | -23.10 | 0.2441 | -#> |.....................| -2.663 | 1.201 | 11.89 | -10.88 | -#> |.....................| -9.982 | -10.81 |...........|...........| -#> | 2| 4109.9562 | 0.3228 | -1.022 | -0.9119 | -0.8965 | -#> |.....................| -0.8458 | -0.1941 | -0.6796 | -0.8709 | -#> |.....................| -0.8672 | -0.8879 | -0.9836 | -0.7677 | -#> |.....................| -0.7789 | -0.7712 |...........|...........| -#> | U| 4109.9562 | 30.05 | -5.326 | -0.9447 | -0.1086 | -#> |.....................| 2.291 | 1.551 | 0.03324 | 1.158 | -#> |.....................| 0.03042 | 0.7495 | 0.7784 | 1.335 | -#> |.....................| 1.167 | 1.178 |...........|...........| -#> | X| 4109.9562 | 30.05 | 0.004866 | 0.2800 | 0.8971 | -#> |.....................| 9.883 | 1.551 | 0.03324 | 1.158 | -#> |.....................| 0.03042 | 0.7495 | 0.7784 | 1.335 | -#> |.....................| 1.167 | 1.178 |...........|...........| -#> | 3| 527.72868 | 0.9323 | -1.002 | -0.9115 | -0.8946 | -#> |.....................| -0.8457 | -0.8012 | -0.8704 | -0.8689 | -#> |.....................| -0.8892 | -0.8779 | -0.8854 | -0.8576 | -#> |.....................| -0.8613 | -0.8605 |...........|...........| -#> | U| 527.72868 | 86.81 | -5.306 | -0.9442 | -0.1067 | -#> |.....................| 2.291 | 1.199 | 0.03037 | 1.159 | -#> |.....................| 0.03009 | 0.7570 | 0.8642 | 1.226 | -#> |.....................| 1.079 | 1.083 |...........|...........| -#> | X| 527.72868 | 86.81 | 0.004964 | 0.2800 | 0.8988 | -#> |.....................| 9.884 | 1.199 | 0.03037 | 1.159 | -#> |.....................| 0.03009 | 0.7570 | 0.8642 | 1.226 | -#> |.....................| 1.079 | 1.083 |...........|...........| -#> | 4| 503.94655 | 0.9891 | -1.000 | -0.9114 | -0.8944 | -#> |.....................| -0.8457 | -0.8578 | -0.8882 | -0.8687 | -#> |.....................| -0.8912 | -0.8770 | -0.8762 | -0.8660 | -#> |.....................| -0.8690 | -0.8688 |...........|...........| -#> | U| 503.94655 | 92.10 | -5.304 | -0.9442 | -0.1066 | -#> |.....................| 2.291 | 1.166 | 0.03011 | 1.160 | -#> |.....................| 0.03006 | 0.7577 | 0.8722 | 1.215 | -#> |.....................| 1.070 | 1.074 |...........|...........| -#> | X| 503.94655 | 92.10 | 0.004973 | 0.2801 | 0.8989 | -#> |.....................| 9.884 | 1.166 | 0.03011 | 1.160 | -#> |.....................| 0.03006 | 0.7577 | 0.8722 | 1.215 | -#> |.....................| 1.070 | 1.074 |...........|...........| -#> | F| Forward Diff. | -83.20 | 2.270 | -0.2572 | 0.1460 | -#> |.....................| -0.3233 | -71.29 | -24.25 | 0.7297 | -#> |.....................| -2.130 | 1.329 | 9.332 | -11.82 | -#> |.....................| -9.604 | -10.42 |...........|...........| -#> | 5| 503.03407 | 1.000 | -1.001 | -0.9114 | -0.8944 | -#> |.....................| -0.8456 | -0.8473 | -0.8847 | -0.8688 | -#> |.....................| -0.8909 | -0.8772 | -0.8776 | -0.8642 | -#> |.....................| -0.8676 | -0.8673 |...........|...........| -#> | U| 503.03407 | 93.15 | -5.304 | -0.9442 | -0.1066 | -#> |.....................| 2.291 | 1.172 | 0.03016 | 1.159 | -#> |.....................| 0.03007 | 0.7575 | 0.8710 | 1.217 | -#> |.....................| 1.072 | 1.075 |...........|...........| -#> | X| 503.03407 | 93.15 | 0.004971 | 0.2801 | 0.8989 | -#> |.....................| 9.884 | 1.172 | 0.03016 | 1.159 | -#> |.....................| 0.03007 | 0.7575 | 0.8710 | 1.217 | -#> |.....................| 1.072 | 1.075 |...........|...........| -#> | F| Forward Diff. | 79.23 | 2.386 | 0.06830 | 0.2424 | -#> |.....................| 0.02121 | -70.84 | -22.28 | -0.5289 | -#> |.....................| -2.713 | 1.149 | 11.82 | -11.86 | -#> |.....................| -9.567 | -10.47 |...........|...........| -#> | 6| 502.12413 | 0.9895 | -1.001 | -0.9114 | -0.8945 | -#> |.....................| -0.8456 | -0.8365 | -0.8812 | -0.8687 | -#> |.....................| -0.8905 | -0.8774 | -0.8794 | -0.8624 | -#> |.....................| -0.8662 | -0.8657 |...........|...........| -#> | U| 502.12413 | 92.14 | -5.304 | -0.9442 | -0.1066 | -#> |.....................| 2.291 | 1.178 | 0.03021 | 1.160 | -#> |.....................| 0.03007 | 0.7574 | 0.8695 | 1.220 | -#> |.....................| 1.073 | 1.077 |...........|...........| -#> | X| 502.12413 | 92.14 | 0.004969 | 0.2801 | 0.8989 | -#> |.....................| 9.884 | 1.178 | 0.03021 | 1.160 | -#> |.....................| 0.03007 | 0.7574 | 0.8695 | 1.220 | -#> |.....................| 1.073 | 1.077 |...........|...........| -#> | F| Forward Diff. | -77.28 | 2.252 | -0.2503 | 0.1427 | -#> |.....................| -0.3238 | -69.21 | -23.25 | 0.3943 | -#> |.....................| -2.493 | 1.092 | 10.79 | -11.67 | -#> |.....................| -9.485 | -10.25 |...........|...........| -#> | 7| 501.24651 | 1.000 | -1.001 | -0.9114 | -0.8945 | -#> |.....................| -0.8456 | -0.8257 | -0.8776 | -0.8688 | -#> |.....................| -0.8901 | -0.8775 | -0.8811 | -0.8606 | -#> |.....................| -0.8647 | -0.8641 |...........|...........| -#> | U| 501.24651 | 93.15 | -5.305 | -0.9441 | -0.1067 | -#> |.....................| 2.291 | 1.184 | 0.03026 | 1.160 | -#> |.....................| 0.03008 | 0.7573 | 0.8680 | 1.222 | -#> |.....................| 1.075 | 1.079 |...........|...........| -#> | X| 501.24651 | 93.15 | 0.004968 | 0.2801 | 0.8988 | -#> |.....................| 9.885 | 1.184 | 0.03026 | 1.160 | -#> |.....................| 0.03008 | 0.7573 | 0.8680 | 1.222 | -#> |.....................| 1.075 | 1.079 |...........|...........| -#> | F| Forward Diff. | 78.96 | 2.363 | 0.07229 | 0.2390 | -#> |.....................| 0.02239 | -67.81 | -20.97 | 0.1381 | -#> |.....................| -2.125 | 1.379 | 9.797 | -11.70 | -#> |.....................| -9.438 | -10.29 |...........|...........| -#> | 8| 500.35160 | 0.9896 | -1.002 | -0.9114 | -0.8945 | -#> |.....................| -0.8456 | -0.8148 | -0.8742 | -0.8688 | -#> |.....................| -0.8898 | -0.8778 | -0.8827 | -0.8587 | -#> |.....................| -0.8632 | -0.8625 |...........|...........| -#> | U| 500.3516 | 92.15 | -5.305 | -0.9441 | -0.1067 | -#> |.....................| 2.291 | 1.191 | 0.03032 | 1.159 | -#> |.....................| 0.03008 | 0.7571 | 0.8666 | 1.224 | -#> |.....................| 1.077 | 1.081 |...........|...........| -#> | X| 500.3516 | 92.15 | 0.004966 | 0.2801 | 0.8988 | -#> |.....................| 9.885 | 1.191 | 0.03032 | 1.159 | -#> |.....................| 0.03008 | 0.7571 | 0.8666 | 1.224 | -#> |.....................| 1.077 | 1.081 |...........|...........| -#> | F| Forward Diff. | -75.23 | 2.232 | -0.2459 | 0.1501 | -#> |.....................| -0.3253 | -66.87 | -22.19 | 0.4436 | -#> |.....................| -2.150 | 0.9434 | 9.182 | -11.49 | -#> |.....................| -9.350 | -10.07 |...........|...........| -#> | 9| 499.45361 | 1.000 | -1.002 | -0.9113 | -0.8946 | -#> |.....................| -0.8455 | -0.8036 | -0.8705 | -0.8689 | -#> |.....................| -0.8894 | -0.8779 | -0.8842 | -0.8568 | -#> |.....................| -0.8616 | -0.8608 |...........|...........| -#> | U| 499.45361 | 93.12 | -5.306 | -0.9441 | -0.1067 | -#> |.....................| 2.291 | 1.197 | 0.03037 | 1.159 | -#> |.....................| 0.03009 | 0.7570 | 0.8653 | 1.226 | -#> |.....................| 1.078 | 1.082 |...........|...........| -#> | X| 499.45361 | 93.12 | 0.004964 | 0.2801 | 0.8988 | -#> |.....................| 9.885 | 1.197 | 0.03037 | 1.159 | -#> |.....................| 0.03009 | 0.7570 | 0.8653 | 1.226 | -#> |.....................| 1.078 | 1.082 |...........|...........| -#> | F| Forward Diff. | 73.21 | 2.337 | 0.06584 | 0.2472 | -#> |.....................| 0.008903 | -65.96 | -20.21 | -0.3457 | -#> |.....................| -2.677 | 1.048 | 11.29 | -11.53 | -#> |.....................| -9.311 | -10.11 |...........|...........| -#> | 10| 498.59105 | 0.9896 | -1.003 | -0.9113 | -0.8946 | -#> |.....................| -0.8455 | -0.7924 | -0.8671 | -0.8688 | -#> |.....................| -0.8890 | -0.8781 | -0.8861 | -0.8548 | -#> |.....................| -0.8600 | -0.8591 |...........|...........| -#> | U| 498.59105 | 92.15 | -5.306 | -0.9441 | -0.1068 | -#> |.....................| 2.291 | 1.204 | 0.03042 | 1.159 | -#> |.....................| 0.03009 | 0.7568 | 0.8636 | 1.229 | -#> |.....................| 1.080 | 1.084 |...........|...........| -#> | X| 498.59105 | 92.15 | 0.004962 | 0.2801 | 0.8987 | -#> |.....................| 9.885 | 1.204 | 0.03042 | 1.159 | -#> |.....................| 0.03009 | 0.7568 | 0.8636 | 1.229 | -#> |.....................| 1.080 | 1.084 |...........|...........| -#> | F| Forward Diff. | -74.43 | 2.211 | -0.2431 | 0.1502 | -#> |.....................| -0.3305 | -64.40 | -21.08 | 0.5329 | -#> |.....................| -2.487 | 0.9319 | 8.926 | -11.33 | -#> |.....................| -9.217 | -9.888 |...........|...........| -#> | 11| 497.71590 | 1.000 | -1.003 | -0.9113 | -0.8946 | -#> |.....................| -0.8455 | -0.7811 | -0.8634 | -0.8689 | -#> |.....................| -0.8885 | -0.8783 | -0.8877 | -0.8529 | -#> |.....................| -0.8584 | -0.8573 |...........|...........| -#> | U| 497.7159 | 93.11 | -5.306 | -0.9441 | -0.1068 | -#> |.....................| 2.291 | 1.210 | 0.03048 | 1.159 | -#> |.....................| 0.03010 | 0.7567 | 0.8622 | 1.231 | -#> |.....................| 1.082 | 1.086 |...........|...........| -#> | X| 497.7159 | 93.11 | 0.004960 | 0.2801 | 0.8987 | -#> |.....................| 9.886 | 1.210 | 0.03048 | 1.159 | -#> |.....................| 0.03010 | 0.7567 | 0.8622 | 1.231 | -#> |.....................| 1.082 | 1.086 |...........|...........| -#> | F| Forward Diff. | 71.79 | 2.312 | 0.07434 | 0.2557 | -#> |.....................| 0.006614 | -63.04 | -18.95 | 0.3164 | -#> |.....................| -2.117 | 1.342 | 9.274 | -11.35 | -#> |.....................| -9.172 | -9.924 |...........|...........| -#> | 12| 496.86264 | 0.9898 | -1.003 | -0.9113 | -0.8947 | -#> |.....................| -0.8455 | -0.7696 | -0.8599 | -0.8690 | -#> |.....................| -0.8881 | -0.8785 | -0.8894 | -0.8508 | -#> |.....................| -0.8567 | -0.8555 |...........|...........| -#> | U| 496.86264 | 92.17 | -5.307 | -0.9441 | -0.1068 | -#> |.....................| 2.291 | 1.217 | 0.03053 | 1.159 | -#> |.....................| 0.03011 | 0.7565 | 0.8607 | 1.234 | -#> |.....................| 1.084 | 1.088 |...........|...........| -#> | X| 496.86264 | 92.17 | 0.004958 | 0.2801 | 0.8987 | -#> |.....................| 9.886 | 1.217 | 0.03053 | 1.159 | -#> |.....................| 0.03011 | 0.7565 | 0.8607 | 1.234 | -#> |.....................| 1.084 | 1.088 |...........|...........| -#> | F| Forward Diff. | -71.54 | 2.190 | -0.2371 | 0.1482 | -#> |.....................| -0.3369 | -61.67 | -19.90 | 0.9419 | -#> |.....................| -2.139 | 1.041 | 7.036 | -11.13 | -#> |.....................| -9.064 | -9.692 |...........|...........| -#> | 13| 495.99097 | 0.9997 | -1.004 | -0.9113 | -0.8947 | -#> |.....................| -0.8454 | -0.7580 | -0.8562 | -0.8692 | -#> |.....................| -0.8877 | -0.8787 | -0.8907 | -0.8487 | -#> |.....................| -0.8550 | -0.8537 |...........|...........| -#> | U| 495.99097 | 93.09 | -5.307 | -0.9441 | -0.1069 | -#> |.....................| 2.291 | 1.224 | 0.03059 | 1.159 | -#> |.....................| 0.03011 | 0.7564 | 0.8596 | 1.236 | -#> |.....................| 1.085 | 1.090 |...........|...........| -#> | X| 495.99097 | 93.09 | 0.004956 | 0.2801 | 0.8987 | -#> |.....................| 9.886 | 1.224 | 0.03059 | 1.159 | -#> |.....................| 0.03011 | 0.7564 | 0.8596 | 1.236 | -#> |.....................| 1.085 | 1.090 |...........|...........| -#> | F| Forward Diff. | 67.48 | 2.282 | 0.05510 | 0.2442 | -#> |.....................| -0.01700 | -60.62 | -17.93 | 0.4372 | -#> |.....................| -2.100 | 1.212 | 9.042 | -11.17 | -#> |.....................| -9.025 | -9.723 |...........|...........| -#> | 14| 495.15472 | 0.9899 | -1.004 | -0.9113 | -0.8948 | -#> |.....................| -0.8454 | -0.7463 | -0.8527 | -0.8693 | -#> |.....................| -0.8873 | -0.8789 | -0.8924 | -0.8465 | -#> |.....................| -0.8533 | -0.8518 |...........|...........| -#> | U| 495.15472 | 92.18 | -5.308 | -0.9441 | -0.1069 | -#> |.....................| 2.291 | 1.231 | 0.03064 | 1.159 | -#> |.....................| 0.03012 | 0.7562 | 0.8581 | 1.239 | -#> |.....................| 1.087 | 1.092 |...........|...........| -#> | X| 495.15472 | 92.18 | 0.004954 | 0.2801 | 0.8986 | -#> |.....................| 9.886 | 1.231 | 0.03064 | 1.159 | -#> |.....................| 0.03012 | 0.7562 | 0.8581 | 1.239 | -#> |.....................| 1.087 | 1.092 |...........|...........| -#> | F| Forward Diff. | -68.93 | 2.171 | -0.2257 | 0.1488 | -#> |.....................| -0.3348 | -59.34 | -18.81 | 1.070 | -#> |.....................| -2.082 | 1.016 | 8.208 | -10.96 | -#> |.....................| -8.930 | -9.498 |...........|...........| -#> | 15| 494.30065 | 0.9995 | -1.005 | -0.9112 | -0.8948 | -#> |.....................| -0.8453 | -0.7344 | -0.8490 | -0.8695 | -#> |.....................| -0.8869 | -0.8792 | -0.8941 | -0.8443 | -#> |.....................| -0.8515 | -0.8499 |...........|...........| -#> | U| 494.30065 | 93.07 | -5.308 | -0.9440 | -0.1069 | -#> |.....................| 2.291 | 1.237 | 0.03069 | 1.159 | -#> |.....................| 0.03013 | 0.7561 | 0.8567 | 1.242 | -#> |.....................| 1.089 | 1.094 |...........|...........| -#> | X| 494.30065 | 93.07 | 0.004951 | 0.2801 | 0.8986 | -#> |.....................| 9.887 | 1.237 | 0.03069 | 1.159 | -#> |.....................| 0.03013 | 0.7561 | 0.8567 | 1.242 | -#> |.....................| 1.089 | 1.094 |...........|...........| -#> | F| Forward Diff. | 65.20 | 2.260 | 0.06851 | 0.2416 | -#> |.....................| -0.02143 | -58.42 | -17.03 | 0.3665 | -#> |.....................| -2.202 | 1.112 | 7.377 | -10.96 | -#> |.....................| -8.866 | -9.510 |...........|...........| -#> | 16| 493.48608 | 0.9901 | -1.005 | -0.9112 | -0.8948 | -#> |.....................| -0.8453 | -0.7225 | -0.8455 | -0.8696 | -#> |.....................| -0.8865 | -0.8794 | -0.8956 | -0.8421 | -#> |.....................| -0.8496 | -0.8479 |...........|...........| -#> | U| 493.48608 | 92.19 | -5.309 | -0.9440 | -0.1070 | -#> |.....................| 2.291 | 1.244 | 0.03075 | 1.159 | -#> |.....................| 0.03013 | 0.7559 | 0.8553 | 1.244 | -#> |.....................| 1.091 | 1.096 |...........|...........| -#> | X| 493.48608 | 92.19 | 0.004949 | 0.2801 | 0.8985 | -#> |.....................| 9.887 | 1.244 | 0.03075 | 1.159 | -#> |.....................| 0.03013 | 0.7559 | 0.8553 | 1.244 | -#> |.....................| 1.091 | 1.096 |...........|...........| -#> | F| Forward Diff. | -66.94 | 2.152 | -0.2367 | 0.1452 | -#> |.....................| -0.3412 | -57.13 | -17.84 | 1.057 | -#> |.....................| -2.129 | 0.9540 | 6.557 | -10.77 | -#> |.....................| -8.770 | -9.285 |...........|...........| -#> | 17| 492.64670 | 0.9993 | -1.006 | -0.9112 | -0.8949 | -#> |.....................| -0.8453 | -0.7105 | -0.8419 | -0.8698 | -#> |.....................| -0.8860 | -0.8796 | -0.8969 | -0.8398 | -#> |.....................| -0.8478 | -0.8460 |...........|...........| -#> | U| 492.6467 | 93.06 | -5.309 | -0.9440 | -0.1070 | -#> |.....................| 2.291 | 1.251 | 0.03080 | 1.159 | -#> |.....................| 0.03014 | 0.7557 | 0.8542 | 1.247 | -#> |.....................| 1.093 | 1.098 |...........|...........| -#> | X| 492.6467 | 93.06 | 0.004947 | 0.2801 | 0.8985 | -#> |.....................| 9.888 | 1.251 | 0.03080 | 1.159 | -#> |.....................| 0.03014 | 0.7557 | 0.8542 | 1.247 | -#> |.....................| 1.093 | 1.098 |...........|...........| -#> | F| Forward Diff. | 62.51 | 2.244 | 0.07930 | 0.2506 | -#> |.....................| -0.02305 | -56.21 | -16.10 | 0.4420 | -#> |.....................| -2.202 | 1.071 | 7.160 | -10.75 | -#> |.....................| -8.705 | -9.292 |...........|...........| -#> | 18| 491.85024 | 0.9902 | -1.006 | -0.9112 | -0.8949 | -#> |.....................| -0.8453 | -0.6983 | -0.8384 | -0.8699 | -#> |.....................| -0.8855 | -0.8798 | -0.8984 | -0.8374 | -#> |.....................| -0.8459 | -0.8439 |...........|...........| -#> | U| 491.85024 | 92.21 | -5.310 | -0.9440 | -0.1071 | -#> |.....................| 2.291 | 1.258 | 0.03085 | 1.159 | -#> |.....................| 0.03015 | 0.7556 | 0.8529 | 1.250 | -#> |.....................| 1.095 | 1.100 |...........|...........| -#> | X| 491.85024 | 92.21 | 0.004944 | 0.2801 | 0.8985 | -#> |.....................| 9.888 | 1.258 | 0.03085 | 1.159 | -#> |.....................| 0.03015 | 0.7556 | 0.8529 | 1.250 | -#> |.....................| 1.095 | 1.100 |...........|...........| -#> | F| Forward Diff. | -64.39 | 2.132 | -0.2231 | 0.1507 | -#> |.....................| -0.3455 | -54.91 | -16.84 | 1.107 | -#> |.....................| -2.130 | 0.9153 | 6.361 | -10.56 | -#> |.....................| -8.604 | -9.065 |...........|...........| -#> | 19| 491.03181 | 0.9992 | -1.007 | -0.9112 | -0.8950 | -#> |.....................| -0.8452 | -0.6860 | -0.8347 | -0.8702 | -#> |.....................| -0.8850 | -0.8800 | -0.8997 | -0.8350 | -#> |.....................| -0.8439 | -0.8419 |...........|...........| -#> | U| 491.03181 | 93.04 | -5.310 | -0.9440 | -0.1071 | -#> |.....................| 2.291 | 1.265 | 0.03091 | 1.159 | -#> |.....................| 0.03015 | 0.7554 | 0.8517 | 1.253 | -#> |.....................| 1.097 | 1.103 |...........|...........| -#> | X| 491.03181 | 93.04 | 0.004942 | 0.2801 | 0.8984 | -#> |.....................| 9.888 | 1.265 | 0.03091 | 1.159 | -#> |.....................| 0.03015 | 0.7554 | 0.8517 | 1.253 | -#> |.....................| 1.097 | 1.103 |...........|...........| -#> | F| Forward Diff. | 59.97 | 2.217 | 0.06954 | 0.2512 | -#> |.....................| -0.03854 | -54.10 | -15.21 | 0.3955 | -#> |.....................| -2.336 | 1.047 | 8.162 | -10.81 | -#> |.....................| -8.706 | -9.233 |...........|...........| -#> | 20| 490.24998 | 0.9904 | -1.007 | -0.9112 | -0.8950 | -#> |.....................| -0.8452 | -0.6737 | -0.8313 | -0.8703 | -#> |.....................| -0.8845 | -0.8803 | -0.9015 | -0.8325 | -#> |.....................| -0.8419 | -0.8397 |...........|...........| -#> | U| 490.24998 | 92.22 | -5.311 | -0.9440 | -0.1072 | -#> |.....................| 2.291 | 1.273 | 0.03096 | 1.159 | -#> |.....................| 0.03016 | 0.7552 | 0.8502 | 1.256 | -#> |.....................| 1.099 | 1.105 |...........|...........| -#> | X| 490.24998 | 92.22 | 0.004939 | 0.2801 | 0.8984 | -#> |.....................| 9.889 | 1.273 | 0.03096 | 1.159 | -#> |.....................| 0.03016 | 0.7552 | 0.8502 | 1.256 | -#> |.....................| 1.099 | 1.105 |...........|...........| -#> | F| Forward Diff. | -61.40 | 2.114 | -0.2172 | 0.1580 | -#> |.....................| -0.3477 | -53.15 | -16.02 | 0.7982 | -#> |.....................| -2.483 | 0.7215 | 9.240 | -10.34 | -#> |.....................| -8.435 | -8.843 |...........|...........| -#> | 21| 489.45580 | 0.9991 | -1.008 | -0.9111 | -0.8951 | -#> |.....................| -0.8451 | -0.6614 | -0.8278 | -0.8705 | -#> |.....................| -0.8839 | -0.8804 | -0.9038 | -0.8300 | -#> |.....................| -0.8398 | -0.8376 |...........|...........| -#> | U| 489.4558 | 93.03 | -5.311 | -0.9439 | -0.1072 | -#> |.....................| 2.291 | 1.280 | 0.03101 | 1.159 | -#> |.....................| 0.03017 | 0.7551 | 0.8482 | 1.259 | -#> |.....................| 1.102 | 1.107 |...........|...........| -#> | X| 489.4558 | 93.03 | 0.004937 | 0.2801 | 0.8983 | -#> |.....................| 9.889 | 1.280 | 0.03101 | 1.159 | -#> |.....................| 0.03017 | 0.7551 | 0.8482 | 1.259 | -#> |.....................| 1.102 | 1.107 |...........|...........| -#> | F| Forward Diff. | 58.20 | 2.191 | 0.07193 | 0.2543 | -#> |.....................| -0.04201 | -51.69 | -14.22 | 0.6968 | -#> |.....................| -2.088 | 1.024 | 8.024 | -10.34 | -#> |.....................| -8.364 | -8.845 |...........|...........| -#> | 22| 488.71859 | 0.9903 | -1.008 | -0.9111 | -0.8951 | -#> |.....................| -0.8451 | -0.6491 | -0.8245 | -0.8707 | -#> |.....................| -0.8833 | -0.8807 | -0.9059 | -0.8275 | -#> |.....................| -0.8378 | -0.8354 |...........|...........| -#> | U| 488.71859 | 92.21 | -5.312 | -0.9439 | -0.1073 | -#> |.....................| 2.291 | 1.287 | 0.03106 | 1.158 | -#> |.....................| 0.03018 | 0.7549 | 0.8463 | 1.262 | -#> |.....................| 1.104 | 1.110 |...........|...........| -#> | X| 488.71859 | 92.21 | 0.004934 | 0.2801 | 0.8983 | -#> |.....................| 9.890 | 1.287 | 0.03106 | 1.158 | -#> |.....................| 0.03018 | 0.7549 | 0.8463 | 1.262 | -#> |.....................| 1.104 | 1.110 |...........|...........| -#> | F| Forward Diff. | -62.72 | 2.087 | -0.2158 | 0.1536 | -#> |.....................| -0.3560 | -50.59 | -14.96 | 1.289 | -#> |.....................| -2.066 | 0.8753 | 7.259 | -10.12 | -#> |.....................| -8.247 | -8.604 |...........|...........| -#> | 23| 487.91801 | 0.9987 | -1.009 | -0.9111 | -0.8952 | -#> |.....................| -0.8450 | -0.6366 | -0.8210 | -0.8711 | -#> |.....................| -0.8828 | -0.8809 | -0.9078 | -0.8248 | -#> |.....................| -0.8356 | -0.8332 |...........|...........| -#> | U| 487.91801 | 93.00 | -5.312 | -0.9439 | -0.1073 | -#> |.....................| 2.292 | 1.294 | 0.03112 | 1.158 | -#> |.....................| 0.03019 | 0.7547 | 0.8446 | 1.265 | -#> |.....................| 1.106 | 1.112 |...........|...........| -#> | X| 487.91801 | 93.00 | 0.004931 | 0.2801 | 0.8982 | -#> |.....................| 9.890 | 1.294 | 0.03112 | 1.158 | -#> |.....................| 0.03019 | 0.7547 | 0.8446 | 1.265 | -#> |.....................| 1.106 | 1.112 |...........|...........| -#> | F| Forward Diff. | 52.73 | 2.162 | 0.07610 | 0.2481 | -#> |.....................| -0.05835 | -50.28 | -13.63 | 0.1991 | -#> |.....................| -2.681 | 0.6961 | 9.479 | -10.12 | -#> |.....................| -8.180 | -8.607 |...........|...........| -#> | 24| 487.19380 | 0.9906 | -1.009 | -0.9111 | -0.8952 | -#> |.....................| -0.8450 | -0.6240 | -0.8177 | -0.8712 | -#> |.....................| -0.8820 | -0.8811 | -0.9103 | -0.8222 | -#> |.....................| -0.8335 | -0.8310 |...........|...........| -#> | U| 487.1938 | 92.24 | -5.313 | -0.9439 | -0.1074 | -#> |.....................| 2.292 | 1.301 | 0.03116 | 1.158 | -#> |.....................| 0.03020 | 0.7546 | 0.8424 | 1.269 | -#> |.....................| 1.108 | 1.114 |...........|...........| -#> | X| 487.1938 | 92.24 | 0.004929 | 0.2801 | 0.8982 | -#> |.....................| 9.891 | 1.301 | 0.03116 | 1.158 | -#> |.....................| 0.03020 | 0.7546 | 0.8424 | 1.269 | -#> |.....................| 1.108 | 1.114 |...........|...........| -#> | F| Forward Diff. | -58.70 | 2.065 | -0.2024 | 0.1592 | -#> |.....................| -0.3563 | -48.58 | -14.05 | 1.280 | -#> |.....................| -2.114 | 0.8980 | 5.535 | -9.882 | -#> |.....................| -8.046 | -8.364 |...........|...........| -#> | 25| 486.45861 | 0.9990 | -1.010 | -0.9111 | -0.8953 | -#> |.....................| -0.8449 | -0.6115 | -0.8144 | -0.8715 | -#> |.....................| -0.8813 | -0.8813 | -0.9121 | -0.8195 | -#> |.....................| -0.8313 | -0.8287 |...........|...........| -#> | U| 486.45861 | 93.03 | -5.313 | -0.9439 | -0.1074 | -#> |.....................| 2.292 | 1.309 | 0.03121 | 1.158 | -#> |.....................| 0.03021 | 0.7545 | 0.8409 | 1.272 | -#> |.....................| 1.111 | 1.117 |...........|...........| -#> | X| 486.45861 | 93.03 | 0.004926 | 0.2801 | 0.8981 | -#> |.....................| 9.892 | 1.309 | 0.03121 | 1.158 | -#> |.....................| 0.03021 | 0.7545 | 0.8409 | 1.272 | -#> |.....................| 1.111 | 1.117 |...........|...........| -#> | F| Forward Diff. | 56.64 | 2.141 | 0.09518 | 0.2574 | -#> |.....................| -0.04938 | -48.45 | -12.81 | 0.1110 | -#> |.....................| -2.819 | 0.7463 | 7.804 | -9.858 | -#> |.....................| -7.976 | -8.366 |...........|...........| -#> | 26| 485.70463 | 0.9912 | -1.011 | -0.9111 | -0.8954 | -#> |.....................| -0.8448 | -0.5987 | -0.8113 | -0.8717 | -#> |.....................| -0.8805 | -0.8815 | -0.9139 | -0.8166 | -#> |.....................| -0.8290 | -0.8264 |...........|...........| -#> | U| 485.70463 | 92.30 | -5.314 | -0.9439 | -0.1075 | -#> |.....................| 2.292 | 1.316 | 0.03126 | 1.158 | -#> |.....................| 0.03022 | 0.7543 | 0.8393 | 1.275 | -#> |.....................| 1.113 | 1.119 |...........|...........| -#> | X| 485.70463 | 92.30 | 0.004923 | 0.2801 | 0.8981 | -#> |.....................| 9.892 | 1.316 | 0.03126 | 1.158 | -#> |.....................| 0.03022 | 0.7543 | 0.8393 | 1.275 | -#> |.....................| 1.113 | 1.119 |...........|...........| -#> | F| Forward Diff. | -49.75 | 2.049 | -0.1896 | 0.1657 | -#> |.....................| -0.3394 | -47.06 | -13.27 | 0.8968 | -#> |.....................| -2.558 | 0.5259 | 7.006 | -9.655 | -#> |.....................| -7.860 | -8.128 |...........|...........| -#> | 27| 485.03383 | 0.9993 | -1.011 | -0.9111 | -0.8954 | -#> |.....................| -0.8447 | -0.5860 | -0.8081 | -0.8719 | -#> |.....................| -0.8796 | -0.8816 | -0.9160 | -0.8138 | -#> |.....................| -0.8267 | -0.8240 |...........|...........| -#> | U| 485.03383 | 93.05 | -5.315 | -0.9439 | -0.1076 | -#> |.....................| 2.292 | 1.323 | 0.03131 | 1.158 | -#> |.....................| 0.03024 | 0.7542 | 0.8375 | 1.279 | -#> |.....................| 1.116 | 1.122 |...........|...........| -#> | X| 485.03383 | 93.05 | 0.004920 | 0.2801 | 0.8980 | -#> |.....................| 9.893 | 1.323 | 0.03131 | 1.158 | -#> |.....................| 0.03024 | 0.7542 | 0.8375 | 1.279 | -#> |.....................| 1.116 | 1.122 |...........|...........| -#> | F| Forward Diff. | 59.36 | 2.117 | 0.1128 | 0.2587 | -#> |.....................| -0.03694 | -45.49 | -11.65 | 0.8714 | -#> |.....................| -2.196 | 0.9711 | 7.208 | -9.629 | -#> |.....................| -7.785 | -8.123 |...........|...........| -#> | 28| 484.30050 | 0.9913 | -1.012 | -0.9111 | -0.8955 | -#> |.....................| -0.8447 | -0.5733 | -0.8052 | -0.8723 | -#> |.....................| -0.8788 | -0.8818 | -0.9181 | -0.8109 | -#> |.....................| -0.8243 | -0.8216 |...........|...........| -#> | U| 484.3005 | 92.30 | -5.315 | -0.9439 | -0.1077 | -#> |.....................| 2.292 | 1.331 | 0.03135 | 1.157 | -#> |.....................| 0.03025 | 0.7541 | 0.8357 | 1.282 | -#> |.....................| 1.118 | 1.124 |...........|...........| -#> | X| 484.3005 | 92.30 | 0.004916 | 0.2801 | 0.8979 | -#> |.....................| 9.894 | 1.331 | 0.03135 | 1.157 | -#> |.....................| 0.03025 | 0.7541 | 0.8357 | 1.282 | -#> |.....................| 1.118 | 1.124 |...........|...........| -#> | F| Forward Diff. | -49.13 | 2.024 | -0.1788 | 0.1668 | -#> |.....................| -0.3408 | -44.74 | -12.30 | 1.348 | -#> |.....................| -2.137 | 0.7757 | 5.010 | -9.393 | -#> |.....................| -7.651 | -7.866 |...........|...........| -#> | 29| 483.61888 | 0.9988 | -1.013 | -0.9110 | -0.8956 | -#> |.....................| -0.8446 | -0.5603 | -0.8022 | -0.8729 | -#> |.....................| -0.8781 | -0.8821 | -0.9194 | -0.8078 | -#> |.....................| -0.8218 | -0.8191 |...........|...........| -#> | U| 483.61888 | 93.00 | -5.316 | -0.9438 | -0.1077 | -#> |.....................| 2.292 | 1.338 | 0.03140 | 1.157 | -#> |.....................| 0.03026 | 0.7539 | 0.8345 | 1.286 | -#> |.....................| 1.121 | 1.127 |...........|...........| -#> | X| 483.61888 | 93.00 | 0.004913 | 0.2801 | 0.8979 | -#> |.....................| 9.895 | 1.338 | 0.03140 | 1.157 | -#> |.....................| 0.03026 | 0.7539 | 0.8345 | 1.286 | -#> |.....................| 1.121 | 1.127 |...........|...........| -#> | F| Forward Diff. | 51.77 | 2.082 | 0.08733 | 0.2462 | -#> |.....................| -0.07383 | -44.60 | -11.22 | 0.3023 | -#> |.....................| -2.722 | 0.5489 | 8.672 | -9.371 | -#> |.....................| -7.562 | -7.848 |...........|...........| -#> | 30| 482.91165 | 0.9915 | -1.013 | -0.9110 | -0.8957 | -#> |.....................| -0.8445 | -0.5473 | -0.7995 | -0.8732 | -#> |.....................| -0.8770 | -0.8822 | -0.9219 | -0.8047 | -#> |.....................| -0.8192 | -0.8165 |...........|...........| -#> | U| 482.91165 | 92.33 | -5.317 | -0.9438 | -0.1078 | -#> |.....................| 2.292 | 1.346 | 0.03144 | 1.157 | -#> |.....................| 0.03027 | 0.7538 | 0.8323 | 1.290 | -#> |.....................| 1.124 | 1.130 |...........|...........| -#> | X| 482.91165 | 92.33 | 0.004909 | 0.2801 | 0.8978 | -#> |.....................| 9.895 | 1.346 | 0.03144 | 1.157 | -#> |.....................| 0.03027 | 0.7538 | 0.8323 | 1.290 | -#> |.....................| 1.124 | 1.130 |...........|...........| -#> | F| Forward Diff. | -45.50 | 2.003 | -0.1660 | 0.1702 | -#> |.....................| -0.3374 | -43.33 | -11.63 | 0.9930 | -#> |.....................| -2.511 | 0.4656 | 7.949 | -9.128 | -#> |.....................| -7.427 | -7.608 |...........|...........| -#> | 31| 482.28997 | 0.9991 | -1.014 | -0.9110 | -0.8957 | -#> |.....................| -0.8444 | -0.5346 | -0.7968 | -0.8735 | -#> |.....................| -0.8759 | -0.8822 | -0.9253 | -0.8017 | -#> |.....................| -0.8168 | -0.8141 |...........|...........| -#> | U| 482.28997 | 93.03 | -5.317 | -0.9438 | -0.1079 | -#> |.....................| 2.292 | 1.353 | 0.03148 | 1.157 | -#> |.....................| 0.03029 | 0.7538 | 0.8294 | 1.293 | -#> |.....................| 1.126 | 1.132 |...........|...........| -#> | X| 482.28997 | 93.03 | 0.004906 | 0.2801 | 0.8977 | -#> |.....................| 9.896 | 1.353 | 0.03148 | 1.157 | -#> |.....................| 0.03029 | 0.7538 | 0.8294 | 1.293 | -#> |.....................| 1.126 | 1.132 |...........|...........| -#> | F| Forward Diff. | 55.95 | 2.054 | 0.1106 | 0.2465 | -#> |.....................| -0.05340 | -42.18 | -10.21 | 0.8261 | -#> |.....................| -2.234 | 0.9104 | 5.096 | -9.114 | -#> |.....................| -7.334 | -7.590 |...........|...........| -#> | 32| 481.60550 | 0.9915 | -1.015 | -0.9110 | -0.8958 | -#> |.....................| -0.8443 | -0.5217 | -0.7945 | -0.8740 | -#> |.....................| -0.8749 | -0.8824 | -0.9274 | -0.7984 | -#> |.....................| -0.8142 | -0.8115 |...........|...........| -#> | U| 481.6055 | 92.33 | -5.318 | -0.9438 | -0.1080 | -#> |.....................| 2.292 | 1.361 | 0.03151 | 1.156 | -#> |.....................| 0.03031 | 0.7536 | 0.8276 | 1.297 | -#> |.....................| 1.129 | 1.135 |...........|...........| -#> | X| 481.6055 | 92.33 | 0.004902 | 0.2801 | 0.8977 | -#> |.....................| 9.897 | 1.361 | 0.03151 | 1.156 | -#> |.....................| 0.03031 | 0.7536 | 0.8276 | 1.297 | -#> |.....................| 1.129 | 1.135 |...........|...........| -#> | F| Forward Diff. | -45.82 | 1.973 | -0.1624 | 0.1674 | -#> |.....................| -0.3387 | -41.15 | -10.74 | 1.410 | -#> |.....................| -2.130 | 0.6088 | 4.422 | -8.852 | -#> |.....................| -7.186 | -7.335 |...........|...........| -#> | 33| 480.97343 | 0.9986 | -1.016 | -0.9110 | -0.8959 | -#> |.....................| -0.8442 | -0.5084 | -0.7922 | -0.8748 | -#> |.....................| -0.8740 | -0.8826 | -0.9278 | -0.7950 | -#> |.....................| -0.8114 | -0.8088 |...........|...........| -#> | U| 480.97343 | 92.98 | -5.319 | -0.9438 | -0.1081 | -#> |.....................| 2.292 | 1.368 | 0.03155 | 1.156 | -#> |.....................| 0.03032 | 0.7534 | 0.8272 | 1.301 | -#> |.....................| 1.132 | 1.138 |...........|...........| -#> | X| 480.97343 | 92.98 | 0.004897 | 0.2801 | 0.8976 | -#> |.....................| 9.898 | 1.368 | 0.03155 | 1.156 | -#> |.....................| 0.03032 | 0.7534 | 0.8272 | 1.301 | -#> |.....................| 1.132 | 1.138 |...........|...........| -#> | F| Forward Diff. | 47.76 | 2.024 | 0.09167 | 0.2404 | -#> |.....................| -0.07393 | -40.22 | -9.470 | 1.031 | -#> |.....................| -2.098 | 0.8752 | 6.346 | -8.797 | -#> |.....................| -7.089 | -7.296 |...........|...........| -#> | 34| 480.33235 | 0.9916 | -1.017 | -0.9110 | -0.8960 | -#> |.....................| -0.8441 | -0.4952 | -0.7903 | -0.8757 | -#> |.....................| -0.8731 | -0.8830 | -0.9294 | -0.7914 | -#> |.....................| -0.8086 | -0.8060 |...........|...........| -#> | U| 480.33235 | 92.33 | -5.320 | -0.9438 | -0.1082 | -#> |.....................| 2.292 | 1.376 | 0.03158 | 1.155 | -#> |.....................| 0.03033 | 0.7532 | 0.8258 | 1.306 | -#> |.....................| 1.135 | 1.141 |...........|...........| -#> | X| 480.33235 | 92.33 | 0.004893 | 0.2801 | 0.8975 | -#> |.....................| 9.899 | 1.376 | 0.03158 | 1.155 | -#> |.....................| 0.03033 | 0.7532 | 0.8258 | 1.306 | -#> |.....................| 1.135 | 1.141 |...........|...........| -#> | F| Forward Diff. | -44.82 | 1.956 | -0.1640 | 0.1653 | -#> |.....................| -0.3374 | -39.36 | -9.982 | 1.432 | -#> |.....................| -2.136 | 0.6770 | 5.747 | -8.552 | -#> |.....................| -6.943 | -7.038 |...........|...........| -#> | 35| 479.71253 | 0.9984 | -1.018 | -0.9110 | -0.8961 | -#> |.....................| -0.8439 | -0.4821 | -0.7885 | -0.8768 | -#> |.....................| -0.8721 | -0.8833 | -0.9319 | -0.7879 | -#> |.....................| -0.8057 | -0.8033 |...........|...........| -#> | U| 479.71253 | 92.97 | -5.321 | -0.9438 | -0.1083 | -#> |.....................| 2.293 | 1.384 | 0.03160 | 1.155 | -#> |.....................| 0.03035 | 0.7529 | 0.8236 | 1.310 | -#> |.....................| 1.138 | 1.144 |...........|...........| -#> | X| 479.71253 | 92.97 | 0.004888 | 0.2801 | 0.8974 | -#> |.....................| 9.901 | 1.384 | 0.03160 | 1.155 | -#> |.....................| 0.03035 | 0.7529 | 0.8236 | 1.310 | -#> |.....................| 1.138 | 1.144 |...........|...........| -#> | F| Forward Diff. | 45.27 | 2.001 | 0.09802 | 0.2411 | -#> |.....................| -0.07361 | -39.48 | -9.147 | 0.2467 | -#> |.....................| -2.886 | 0.4583 | 7.836 | -8.475 | -#> |.....................| -6.831 | -7.001 |...........|...........| -#> | 36| 479.08241 | 0.9920 | -1.019 | -0.9110 | -0.8962 | -#> |.....................| -0.8438 | -0.4691 | -0.7871 | -0.8771 | -#> |.....................| -0.8704 | -0.8833 | -0.9359 | -0.7844 | -#> |.....................| -0.8029 | -0.8006 |...........|...........| -#> | U| 479.08241 | 92.37 | -5.322 | -0.9438 | -0.1084 | -#> |.....................| 2.293 | 1.391 | 0.03163 | 1.155 | -#> |.....................| 0.03037 | 0.7529 | 0.8201 | 1.314 | -#> |.....................| 1.141 | 1.147 |...........|...........| -#> | X| 479.08241 | 92.37 | 0.004883 | 0.2801 | 0.8973 | -#> |.....................| 9.902 | 1.391 | 0.03163 | 1.155 | -#> |.....................| 0.03037 | 0.7529 | 0.8201 | 1.314 | -#> |.....................| 1.141 | 1.147 |...........|...........| -#> | F| Forward Diff. | -39.48 | 1.926 | -0.1378 | 0.1752 | -#> |.....................| -0.3206 | -38.45 | -9.498 | 0.8453 | -#> |.....................| -2.699 | 0.3871 | 5.589 | -8.242 | -#> |.....................| -6.674 | -6.762 |...........|...........| -#> | 37| 478.53604 | 0.9990 | -1.019 | -0.9110 | -0.8964 | -#> |.....................| -0.8437 | -0.4561 | -0.7854 | -0.8772 | -#> |.....................| -0.8684 | -0.8832 | -0.9392 | -0.7811 | -#> |.....................| -0.8002 | -0.7981 |...........|...........| -#> | U| 478.53604 | 93.02 | -5.323 | -0.9438 | -0.1085 | -#> |.....................| 2.293 | 1.399 | 0.03165 | 1.155 | -#> |.....................| 0.03040 | 0.7530 | 0.8172 | 1.318 | -#> |.....................| 1.144 | 1.150 |...........|...........| -#> | X| 478.53604 | 93.02 | 0.004879 | 0.2801 | 0.8972 | -#> |.....................| 9.903 | 1.399 | 0.03165 | 1.155 | -#> |.....................| 0.03040 | 0.7530 | 0.8172 | 1.318 | -#> |.....................| 1.144 | 1.150 |...........|...........| -#> | F| Forward Diff. | 52.06 | 1.969 | 0.1359 | 0.2508 | -#> |.....................| -0.04337 | -37.95 | -8.435 | 0.2680 | -#> |.....................| -2.930 | 0.5186 | 5.955 | -8.188 | -#> |.....................| -6.576 | -6.741 |...........|...........| -#> | 38| 477.90297 | 0.9924 | -1.021 | -0.9111 | -0.8965 | -#> |.....................| -0.8436 | -0.4428 | -0.7846 | -0.8771 | -#> |.....................| -0.8659 | -0.8830 | -0.9416 | -0.7776 | -#> |.....................| -0.7975 | -0.7955 |...........|...........| -#> | U| 477.90297 | 92.41 | -5.324 | -0.9439 | -0.1086 | -#> |.....................| 2.293 | 1.406 | 0.03166 | 1.155 | -#> |.....................| 0.03044 | 0.7531 | 0.8151 | 1.323 | -#> |.....................| 1.147 | 1.152 |...........|...........| -#> | X| 477.90297 | 92.41 | 0.004873 | 0.2801 | 0.8971 | -#> |.....................| 9.904 | 1.406 | 0.03166 | 1.155 | -#> |.....................| 0.03044 | 0.7531 | 0.8151 | 1.323 | -#> |.....................| 1.147 | 1.152 |...........|...........| -#> | F| Forward Diff. | -35.48 | 1.900 | -0.1171 | 0.1805 | -#> |.....................| -0.3013 | -36.12 | -8.554 | 1.521 | -#> |.....................| -2.082 | 0.5139 | 5.057 | -7.934 | -#> |.....................| -6.421 | -6.501 |...........|...........| -#> | 39| 477.39487 | 0.9991 | -1.022 | -0.9111 | -0.8966 | -#> |.....................| -0.8434 | -0.4296 | -0.7836 | -0.8780 | -#> |.....................| -0.8642 | -0.8831 | -0.9436 | -0.7740 | -#> |.....................| -0.7946 | -0.7928 |...........|...........| -#> | U| 477.39487 | 93.04 | -5.325 | -0.9439 | -0.1088 | -#> |.....................| 2.293 | 1.414 | 0.03168 | 1.154 | -#> |.....................| 0.03047 | 0.7531 | 0.8134 | 1.327 | -#> |.....................| 1.150 | 1.155 |...........|...........| -#> | X| 477.39487 | 93.04 | 0.004868 | 0.2801 | 0.8969 | -#> |.....................| 9.906 | 1.414 | 0.03168 | 1.154 | -#> |.....................| 0.03047 | 0.7531 | 0.8134 | 1.327 | -#> |.....................| 1.150 | 1.155 |...........|...........| -#> | F| Forward Diff. | 53.22 | 1.947 | 0.1564 | 0.2562 | -#> |.....................| -0.02756 | -35.38 | -7.440 | 1.129 | -#> |.....................| -2.109 | 0.8531 | 5.389 | -7.888 | -#> |.....................| -6.311 | -6.462 |...........|...........| -#> | 40| 476.77835 | 0.9927 | -1.023 | -0.9112 | -0.8968 | -#> |.....................| -0.8433 | -0.4165 | -0.7840 | -0.8801 | -#> |.....................| -0.8630 | -0.8835 | -0.9455 | -0.7699 | -#> |.....................| -0.7913 | -0.7897 |...........|...........| -#> | U| 476.77835 | 92.44 | -5.326 | -0.9439 | -0.1090 | -#> |.....................| 2.293 | 1.422 | 0.03167 | 1.153 | -#> |.....................| 0.03048 | 0.7527 | 0.8117 | 1.332 | -#> |.....................| 1.153 | 1.159 |...........|...........| -#> | X| 476.77835 | 92.44 | 0.004861 | 0.2801 | 0.8968 | -#> |.....................| 9.907 | 1.422 | 0.03167 | 1.153 | -#> |.....................| 0.03048 | 0.7527 | 0.8117 | 1.332 | -#> |.....................| 1.153 | 1.159 |...........|...........| -#> | F| Forward Diff. | -31.48 | 1.878 | -0.09989 | 0.1868 | -#> |.....................| -0.2862 | -34.69 | -7.934 | 1.303 | -#> |.....................| -2.230 | 0.5238 | 3.299 | -7.623 | -#> |.....................| -6.137 | -6.207 |...........|...........| -#> | 41| 476.29140 | 0.9988 | -1.024 | -0.9112 | -0.8970 | -#> |.....................| -0.8432 | -0.4030 | -0.7837 | -0.8817 | -#> |.....................| -0.8615 | -0.8839 | -0.9453 | -0.7660 | -#> |.....................| -0.7883 | -0.7869 |...........|...........| -#> | U| 476.2914 | 93.01 | -5.328 | -0.9440 | -0.1091 | -#> |.....................| 2.293 | 1.430 | 0.03168 | 1.152 | -#> |.....................| 0.03051 | 0.7524 | 0.8119 | 1.337 | -#> |.....................| 1.157 | 1.162 |...........|...........| -#> | X| 476.2914 | 93.01 | 0.004855 | 0.2801 | 0.8966 | -#> |.....................| 9.909 | 1.430 | 0.03168 | 1.152 | -#> |.....................| 0.03051 | 0.7524 | 0.8119 | 1.337 | -#> |.....................| 1.157 | 1.162 |...........|...........| -#> | F| Forward Diff. | 48.73 | 1.930 | 0.1514 | 0.2545 | -#> |.....................| -0.03521 | -34.01 | -6.934 | 1.004 | -#> |.....................| -2.133 | 0.7968 | 5.252 | -7.528 | -#> |.....................| -6.021 | -6.137 |...........|...........| -#> | 42| 475.72593 | 0.9927 | -1.026 | -0.9113 | -0.8972 | -#> |.....................| -0.8430 | -0.3897 | -0.7848 | -0.8834 | -#> |.....................| -0.8598 | -0.8844 | -0.9451 | -0.7619 | -#> |.....................| -0.7851 | -0.7840 |...........|...........| -#> | U| 475.72593 | 92.44 | -5.329 | -0.9441 | -0.1094 | -#> |.....................| 2.294 | 1.437 | 0.03166 | 1.151 | -#> |.....................| 0.03053 | 0.7521 | 0.8121 | 1.342 | -#> |.....................| 1.160 | 1.165 |...........|...........| -#> | X| 475.72593 | 92.44 | 0.004847 | 0.2801 | 0.8964 | -#> |.....................| 9.910 | 1.437 | 0.03166 | 1.151 | -#> |.....................| 0.03053 | 0.7521 | 0.8121 | 1.342 | -#> |.....................| 1.160 | 1.165 |...........|...........| -#> | F| Forward Diff. | -31.62 | 1.868 | -0.1026 | 0.1833 | -#> |.....................| -0.2884 | -33.06 | -7.282 | 1.547 | -#> |.....................| -2.194 | 0.5347 | 3.320 | -7.249 | -#> |.....................| -5.852 | -5.889 |...........|...........| -#> | 43| 475.25217 | 0.9986 | -1.027 | -0.9113 | -0.8974 | -#> |.....................| -0.8428 | -0.3762 | -0.7856 | -0.8854 | -#> |.....................| -0.8580 | -0.8849 | -0.9453 | -0.7580 | -#> |.....................| -0.7821 | -0.7812 |...........|...........| -#> | U| 475.25217 | 92.99 | -5.331 | -0.9441 | -0.1096 | -#> |.....................| 2.294 | 1.445 | 0.03165 | 1.150 | -#> |.....................| 0.03056 | 0.7517 | 0.8119 | 1.346 | -#> |.....................| 1.163 | 1.168 |...........|...........| -#> | X| 475.25217 | 92.99 | 0.004840 | 0.2801 | 0.8962 | -#> |.....................| 9.912 | 1.445 | 0.03165 | 1.150 | -#> |.....................| 0.03056 | 0.7517 | 0.8119 | 1.346 | -#> |.....................| 1.163 | 1.168 |...........|...........| -#> | F| Forward Diff. | 45.01 | 1.918 | 0.1424 | 0.2472 | -#> |.....................| -0.04139 | -32.61 | -6.424 | 0.9161 | -#> |.....................| -2.151 | 0.6354 | 5.209 | -7.174 | -#> |.....................| -5.746 | -5.822 |...........|...........| -#> | 44| 474.72079 | 0.9929 | -1.029 | -0.9114 | -0.8977 | -#> |.....................| -0.8427 | -0.3629 | -0.7879 | -0.8876 | -#> |.....................| -0.8559 | -0.8852 | -0.9458 | -0.7541 | -#> |.....................| -0.7790 | -0.7785 |...........|...........| -#> | U| 474.72079 | 92.46 | -5.333 | -0.9442 | -0.1098 | -#> |.....................| 2.294 | 1.453 | 0.03161 | 1.149 | -#> |.....................| 0.03059 | 0.7515 | 0.8114 | 1.351 | -#> |.....................| 1.167 | 1.171 |...........|...........| -#> | X| 474.72079 | 92.46 | 0.004831 | 0.2800 | 0.8960 | -#> |.....................| 9.913 | 1.453 | 0.03161 | 1.149 | -#> |.....................| 0.03059 | 0.7515 | 0.8114 | 1.351 | -#> |.....................| 1.167 | 1.171 |...........|...........| -#> | F| Forward Diff. | -29.98 | 1.856 | -0.09377 | 0.1852 | -#> |.....................| -0.2753 | -32.15 | -6.889 | 1.072 | -#> |.....................| -2.266 | 0.4091 | 3.274 | -6.876 | -#> |.....................| -5.564 | -5.585 |...........|...........| -#> | 45| 474.26379 | 0.9985 | -1.031 | -0.9115 | -0.8979 | -#> |.....................| -0.8425 | -0.3491 | -0.7895 | -0.8887 | -#> |.....................| -0.8536 | -0.8852 | -0.9464 | -0.7506 | -#> |.....................| -0.7762 | -0.7761 |...........|...........| -#> | U| 474.26379 | 92.98 | -5.335 | -0.9443 | -0.1101 | -#> |.....................| 2.294 | 1.461 | 0.03159 | 1.148 | -#> |.....................| 0.03063 | 0.7515 | 0.8109 | 1.355 | -#> |.....................| 1.170 | 1.173 |...........|...........| -#> | X| 474.26379 | 92.98 | 0.004822 | 0.2800 | 0.8958 | -#> |.....................| 9.915 | 1.461 | 0.03159 | 1.148 | -#> |.....................| 0.03063 | 0.7515 | 0.8109 | 1.355 | -#> |.....................| 1.170 | 1.173 |...........|...........| -#> | F| Forward Diff. | 42.78 | 1.902 | 0.1464 | 0.2388 | -#> |.....................| -0.03417 | -31.28 | -5.931 | 0.8375 | -#> |.....................| -2.202 | 0.7305 | 5.128 | -6.841 | -#> |.....................| -5.479 | -5.554 |...........|...........| -#> | 46| 473.76810 | 0.9929 | -1.033 | -0.9117 | -0.8982 | -#> |.....................| -0.8424 | -0.3358 | -0.7928 | -0.8897 | -#> |.....................| -0.8508 | -0.8855 | -0.9473 | -0.7471 | -#> |.....................| -0.7734 | -0.7737 |...........|...........| -#> | U| 473.7681 | 92.46 | -5.337 | -0.9444 | -0.1104 | -#> |.....................| 2.294 | 1.469 | 0.03154 | 1.147 | -#> |.....................| 0.03067 | 0.7512 | 0.8101 | 1.360 | -#> |.....................| 1.173 | 1.176 |...........|...........| -#> | X| 473.7681 | 92.46 | 0.004812 | 0.2800 | 0.8955 | -#> |.....................| 9.917 | 1.469 | 0.03154 | 1.147 | -#> |.....................| 0.03067 | 0.7512 | 0.8101 | 1.360 | -#> |.....................| 1.173 | 1.176 |...........|...........| -#> | F| Forward Diff. | -30.83 | 1.832 | -0.1003 | 0.1743 | -#> |.....................| -0.2686 | -30.77 | -6.362 | 1.107 | -#> |.....................| -2.234 | 0.4249 | 4.678 | -6.593 | -#> |.....................| -5.329 | -5.340 |...........|...........| -#> | 47| 473.32508 | 0.9983 | -1.035 | -0.9117 | -0.8984 | -#> |.....................| -0.8422 | -0.3229 | -0.7959 | -0.8909 | -#> |.....................| -0.8482 | -0.8859 | -0.9520 | -0.7438 | -#> |.....................| -0.7708 | -0.7715 |...........|...........| -#> | U| 473.32508 | 92.96 | -5.339 | -0.9445 | -0.1106 | -#> |.....................| 2.294 | 1.476 | 0.03149 | 1.147 | -#> |.....................| 0.03071 | 0.7509 | 0.8061 | 1.364 | -#> |.....................| 1.175 | 1.178 |...........|...........| -#> | X| 473.32508 | 92.96 | 0.004802 | 0.2800 | 0.8953 | -#> |.....................| 9.918 | 1.476 | 0.03149 | 1.147 | -#> |.....................| 0.03071 | 0.7509 | 0.8061 | 1.364 | -#> |.....................| 1.175 | 1.178 |...........|...........| -#> | F| Forward Diff. | 38.19 | 1.865 | 0.1554 | 0.2504 | -#> |.....................| -0.02116 | -30.15 | -5.522 | 0.8218 | -#> |.....................| -2.215 | 0.6878 | 4.772 | -6.537 | -#> |.....................| -5.232 | -5.315 |...........|...........| -#> | 48| 472.87290 | 0.9930 | -1.038 | -0.9119 | -0.8988 | -#> |.....................| -0.8421 | -0.3103 | -0.8002 | -0.8921 | -#> |.....................| -0.8451 | -0.8864 | -0.9564 | -0.7407 | -#> |.....................| -0.7684 | -0.7695 |...........|...........| -#> | U| 472.8729 | 92.47 | -5.341 | -0.9447 | -0.1109 | -#> |.....................| 2.294 | 1.483 | 0.03143 | 1.146 | -#> |.....................| 0.03075 | 0.7506 | 0.8022 | 1.367 | -#> |.....................| 1.178 | 1.180 |...........|...........| -#> | X| 472.8729 | 92.47 | 0.004791 | 0.2800 | 0.8950 | -#> |.....................| 9.919 | 1.483 | 0.03143 | 1.146 | -#> |.....................| 0.03075 | 0.7506 | 0.8022 | 1.367 | -#> |.....................| 1.178 | 1.180 |...........|...........| -#> | F| Forward Diff. | -31.43 | 1.786 | -0.07853 | 0.1828 | -#> |.....................| -0.2451 | -29.69 | -5.937 | 1.129 | -#> |.....................| -2.237 | 0.5225 | 4.143 | -6.356 | -#> |.....................| -5.097 | -5.139 |...........|...........| -#> | 49| 472.45068 | 0.9981 | -1.040 | -0.9121 | -0.8991 | -#> |.....................| -0.8421 | -0.2974 | -0.8046 | -0.8935 | -#> |.....................| -0.8420 | -0.8871 | -0.9597 | -0.7375 | -#> |.....................| -0.7660 | -0.7674 |...........|...........| -#> | U| 472.45068 | 92.94 | -5.343 | -0.9449 | -0.1112 | -#> |.....................| 2.294 | 1.491 | 0.03136 | 1.145 | -#> |.....................| 0.03080 | 0.7500 | 0.7993 | 1.371 | -#> |.....................| 1.180 | 1.183 |...........|...........| -#> | X| 472.45068 | 92.94 | 0.004780 | 0.2799 | 0.8947 | -#> |.....................| 9.919 | 1.491 | 0.03136 | 1.145 | -#> |.....................| 0.03080 | 0.7500 | 0.7993 | 1.371 | -#> |.....................| 1.180 | 1.183 |...........|...........| -#> | F| Forward Diff. | 34.69 | 1.825 | 0.1712 | 0.2558 | -#> |.....................| 0.0008262 | -30.15 | -5.461 | 0.02383 | -#> |.....................| -3.011 | 0.3236 | 4.609 | -6.242 | -#> |.....................| -4.997 | -5.107 |...........|...........| -#> | 50| 472.02915 | 0.9936 | -1.042 | -0.9125 | -0.8995 | -#> |.....................| -0.8422 | -0.2847 | -0.8092 | -0.8923 | -#> |.....................| -0.8364 | -0.8868 | -0.9626 | -0.7353 | -#> |.....................| -0.7644 | -0.7660 |...........|...........| -#> | U| 472.02915 | 92.52 | -5.345 | -0.9452 | -0.1116 | -#> |.....................| 2.294 | 1.498 | 0.03129 | 1.146 | -#> |.....................| 0.03088 | 0.7503 | 0.7968 | 1.374 | -#> |.....................| 1.182 | 1.184 |...........|...........| -#> | X| 472.02915 | 92.52 | 0.004770 | 0.2799 | 0.8944 | -#> |.....................| 9.918 | 1.498 | 0.03129 | 1.146 | -#> |.....................| 0.03088 | 0.7503 | 0.7968 | 1.374 | -#> |.....................| 1.182 | 1.184 |...........|...........| -#> | F| Forward Diff. | -26.29 | 1.758 | -0.04843 | 0.1910 | -#> |.....................| -0.1997 | -28.69 | -5.506 | 1.097 | -#> |.....................| -2.285 | 0.4947 | 2.297 | -6.079 | -#> |.....................| -4.892 | -4.970 |...........|...........| -#> | 51| 471.69520 | 0.9992 | -1.044 | -0.9127 | -0.8998 | -#> |.....................| -0.8423 | -0.2715 | -0.8127 | -0.8918 | -#> |.....................| -0.8317 | -0.8866 | -0.9606 | -0.7330 | -#> |.....................| -0.7627 | -0.7642 |...........|...........| -#> | U| 471.6952 | 93.04 | -5.347 | -0.9454 | -0.1120 | -#> |.....................| 2.294 | 1.506 | 0.03124 | 1.146 | -#> |.....................| 0.03096 | 0.7504 | 0.7985 | 1.377 | -#> |.....................| 1.184 | 1.186 |...........|...........| -#> | X| 471.6952 | 93.04 | 0.004761 | 0.2798 | 0.8941 | -#> |.....................| 9.917 | 1.506 | 0.03124 | 1.146 | -#> |.....................| 0.03096 | 0.7504 | 0.7985 | 1.377 | -#> |.....................| 1.184 | 1.186 |...........|...........| -#> | F| Forward Diff. | 46.70 | 1.815 | 0.2108 | 0.2607 | -#> |.....................| 0.05766 | -27.95 | -4.639 | 0.9041 | -#> |.....................| -2.201 | 0.7590 | 4.326 | -6.078 | -#> |.....................| -4.851 | -4.972 |...........|...........| -#> | 52| 471.30240 | 0.9939 | -1.046 | -0.9131 | -0.9002 | -#> |.....................| -0.8425 | -0.2596 | -0.8187 | -0.8939 | -#> |.....................| -0.8280 | -0.8876 | -0.9571 | -0.7302 | -#> |.....................| -0.7606 | -0.7622 |...........|...........| -#> | U| 471.3024 | 92.55 | -5.350 | -0.9458 | -0.1124 | -#> |.....................| 2.294 | 1.513 | 0.03115 | 1.145 | -#> |.....................| 0.03101 | 0.7497 | 0.8016 | 1.380 | -#> |.....................| 1.186 | 1.188 |...........|...........| -#> | X| 471.3024 | 92.55 | 0.004750 | 0.2797 | 0.8937 | -#> |.....................| 9.915 | 1.513 | 0.03115 | 1.145 | -#> |.....................| 0.03101 | 0.7497 | 0.8016 | 1.380 | -#> |.....................| 1.186 | 1.188 |...........|...........| -#> | F| Forward Diff. | -23.61 | 1.763 | -0.06060 | 0.1836 | -#> |.....................| -0.1912 | -28.31 | -5.279 | 0.6597 | -#> |.....................| -2.739 | 0.2048 | 5.941 | -5.864 | -#> |.....................| -4.747 | -4.787 |...........|...........| -#> | 53| 470.94339 | 0.9985 | -1.048 | -0.9133 | -0.9006 | -#> |.....................| -0.8426 | -0.2476 | -0.8235 | -0.8946 | -#> |.....................| -0.8237 | -0.8877 | -0.9629 | -0.7278 | -#> |.....................| -0.7587 | -0.7604 |...........|...........| -#> | U| 470.94339 | 92.98 | -5.352 | -0.9460 | -0.1127 | -#> |.....................| 2.294 | 1.520 | 0.03108 | 1.145 | -#> |.....................| 0.03108 | 0.7496 | 0.7965 | 1.383 | -#> |.....................| 1.188 | 1.190 |...........|...........| -#> | X| 470.94339 | 92.98 | 0.004740 | 0.2797 | 0.8934 | -#> |.....................| 9.914 | 1.520 | 0.03108 | 1.145 | -#> |.....................| 0.03108 | 0.7496 | 0.7965 | 1.383 | -#> |.....................| 1.188 | 1.190 |...........|...........| -#> | F| Forward Diff. | 36.04 | 1.791 | 0.1836 | 0.2544 | -#> |.....................| 0.04274 | -27.03 | -4.370 | 0.9159 | -#> |.....................| -2.217 | 0.6791 | 4.141 | -5.840 | -#> |.....................| -4.667 | -4.764 |...........|...........| -#> | 54| 470.60274 | 0.9931 | -1.051 | -0.9136 | -0.9010 | -#> |.....................| -0.8428 | -0.2366 | -0.8300 | -0.8957 | -#> |.....................| -0.8190 | -0.8879 | -0.9681 | -0.7257 | -#> |.....................| -0.7570 | -0.7588 |...........|...........| -#> | U| 470.60274 | 92.48 | -5.354 | -0.9463 | -0.1131 | -#> |.....................| 2.294 | 1.526 | 0.03098 | 1.144 | -#> |.....................| 0.03115 | 0.7494 | 0.7919 | 1.386 | -#> |.....................| 1.190 | 1.192 |...........|...........| -#> | X| 470.60274 | 92.48 | 0.004728 | 0.2796 | 0.8930 | -#> |.....................| 9.912 | 1.526 | 0.03098 | 1.144 | -#> |.....................| 0.03115 | 0.7494 | 0.7919 | 1.386 | -#> |.....................| 1.190 | 1.192 |...........|...........| -#> | F| Forward Diff. | -35.91 | 1.718 | -0.07847 | 0.1786 | -#> |.....................| -0.1996 | -26.69 | -4.843 | 1.231 | -#> |.....................| -2.229 | 0.5625 | 3.489 | -5.662 | -#> |.....................| -4.557 | -4.604 |...........|...........| -#> | 55| 470.25392 | 0.9977 | -1.054 | -0.9140 | -0.9015 | -#> |.....................| -0.8431 | -0.2250 | -0.8375 | -0.8987 | -#> |.....................| -0.8153 | -0.8894 | -0.9673 | -0.7229 | -#> |.....................| -0.7550 | -0.7569 |...........|...........| -#> | U| 470.25392 | 92.90 | -5.357 | -0.9467 | -0.1136 | -#> |.....................| 2.293 | 1.533 | 0.03087 | 1.142 | -#> |.....................| 0.03120 | 0.7483 | 0.7927 | 1.389 | -#> |.....................| 1.192 | 1.194 |...........|...........| -#> | X| 470.25392 | 92.90 | 0.004715 | 0.2796 | 0.8926 | -#> |.....................| 9.909 | 1.533 | 0.03087 | 1.142 | -#> |.....................| 0.03120 | 0.7483 | 0.7927 | 1.389 | -#> |.....................| 1.192 | 1.194 |...........|...........| -#> | F| Forward Diff. | 23.42 | 1.753 | 0.1414 | 0.2393 | -#> |.....................| 0.01691 | -26.51 | -4.262 | 0.6993 | -#> |.....................| -2.408 | 0.5525 | 2.318 | -5.573 | -#> |.....................| -4.475 | -4.572 |...........|...........| -#> | 56| 469.96066 | 0.9934 | -1.056 | -0.9144 | -0.9019 | -#> |.....................| -0.8434 | -0.2128 | -0.8432 | -0.9002 | -#> |.....................| -0.8113 | -0.8903 | -0.9627 | -0.7205 | -#> |.....................| -0.7531 | -0.7551 |...........|...........| -#> | U| 469.96066 | 92.50 | -5.359 | -0.9470 | -0.1140 | -#> |.....................| 2.293 | 1.540 | 0.03078 | 1.141 | -#> |.....................| 0.03126 | 0.7476 | 0.7967 | 1.392 | -#> |.....................| 1.194 | 1.196 |...........|...........| -#> | X| 469.96066 | 92.50 | 0.004704 | 0.2795 | 0.8922 | -#> |.....................| 9.906 | 1.540 | 0.03078 | 1.141 | -#> |.....................| 0.03126 | 0.7476 | 0.7967 | 1.392 | -#> |.....................| 1.194 | 1.196 |...........|...........| -#> | F| Forward Diff. | -33.10 | 1.713 | -0.09549 | 0.1710 | -#> |.....................| -0.1943 | -25.89 | -4.557 | 1.045 | -#> |.....................| -2.243 | 0.5648 | 3.834 | -5.392 | -#> |.....................| -4.399 | -4.402 |...........|...........| -#> | 57| 469.66426 | 0.9983 | -1.059 | -0.9147 | -0.9023 | -#> |.....................| -0.8437 | -0.2012 | -0.8503 | -0.9014 | -#> |.....................| -0.8068 | -0.8914 | -0.9589 | -0.7186 | -#> |.....................| -0.7515 | -0.7537 |...........|...........| -#> | U| 469.66426 | 92.95 | -5.362 | -0.9473 | -0.1144 | -#> |.....................| 2.293 | 1.547 | 0.03068 | 1.141 | -#> |.....................| 0.03133 | 0.7468 | 0.8000 | 1.394 | -#> |.....................| 1.196 | 1.197 |...........|...........| -#> | X| 469.66426 | 92.95 | 0.004691 | 0.2794 | 0.8919 | -#> |.....................| 9.903 | 1.547 | 0.03068 | 1.141 | -#> |.....................| 0.03133 | 0.7468 | 0.8000 | 1.394 | -#> |.....................| 1.196 | 1.197 |...........|...........| -#> | F| Forward Diff. | 29.48 | 1.769 | 0.1441 | 0.2362 | -#> |.....................| 0.03493 | -25.40 | -3.876 | 0.7581 | -#> |.....................| -2.246 | 0.6653 | 4.370 | -5.362 | -#> |.....................| -4.340 | -4.389 |...........|...........| -#> | 58| 469.35361 | 0.9940 | -1.062 | -0.9149 | -0.9027 | -#> |.....................| -0.8440 | -0.1900 | -0.8585 | -0.9032 | -#> |.....................| -0.8026 | -0.8931 | -0.9615 | -0.7168 | -#> |.....................| -0.7497 | -0.7523 |...........|...........| -#> | U| 469.35361 | 92.56 | -5.365 | -0.9475 | -0.1149 | -#> |.....................| 2.293 | 1.553 | 0.03055 | 1.140 | -#> |.....................| 0.03139 | 0.7454 | 0.7977 | 1.396 | -#> |.....................| 1.198 | 1.199 |...........|...........| -#> | X| 469.35361 | 92.56 | 0.004677 | 0.2794 | 0.8915 | -#> |.....................| 9.900 | 1.553 | 0.03055 | 1.140 | -#> |.....................| 0.03139 | 0.7454 | 0.7977 | 1.396 | -#> |.....................| 1.198 | 1.199 |...........|...........| -#> | F| Forward Diff. | -26.71 | 1.702 | -0.07338 | 0.1729 | -#> |.....................| -0.1601 | -26.00 | -4.465 | 0.4354 | -#> |.....................| -2.821 | 0.3110 | 5.728 | -5.228 | -#> |.....................| -4.240 | -4.266 |...........|...........| -#> | 59| 469.04262 | 0.9978 | -1.064 | -0.9151 | -0.9031 | -#> |.....................| -0.8443 | -0.1798 | -0.8657 | -0.9030 | -#> |.....................| -0.7971 | -0.8938 | -0.9685 | -0.7157 | -#> |.....................| -0.7487 | -0.7515 |...........|...........| -#> | U| 469.04262 | 92.91 | -5.368 | -0.9477 | -0.1152 | -#> |.....................| 2.292 | 1.559 | 0.03044 | 1.140 | -#> |.....................| 0.03147 | 0.7450 | 0.7916 | 1.398 | -#> |.....................| 1.199 | 1.200 |...........|...........| -#> | X| 469.04262 | 92.91 | 0.004665 | 0.2794 | 0.8912 | -#> |.....................| 9.897 | 1.559 | 0.03044 | 1.140 | -#> |.....................| 0.03147 | 0.7450 | 0.7916 | 1.398 | -#> |.....................| 1.199 | 1.200 |...........|...........| -#> | 60| 468.78438 | 0.9975 | -1.068 | -0.9154 | -0.9036 | -#> |.....................| -0.8447 | -0.1709 | -0.8764 | -0.9025 | -#> |.....................| -0.7900 | -0.8946 | -0.9771 | -0.7153 | -#> |.....................| -0.7482 | -0.7514 |...........|...........| -#> | U| 468.78438 | 92.88 | -5.371 | -0.9479 | -0.1157 | -#> |.....................| 2.292 | 1.564 | 0.03028 | 1.140 | -#> |.....................| 0.03158 | 0.7443 | 0.7841 | 1.398 | -#> |.....................| 1.200 | 1.200 |...........|...........| -#> | X| 468.78438 | 92.88 | 0.004649 | 0.2793 | 0.8907 | -#> |.....................| 9.893 | 1.564 | 0.03028 | 1.140 | -#> |.....................| 0.03158 | 0.7443 | 0.7841 | 1.398 | -#> |.....................| 1.200 | 1.200 |...........|...........| -#> | 61| 467.65199 | 0.9960 | -1.083 | -0.9167 | -0.9058 | -#> |.....................| -0.8469 | -0.1283 | -0.9284 | -0.9002 | -#> |.....................| -0.7560 | -0.8987 | -1.018 | -0.7133 | -#> |.....................| -0.7456 | -0.7506 |...........|...........| -#> | U| 467.65199 | 92.74 | -5.387 | -0.9492 | -0.1179 | -#> |.....................| 2.290 | 1.589 | 0.02950 | 1.141 | -#> |.....................| 0.03209 | 0.7413 | 0.7481 | 1.401 | -#> |.....................| 1.202 | 1.201 |...........|...........| -#> | X| 467.65199 | 92.74 | 0.004577 | 0.2791 | 0.8887 | -#> |.....................| 9.872 | 1.589 | 0.02950 | 1.141 | -#> |.....................| 0.03209 | 0.7413 | 0.7481 | 1.401 | -#> |.....................| 1.202 | 1.201 |...........|...........| -#> | 62| 464.96560 | 0.9898 | -1.148 | -0.9222 | -0.9151 | -#> |.....................| -0.8556 | 0.04847 | -1.144 | -0.8910 | -#> |.....................| -0.6148 | -0.9154 | -1.189 | -0.7051 | -#> |.....................| -0.7350 | -0.7474 |...........|...........| -#> | U| 464.9656 | 92.17 | -5.451 | -0.9543 | -0.1273 | -#> |.....................| 2.281 | 1.691 | 0.02626 | 1.147 | -#> |.....................| 0.03421 | 0.7285 | 0.5986 | 1.411 | -#> |.....................| 1.214 | 1.204 |...........|...........| -#> | X| 464.9656 | 92.17 | 0.004291 | 0.2780 | 0.8805 | -#> |.....................| 9.786 | 1.691 | 0.02626 | 1.147 | -#> |.....................| 0.03421 | 0.7285 | 0.5986 | 1.411 | -#> |.....................| 1.214 | 1.204 |...........|...........| -#> | F| Forward Diff. | -134.9 | 0.8693 | 0.2607 | 0.2086 | -#> |.....................| 0.2111 | -19.53 | -3.427 | 3.399 | -#> |.....................| -2.172 | 1.526 | -11.79 | -4.993 | -#> |.....................| -3.321 | -4.659 |...........|...........| -#> | 63| 458.88877 | 1.003 | -1.235 | -0.9465 | -0.9328 | -#> |.....................| -0.8841 | 0.3192 | -1.460 | -0.9475 | -#> |.....................| -0.4237 | -0.9768 | -1.134 | -0.6574 | -#> |.....................| -0.7075 | -0.6995 |...........|...........| -#> | U| 458.88877 | 93.40 | -5.538 | -0.9774 | -0.1450 | -#> |.....................| 2.252 | 1.848 | 0.02152 | 1.114 | -#> |.....................| 0.03709 | 0.6820 | 0.6469 | 1.468 | -#> |.....................| 1.243 | 1.255 |...........|...........| -#> | X| 458.88877 | 93.40 | 0.003933 | 0.2734 | 0.8651 | -#> |.....................| 9.511 | 1.848 | 0.02152 | 1.114 | -#> |.....................| 0.03709 | 0.6820 | 0.6469 | 1.468 | -#> |.....................| 1.243 | 1.255 |...........|...........| -#> | 64| 455.19412 | 1.006 | -1.330 | -0.9732 | -0.9522 | -#> |.....................| -0.9154 | 0.6143 | -1.806 | -1.009 | -#> |.....................| -0.2144 | -1.044 | -1.075 | -0.6056 | -#> |.....................| -0.6776 | -0.6473 |...........|...........| -#> | U| 455.19412 | 93.67 | -5.634 | -1.003 | -0.1644 | -#> |.....................| 2.221 | 2.019 | 0.01631 | 1.078 | -#> |.....................| 0.04023 | 0.6311 | 0.6989 | 1.531 | -#> |.....................| 1.275 | 1.311 |...........|...........| -#> | X| 455.19412 | 93.67 | 0.003576 | 0.2684 | 0.8484 | -#> |.....................| 9.218 | 2.019 | 0.01631 | 1.078 | -#> |.....................| 0.04023 | 0.6311 | 0.6989 | 1.531 | -#> |.....................| 1.275 | 1.311 |...........|...........| -#> | F| Forward Diff. | 18.82 | 0.9889 | -1.032 | -0.1489 | -#> |.....................| 0.2009 | -8.117 | -0.5123 | 0.1656 | -#> |.....................| -2.314 | -3.473 | -0.8284 | 0.3432 | -#> |.....................| -0.8357 | 0.04588 |...........|...........| -#> | 65| 458.62552 | 1.004 | -1.494 | -0.8145 | -0.9319 | -#> |.....................| -0.9630 | 1.033 | -2.192 | -1.036 | -#> |.....................| 0.2529 | -0.5036 | -0.8838 | -0.8679 | -#> |.....................| -0.7178 | -0.8209 |...........|...........| -#> | U| 458.62552 | 93.52 | -5.797 | -0.8527 | -0.1440 | -#> |.....................| 2.174 | 2.262 | 0.01051 | 1.062 | -#> |.....................| 0.04725 | 1.041 | 0.8656 | 1.213 | -#> |.....................| 1.232 | 1.125 |...........|...........| -#> | X| 458.62552 | 93.52 | 0.003036 | 0.2989 | 0.8659 | -#> |.....................| 8.789 | 2.262 | 0.01051 | 1.062 | -#> |.....................| 0.04725 | 1.041 | 0.8656 | 1.213 | -#> |.....................| 1.232 | 1.125 |...........|...........| -#> | 66| 454.48694 | 1.003 | -1.384 | -0.9206 | -0.9455 | -#> |.....................| -0.9312 | 0.7538 | -1.934 | -1.018 | -#> |.....................| -0.05956 | -0.8649 | -1.011 | -0.6924 | -#> |.....................| -0.6908 | -0.7048 |...........|...........| -#> | U| 454.48694 | 93.41 | -5.688 | -0.9529 | -0.1576 | -#> |.....................| 2.205 | 2.100 | 0.01439 | 1.073 | -#> |.....................| 0.04256 | 0.7669 | 0.7542 | 1.426 | -#> |.....................| 1.261 | 1.250 |...........|...........| -#> | X| 454.48694 | 93.41 | 0.003387 | 0.2783 | 0.8542 | -#> |.....................| 9.074 | 2.100 | 0.01439 | 1.073 | -#> |.....................| 0.04256 | 0.7669 | 0.7542 | 1.426 | -#> |.....................| 1.261 | 1.250 |...........|...........| -#> | F| Forward Diff. | -11.88 | 0.8805 | 1.030 | 0.0001663 | -#> |.....................| -0.3119 | -6.748 | -1.151 | 0.2517 | -#> |.....................| -3.379 | 3.981 | 5.317 | -4.395 | -#> |.....................| -1.890 | -2.785 |...........|...........| -#> | 67| 453.47854 | 1.004 | -1.455 | -0.9097 | -0.9308 | -#> |.....................| -0.9364 | 0.8078 | -2.047 | -1.046 | -#> |.....................| 0.2383 | -0.8443 | -0.9977 | -0.6524 | -#> |.....................| -0.6789 | -0.6970 |...........|...........| -#> | U| 453.47854 | 93.48 | -5.759 | -0.9426 | -0.1429 | -#> |.....................| 2.200 | 2.132 | 0.01270 | 1.056 | -#> |.....................| 0.04703 | 0.7825 | 0.7661 | 1.474 | -#> |.....................| 1.274 | 1.258 |...........|...........| -#> | X| 453.47854 | 93.48 | 0.003156 | 0.2804 | 0.8668 | -#> |.....................| 9.026 | 2.132 | 0.01270 | 1.056 | -#> |.....................| 0.04703 | 0.7825 | 0.7661 | 1.474 | -#> |.....................| 1.274 | 1.258 |...........|...........| -#> | F| Forward Diff. | -7.580 | 0.7096 | 1.748 | 0.4450 | -#> |.....................| -0.3063 | -5.686 | -1.090 | 2.089 | -#> |.....................| -1.806 | 4.661 | 3.477 | -2.550 | -#> |.....................| -1.063 | -2.646 |...........|...........| -#> | 68| 452.65869 | 1.010 | -1.604 | -0.9910 | -0.9601 | -#> |.....................| -0.9321 | 0.9548 | -2.236 | -1.333 | -#> |.....................| 0.7427 | -0.9083 | -1.017 | -0.7899 | -#> |.....................| -0.7453 | -0.6781 |...........|...........| -#> | U| 452.65869 | 94.06 | -5.907 | -1.019 | -0.1723 | -#> |.....................| 2.204 | 2.217 | 0.009851 | 0.8906 | -#> |.....................| 0.05461 | 0.7340 | 0.7490 | 1.308 | -#> |.....................| 1.203 | 1.278 |...........|...........| -#> | X| 452.65869 | 94.06 | 0.002719 | 0.2652 | 0.8418 | -#> |.....................| 9.065 | 2.217 | 0.009851 | 0.8906 | -#> |.....................| 0.05461 | 0.7340 | 0.7490 | 1.308 | -#> |.....................| 1.203 | 1.278 |...........|...........| -#> | F| Forward Diff. | 87.74 | 0.4343 | -0.7887 | -0.2527 | -#> |.....................| -0.1232 | -3.287 | -0.3715 | -5.728 | -#> |.....................| -3.469 | 4.620 | 5.104 | -8.863 | -#> |.....................| -5.024 | -1.180 |...........|...........| -#> | 69| 455.46876 | 1.000 | -1.721 | -0.9929 | -1.109 | -#> |.....................| -0.8905 | 1.109 | -2.343 | -1.386 | -#> |.....................| 1.193 | -1.162 | -0.9750 | -0.9277 | -#> |.....................| -0.5804 | -0.9245 |...........|...........| -#> | U| 455.46876 | 93.13 | -6.025 | -1.021 | -0.3216 | -#> |.....................| 2.246 | 2.306 | 0.008241 | 0.8595 | -#> |.....................| 0.06138 | 0.5417 | 0.7859 | 1.140 | -#> |.....................| 1.379 | 1.014 |...........|...........| -#> | X| 455.46876 | 93.13 | 0.002419 | 0.2648 | 0.7250 | -#> |.....................| 9.450 | 2.306 | 0.008241 | 0.8595 | -#> |.....................| 0.06138 | 0.5417 | 0.7859 | 1.140 | -#> |.....................| 1.379 | 1.014 |...........|...........| -#> | 70| 453.13548 | 0.9926 | -1.633 | -0.9913 | -0.9976 | -#> |.....................| -0.9216 | 0.9941 | -2.263 | -1.345 | -#> |.....................| 0.8563 | -0.9728 | -1.008 | -0.8230 | -#> |.....................| -0.7030 | -0.7398 |...........|...........| -#> | U| 453.13548 | 92.43 | -5.937 | -1.020 | -0.2097 | -#> |.....................| 2.215 | 2.240 | 0.009448 | 0.8833 | -#> |.....................| 0.05632 | 0.6851 | 0.7575 | 1.268 | -#> |.....................| 1.248 | 1.212 |...........|...........| -#> | X| 453.13548 | 92.43 | 0.002640 | 0.2651 | 0.8108 | -#> |.....................| 9.161 | 2.240 | 0.009448 | 0.8833 | -#> |.....................| 0.05632 | 0.6851 | 0.7575 | 1.268 | -#> |.....................| 1.248 | 1.212 |...........|...........| -#> | 71| 453.54485 | 0.9910 | -1.615 | -0.9910 | -0.9747 | -#> |.....................| -0.9280 | 0.9706 | -2.247 | -1.337 | -#> |.....................| 0.7875 | -0.9341 | -1.014 | -0.8015 | -#> |.....................| -0.7281 | -0.7020 |...........|...........| -#> | U| 453.54485 | 92.28 | -5.919 | -1.019 | -0.1868 | -#> |.....................| 2.209 | 2.226 | 0.009694 | 0.8882 | -#> |.....................| 0.05529 | 0.7144 | 0.7517 | 1.294 | -#> |.....................| 1.221 | 1.253 |...........|...........| -#> | X| 453.54485 | 92.28 | 0.002688 | 0.2651 | 0.8296 | -#> |.....................| 9.103 | 2.226 | 0.009694 | 0.8882 | -#> |.....................| 0.05529 | 0.7144 | 0.7517 | 1.294 | -#> |.....................| 1.221 | 1.253 |...........|...........| -#> | 72| 453.87696 | 0.9902 | -1.606 | -0.9909 | -0.9627 | -#> |.....................| -0.9313 | 0.9582 | -2.238 | -1.332 | -#> |.....................| 0.7513 | -0.9138 | -1.018 | -0.7903 | -#> |.....................| -0.7413 | -0.6822 |...........|...........| -#> | U| 453.87696 | 92.21 | -5.909 | -1.019 | -0.1748 | -#> |.....................| 2.205 | 2.219 | 0.009824 | 0.8908 | -#> |.....................| 0.05474 | 0.7298 | 0.7487 | 1.307 | -#> |.....................| 1.207 | 1.274 |...........|...........| -#> | X| 453.87696 | 92.21 | 0.002714 | 0.2652 | 0.8396 | -#> |.....................| 9.072 | 2.219 | 0.009824 | 0.8908 | -#> |.....................| 0.05474 | 0.7298 | 0.7487 | 1.307 | -#> |.....................| 1.207 | 1.274 |...........|...........| -#> | 73| 452.40810 | 1.003 | -1.604 | -0.9910 | -0.9601 | -#> |.....................| -0.9321 | 0.9550 | -2.236 | -1.332 | -#> |.....................| 0.7430 | -0.9087 | -1.018 | -0.7892 | -#> |.....................| -0.7449 | -0.6781 |...........|...........| -#> | U| 452.4081 | 93.41 | -5.907 | -1.019 | -0.1722 | -#> |.....................| 2.204 | 2.217 | 0.009851 | 0.8908 | -#> |.....................| 0.05462 | 0.7337 | 0.7487 | 1.309 | -#> |.....................| 1.203 | 1.278 |...........|...........| -#> | X| 452.4081 | 93.41 | 0.002719 | 0.2652 | 0.8418 | -#> |.....................| 9.065 | 2.217 | 0.009851 | 0.8908 | -#> |.....................| 0.05462 | 0.7337 | 0.7487 | 1.309 | -#> |.....................| 1.203 | 1.278 |...........|...........| -#> | F| Forward Diff. | -20.28 | 0.3985 | -0.9900 | -0.3302 | -#> |.....................| -0.4580 | -3.509 | -0.7634 | -5.125 | -#> |.....................| -3.224 | 3.921 | 4.784 | -8.607 | -#> |.....................| -4.910 | -1.049 |...........|...........| -#> | 74| 452.35774 | 1.005 | -1.605 | -0.9906 | -0.9617 | -#> |.....................| -0.9314 | 0.9567 | -2.238 | -1.332 | -#> |.....................| 0.7462 | -0.9112 | -1.018 | -0.7890 | -#> |.....................| -0.7417 | -0.6810 |...........|...........| -#> | U| 452.35774 | 93.58 | -5.909 | -1.019 | -0.1738 | -#> |.....................| 2.205 | 2.218 | 0.009828 | 0.8908 | -#> |.....................| 0.05467 | 0.7317 | 0.7485 | 1.309 | -#> |.....................| 1.207 | 1.275 |...........|...........| -#> | X| 452.35774 | 93.58 | 0.002715 | 0.2652 | 0.8405 | -#> |.....................| 9.072 | 2.218 | 0.009828 | 0.8908 | -#> |.....................| 0.05467 | 0.7317 | 0.7485 | 1.309 | -#> |.....................| 1.207 | 1.275 |...........|...........| -#> | F| Forward Diff. | 9.319 | 0.4042 | -0.9262 | -0.3428 | -#> |.....................| -0.3413 | -3.482 | -0.6441 | -5.151 | -#> |.....................| -3.223 | 3.864 | 4.863 | -8.623 | -#> |.....................| -4.770 | -1.217 |...........|...........| -#> | 75| 452.31017 | 1.003 | -1.607 | -0.9902 | -0.9631 | -#> |.....................| -0.9307 | 0.9586 | -2.239 | -1.332 | -#> |.....................| 0.7493 | -0.9137 | -1.019 | -0.7876 | -#> |.....................| -0.7383 | -0.6834 |...........|...........| -#> | U| 452.31017 | 93.41 | -5.910 | -1.019 | -0.1752 | -#> |.....................| 2.206 | 2.219 | 0.009807 | 0.8910 | -#> |.....................| 0.05471 | 0.7298 | 0.7478 | 1.310 | -#> |.....................| 1.210 | 1.273 |...........|...........| -#> | X| 452.31017 | 93.41 | 0.002711 | 0.2653 | 0.8393 | -#> |.....................| 9.078 | 2.219 | 0.009807 | 0.8910 | -#> |.....................| 0.05471 | 0.7298 | 0.7478 | 1.310 | -#> |.....................| 1.210 | 1.273 |...........|...........| -#> | F| Forward Diff. | -20.20 | 0.3903 | -0.9767 | -0.3983 | -#> |.....................| -0.4106 | -3.495 | -0.7375 | -5.052 | -#> |.....................| -3.297 | 3.718 | 4.704 | -8.538 | -#> |.....................| -4.606 | -1.295 |...........|...........| -#> | 76| 452.25868 | 1.005 | -1.609 | -0.9898 | -0.9648 | -#> |.....................| -0.9300 | 0.9604 | -2.241 | -1.332 | -#> |.....................| 0.7529 | -0.9160 | -1.019 | -0.7870 | -#> |.....................| -0.7354 | -0.6858 |...........|...........| -#> | U| 452.25868 | 93.58 | -5.912 | -1.018 | -0.1770 | -#> |.....................| 2.207 | 2.220 | 0.009778 | 0.8908 | -#> |.....................| 0.05477 | 0.7281 | 0.7476 | 1.311 | -#> |.....................| 1.213 | 1.270 |...........|...........| -#> | X| 452.25868 | 93.58 | 0.002707 | 0.2654 | 0.8378 | -#> |.....................| 9.084 | 2.220 | 0.009778 | 0.8908 | -#> |.....................| 0.05477 | 0.7281 | 0.7476 | 1.311 | -#> |.....................| 1.213 | 1.270 |...........|...........| -#> | F| Forward Diff. | 8.768 | 0.3959 | -0.9108 | -0.4152 | -#> |.....................| -0.2985 | -3.789 | -0.7277 | -5.480 | -#> |.....................| -3.800 | 3.463 | 7.165 | -8.525 | -#> |.....................| -4.480 | -1.429 |...........|...........| -#> | 77| 452.20380 | 1.003 | -1.610 | -0.9896 | -0.9665 | -#> |.....................| -0.9299 | 0.9625 | -2.243 | -1.331 | -#> |.....................| 0.7574 | -0.9182 | -1.020 | -0.7855 | -#> |.....................| -0.7330 | -0.6868 |...........|...........| -#> | U| 452.2038 | 93.42 | -5.913 | -1.018 | -0.1787 | -#> |.....................| 2.207 | 2.221 | 0.009753 | 0.8912 | -#> |.....................| 0.05483 | 0.7265 | 0.7464 | 1.313 | -#> |.....................| 1.216 | 1.269 |...........|...........| -#> | X| 452.2038 | 93.42 | 0.002704 | 0.2654 | 0.8364 | -#> |.....................| 9.085 | 2.221 | 0.009753 | 0.8912 | -#> |.....................| 0.05483 | 0.7265 | 0.7464 | 1.313 | -#> |.....................| 1.216 | 1.269 |...........|...........| -#> | F| Forward Diff. | -17.51 | 0.3875 | -0.9566 | -0.4713 | -#> |.....................| -0.3666 | -3.384 | -0.7134 | -4.862 | -#> |.....................| -3.257 | 3.566 | 3.539 | -8.382 | -#> |.....................| -4.308 | -1.428 |...........|...........| -#> | 78| 452.15674 | 1.006 | -1.611 | -0.9895 | -0.9681 | -#> |.....................| -0.9296 | 0.9646 | -2.244 | -1.331 | -#> |.....................| 0.7624 | -0.9204 | -1.020 | -0.7847 | -#> |.....................| -0.7317 | -0.6876 |...........|...........| -#> | U| 452.15674 | 93.63 | -5.915 | -1.018 | -0.1803 | -#> |.....................| 2.207 | 2.222 | 0.009729 | 0.8915 | -#> |.....................| 0.05491 | 0.7248 | 0.7463 | 1.314 | -#> |.....................| 1.217 | 1.268 |...........|...........| -#> | X| 452.15674 | 93.63 | 0.002700 | 0.2654 | 0.8350 | -#> |.....................| 9.088 | 2.222 | 0.009729 | 0.8915 | -#> |.....................| 0.05491 | 0.7248 | 0.7463 | 1.314 | -#> |.....................| 1.217 | 1.268 |...........|...........| -#> | F| Forward Diff. | 16.34 | 0.3942 | -0.8917 | -0.4820 | -#> |.....................| -0.2498 | -3.403 | -0.6022 | -5.023 | -#> |.....................| -3.383 | 3.482 | 3.627 | -8.397 | -#> |.....................| -4.266 | -1.517 |...........|...........| -#> | 79| 452.11013 | 1.004 | -1.613 | -0.9892 | -0.9692 | -#> |.....................| -0.9285 | 0.9667 | -2.245 | -1.330 | -#> |.....................| 0.7674 | -0.9230 | -1.020 | -0.7840 | -#> |.....................| -0.7312 | -0.6887 |...........|...........| -#> | U| 452.11013 | 93.48 | -5.917 | -1.018 | -0.1814 | -#> |.....................| 2.208 | 2.224 | 0.009710 | 0.8921 | -#> |.....................| 0.05499 | 0.7229 | 0.7466 | 1.315 | -#> |.....................| 1.218 | 1.267 |...........|...........| -#> | X| 452.11013 | 93.48 | 0.002694 | 0.2655 | 0.8341 | -#> |.....................| 9.098 | 2.224 | 0.009710 | 0.8921 | -#> |.....................| 0.05499 | 0.7229 | 0.7466 | 1.315 | -#> |.....................| 1.218 | 1.267 |...........|...........| -#> | F| Forward Diff. | -8.858 | 0.3784 | -0.9339 | -0.5242 | -#> |.....................| -0.2958 | -3.274 | -0.6451 | -4.716 | -#> |.....................| -3.235 | 3.524 | 3.578 | -8.323 | -#> |.....................| -4.226 | -1.527 |...........|...........| -#> | 80| 452.06081 | 1.006 | -1.615 | -0.9885 | -0.9698 | -#> |.....................| -0.9277 | 0.9688 | -2.247 | -1.329 | -#> |.....................| 0.7723 | -0.9255 | -1.020 | -0.7822 | -#> |.....................| -0.7302 | -0.6891 |...........|...........| -#> | U| 452.06081 | 93.65 | -5.919 | -1.017 | -0.1820 | -#> |.....................| 2.209 | 2.225 | 0.009693 | 0.8927 | -#> |.....................| 0.05506 | 0.7209 | 0.7465 | 1.317 | -#> |.....................| 1.219 | 1.266 |...........|...........| -#> | X| 452.06081 | 93.65 | 0.002689 | 0.2656 | 0.8336 | -#> |.....................| 9.105 | 2.225 | 0.009693 | 0.8927 | -#> |.....................| 0.05506 | 0.7209 | 0.7465 | 1.317 | -#> |.....................| 1.219 | 1.266 |...........|...........| -#> | F| Forward Diff. | 18.08 | 0.3814 | -0.8701 | -0.5179 | -#> |.....................| -0.1901 | -3.027 | -0.4828 | -4.583 | -#> |.....................| -3.046 | 3.385 | 4.724 | -8.292 | -#> |.....................| -4.215 | -1.583 |...........|...........| -#> | 81| 452.00089 | 1.004 | -1.618 | -0.9864 | -0.9698 | -#> |.....................| -0.9276 | 0.9701 | -2.249 | -1.331 | -#> |.....................| 0.7751 | -0.9261 | -1.021 | -0.7787 | -#> |.....................| -0.7281 | -0.6889 |...........|...........| -#> | U| 452.00089 | 93.48 | -5.921 | -1.015 | -0.1820 | -#> |.....................| 2.209 | 2.226 | 0.009656 | 0.8916 | -#> |.....................| 0.05510 | 0.7205 | 0.7459 | 1.321 | -#> |.....................| 1.221 | 1.267 |...........|...........| -#> | X| 452.00089 | 93.48 | 0.002683 | 0.2660 | 0.8336 | -#> |.....................| 9.107 | 2.226 | 0.009656 | 0.8916 | -#> |.....................| 0.05510 | 0.7205 | 0.7459 | 1.321 | -#> |.....................| 1.221 | 1.267 |...........|...........| -#> | F| Forward Diff. | -8.141 | 0.3688 | -0.8752 | -0.5418 | -#> |.....................| -0.2687 | -3.191 | -0.6153 | -4.612 | -#> |.....................| -3.168 | 3.248 | 4.602 | -8.159 | -#> |.....................| -4.118 | -1.545 |...........|...........| -#> | 82| 451.94404 | 1.006 | -1.619 | -0.9850 | -0.9696 | -#> |.....................| -0.9279 | 0.9711 | -2.251 | -1.332 | -#> |.....................| 0.7767 | -0.9258 | -1.022 | -0.7739 | -#> |.....................| -0.7256 | -0.6877 |...........|...........| -#> | U| 451.94404 | 93.65 | -5.922 | -1.014 | -0.1817 | -#> |.....................| 2.209 | 2.226 | 0.009627 | 0.8908 | -#> |.....................| 0.05512 | 0.7207 | 0.7445 | 1.327 | -#> |.....................| 1.224 | 1.268 |...........|...........| -#> | X| 451.94404 | 93.65 | 0.002679 | 0.2663 | 0.8338 | -#> |.....................| 9.104 | 2.226 | 0.009627 | 0.8908 | -#> |.....................| 0.05512 | 0.7207 | 0.7445 | 1.327 | -#> |.....................| 1.224 | 1.268 |...........|...........| -#> | 83| 451.90577 | 1.006 | -1.621 | -0.9832 | -0.9693 | -#> |.....................| -0.9284 | 0.9716 | -2.254 | -1.336 | -#> |.....................| 0.7778 | -0.9242 | -1.023 | -0.7696 | -#> |.....................| -0.7233 | -0.6864 |...........|...........| -#> | U| 451.90577 | 93.65 | -5.925 | -1.012 | -0.1815 | -#> |.....................| 2.208 | 2.227 | 0.009581 | 0.8887 | -#> |.....................| 0.05514 | 0.7219 | 0.7437 | 1.332 | -#> |.....................| 1.226 | 1.269 |...........|...........| -#> | X| 451.90577 | 93.65 | 0.002673 | 0.2666 | 0.8340 | -#> |.....................| 9.099 | 2.227 | 0.009581 | 0.8887 | -#> |.....................| 0.05514 | 0.7219 | 0.7437 | 1.332 | -#> |.....................| 1.226 | 1.269 |...........|...........| -#> | 84| 451.74017 | 1.006 | -1.632 | -0.9740 | -0.9682 | -#> |.....................| -0.9311 | 0.9738 | -2.270 | -1.354 | -#> |.....................| 0.7839 | -0.9163 | -1.028 | -0.7474 | -#> |.....................| -0.7117 | -0.6796 |...........|...........| -#> | U| 451.74017 | 93.64 | -5.935 | -1.003 | -0.1804 | -#> |.....................| 2.205 | 2.228 | 0.009348 | 0.8780 | -#> |.....................| 0.05523 | 0.7279 | 0.7400 | 1.359 | -#> |.....................| 1.239 | 1.277 |...........|...........| -#> | X| 451.74017 | 93.64 | 0.002645 | 0.2683 | 0.8350 | -#> |.....................| 9.074 | 2.228 | 0.009348 | 0.8780 | -#> |.....................| 0.05523 | 0.7279 | 0.7400 | 1.359 | -#> |.....................| 1.239 | 1.277 |...........|...........| -#> | 85| 451.58673 | 1.005 | -1.675 | -0.9364 | -0.9637 | -#> |.....................| -0.9422 | 0.9828 | -2.333 | -1.429 | -#> |.....................| 0.8084 | -0.8841 | -1.045 | -0.6570 | -#> |.....................| -0.6645 | -0.6522 |...........|...........| -#> | U| 451.58673 | 93.57 | -5.978 | -0.9678 | -0.1758 | -#> |.....................| 2.194 | 2.233 | 0.008399 | 0.8346 | -#> |.....................| 0.05560 | 0.7523 | 0.7245 | 1.469 | -#> |.....................| 1.289 | 1.306 |...........|...........| -#> | X| 451.58673 | 93.57 | 0.002533 | 0.2753 | 0.8388 | -#> |.....................| 8.974 | 2.233 | 0.008399 | 0.8346 | -#> |.....................| 0.05560 | 0.7523 | 0.7245 | 1.469 | -#> |.....................| 1.289 | 1.306 |...........|...........| -#> | F| Forward Diff. | 7.829 | 0.3494 | 0.8366 | -0.4922 | -#> |.....................| -0.7083 | -3.782 | -0.9020 | -9.523 | -#> |.....................| -4.571 | 4.733 | 3.935 | -3.194 | -#> |.....................| -1.280 | 0.5510 |...........|...........| -#> | 86| 450.56328 | 1.003 | -1.760 | -0.9418 | -0.9563 | -#> |.....................| -0.9480 | 1.050 | -2.445 | -1.421 | -#> |.....................| 0.9402 | -0.9310 | -1.041 | -0.6107 | -#> |.....................| -0.6547 | -0.6413 |...........|...........| -#> | U| 450.56328 | 93.41 | -6.064 | -0.9728 | -0.1684 | -#> |.....................| 2.189 | 2.272 | 0.006706 | 0.8396 | -#> |.....................| 0.05758 | 0.7168 | 0.7280 | 1.525 | -#> |.....................| 1.300 | 1.318 |...........|...........| -#> | X| 450.56328 | 93.41 | 0.002326 | 0.2743 | 0.8450 | -#> |.....................| 8.923 | 2.272 | 0.006706 | 0.8396 | -#> |.....................| 0.05758 | 0.7168 | 0.7280 | 1.525 | -#> |.....................| 1.300 | 1.318 |...........|...........| -#> | 87| 449.70344 | 1.004 | -1.916 | -0.9511 | -0.9429 | -#> |.....................| -0.9589 | 1.170 | -2.653 | -1.409 | -#> |.....................| 1.180 | -1.015 | -1.032 | -0.5274 | -#> |.....................| -0.6372 | -0.6210 |...........|...........| -#> | U| 449.70344 | 93.47 | -6.220 | -0.9817 | -0.1550 | -#> |.....................| 2.178 | 2.342 | 0.003591 | 0.8462 | -#> |.....................| 0.06119 | 0.6534 | 0.7360 | 1.626 | -#> |.....................| 1.318 | 1.340 |...........|...........| -#> | X| 449.70344 | 93.47 | 0.001990 | 0.2726 | 0.8564 | -#> |.....................| 8.826 | 2.342 | 0.003591 | 0.8462 | -#> |.....................| 0.06119 | 0.6534 | 0.7360 | 1.626 | -#> |.....................| 1.318 | 1.340 |...........|...........| -#> | F| Forward Diff. | -19.90 | -0.3168 | 0.4549 | 0.1875 | -#> |.....................| -1.116 | -0.4934 | -0.07687 | -3.113 | -#> |.....................| -2.715 | -1.586 | 5.430 | 3.365 | -#> |.....................| 0.3009 | 1.974 |...........|...........| -#> | 88| 451.98935 | 1.002 | -1.890 | -1.062 | -1.052 | -#> |.....................| -0.7983 | 1.243 | -2.828 | -1.513 | -#> |.....................| 1.600 | -1.043 | -1.029 | -0.6268 | -#> |.....................| -0.3463 | -0.6648 |...........|...........| -#> | U| 451.98935 | 93.35 | -6.193 | -1.087 | -0.2643 | -#> |.....................| 2.338 | 2.384 | 0.0009551 | 0.7857 | -#> |.....................| 0.06749 | 0.6319 | 0.7389 | 1.506 | -#> |.....................| 1.629 | 1.293 |...........|...........| -#> | X| 451.98935 | 93.35 | 0.002043 | 0.2523 | 0.7677 | -#> |.....................| 10.36 | 2.384 | 0.0009551 | 0.7857 | -#> |.....................| 0.06749 | 0.6319 | 0.7389 | 1.506 | -#> |.....................| 1.629 | 1.293 |...........|...........| -#> | 89| 449.56377 | 1.005 | -1.911 | -0.9716 | -0.9631 | -#> |.....................| -0.9292 | 1.184 | -2.685 | -1.428 | -#> |.....................| 1.258 | -1.020 | -1.032 | -0.5459 | -#> |.....................| -0.5835 | -0.6292 |...........|...........| -#> | U| 449.56377 | 93.56 | -6.215 | -1.001 | -0.1752 | -#> |.....................| 2.207 | 2.350 | 0.003105 | 0.8351 | -#> |.....................| 0.06235 | 0.6495 | 0.7362 | 1.604 | -#> |.....................| 1.376 | 1.331 |...........|...........| -#> | X| 449.56377 | 93.56 | 0.002000 | 0.2687 | 0.8393 | -#> |.....................| 9.092 | 2.350 | 0.003105 | 0.8351 | -#> |.....................| 0.06235 | 0.6495 | 0.7362 | 1.604 | -#> |.....................| 1.376 | 1.331 |...........|...........| -#> | F| Forward Diff. | -8.503 | -0.3085 | -0.7128 | -0.4858 | -#> |.....................| -0.1462 | -0.3349 | -0.04630 | -2.615 | -#> |.....................| -2.539 | -1.761 | 5.421 | 2.664 | -#> |.....................| 3.069 | 1.771 |...........|...........| -#> | 90| 449.37295 | 1.008 | -1.883 | -0.9569 | -0.9753 | -#> |.....................| -0.9112 | 1.201 | -2.710 | -1.458 | -#> |.....................| 1.352 | -1.030 | -1.036 | -0.5467 | -#> |.....................| -0.5933 | -0.6460 |...........|...........| -#> | U| 449.37295 | 93.89 | -6.186 | -0.9871 | -0.1875 | -#> |.....................| 2.225 | 2.360 | 0.002726 | 0.8181 | -#> |.....................| 0.06377 | 0.6417 | 0.7326 | 1.603 | -#> |.....................| 1.365 | 1.313 |...........|...........| -#> | X| 449.37295 | 93.89 | 0.002058 | 0.2715 | 0.8291 | -#> |.....................| 9.256 | 2.360 | 0.002726 | 0.8181 | -#> |.....................| 0.06377 | 0.6417 | 0.7326 | 1.603 | -#> |.....................| 1.365 | 1.313 |...........|...........| -#> | F| Forward Diff. | 31.95 | -0.2055 | 0.2861 | -0.8772 | -#> |.....................| 0.4589 | 0.008909 | 0.01409 | -2.994 | -#> |.....................| -2.511 | -2.129 | 5.021 | 2.567 | -#> |.....................| 2.446 | 1.004 |...........|...........| -#> | 91| 449.07232 | 1.007 | -1.848 | -0.9883 | -0.9607 | -#> |.....................| -0.9269 | 1.208 | -2.721 | -1.473 | -#> |.....................| 1.446 | -1.013 | -1.041 | -0.5472 | -#> |.....................| -0.6000 | -0.6251 |...........|...........| -#> | U| 449.07232 | 93.73 | -6.151 | -1.017 | -0.1729 | -#> |.....................| 2.210 | 2.364 | 0.002568 | 0.8093 | -#> |.....................| 0.06518 | 0.6543 | 0.7283 | 1.602 | -#> |.....................| 1.358 | 1.335 |...........|...........| -#> | X| 449.07232 | 93.73 | 0.002130 | 0.2656 | 0.8412 | -#> |.....................| 9.113 | 2.364 | 0.002568 | 0.8093 | -#> |.....................| 0.06518 | 0.6543 | 0.7283 | 1.602 | -#> |.....................| 1.358 | 1.335 |...........|...........| -#> | 92| 449.34581 | 1.013 | -1.744 | -1.083 | -0.9172 | -#> |.....................| -0.9739 | 1.229 | -2.752 | -1.520 | -#> |.....................| 1.728 | -0.9642 | -1.054 | -0.5478 | -#> |.....................| -0.6192 | -0.5619 |...........|...........| -#> | U| 449.34581 | 94.33 | -6.047 | -1.106 | -0.1294 | -#> |.....................| 2.163 | 2.376 | 0.002092 | 0.7821 | -#> |.....................| 0.06942 | 0.6916 | 0.7169 | 1.601 | -#> |.....................| 1.337 | 1.403 |...........|...........| -#> | X| 449.34581 | 94.33 | 0.002364 | 0.2486 | 0.8787 | -#> |.....................| 8.694 | 2.376 | 0.002092 | 0.7821 | -#> |.....................| 0.06942 | 0.6916 | 0.7169 | 1.601 | -#> |.....................| 1.337 | 1.403 |...........|...........| -#> | F| Forward Diff. | 11.36 | -0.08356 | -1.544 | -0.3785 | -#> |.....................| -0.02879 | 0.1985 | 0.04898 | -2.532 | -#> |.....................| -2.210 | -1.428 | 5.624 | 2.440 | -#> |.....................| 2.104 | 1.894 |...........|...........| -#> | 93| 449.83746 | 0.9966 | -1.806 | -0.8436 | -0.9213 | -#> |.....................| -1.016 | 1.236 | -2.752 | -1.567 | -#> |.....................| 1.816 | -1.085 | -1.056 | -0.6567 | -#> |.....................| -0.5363 | -0.5852 |...........|...........| -#> | U| 449.83746 | 92.80 | -6.109 | -0.8802 | -0.1335 | -#> |.....................| 2.121 | 2.380 | 0.002093 | 0.7548 | -#> |.....................| 0.07074 | 0.5997 | 0.7149 | 1.469 | -#> |.....................| 1.426 | 1.378 |...........|...........| -#> | X| 449.83746 | 92.80 | 0.002222 | 0.2931 | 0.8750 | -#> |.....................| 8.340 | 2.380 | 0.002093 | 0.7548 | -#> |.....................| 0.07074 | 0.5997 | 0.7149 | 1.469 | -#> |.....................| 1.426 | 1.378 |...........|...........| -#> | 94| 449.05525 | 1.000 | -1.836 | -0.9477 | -0.9497 | -#> |.....................| -0.9515 | 1.216 | -2.730 | -1.498 | -#> |.....................| 1.549 | -1.033 | -1.047 | -0.5784 | -#> |.....................| -0.5830 | -0.6146 |...........|...........| -#> | U| 449.05525 | 93.13 | -6.140 | -0.9784 | -0.1618 | -#> |.....................| 2.185 | 2.368 | 0.002436 | 0.7946 | -#> |.....................| 0.06673 | 0.6395 | 0.7230 | 1.564 | -#> |.....................| 1.376 | 1.346 |...........|...........| -#> | X| 449.05525 | 93.13 | 0.002156 | 0.2732 | 0.8506 | -#> |.....................| 8.891 | 2.368 | 0.002436 | 0.7946 | -#> |.....................| 0.06673 | 0.6395 | 0.7230 | 1.564 | -#> |.....................| 1.376 | 1.346 |...........|...........| -#> | F| Forward Diff. | -56.82 | -0.05113 | 0.4930 | -0.04031 | -#> |.....................| -1.049 | 0.03445 | -0.05944 | -2.319 | -#> |.....................| -2.208 | -2.328 | 3.545 | 0.3775 | -#> |.....................| 2.643 | 2.387 |...........|...........| -#> | 95| 448.75128 | 1.006 | -1.837 | -0.9543 | -0.9497 | -#> |.....................| -0.9537 | 1.219 | -2.732 | -1.514 | -#> |.....................| 1.608 | -1.030 | -1.050 | -0.5750 | -#> |.....................| -0.5860 | -0.6263 |...........|...........| -#> | U| 448.75128 | 93.69 | -6.140 | -0.9847 | -0.1618 | -#> |.....................| 2.183 | 2.370 | 0.002396 | 0.7854 | -#> |.....................| 0.06761 | 0.6415 | 0.7208 | 1.568 | -#> |.....................| 1.373 | 1.334 |...........|...........| -#> | X| 448.75128 | 93.69 | 0.002154 | 0.2720 | 0.8506 | -#> |.....................| 8.872 | 2.370 | 0.002396 | 0.7854 | -#> |.....................| 0.06761 | 0.6415 | 0.7208 | 1.568 | -#> |.....................| 1.373 | 1.334 |...........|...........| -#> | F| Forward Diff. | 6.795 | -0.02569 | 0.3964 | 0.03329 | -#> |.....................| -0.8574 | 0.1774 | 0.01390 | -2.462 | -#> |.....................| -2.149 | -2.476 | 3.910 | 1.045 | -#> |.....................| 2.743 | 2.014 |...........|...........| -#> | 96| 448.60805 | 1.005 | -1.844 | -0.9658 | -0.9658 | -#> |.....................| -0.9330 | 1.222 | -2.731 | -1.528 | -#> |.....................| 1.652 | -1.023 | -1.051 | -0.5597 | -#> |.....................| -0.5993 | -0.6478 |...........|...........| -#> | U| 448.60805 | 93.55 | -6.147 | -0.9955 | -0.1780 | -#> |.....................| 2.204 | 2.372 | 0.002406 | 0.7773 | -#> |.....................| 0.06828 | 0.6470 | 0.7198 | 1.587 | -#> |.....................| 1.359 | 1.311 |...........|...........| -#> | X| 448.60805 | 93.55 | 0.002140 | 0.2698 | 0.8370 | -#> |.....................| 9.057 | 2.372 | 0.002406 | 0.7773 | -#> |.....................| 0.06828 | 0.6470 | 0.7198 | 1.587 | -#> |.....................| 1.359 | 1.311 |...........|...........| -#> | 97| 448.54893 | 1.004 | -1.854 | -0.9831 | -0.9905 | -#> |.....................| -0.9018 | 1.226 | -2.730 | -1.550 | -#> |.....................| 1.719 | -1.013 | -1.051 | -0.5361 | -#> |.....................| -0.6188 | -0.6800 |...........|...........| -#> | U| 448.54893 | 93.53 | -6.157 | -1.012 | -0.2026 | -#> |.....................| 2.235 | 2.374 | 0.002422 | 0.7645 | -#> |.....................| 0.06928 | 0.6548 | 0.7192 | 1.616 | -#> |.....................| 1.338 | 1.276 |...........|...........| -#> | X| 448.54893 | 93.53 | 0.002118 | 0.2666 | 0.8166 | -#> |.....................| 9.344 | 2.374 | 0.002422 | 0.7645 | -#> |.....................| 0.06928 | 0.6548 | 0.7192 | 1.616 | -#> |.....................| 1.338 | 1.276 |...........|...........| -#> | F| Forward Diff. | -11.31 | -0.05480 | -1.344 | -1.332 | -#> |.....................| 0.5363 | 0.1616 | -0.02955 | -2.282 | -#> |.....................| -1.949 | -1.541 | 5.051 | 2.875 | -#> |.....................| 1.005 | -0.6800 |...........|...........| -#> | 98| 448.23423 | 1.005 | -1.862 | -0.9802 | -0.9885 | -#> |.....................| -0.8649 | 1.225 | -2.731 | -1.570 | -#> |.....................| 1.863 | -0.9934 | -1.058 | -0.5422 | -#> |.....................| -0.6330 | -0.6404 |...........|...........| -#> | U| 448.23423 | 93.60 | -6.165 | -1.009 | -0.2007 | -#> |.....................| 2.272 | 2.374 | 0.002415 | 0.7529 | -#> |.....................| 0.07145 | 0.6695 | 0.7131 | 1.608 | -#> |.....................| 1.323 | 1.319 |...........|...........| -#> | X| 448.23423 | 93.60 | 0.002101 | 0.2671 | 0.8182 | -#> |.....................| 9.695 | 2.374 | 0.002415 | 0.7529 | -#> |.....................| 0.07145 | 0.6695 | 0.7131 | 1.608 | -#> |.....................| 1.323 | 1.319 |...........|...........| -#> | 99| 448.52797 | 1.003 | -1.887 | -0.9721 | -0.9832 | -#> |.....................| -0.7539 | 1.222 | -2.732 | -1.631 | -#> |.....................| 2.296 | -0.9358 | -1.078 | -0.5592 | -#> |.....................| -0.6753 | -0.5215 |...........|...........| -#> | U| 448.52797 | 93.41 | -6.190 | -1.001 | -0.1954 | -#> |.....................| 2.383 | 2.371 | 0.002396 | 0.7173 | -#> |.....................| 0.07796 | 0.7131 | 0.6963 | 1.588 | -#> |.....................| 1.277 | 1.446 |...........|...........| -#> | X| 448.52797 | 93.41 | 0.002050 | 0.2687 | 0.8225 | -#> |.....................| 10.83 | 2.371 | 0.002396 | 0.7173 | -#> |.....................| 0.07796 | 0.7131 | 0.6963 | 1.588 | -#> |.....................| 1.277 | 1.446 |...........|...........| -#> | F| Forward Diff. | -1.417 | -0.03842 | -1.058 | -1.257 | -#> |.....................| 1.697 | 0.2446 | 0.02601 | -1.725 | -#> |.....................| -1.728 | -0.7541 | 3.822 | 2.423 | -#> |.....................| 0.4552 | 1.132 |...........|...........| -#> | 100| 447.48636 | 1.010 | -1.889 | -1.018 | -0.9136 | -#> |.....................| -0.9465 | 1.241 | -2.741 | -1.706 | -#> |.....................| 2.465 | -0.9635 | -1.095 | -0.5705 | -#> |.....................| -0.6276 | -0.6598 |...........|...........| -#> | U| 447.48636 | 94.00 | -6.193 | -1.045 | -0.1257 | -#> |.....................| 2.190 | 2.383 | 0.002265 | 0.6743 | -#> |.....................| 0.08050 | 0.6921 | 0.6807 | 1.574 | -#> |.....................| 1.328 | 1.298 |...........|...........| -#> | X| 447.48636 | 94.00 | 0.002044 | 0.2602 | 0.8818 | -#> |.....................| 8.935 | 2.383 | 0.002265 | 0.6743 | -#> |.....................| 0.08050 | 0.6921 | 0.6807 | 1.574 | -#> |.....................| 1.328 | 1.298 |...........|...........| -#> | F| Forward Diff. | 49.18 | 0.06228 | -2.520 | 1.219 | -#> |.....................| -0.3402 | 0.5332 | 0.01803 | -1.013 | -#> |.....................| -0.7363 | 0.9697 | 2.720 | 0.6118 | -#> |.....................| 0.4882 | -0.1519 |...........|...........| -#> | 101| 448.59314 | 1.009 | -1.906 | -0.9798 | -1.202 | -#> |.....................| -1.107 | 1.243 | -2.730 | -1.791 | -#> |.....................| 2.989 | -0.9474 | -1.110 | -0.5914 | -#> |.....................| -0.6423 | -0.5882 |...........|...........| -#> | U| 448.59314 | 93.96 | -6.209 | -1.009 | -0.4139 | -#> |.....................| 2.029 | 2.384 | 0.002422 | 0.6247 | -#> |.....................| 0.08837 | 0.7043 | 0.6679 | 1.549 | -#> |.....................| 1.313 | 1.375 |...........|...........| -#> | X| 448.59314 | 93.96 | 0.002010 | 0.2672 | 0.6611 | -#> |.....................| 7.610 | 2.384 | 0.002422 | 0.6247 | -#> |.....................| 0.08837 | 0.7043 | 0.6679 | 1.549 | -#> |.....................| 1.313 | 1.375 |...........|...........| -#> | 102| 447.34338 | 1.004 | -1.893 | -1.010 | -0.9727 | -#> |.....................| -0.9794 | 1.241 | -2.739 | -1.723 | -#> |.....................| 2.572 | -0.9603 | -1.099 | -0.5748 | -#> |.....................| -0.6307 | -0.6452 |...........|...........| -#> | U| 447.34338 | 93.48 | -6.196 | -1.037 | -0.1848 | -#> |.....................| 2.157 | 2.383 | 0.002297 | 0.6642 | -#> |.....................| 0.08211 | 0.6946 | 0.6778 | 1.569 | -#> |.....................| 1.325 | 1.314 |...........|...........| -#> | X| 447.34338 | 93.48 | 0.002037 | 0.2617 | 0.8313 | -#> |.....................| 8.647 | 2.383 | 0.002297 | 0.6642 | -#> |.....................| 0.08211 | 0.6946 | 0.6778 | 1.569 | -#> |.....................| 1.325 | 1.314 |...........|...........| -#> | F| Forward Diff. | -27.99 | 0.05620 | -2.283 | -0.5861 | -#> |.....................| -1.399 | 0.3409 | -0.05316 | -0.7185 | -#> |.....................| -0.6589 | 0.7167 | 1.472 | 0.2167 | -#> |.....................| 0.2339 | 0.7351 |...........|...........| -#> | 103| 447.24116 | 1.004 | -1.898 | -0.9880 | -0.9438 | -#> |.....................| -0.9421 | 1.243 | -2.723 | -1.759 | -#> |.....................| 2.683 | -0.9737 | -1.096 | -0.5790 | -#> |.....................| -0.6284 | -0.6557 |...........|...........| -#> | U| 447.24116 | 93.50 | -6.201 | -1.017 | -0.1559 | -#> |.....................| 2.195 | 2.384 | 0.002530 | 0.6435 | -#> |.....................| 0.08377 | 0.6844 | 0.6802 | 1.564 | -#> |.....................| 1.328 | 1.302 |...........|...........| -#> | X| 447.24116 | 93.50 | 0.002027 | 0.2657 | 0.8556 | -#> |.....................| 8.976 | 2.384 | 0.002530 | 0.6435 | -#> |.....................| 0.08377 | 0.6844 | 0.6802 | 1.564 | -#> |.....................| 1.328 | 1.302 |...........|...........| -#> | F| Forward Diff. | -22.25 | 0.02611 | -1.124 | 0.2366 | -#> |.....................| -0.4078 | 0.2597 | -0.06938 | -0.8187 | -#> |.....................| -0.5375 | 0.002218 | 1.533 | -0.1306 | -#> |.....................| 0.2372 | 0.1318 |...........|...........| -#> | 104| 447.36545 | 1.010 | -1.910 | -0.9563 | -1.018 | -#> |.....................| -0.9640 | 1.238 | -2.696 | -1.806 | -#> |.....................| 2.921 | -0.9760 | -1.100 | -0.5866 | -#> |.....................| -0.6320 | -0.6434 |...........|...........| -#> | U| 447.36545 | 94.05 | -6.214 | -0.9866 | -0.2304 | -#> |.....................| 2.173 | 2.381 | 0.002941 | 0.6159 | -#> |.....................| 0.08734 | 0.6827 | 0.6770 | 1.554 | -#> |.....................| 1.324 | 1.315 |...........|...........| -#> | X| 447.36545 | 94.05 | 0.002002 | 0.2716 | 0.7942 | -#> |.....................| 8.780 | 2.381 | 0.002941 | 0.6159 | -#> |.....................| 0.08734 | 0.6827 | 0.6770 | 1.554 | -#> |.....................| 1.324 | 1.315 |...........|...........| -#> | 105| 447.25244 | 1.009 | -1.902 | -0.9770 | -0.9694 | -#> |.....................| -0.9495 | 1.241 | -2.714 | -1.775 | -#> |.....................| 2.765 | -0.9745 | -1.097 | -0.5816 | -#> |.....................| -0.6297 | -0.6515 |...........|...........| -#> | U| 447.25244 | 93.94 | -6.205 | -1.006 | -0.1815 | -#> |.....................| 2.187 | 2.383 | 0.002671 | 0.6341 | -#> |.....................| 0.08500 | 0.6838 | 0.6790 | 1.560 | -#> |.....................| 1.326 | 1.307 |...........|...........| -#> | X| 447.25244 | 93.94 | 0.002018 | 0.2677 | 0.8340 | -#> |.....................| 8.909 | 2.383 | 0.002671 | 0.6341 | -#> |.....................| 0.08500 | 0.6838 | 0.6790 | 1.560 | -#> |.....................| 1.326 | 1.307 |...........|...........| -#> | 106| 447.24908 | 1.008 | -1.900 | -0.9828 | -0.9557 | -#> |.....................| -0.9455 | 1.242 | -2.719 | -1.766 | -#> |.....................| 2.721 | -0.9741 | -1.097 | -0.5802 | -#> |.....................| -0.6290 | -0.6537 |...........|...........| -#> | U| 447.24908 | 93.91 | -6.203 | -1.012 | -0.1678 | -#> |.....................| 2.191 | 2.383 | 0.002596 | 0.6392 | -#> |.....................| 0.08434 | 0.6841 | 0.6795 | 1.562 | -#> |.....................| 1.327 | 1.304 |...........|...........| -#> | X| 447.24908 | 93.91 | 0.002023 | 0.2667 | 0.8455 | -#> |.....................| 8.945 | 2.383 | 0.002596 | 0.6392 | -#> |.....................| 0.08434 | 0.6841 | 0.6795 | 1.562 | -#> |.....................| 1.327 | 1.304 |...........|...........| -#> | 107| 447.25180 | 1.008 | -1.899 | -0.9855 | -0.9493 | -#> |.....................| -0.9436 | 1.242 | -2.721 | -1.762 | -#> |.....................| 2.700 | -0.9739 | -1.097 | -0.5796 | -#> |.....................| -0.6287 | -0.6548 |...........|...........| -#> | U| 447.2518 | 93.89 | -6.202 | -1.014 | -0.1614 | -#> |.....................| 2.193 | 2.383 | 0.002560 | 0.6416 | -#> |.....................| 0.08403 | 0.6843 | 0.6798 | 1.563 | -#> |.....................| 1.327 | 1.303 |...........|...........| -#> | X| 447.2518 | 93.89 | 0.002025 | 0.2662 | 0.8509 | -#> |.....................| 8.962 | 2.383 | 0.002560 | 0.6416 | -#> |.....................| 0.08403 | 0.6843 | 0.6798 | 1.563 | -#> |.....................| 1.327 | 1.303 |...........|...........| -#> | 108| 447.25421 | 1.008 | -1.898 | -0.9869 | -0.9460 | -#> |.....................| -0.9426 | 1.242 | -2.722 | -1.760 | -#> |.....................| 2.690 | -0.9738 | -1.096 | -0.5792 | -#> |.....................| -0.6286 | -0.6553 |...........|...........| -#> | U| 447.25421 | 93.88 | -6.202 | -1.015 | -0.1582 | -#> |.....................| 2.194 | 2.384 | 0.002542 | 0.6428 | -#> |.....................| 0.08388 | 0.6843 | 0.6799 | 1.563 | -#> |.....................| 1.327 | 1.303 |...........|...........| -#> | X| 447.25421 | 93.88 | 0.002026 | 0.2659 | 0.8537 | -#> |.....................| 8.970 | 2.384 | 0.002542 | 0.6428 | -#> |.....................| 0.08388 | 0.6843 | 0.6799 | 1.563 | -#> |.....................| 1.327 | 1.303 |...........|...........| -#> | 109| 447.24978 | 1.008 | -1.898 | -0.9878 | -0.9438 | -#> |.....................| -0.9420 | 1.242 | -2.723 | -1.759 | -#> |.....................| 2.683 | -0.9737 | -1.096 | -0.5790 | -#> |.....................| -0.6285 | -0.6557 |...........|...........| -#> | U| 447.24978 | 93.86 | -6.201 | -1.016 | -0.1560 | -#> |.....................| 2.195 | 2.384 | 0.002530 | 0.6436 | -#> |.....................| 0.08377 | 0.6844 | 0.6800 | 1.564 | -#> |.....................| 1.328 | 1.302 |...........|...........| -#> | X| 447.24978 | 93.86 | 0.002027 | 0.2657 | 0.8556 | -#> |.....................| 8.976 | 2.384 | 0.002530 | 0.6436 | -#> |.....................| 0.08377 | 0.6844 | 0.6800 | 1.564 | -#> |.....................| 1.328 | 1.302 |...........|...........| -#> | 110| 447.22094 | 1.006 | -1.898 | -0.9879 | -0.9438 | -#> |.....................| -0.9420 | 1.243 | -2.723 | -1.759 | -#> |.....................| 2.683 | -0.9737 | -1.096 | -0.5790 | -#> |.....................| -0.6284 | -0.6557 |...........|...........| -#> | U| 447.22094 | 93.66 | -6.201 | -1.016 | -0.1560 | -#> |.....................| 2.195 | 2.384 | 0.002530 | 0.6435 | -#> |.....................| 0.08377 | 0.6844 | 0.6801 | 1.564 | -#> |.....................| 1.328 | 1.302 |...........|...........| -#> | X| 447.22094 | 93.66 | 0.002027 | 0.2657 | 0.8556 | -#> |.....................| 8.976 | 2.384 | 0.002530 | 0.6435 | -#> |.....................| 0.08377 | 0.6844 | 0.6801 | 1.564 | -#> |.....................| 1.328 | 1.302 |...........|...........| -#> | F| Forward Diff. | 0.7136 | 0.03206 | -1.028 | 0.2620 | -#> |.....................| -0.3312 | 0.3050 | -0.05505 | -0.8960 | -#> |.....................| -0.4549 | 0.03409 | 2.494 | -0.1555 | -#> |.....................| 0.2265 | 0.1085 |...........|...........| -#> | 111| 447.21344 | 1.005 | -1.898 | -0.9873 | -0.9440 | -#> |.....................| -0.9418 | 1.242 | -2.723 | -1.758 | -#> |.....................| 2.683 | -0.9737 | -1.098 | -0.5789 | -#> |.....................| -0.6286 | -0.6557 |...........|...........| -#> | U| 447.21344 | 93.62 | -6.201 | -1.016 | -0.1561 | -#> |.....................| 2.195 | 2.384 | 0.002531 | 0.6439 | -#> |.....................| 0.08377 | 0.6844 | 0.6789 | 1.564 | -#> |.....................| 1.327 | 1.302 |...........|...........| -#> | X| 447.21344 | 93.62 | 0.002027 | 0.2658 | 0.8555 | -#> |.....................| 8.978 | 2.384 | 0.002531 | 0.6439 | -#> |.....................| 0.08377 | 0.6844 | 0.6789 | 1.564 | -#> |.....................| 1.327 | 1.302 |...........|...........| -#> | F| Forward Diff. | -4.689 | 0.03686 | -1.013 | 0.2539 | -#> |.....................| -0.3408 | 0.6592 | 0.03740 | -0.5502 | -#> |.....................| -0.2201 | 0.3219 | 2.382 | -0.1778 | -#> |.....................| 0.2028 | 0.08770 |...........|...........| -#> | 112| 447.19216 | 1.006 | -1.899 | -0.9854 | -0.9463 | -#> |.....................| -0.9420 | 1.239 | -2.724 | -1.756 | -#> |.....................| 2.680 | -0.9744 | -1.101 | -0.5784 | -#> |.....................| -0.6293 | -0.6560 |...........|...........| -#> | U| 447.19216 | 93.64 | -6.203 | -1.014 | -0.1585 | -#> |.....................| 2.195 | 2.382 | 0.002523 | 0.6453 | -#> |.....................| 0.08373 | 0.6839 | 0.6759 | 1.564 | -#> |.....................| 1.327 | 1.302 |...........|...........| -#> | X| 447.19216 | 93.64 | 0.002024 | 0.2662 | 0.8535 | -#> |.....................| 8.976 | 2.382 | 0.002523 | 0.6453 | -#> |.....................| 0.08373 | 0.6839 | 0.6759 | 1.564 | -#> |.....................| 1.327 | 1.302 |...........|...........| -#> | 113| 447.14896 | 1.005 | -1.904 | -0.9796 | -0.9535 | -#> |.....................| -0.9426 | 1.230 | -2.725 | -1.748 | -#> |.....................| 2.670 | -0.9764 | -1.111 | -0.5767 | -#> |.....................| -0.6315 | -0.6570 |...........|...........| -#> | U| 447.14896 | 93.56 | -6.208 | -1.009 | -0.1657 | -#> |.....................| 2.194 | 2.376 | 0.002500 | 0.6498 | -#> |.....................| 0.08358 | 0.6823 | 0.6675 | 1.566 | -#> |.....................| 1.324 | 1.301 |...........|...........| -#> | X| 447.14896 | 93.56 | 0.002014 | 0.2673 | 0.8473 | -#> |.....................| 8.971 | 2.376 | 0.002500 | 0.6498 | -#> |.....................| 0.08358 | 0.6823 | 0.6675 | 1.566 | -#> |.....................| 1.324 | 1.301 |...........|...........| -#> | 114| 447.12523 | 1.003 | -1.923 | -0.9566 | -0.9821 | -#> |.....................| -0.9448 | 1.194 | -2.731 | -1.717 | -#> |.....................| 2.632 | -0.9846 | -1.149 | -0.5701 | -#> |.....................| -0.6401 | -0.6607 |...........|...........| -#> | U| 447.12523 | 93.36 | -6.227 | -0.9868 | -0.1943 | -#> |.....................| 2.192 | 2.355 | 0.002410 | 0.6677 | -#> |.....................| 0.08300 | 0.6762 | 0.6336 | 1.574 | -#> |.....................| 1.315 | 1.297 |...........|...........| -#> | X| 447.12523 | 93.36 | 0.001976 | 0.2715 | 0.8234 | -#> |.....................| 8.951 | 2.355 | 0.002410 | 0.6677 | -#> |.....................| 0.08300 | 0.6762 | 0.6336 | 1.574 | -#> |.....................| 1.315 | 1.297 |...........|...........| -#> | F| Forward Diff. | -42.78 | 0.1470 | 0.5793 | -0.8455 | -#> |.....................| -0.3546 | -0.4331 | -0.1071 | -0.02049 | -#> |.....................| -0.3358 | -0.3904 | -2.177 | 0.2043 | -#> |.....................| -0.1377 | -0.3207 |...........|...........| -#> | 115| 447.09924 | 1.007 | -1.940 | -0.9416 | -1.018 | -#> |.....................| -0.9550 | 1.181 | -2.719 | -1.734 | -#> |.....................| 2.734 | -0.9861 | -1.153 | -0.5706 | -#> |.....................| -0.6433 | -0.6564 |...........|...........| -#> | U| 447.09924 | 93.80 | -6.243 | -0.9727 | -0.2297 | -#> |.....................| 2.182 | 2.348 | 0.002591 | 0.6578 | -#> |.....................| 0.08453 | 0.6750 | 0.6303 | 1.574 | -#> |.....................| 1.312 | 1.301 |...........|...........| -#> | X| 447.09924 | 93.80 | 0.001943 | 0.2743 | 0.7947 | -#> |.....................| 8.860 | 2.348 | 0.002591 | 0.6578 | -#> |.....................| 0.08453 | 0.6750 | 0.6303 | 1.574 | -#> |.....................| 1.312 | 1.301 |...........|...........| -#> | F| Forward Diff. | 15.04 | 0.1387 | 1.646 | -1.777 | -#> |.....................| -0.3749 | -0.5049 | -0.07528 | 0.1505 | -#> |.....................| -0.2071 | -0.6675 | -2.129 | 0.2735 | -#> |.....................| -0.05533 | -0.2849 |...........|...........| -#> | 116| 447.06926 | 1.008 | -1.968 | -0.9759 | -0.9363 | -#> |.....................| -0.9300 | 1.192 | -2.714 | -1.733 | -#> |.....................| 2.676 | -0.9757 | -1.142 | -0.5672 | -#> |.....................| -0.6383 | -0.6598 |...........|...........| -#> | U| 447.06926 | 93.90 | -6.272 | -1.005 | -0.1484 | -#> |.....................| 2.207 | 2.354 | 0.002664 | 0.6586 | -#> |.....................| 0.08367 | 0.6829 | 0.6398 | 1.578 | -#> |.....................| 1.317 | 1.298 |...........|...........| -#> | X| 447.06926 | 93.90 | 0.001889 | 0.2679 | 0.8621 | -#> |.....................| 9.084 | 2.354 | 0.002664 | 0.6586 | -#> |.....................| 0.08367 | 0.6829 | 0.6398 | 1.578 | -#> |.....................| 1.317 | 1.298 |...........|...........| -#> | F| Forward Diff. | 31.57 | 0.06960 | -0.1881 | 0.5445 | -#> |.....................| 0.2088 | -0.3879 | -0.06801 | -0.3419 | -#> |.....................| -0.4021 | 0.02711 | -1.273 | 0.2199 | -#> |.....................| -0.1004 | -0.4182 |...........|...........| -#> | 117| 447.12806 | 1.006 | -2.047 | -0.9734 | -0.9587 | -#> |.....................| -0.9336 | 1.189 | -2.704 | -1.764 | -#> |.....................| 2.737 | -0.9879 | -1.112 | -0.5826 | -#> |.....................| -0.6349 | -0.6438 |...........|...........| -#> | U| 447.12806 | 93.67 | -6.350 | -1.003 | -0.1708 | -#> |.....................| 2.203 | 2.352 | 0.002825 | 0.6405 | -#> |.....................| 0.08458 | 0.6737 | 0.6664 | 1.559 | -#> |.....................| 1.321 | 1.315 |...........|...........| -#> | X| 447.12806 | 93.67 | 0.001747 | 0.2684 | 0.8430 | -#> |.....................| 9.052 | 2.352 | 0.002825 | 0.6405 | -#> |.....................| 0.08458 | 0.6737 | 0.6664 | 1.559 | -#> |.....................| 1.321 | 1.315 |...........|...........| -#> | 118| 447.05003 | 1.006 | -1.997 | -0.9750 | -0.9445 | -#> |.....................| -0.9313 | 1.191 | -2.710 | -1.744 | -#> |.....................| 2.698 | -0.9801 | -1.131 | -0.5728 | -#> |.....................| -0.6370 | -0.6539 |...........|...........| -#> | U| 447.05003 | 93.71 | -6.300 | -1.004 | -0.1566 | -#> |.....................| 2.205 | 2.354 | 0.002723 | 0.6520 | -#> |.....................| 0.08400 | 0.6796 | 0.6495 | 1.571 | -#> |.....................| 1.318 | 1.304 |...........|...........| -#> | X| 447.05003 | 93.71 | 0.001836 | 0.2681 | 0.8551 | -#> |.....................| 9.073 | 2.354 | 0.002723 | 0.6520 | -#> |.....................| 0.08400 | 0.6796 | 0.6495 | 1.571 | -#> |.....................| 1.318 | 1.304 |...........|...........| -#> | F| Forward Diff. | 4.860 | -0.01375 | -0.2473 | 0.2780 | -#> |.....................| 0.08862 | -0.4372 | -0.08802 | -0.3404 | -#> |.....................| -0.3654 | -0.2345 | -0.3468 | 0.08396 | -#> |.....................| -0.01035 | -0.06837 |...........|...........| -#> | 119| 447.04716 | 1.006 | -1.989 | -0.9725 | -0.9518 | -#> |.....................| -0.9334 | 1.193 | -2.718 | -1.756 | -#> |.....................| 2.735 | -0.9825 | -1.129 | -0.5738 | -#> |.....................| -0.6372 | -0.6523 |...........|...........| -#> | U| 447.04716 | 93.69 | -6.292 | -1.002 | -0.1639 | -#> |.....................| 2.203 | 2.355 | 0.002610 | 0.6452 | -#> |.....................| 0.08456 | 0.6777 | 0.6509 | 1.570 | -#> |.....................| 1.318 | 1.306 |...........|...........| -#> | X| 447.04716 | 93.69 | 0.001850 | 0.2686 | 0.8488 | -#> |.....................| 9.053 | 2.355 | 0.002610 | 0.6452 | -#> |.....................| 0.08456 | 0.6777 | 0.6509 | 1.570 | -#> |.....................| 1.318 | 1.306 |...........|...........| -#> | F| Forward Diff. | 2.456 | -0.007589 | -0.1181 | 0.06051 | -#> |.....................| 0.03158 | -0.4028 | -0.08358 | -0.4018 | -#> |.....................| -0.3358 | -0.3459 | -0.2609 | 0.03632 | -#> |.....................| -0.03277 | 0.02331 |...........|...........| -#> | 120| 447.04716 | 1.006 | -1.989 | -0.9725 | -0.9518 | -#> |.....................| -0.9334 | 1.193 | -2.718 | -1.756 | -#> |.....................| 2.735 | -0.9825 | -1.129 | -0.5738 | -#> |.....................| -0.6372 | -0.6523 |...........|...........| -#> | U| 447.04716 | 93.69 | -6.292 | -1.002 | -0.1639 | -#> |.....................| 2.203 | 2.355 | 0.002610 | 0.6452 | -#> |.....................| 0.08456 | 0.6777 | 0.6509 | 1.570 | -#> |.....................| 1.318 | 1.306 |...........|...........| -#> | X| 447.04716 | 93.69 | 0.001850 | 0.2686 | 0.8488 | -#> |.....................| 9.053 | 2.355 | 0.002610 | 0.6452 | -#> |.....................| 0.08456 | 0.6777 | 0.6509 | 1.570 | -#> |.....................| 1.318 | 1.306 |...........|...........| -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[4]+THETA[4]; +#> rx_expr_8~ETA[5]+THETA[5]; +#> rx_expr_12~exp(-(rx_expr_8)); +#> rx_expr_14~t*rx_expr_12; +#> rx_expr_15~1+rx_expr_14; +#> rx_expr_17~rx_expr_7-(rx_expr_8); +#> rx_expr_19~exp(rx_expr_17); +#> d/dt(parent)=-rx_expr_19*parent/(rx_expr_15); +#> rx_expr_9~ETA[2]+THETA[2]; +#> rx_expr_11~exp(rx_expr_9); +#> d/dt(A1)=-rx_expr_11*A1+rx_expr_19*parent*f_parent_to_A1/(rx_expr_15); +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_13~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_13+rx_expr_3; +#> rx_hi_~rx_expr_13+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_10~parent*(rx_expr_2); +#> rx_expr_16~rx_expr_10*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1); +#> rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1)),2)*Rx_pow_di(THETA[9],2)+Rx_pow_di(THETA[8],2))*(rx_expr_0)+(Rx_pow_di(THETA[7],2)*Rx_pow_di(((rx_expr_4+rx_expr_16)*(rx_expr_1)),2)+Rx_pow_di(THETA[6],2))*(rx_expr_2)*(rx_expr_1); +#> parent_0=THETA[1]; +#> log_k_A1=THETA[2]; +#> f_parent_qlogis=THETA[3]; +#> log_alpha=THETA[4]; +#> log_beta=THETA[5]; +#> sigma_low_parent=THETA[6]; +#> rsd_high_parent=THETA[7]; +#> sigma_low_A1=THETA[8]; +#> rsd_high_A1=THETA[9]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_A1=ETA[2]; +#> eta.f_parent_qlogis=ETA[3]; +#> eta.log_alpha=ETA[4]; +#> eta.log_beta=ETA[5]; +#> parent_0_model=rx_expr_6; +#> k_A1=rx_expr_11; +#> alpha=exp(rx_expr_7); +#> beta=exp(rx_expr_8); +#> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.196 0.388 8.584
                                                                f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", error_model = "obs_tc") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> 1: 9.1294e+01 -5.0486e+00 -1.7441e+00 -3.5640e+00 -2.1387e+00 4.8639e-01 5.5948e+00 1.4680e+00 1.1057e+00 2.3810e+00 4.8150e-01 4.3452e-01 1.0359e+01 2.3790e-05 7.8082e+00 5.1813e-01 -#> 2: 9.1224e+01 -5.2308e+00 -1.9743e+00 -4.0115e+00 -1.8311e+00 9.8058e-02 5.3151e+00 1.3946e+00 1.0504e+00 2.8908e+00 4.5742e-01 5.2252e-01 5.9132e+00 5.7000e-04 6.5362e+00 1.8571e-07 -#> 3: 9.1371e+01 -5.5075e+00 -2.1136e+00 -4.0542e+00 -1.4871e+00 -4.1222e-02 5.0493e+00 1.3249e+00 9.9785e-01 3.4546e+00 4.3455e-01 6.3380e-01 4.0626e+00 1.0302e-05 4.6845e+00 5.0378e-04 -#> 4: 91.3391 -5.7912 -2.1450 -3.9623 -1.3302 -0.1356 4.7969 1.2586 0.9480 3.2819 0.4128 0.6021 3.3624 0.0249 3.6770 0.0248 -#> 5: 91.5018 -6.0214 -2.1492 -3.9323 -1.2118 -0.0647 4.5570 1.1957 0.9006 3.1178 0.3922 0.5720 2.9393 0.0349 3.1610 0.0371 -#> 6: 91.4496 -5.8734 -2.0974 -3.9977 -1.0936 -0.0608 4.3292 1.3347 0.8695 3.1231 0.3726 0.5434 2.5921 0.0366 2.7534 0.0396 -#> 7: 91.6540 -5.8545 -2.1019 -3.9268 -0.9717 -0.1622 4.1127 1.8221 0.8771 2.9670 0.3539 0.5162 2.3468 0.0466 2.4323 0.0474 -#> 8: 91.7226 -5.8139 -2.0764 -4.0030 -0.9804 -0.1283 3.9071 2.3972 0.9978 2.9945 0.3362 0.4904 2.0001 0.0405 2.0620 0.0557 -#> 9: 91.9975 -5.6339 -2.0812 -3.9379 -0.9156 -0.0654 4.4265 2.2773 0.9479 3.0945 0.3194 0.4659 1.8817 0.0397 1.4473 0.0845 -#> 10: 91.9477 -5.6101 -2.0459 -3.8821 -0.9368 -0.0428 4.9868 2.2787 0.9297 3.2162 0.3035 0.4426 1.6910 0.0397 1.3759 0.0892 -#> 11: 92.1798 -5.5425 -2.0676 -3.9349 -0.9248 -0.0339 5.0312 2.1648 0.8832 3.0554 0.2883 0.4205 1.6613 0.0375 1.3387 0.0823 -#> 12: 92.1456 -5.6294 -2.1011 -3.8899 -0.9195 -0.0410 4.7796 2.0565 0.9077 3.1066 0.3013 0.3995 1.6018 0.0393 1.5496 0.0691 -#> 13: 91.6764 -5.5607 -2.0911 -3.8832 -0.9268 -0.0367 4.5407 1.9537 0.9246 3.0425 0.2862 0.3795 1.6900 0.0350 1.4050 0.0712 -#> 14: 91.4832 -5.5007 -2.1133 -3.8869 -0.9208 -0.0202 4.3136 1.8560 0.8795 3.0389 0.2719 0.3605 1.5526 0.0389 1.7056 0.0502 -#> 15: 91.7854 -5.4454 -2.1124 -3.8750 -0.8842 -0.0608 4.0979 1.7632 0.9004 3.0463 0.2583 0.3425 1.6201 0.0384 1.2463 0.0747 -#> 16: 91.7608 -5.4097 -2.1449 -3.8750 -0.8797 -0.0532 3.8930 1.6751 0.9666 3.0463 0.2454 0.3254 1.6086 0.0384 1.0840 0.0850 -#> 17: 91.6692 -5.5401 -2.1688 -3.8762 -0.9022 -0.0101 3.6984 1.8405 1.0323 2.9672 0.2331 0.3091 1.4625 0.0371 1.1135 0.0841 -#> 18: 91.3169 -5.5720 -2.1777 -3.8851 -0.9396 0.0040 3.5135 1.8186 1.0419 3.0783 0.2215 0.2936 1.4778 0.0396 1.3403 0.0732 -#> 19: 91.4384 -5.6696 -2.1469 -3.8892 -0.9318 -0.0103 3.3378 2.2700 1.0489 3.0592 0.2128 0.2790 1.3854 0.0379 1.1760 0.0858 -#> 20: 91.3273 -5.7800 -2.1388 -3.9004 -0.9536 -0.0159 3.1709 2.7506 1.0297 3.0477 0.2021 0.2650 1.4542 0.0419 1.1576 0.0856 -#> 21: 91.7477 -5.7952 -2.1436 -3.9164 -0.9263 -0.0184 3.0124 3.0737 1.0414 3.0435 0.1948 0.2518 1.5026 0.0398 1.1833 0.0791 -#> 22: 91.6492 -6.0575 -2.1196 -3.9168 -0.9471 -0.0153 2.8617 4.1317 1.0322 3.0494 0.1850 0.2392 1.4351 0.0409 1.0739 0.0873 -#> 23: 91.8536 -6.2824 -2.1596 -3.9174 -0.9405 0.0031 2.7187 5.3935 1.0143 3.1085 0.1758 0.2272 1.4534 0.0404 1.0651 0.0805 -#> 24: 92.1616 -6.2246 -2.0912 -3.9224 -0.9338 0.0118 2.5827 5.7533 0.9636 3.0780 0.1741 0.2158 1.5863 0.0336 1.0915 0.0804 -#> 25: 92.2576 -6.2746 -2.1058 -3.9587 -0.9355 0.0189 2.4536 5.4656 0.9706 3.3477 0.1780 0.2051 1.4555 0.0365 1.0838 0.0782 -#> 26: 92.3314 -6.1739 -2.1211 -3.9676 -0.9474 0.0525 2.4934 5.5785 0.9981 3.3705 0.1835 0.1948 1.4433 0.0379 1.1300 0.0783 -#> 27: 92.8206 -6.1111 -2.0900 -3.9787 -0.9472 0.0058 2.5201 5.4329 1.0145 3.5013 0.1856 0.1851 1.4484 0.0391 1.1809 0.0723 -#> 28: 92.8685 -6.0934 -2.0963 -3.9872 -0.9693 0.0053 2.9812 5.1612 0.9925 3.5416 0.1816 0.1758 1.4713 0.0389 1.1766 0.0704 -#> 29: 92.6774 -5.8779 -2.0833 -3.9954 -0.9546 -0.0099 4.3751 4.9032 1.0762 3.5483 0.1755 0.1670 1.4844 0.0378 1.3435 0.0599 -#> 30: 92.6704 -5.9657 -2.0746 -3.9920 -0.9342 -0.0329 4.1563 4.6580 1.0571 3.5382 0.1667 0.1587 1.4510 0.0427 1.2218 0.0678 -#> 31: 92.4139 -5.7428 -2.0922 -3.9765 -0.9178 -0.0302 3.9485 4.4251 1.0210 3.5601 0.1596 0.1507 1.5981 0.0349 1.3086 0.0619 -#> 32: 92.8243 -5.8072 -2.1154 -3.9699 -0.9130 0.0065 3.7511 4.2039 1.0622 3.4768 0.1667 0.1432 1.5321 0.0333 1.3779 0.0611 -#> 33: 92.8737 -5.6655 -2.1132 -3.9763 -0.9155 0.0183 3.5635 3.9937 1.1068 3.5075 0.1583 0.1360 1.5351 0.0341 1.2700 0.0673 -#> 34: 93.0233 -5.7429 -2.1022 -3.9648 -0.9057 0.0202 3.3853 3.7940 1.0830 3.4532 0.1504 0.1292 1.5128 0.0368 1.1942 0.0702 -#> 35: 93.1333 -5.7707 -2.1003 -4.0004 -0.9031 0.0201 3.2161 3.6043 1.1161 3.4701 0.1429 0.1228 1.6003 0.0307 1.1387 0.0734 -#> 36: 93.1398 -5.7700 -2.1168 -3.9678 -0.9038 0.0107 3.0553 3.4241 1.1209 3.4126 0.1358 0.1166 1.4919 0.0331 1.0642 0.0755 -#> 37: 92.8847 -5.6651 -2.1538 -3.9634 -0.9176 0.0364 2.9995 3.2529 1.1108 3.3776 0.1402 0.1173 1.5093 0.0396 1.1550 0.0693 -#> 38: 93.2326 -5.5244 -2.1571 -3.9909 -0.9231 0.0179 2.8832 3.0902 1.0763 3.5170 0.1332 0.1205 1.4962 0.0472 1.1657 0.0679 -#> 39: 92.9946 -5.4516 -2.1475 -3.9365 -0.9067 0.0309 3.0986 2.9357 1.0562 3.4194 0.1265 0.1251 1.4786 0.0464 1.1183 0.0721 -#> 40: 93.2028 -5.6148 -2.1367 -3.9235 -0.9048 0.0099 2.9436 2.7889 1.1256 3.3460 0.1241 0.1288 1.4515 0.0459 1.0449 0.0753 -#> 41: 93.1297 -5.4665 -2.0545 -4.0108 -0.9136 -0.0216 2.7964 2.6495 1.1471 3.4754 0.1281 0.1223 1.7359 0.0321 1.0876 0.0780 -#> 42: 93.0469 -5.3767 -2.0820 -4.0213 -0.9361 -0.0264 2.6566 2.5170 1.0897 3.5120 0.1411 0.1162 1.7070 0.0276 1.2377 0.0691 -#> 43: 93.3305 -5.4943 -2.0910 -4.0226 -0.9414 -0.0201 2.5238 2.3912 1.0896 3.4589 0.1621 0.1126 1.5584 0.0393 1.1485 0.0705 -#> 44: 93.2566 -5.4919 -2.1016 -4.0718 -0.9373 0.0024 2.3976 2.2716 1.0451 3.8959 0.1612 0.1162 1.5769 0.0286 1.2778 0.0693 -#> 45: 93.0284 -5.4885 -2.1012 -4.0740 -0.9202 -0.0197 2.2777 2.1580 1.0268 3.9297 0.1553 0.1104 1.5589 0.0289 1.1388 0.0778 -#> 46: 92.7188 -5.5807 -2.1102 -4.0875 -0.9465 0.0076 2.1638 2.2084 0.9840 4.0322 0.1475 0.1048 1.6729 0.0295 1.2763 0.0735 -#> 47: 92.6718 -5.5108 -2.1268 -4.0638 -0.9220 0.0131 2.0556 2.0980 1.0064 3.8306 0.1475 0.0996 1.6527 0.0271 1.3190 0.0659 -#> 48: 92.6727 -5.5268 -2.1326 -4.0693 -0.8999 0.0259 1.9529 2.2445 1.0387 3.8064 0.1459 0.0946 1.6587 0.0283 1.3555 0.0604 -#> 49: 92.5230 -5.5592 -2.1701 -4.0595 -0.9087 0.0350 1.8552 2.5181 1.0238 3.7514 0.1552 0.0899 1.5473 0.0307 1.2437 0.0662 -#> 50: 92.4920 -5.5778 -2.1309 -4.0711 -0.9317 0.0383 1.7625 2.6771 1.0203 3.7435 0.1587 0.0854 1.5727 0.0330 1.2555 0.0611 -#> 51: 92.4606 -5.5485 -2.1346 -4.0687 -0.9148 0.0638 1.6743 2.8079 1.0402 3.6978 0.1513 0.0811 1.5476 0.0335 1.2744 0.0658 -#> 52: 92.6305 -5.6829 -2.1658 -4.0697 -0.9298 0.0848 1.5906 2.8530 1.0565 3.6998 0.1644 0.0798 1.4751 0.0296 1.1351 0.0747 -#> 53: 92.6412 -5.5519 -2.1984 -4.1605 -0.9472 0.0803 1.8328 2.7103 1.0501 4.4111 0.1626 0.0758 1.5735 0.0343 1.2247 0.0643 -#> 54: 92.7616 -5.5718 -2.1826 -4.2028 -0.9382 0.0939 1.9108 2.5748 1.0708 4.7287 0.1775 0.0720 1.4860 0.0299 1.2190 0.0638 -#> 55: 92.8466 -5.6434 -2.1590 -4.0501 -0.9219 0.0660 2.3709 2.4461 1.0399 4.4922 0.1686 0.0684 1.5899 0.0297 1.2586 0.0598 -#> 56: 92.8839 -5.6503 -2.1758 -4.0467 -0.9265 0.0765 2.2523 2.3238 1.0755 4.2676 0.1698 0.0666 1.5357 0.0319 1.1854 0.0633 -#> 57: 92.8882 -5.3950 -2.1926 -4.0282 -0.9455 0.0600 2.4994 2.2076 1.0411 4.0542 0.1684 0.0633 1.5839 0.0342 1.2789 0.0612 -#> 58: 92.9510 -5.4362 -2.1993 -4.0402 -0.9349 0.0576 2.3744 2.0972 1.0184 3.8515 0.1757 0.0604 1.5796 0.0328 1.3027 0.0570 -#> 59: 92.8806 -5.4605 -2.2176 -4.2201 -0.9360 0.0998 2.2557 1.9923 1.0248 5.1421 0.1904 0.0573 1.6469 0.0325 1.4177 0.0534 -#> 60: 92.8606 -5.4697 -2.2016 -4.1707 -0.9218 0.0747 2.1429 1.8927 1.0489 4.8850 0.1809 0.0545 1.5984 0.0318 1.2879 0.0589 -#> 61: 92.8939 -5.5167 -2.2169 -4.1567 -0.9434 0.0680 2.1067 1.9160 1.0677 4.6408 0.1775 0.0517 1.5223 0.0404 1.2033 0.0623 -#> 62: 93.1569 -5.6121 -2.2073 -4.1427 -0.9431 0.0717 2.5977 2.0627 1.0518 4.5133 0.1758 0.0494 1.4644 0.0364 1.1857 0.0621 -#> 63: 93.2362 -5.5056 -2.1832 -4.0832 -0.9433 0.0754 3.4639 1.9596 1.0905 4.2877 0.1851 0.0536 1.5500 0.0320 1.2533 0.0610 -#> 64: 93.3935 -5.4320 -2.1735 -4.0754 -0.9601 0.0719 5.0337 1.8616 1.0723 4.0733 0.1907 0.0649 1.5436 0.0270 1.4154 0.0546 -#> 65: 93.1102 -5.5419 -2.1870 -4.0496 -0.9481 0.0753 5.0250 1.9760 1.1263 3.8696 0.1902 0.0617 1.4779 0.0262 1.1326 0.0712 -#> 66: 92.9832 -5.7640 -2.1941 -4.0532 -0.9444 0.0635 5.2049 2.6553 1.1258 3.7699 0.1915 0.0586 1.4926 0.0307 1.0960 0.0645 -#> 67: 92.6674 -5.6976 -2.1858 -4.0855 -0.9209 0.0562 4.9447 2.5225 1.1285 4.0204 0.1948 0.0556 1.4667 0.0315 1.1023 0.0650 -#> 68: 92.7718 -5.7724 -2.1760 -4.0242 -0.9354 0.0441 4.6975 2.8536 1.1471 3.8194 0.1922 0.0529 1.4283 0.0329 1.1174 0.0664 -#> 69: 92.8377 -5.7554 -2.1833 -4.0670 -0.9412 0.0834 4.4626 2.7404 1.1565 3.7904 0.1826 0.0502 1.4628 0.0318 1.0793 0.0747 -#> 70: 92.6830 -5.9071 -2.2266 -4.0604 -0.9399 0.0730 4.2394 3.5629 1.1459 3.7282 0.1734 0.0477 1.4892 0.0331 1.1526 0.0683 -#> 71: 92.5729 -5.8185 -2.2009 -4.0623 -0.9401 0.0878 4.0275 3.3847 1.0886 3.7348 0.1648 0.0453 1.4739 0.0373 1.0902 0.0678 -#> 72: 92.1755 -6.0270 -2.2108 -4.1507 -0.9564 0.0665 3.8261 3.9851 1.1200 4.1726 0.1617 0.0431 1.4478 0.0348 1.1400 0.0673 -#> 73: 91.8986 -6.0175 -2.1916 -4.1416 -0.9347 0.0243 3.6348 4.0607 1.1553 4.0576 0.1802 0.0409 1.4330 0.0406 1.0914 0.0712 -#> 74: 91.7729 -5.8767 -2.1898 -4.0934 -0.9122 0.0184 3.4531 3.8577 1.1254 3.8547 0.1827 0.0389 1.3372 0.0524 1.0717 0.0687 -#> 75: 91.3098 -5.9950 -2.1572 -4.1349 -0.9427 0.0190 3.4756 3.8000 1.1626 3.8402 0.1969 0.0369 1.3378 0.0501 1.1602 0.0685 -#> 76: 91.3766 -5.8701 -2.2042 -4.1128 -0.9081 0.0539 3.9350 3.6100 1.2348 3.7994 0.1891 0.0369 1.3400 0.0495 1.0656 0.0738 -#> 77: 91.6057 -5.7437 -2.1988 -4.1241 -0.8890 0.0500 5.0868 3.4295 1.1971 3.8470 0.1950 0.0469 1.4928 0.0397 1.1129 0.0700 -#> 78: 91.7868 -5.7832 -2.1844 -4.1102 -0.9104 0.0698 4.8325 3.2580 1.1670 3.6547 0.1993 0.0502 1.4336 0.0340 0.9512 0.0805 -#> 79: 91.7221 -5.7881 -2.2166 -4.1137 -0.9160 0.0672 4.5909 3.0951 1.1582 3.5765 0.1928 0.0486 1.4632 0.0352 1.0210 0.0728 -#> 80: 91.8608 -5.8064 -2.2006 -4.0971 -0.9209 0.0642 4.3613 3.2163 1.1481 3.4758 0.1832 0.0462 1.4368 0.0356 1.0605 0.0710 -#> 81: 91.6423 -5.8749 -2.2037 -4.0893 -0.9187 0.0503 4.1432 3.5329 1.0907 3.5148 0.2011 0.0451 1.4719 0.0346 1.1684 0.0646 -#> 82: 91.8319 -6.0898 -2.2251 -4.0826 -0.9368 0.0842 4.1509 4.4964 1.0606 3.4836 0.1910 0.0428 1.4468 0.0387 1.1605 0.0637 -#> 83: 91.9794 -6.0417 -2.1947 -4.1042 -0.9114 0.0741 6.5949 4.5668 1.1113 3.6409 0.1815 0.0407 1.4780 0.0346 1.1277 0.0634 -#> 84: 91.8669 -6.1877 -2.1979 -4.1052 -0.9300 0.0807 6.2651 5.1958 1.1750 3.6752 0.1724 0.0386 1.4931 0.0278 1.0401 0.0685 -#> 85: 91.6789 -6.0634 -2.1896 -4.1357 -0.9371 0.0933 5.9519 4.9360 1.1259 3.8493 0.1732 0.0367 1.5058 0.0275 1.1356 0.0670 -#> 86: 91.6989 -6.2114 -2.2056 -4.1542 -0.9646 0.0882 5.6543 5.0411 1.1091 3.9411 0.1988 0.0349 1.4099 0.0338 1.1811 0.0636 -#> 87: 92.3758 -6.3779 -2.2062 -4.1739 -0.9385 0.0916 5.3716 6.2290 1.1213 4.0290 0.1889 0.0331 1.4809 0.0306 1.1443 0.0626 -#> 88: 92.2757 -6.2016 -2.2215 -4.1389 -0.9582 0.0942 5.1030 5.9176 1.0797 4.0768 0.1990 0.0315 1.4282 0.0386 1.2235 0.0629 -#> 89: 92.1970 -6.3356 -2.2081 -4.1412 -0.9555 0.1057 4.8478 5.9597 1.1474 4.0677 0.1890 0.0299 1.3856 0.0377 1.1807 0.0640 -#> 90: 92.0813 -6.4550 -2.2045 -4.1524 -0.9553 0.0885 4.6054 6.9999 1.1542 3.9901 0.1880 0.0284 1.3416 0.0416 1.1379 0.0653 -#> 91: 91.7111 -6.5289 -2.2203 -4.1763 -0.9288 0.0823 5.4933 6.9237 1.1601 4.0435 0.1839 0.0360 1.3387 0.0401 1.1768 0.0591 -#> 92: 92.1217 -6.5567 -2.2232 -4.2082 -0.9411 0.0815 8.0692 6.7286 1.1684 3.9422 0.1763 0.0411 1.3740 0.0463 1.1538 0.0613 -#> 93: 92.7497 -6.3512 -2.2463 -4.1806 -0.9633 0.0724 7.6657 6.3922 1.1870 3.8858 0.1796 0.0391 1.4232 0.0454 1.3749 0.0497 -#> 94: 92.2679 -6.3542 -2.2473 -4.1873 -0.9382 0.0711 7.2824 6.0726 1.1940 3.8847 0.1956 0.0371 1.3812 0.0465 1.2897 0.0521 -#> 95: 92.0257 -6.2448 -2.2624 -4.1681 -0.9624 0.0810 6.9183 5.7690 1.1345 3.8091 0.1858 0.0359 1.3026 0.0509 1.3000 0.0530 -#> 96: 91.5166 -5.9442 -2.2924 -4.2449 -0.9238 0.1058 7.1159 5.4805 1.1231 4.2529 0.1953 0.0343 1.4063 0.0445 1.3479 0.0482 -#> 97: 91.1606 -5.8541 -2.2912 -4.2398 -0.8875 0.1101 9.4515 5.2065 1.1256 4.3194 0.2081 0.0337 1.3436 0.0498 1.3317 0.0496 -#> 98: 91.2787 -6.0967 -2.2703 -4.2641 -0.9260 0.0869 8.9789 4.9462 1.2070 4.2238 0.1977 0.0373 1.3124 0.0495 1.1362 0.0653 -#> 99: 91.6449 -5.9441 -2.2562 -4.2355 -0.9312 0.1237 8.5300 4.6988 1.2343 4.0468 0.1878 0.0369 1.3508 0.0462 1.0542 0.0704 -#> 100: 91.7795 -5.8857 -2.2516 -4.3381 -0.9344 0.1291 8.1035 4.4639 1.2355 4.6941 0.1968 0.0393 1.4327 0.0358 1.1170 0.0668 -#> 101: 92.2537 -5.7930 -2.2345 -4.3477 -0.9272 0.1340 8.3402 4.2407 1.1961 4.7638 0.1933 0.0402 1.4683 0.0375 1.1216 0.0626 -#> 102: 92.3920 -6.0193 -2.2332 -4.3487 -0.9155 0.1565 11.1006 4.2977 1.1700 4.8048 0.2260 0.0444 1.4443 0.0342 1.0888 0.0674 -#> 103: 92.0043 -5.7825 -2.2376 -4.2616 -0.9043 0.1686 10.5455 4.0829 1.1587 4.5646 0.2147 0.0422 1.4198 0.0338 1.1639 0.0625 -#> 104: 92.1575 -5.8497 -2.2470 -4.2456 -0.9128 0.1762 10.0183 3.8787 1.1405 4.3364 0.2040 0.0440 1.3919 0.0379 1.2040 0.0582 -#> 105: 92.2784 -5.7971 -2.2582 -4.2100 -0.9128 0.1731 9.5173 3.6848 1.1351 4.1196 0.1938 0.0418 1.3982 0.0404 1.1069 0.0656 -#> 106: 92.4336 -5.7752 -2.2690 -4.3771 -0.8925 0.1644 9.0415 3.5005 1.1547 5.0970 0.1841 0.0476 1.3670 0.0423 1.1716 0.0625 -#> 107: 92.5128 -5.8328 -2.2549 -4.4193 -0.9403 0.2268 8.5894 3.3255 1.1160 5.2711 0.1749 0.0453 1.4023 0.0347 1.0279 0.0757 -#> 108: 92.8926 -5.7266 -2.2606 -4.5037 -0.9392 0.2394 8.1599 3.1592 1.1293 5.9652 0.1661 0.0447 1.3837 0.0346 0.9545 0.0747 -#> 109: 92.4657 -5.8687 -2.2884 -4.4108 -0.9043 0.2611 7.7519 4.0001 1.0729 5.6669 0.1578 0.0424 1.3441 0.0351 0.9758 0.0708 -#> 110: 92.6620 -5.6900 -2.2825 -4.4337 -0.9003 0.2602 7.3643 3.8001 1.0843 5.3836 0.1499 0.0433 1.4652 0.0302 0.9950 0.0722 -#> 111: 92.8949 -5.6946 -2.2661 -4.5240 -0.9233 0.2372 6.9961 3.6101 1.0845 5.8133 0.1551 0.0411 1.5005 0.0327 0.9284 0.0753 -#> 112: 93.4237 -5.6562 -2.2474 -4.4809 -0.9441 0.2322 6.6463 3.4296 1.1498 5.5227 0.1474 0.0409 1.4612 0.0317 0.9336 0.0762 -#> 113: 93.1883 -5.6891 -2.2846 -4.3984 -0.9416 0.2317 6.3140 3.2581 1.1062 5.2465 0.1596 0.0463 1.3924 0.0380 1.0268 0.0698 -#> 114: 93.4464 -5.7087 -2.2902 -4.4274 -0.9401 0.2638 5.9983 3.0952 1.1170 5.0203 0.1516 0.0495 1.4108 0.0361 1.0355 0.0682 -#> 115: 93.1873 -5.8732 -2.2668 -4.5086 -0.9636 0.2516 5.6984 3.3427 1.1141 5.7549 0.1440 0.0490 1.5010 0.0309 1.0443 0.0679 -#> 116: 92.6878 -5.8520 -2.2903 -4.5349 -0.9663 0.2612 5.4135 3.2444 1.1048 5.8809 0.1471 0.0511 1.3910 0.0360 1.0423 0.0702 -#> 117: 92.7775 -5.7892 -2.2897 -4.4572 -0.9544 0.2380 5.1428 3.0822 1.0731 5.5869 0.1397 0.0703 1.3493 0.0360 0.9831 0.0713 -#> 118: 93.1533 -5.8045 -2.2859 -4.4787 -0.9667 0.2150 4.8857 3.0277 1.0872 5.6786 0.1439 0.0812 1.3838 0.0373 1.0547 0.0696 -#> 119: 92.8370 -5.7208 -2.2738 -4.4627 -0.9462 0.2095 4.6414 2.8764 1.1172 5.6197 0.1643 0.0772 1.3394 0.0348 0.9180 0.0803 -#> 120: 92.5430 -5.7795 -2.3004 -4.4203 -0.9479 0.2313 4.4093 2.8377 1.1312 5.3387 0.1655 0.0803 1.2967 0.0360 1.0699 0.0761 -#> 121: 92.5318 -5.6550 -2.2866 -4.5065 -0.9166 0.2321 4.1888 2.6959 1.0994 6.0180 0.1686 0.0763 1.3882 0.0322 0.9895 0.0733 -#> 122: 92.7380 -5.6688 -2.2968 -4.4523 -0.9279 0.2529 3.9794 2.5611 1.0642 5.7171 0.1601 0.0851 1.3786 0.0316 0.9358 0.0742 -#> 123: 93.0753 -5.7451 -2.2896 -4.5423 -0.9371 0.2724 3.7804 2.9938 1.0758 5.9349 0.1521 0.0808 1.4275 0.0339 0.9652 0.0727 -#> 124: 93.2708 -5.8004 -2.2782 -4.4951 -0.9451 0.2590 3.5914 3.0594 1.0875 5.6382 0.1607 0.0768 1.3628 0.0340 1.0577 0.0693 -#> 125: 93.4025 -5.7710 -2.2990 -4.4498 -0.9661 0.2633 3.4118 2.9276 1.0809 5.3563 0.1527 0.0730 1.3816 0.0406 1.0295 0.0671 -#> 126: 93.4928 -5.7054 -2.3002 -4.4087 -0.9394 0.2965 3.4732 2.7812 1.1275 5.0884 0.1481 0.0693 1.2949 0.0423 0.9084 0.0726 -#> 127: 93.6449 -5.6593 -2.2683 -4.3418 -0.9194 0.2560 4.2986 2.6422 1.1070 4.8340 0.1449 0.0707 1.4258 0.0341 0.8802 0.0777 -#> 128: 93.7430 -5.6359 -2.2686 -4.4174 -0.9500 0.2279 5.2477 2.5101 1.1046 5.5376 0.1512 0.0859 1.4523 0.0327 0.8659 0.0826 -#> 129: 93.7432 -5.6851 -2.2849 -4.2019 -0.9660 0.1995 7.2497 2.8789 1.1315 5.2607 0.1762 0.0972 1.3901 0.0357 1.1264 0.0743 -#> 130: 93.2409 -5.8965 -2.2946 -4.1880 -0.9774 0.1719 7.4467 3.2276 1.1464 4.9977 0.1720 0.0924 1.3517 0.0446 1.0461 0.0705 -#> 131: 92.7780 -6.0551 -2.2647 -4.1894 -0.9579 0.1391 7.0744 3.7584 1.1291 4.7478 0.1714 0.0995 1.2542 0.0438 0.9139 0.0777 -#> 132: 92.7157 -6.1161 -2.2501 -4.1784 -0.9651 0.1146 6.7207 3.9259 1.1674 4.5104 0.1712 0.0957 1.2549 0.0473 0.8964 0.0803 -#> 133: 92.2696 -5.8545 -2.2717 -4.1907 -0.9782 0.0985 6.3846 3.7296 1.1652 4.2849 0.1626 0.1198 1.2208 0.0498 0.9730 0.0822 -#> 134: 92.2067 -5.8603 -2.2743 -4.2095 -0.9754 0.1398 6.0654 3.5431 1.1551 4.0706 0.1695 0.1138 1.3022 0.0432 0.9960 0.0795 -#> 135: 92.3979 -5.9500 -2.3053 -4.1938 -0.9425 0.1134 5.7621 3.3660 1.1771 3.8671 0.1610 0.1081 1.3373 0.0462 1.1323 0.0665 -#> 136: 92.3749 -5.8701 -2.2979 -4.2493 -0.9386 0.1504 5.4740 3.3090 1.1638 3.9609 0.1724 0.1027 1.3578 0.0389 1.1943 0.0650 -#> 137: 92.6942 -5.9020 -2.2755 -4.2318 -0.9464 0.1541 5.2003 3.5521 1.1704 3.8948 0.1685 0.0976 1.4170 0.0399 1.1472 0.0626 -#> 138: 92.7234 -5.8085 -2.2653 -4.2164 -0.9662 0.1808 4.9403 3.3745 1.1977 3.8348 0.1694 0.0927 1.4229 0.0387 1.0934 0.0708 -#> 139: 92.7341 -5.7737 -2.2685 -4.1759 -0.9334 0.1554 4.6933 3.2057 1.1971 3.6962 0.1917 0.0881 1.4324 0.0363 1.1669 0.0652 -#> 140: 92.1593 -5.6287 -2.2576 -4.1977 -0.9232 0.1345 4.6967 3.0455 1.1676 3.8133 0.2060 0.0837 1.5032 0.0349 1.1418 0.0678 -#> 141: 92.3199 -5.8323 -2.2451 -4.1948 -0.9447 0.1295 4.9624 3.3893 1.1408 3.8423 0.1957 0.0795 1.4470 0.0325 1.0892 0.0739 -#> 142: 92.7246 -6.1252 -2.2304 -4.1984 -0.9160 0.0816 4.7143 4.6501 1.1420 3.8554 0.1901 0.0755 1.4847 0.0386 1.2815 0.0576 -#> 143: 92.4130 -6.0231 -2.2261 -4.2205 -0.9495 0.1020 4.4786 4.4176 1.1454 4.0301 0.1929 0.0717 1.4103 0.0410 1.0418 0.0739 -#> 144: 92.4006 -5.9898 -2.2232 -4.2429 -0.9553 0.1131 4.2547 4.1967 1.1579 4.2583 0.1904 0.0681 1.4272 0.0339 1.0591 0.0737 -#> 145: 92.5011 -6.2340 -2.2232 -4.1872 -0.9560 0.1322 6.1775 4.8941 1.1594 4.0453 0.1811 0.0647 1.4059 0.0298 1.0219 0.0752 -#> 146: 92.7460 -6.2989 -2.2417 -4.2501 -0.9650 0.1527 5.8686 5.6454 1.1154 4.0076 0.1720 0.0758 1.4027 0.0348 1.1220 0.0689 -#> 147: 93.0630 -6.0839 -2.2217 -4.1822 -0.9661 0.1634 5.5752 5.3631 1.0596 3.8072 0.1743 0.0733 1.3682 0.0393 1.0992 0.0700 -#> 148: 92.7639 -5.8682 -2.2550 -4.1926 -0.9440 0.1599 5.8048 5.0950 1.0858 3.6230 0.1749 0.0696 1.3364 0.0436 1.0967 0.0721 -#> 149: 92.6183 -6.1270 -2.2379 -4.1103 -0.9643 0.1202 5.8027 4.8402 1.1089 3.4860 0.1661 0.0661 1.3061 0.0457 1.0014 0.0724 -#> 150: 92.7472 -6.1515 -2.2199 -4.1027 -0.9611 0.1014 5.6767 4.5982 1.1061 3.6113 0.1578 0.0654 1.3543 0.0405 1.0847 0.0707 -#> 151: 92.9566 -5.8911 -2.2174 -4.0722 -0.9516 0.0992 5.9638 4.3683 1.1057 3.5122 0.1767 0.0621 1.3619 0.0396 1.0158 0.0734 -#> 152: 93.0035 -5.8395 -2.2559 -4.0650 -0.9389 0.0928 4.4799 3.2331 1.0387 3.4826 0.1713 0.0604 1.3425 0.0428 1.1101 0.0635 -#> 153: 92.7242 -5.7832 -2.2538 -4.1288 -0.9159 0.1047 4.6102 3.0838 1.0527 3.8052 0.1718 0.0597 1.3905 0.0398 1.1371 0.0635 -#> 154: 92.2125 -5.9077 -2.2400 -4.0922 -0.9106 0.1033 4.4732 3.8350 1.0261 3.6148 0.1955 0.0643 1.3176 0.0419 1.1130 0.0635 -#> 155: 92.6226 -5.6271 -2.2239 -4.0122 -0.8948 0.0647 4.5553 2.5675 1.0412 3.0513 0.1845 0.0866 1.3266 0.0459 1.0244 0.0680 -#> 156: 92.6532 -5.5576 -2.2251 -4.0066 -0.9006 0.0922 3.8517 2.3273 1.0455 3.0971 0.1928 0.0863 1.4000 0.0394 0.9203 0.0754 -#> 157: 92.5192 -5.4834 -2.2356 -4.0069 -0.9321 0.0904 3.0410 1.8841 0.9867 3.1990 0.1905 0.0816 1.3927 0.0407 1.1517 0.0614 -#> 158: 92.5628 -5.5318 -2.2044 -4.0269 -0.9319 0.0742 3.5124 1.9585 1.0692 3.1835 0.1958 0.0934 1.4038 0.0324 0.9680 0.0758 -#> 159: 92.9690 -5.6416 -2.2134 -4.0156 -0.9556 0.0560 4.3830 2.2442 1.0543 3.2358 0.1873 0.0951 1.3624 0.0375 1.1207 0.0696 -#> 160: 92.9861 -5.5872 -2.2207 -3.9908 -0.9190 0.0417 4.1202 2.1685 1.0711 3.1521 0.1766 0.0913 1.3760 0.0371 1.0970 0.0713 -#> 161: 93.3139 -5.5349 -2.1972 -3.9860 -0.9365 0.0011 4.2865 1.8741 1.0759 3.0304 0.2007 0.0750 1.3650 0.0411 1.1220 0.0662 -#> 162: 93.3324 -5.6135 -2.1579 -4.0151 -0.9507 -0.0091 4.6402 2.0208 1.0535 3.0349 0.1935 0.0764 1.4069 0.0383 1.2550 0.0598 -#> 163: 93.0110 -5.5253 -2.1419 -4.0151 -0.9197 -0.0072 5.8946 1.9087 1.0965 3.0349 0.1833 0.0814 1.5095 0.0290 1.1314 0.0665 -#> 164: 93.0848 -5.4980 -2.1670 -4.0213 -0.9345 0.0150 4.9128 1.8293 1.0379 3.0653 0.1728 0.0835 1.4913 0.0343 1.0589 0.0687 -#> 165: 92.9407 -5.3978 -2.1707 -4.0090 -0.9480 0.0126 3.4620 1.3870 1.0594 3.0115 0.1702 0.0982 1.5550 0.0296 1.0978 0.0694 -#> 166: 93.1504 -5.4880 -2.1890 -3.9958 -0.9511 0.0316 2.7859 1.8457 1.0294 3.0739 0.1738 0.1031 1.5109 0.0308 1.1800 0.0651 -#> 167: 92.8442 -5.4673 -2.1984 -4.0259 -0.9262 0.0243 2.0497 1.6348 1.0469 3.1258 0.1650 0.0981 1.6185 0.0291 1.1733 0.0655 -#> 168: 92.9484 -5.6255 -2.2012 -4.0136 -0.9309 0.0199 1.8121 2.0784 1.0415 3.1795 0.1816 0.0929 1.5727 0.0268 1.4222 0.0543 -#> 169: 93.0266 -5.6135 -2.1677 -4.0179 -0.9279 0.0375 1.7553 2.1663 1.0298 3.1675 0.2013 0.0926 1.5356 0.0274 1.2960 0.0596 -#> 170: 92.9844 -5.6286 -2.1839 -4.0509 -0.9471 0.0414 1.9485 2.4078 1.0656 3.2787 0.2112 0.0950 1.5210 0.0265 1.3069 0.0616 -#> 171: 92.6832 -5.6238 -2.2059 -4.0710 -0.9175 0.0383 1.5941 2.2918 1.1095 3.3435 0.1921 0.0895 1.4678 0.0345 1.2189 0.0618 -#> 172: 92.5302 -5.5653 -2.2086 -4.0429 -0.9412 0.0773 1.5302 2.2565 1.1293 3.2157 0.1924 0.0680 1.4438 0.0367 1.2084 0.0661 -#> 173: 92.3877 -5.5357 -2.2141 -4.0246 -0.9268 0.0866 1.2153 2.0588 1.0844 3.2941 0.2060 0.0726 1.4686 0.0359 1.3683 0.0596 -#> 174: 92.4410 -5.4921 -2.1955 -4.0398 -0.9269 0.0645 1.6903 2.0042 1.1236 3.3646 0.1847 0.0804 1.5533 0.0310 1.2320 0.0675 -#> 175: 92.4192 -5.4726 -2.1945 -4.0271 -0.9222 0.0728 1.1344 1.9292 1.1085 3.3173 0.1875 0.0912 1.5350 0.0302 1.2461 0.0679 -#> 176: 92.3581 -5.5256 -2.2055 -3.9958 -0.9211 0.0720 1.1140 1.8097 1.0898 3.1459 0.2018 0.1104 1.4391 0.0323 1.2240 0.0677 -#> 177: 92.2144 -5.6699 -2.2357 -4.0017 -0.9402 0.0785 1.1932 2.6190 1.0355 3.1852 0.2266 0.1125 1.4705 0.0327 1.2866 0.0621 -#> 178: 92.3608 -5.7040 -2.2245 -4.0242 -0.9642 0.0596 0.7932 2.6061 0.9408 3.1080 0.1958 0.1180 1.5158 0.0365 1.3571 0.0600 -#> 179: 92.4358 -5.6877 -2.2243 -4.0166 -0.9486 0.0595 0.7591 2.3791 0.9241 3.0638 0.1900 0.1257 1.4317 0.0363 1.2359 0.0686 -#> 180: 92.5146 -5.7856 -2.2343 -4.0098 -0.9522 0.0522 0.4573 2.6882 0.9636 3.0406 0.1835 0.1270 1.4631 0.0361 1.2192 0.0701 -#> 181: 92.5469 -5.7684 -2.2220 -4.0549 -0.9488 0.0901 0.4189 2.4963 0.9873 3.1470 0.1744 0.1268 1.5165 0.0336 1.1359 0.0760 -#> 182: 92.5829 -5.7658 -2.2385 -4.0362 -0.9723 0.0572 0.3720 2.5387 0.9203 3.0397 0.1769 0.1636 1.4781 0.0375 1.2697 0.0677 -#> 183: 92.5737 -5.9187 -2.2130 -4.0638 -0.9876 0.0797 0.3084 3.3137 0.9467 3.0532 0.1737 0.1599 1.4288 0.0309 1.3024 0.0617 -#> 184: 92.4989 -5.9837 -2.1994 -4.0476 -0.9737 0.0594 0.2533 3.6658 0.9248 3.1230 0.1776 0.1552 1.3829 0.0316 1.2818 0.0621 -#> 185: 92.5677 -6.0227 -2.2084 -4.0403 -0.9584 0.0609 0.2215 3.8810 0.9134 3.0961 0.1739 0.1473 1.4202 0.0319 1.2731 0.0579 -#> 186: 92.7090 -5.9641 -2.2218 -4.0319 -0.9573 0.0575 0.2917 3.9574 0.9373 3.0666 0.1691 0.1703 1.4378 0.0296 1.2775 0.0601 -#> 187: 92.7358 -6.2503 -2.2003 -4.0534 -0.9742 0.0691 0.3037 5.2011 0.9333 3.0796 0.1647 0.1553 1.4254 0.0293 1.1987 0.0629 -#> 188: 92.6733 -6.1434 -2.1988 -4.0792 -0.9878 0.0860 0.3122 4.9451 0.9080 3.1891 0.1628 0.1558 1.4099 0.0317 1.3162 0.0593 -#> 189: 92.7256 -6.0886 -2.1766 -4.0419 -0.9672 0.0550 0.3758 4.3461 0.9140 3.0795 0.1697 0.1649 1.5310 0.0301 1.3258 0.0566 -#> 190: 92.5144 -6.1827 -2.2159 -4.0525 -0.9677 0.0728 0.3855 4.3370 0.9706 3.0518 0.1486 0.1841 1.4390 0.0295 1.1259 0.0740 -#> 191: 92.6209 -6.1257 -2.2287 -4.1095 -0.9670 0.1034 0.3340 4.3051 0.9486 3.1970 0.1549 0.1776 1.4397 0.0296 1.2004 0.0684 -#> 192: 92.6156 -6.1289 -2.2067 -4.1191 -0.9900 0.1090 0.3069 4.1314 0.9134 3.1476 0.1596 0.1912 1.4380 0.0301 1.1238 0.0720 -#> 193: 92.5434 -5.9782 -2.1800 -4.0845 -0.9547 0.1173 0.2694 3.6834 0.9005 2.9479 0.1582 0.1733 1.4538 0.0294 0.8798 0.0866 -#> 194: 92.5884 -5.7815 -2.2110 -4.0714 -0.9510 0.0928 0.2493 2.8236 0.9615 2.9852 0.1488 0.1730 1.4409 0.0297 1.1446 0.0677 -#> 195: 92.6180 -5.9277 -2.2213 -4.0714 -0.9379 0.1177 0.1993 3.5172 0.8976 2.9852 0.1449 0.1735 1.5012 0.0299 1.2131 0.0618 -#> 196: 92.5920 -5.7723 -2.2496 -4.0669 -0.9184 0.1262 0.2595 3.2454 0.9419 2.9697 0.1600 0.1881 1.4017 0.0338 0.9594 0.0790 -#> 197: 92.6292 -5.8658 -2.2434 -4.0640 -0.9365 0.1216 0.2491 3.3540 0.9267 2.9523 0.1598 0.1749 1.3953 0.0383 1.0788 0.0702 -#> 198: 92.6911 -5.8407 -2.2605 -4.0640 -0.9319 0.1264 0.1930 3.2321 0.8884 2.9523 0.1320 0.1940 1.4026 0.0358 1.0613 0.0704 -#> 199: 92.6480 -5.6988 -2.2599 -4.0668 -0.9395 0.1328 0.1412 2.6535 0.8915 2.9610 0.1573 0.2052 1.4353 0.0360 0.9900 0.0742 -#> 200: 92.7139 -5.6152 -2.2522 -4.0684 -0.9192 0.1589 0.1686 2.4362 0.9098 3.0185 0.1702 0.1705 1.4153 0.0338 1.1747 0.0705 -#> 201: 92.7134 -5.7029 -2.2504 -4.0502 -0.9270 0.1453 0.1499 2.6851 0.8909 2.9484 0.1749 0.1772 1.3851 0.0363 1.1255 0.0714 -#> 202: 92.7087 -5.7236 -2.2421 -4.0499 -0.9364 0.1238 0.1324 2.7215 0.8810 2.9507 0.1694 0.1913 1.3864 0.0365 1.1192 0.0705 -#> 203: 92.7013 -5.7563 -2.2293 -4.0494 -0.9394 0.1134 0.1269 2.8279 0.8866 2.9501 0.1618 0.1915 1.3981 0.0356 1.0942 0.0710 -#> 204: 92.6964 -5.8134 -2.2208 -4.0646 -0.9373 0.1144 0.1192 3.1058 0.8973 3.0279 0.1523 0.1983 1.4126 0.0345 1.0629 0.0723 -#> 205: 92.6936 -5.8441 -2.2195 -4.0787 -0.9373 0.1144 0.1068 3.2553 0.9029 3.0962 0.1473 0.2001 1.4217 0.0344 1.0532 0.0719 -#> 206: 92.6881 -5.8805 -2.2209 -4.0887 -0.9432 0.1187 0.1016 3.4269 0.9126 3.1477 0.1479 0.1957 1.4251 0.0348 1.0697 0.0712 -#> 207: 92.6929 -5.9304 -2.2259 -4.0987 -0.9473 0.1234 0.1028 3.6444 0.9261 3.1982 0.1469 0.1910 1.4170 0.0348 1.0586 0.0717 -#> 208: 92.6907 -5.9413 -2.2275 -4.1043 -0.9482 0.1267 0.1038 3.6864 0.9313 3.2244 0.1467 0.1889 1.4121 0.0343 1.0499 0.0718 -#> 209: 92.6917 -5.9265 -2.2304 -4.1109 -0.9498 0.1289 0.1022 3.5975 0.9363 3.2487 0.1478 0.1863 1.4053 0.0344 1.0521 0.0716 -#> 210: 92.6966 -5.9218 -2.2322 -4.1164 -0.9516 0.1337 0.0984 3.5650 0.9413 3.2688 0.1493 0.1874 1.3949 0.0342 1.0499 0.0719 -#> 211: 92.7020 -5.9160 -2.2351 -4.1209 -0.9542 0.1385 0.0958 3.5091 0.9390 3.2968 0.1503 0.1873 1.3925 0.0345 1.0547 0.0718 -#> 212: 92.7065 -5.9119 -2.2376 -4.1247 -0.9564 0.1432 0.0933 3.4520 0.9373 3.3205 0.1531 0.1901 1.3869 0.0346 1.0625 0.0717 -#> 213: 92.7107 -5.9047 -2.2402 -4.1286 -0.9575 0.1455 0.0930 3.3990 0.9361 3.3369 0.1536 0.1932 1.3814 0.0349 1.0698 0.0712 -#> 214: 92.7110 -5.9061 -2.2415 -4.1321 -0.9585 0.1483 0.0921 3.3864 0.9364 3.3517 0.1542 0.1963 1.3794 0.0348 1.0721 0.0712 -#> 215: 92.7116 -5.9128 -2.2417 -4.1360 -0.9581 0.1510 0.0941 3.4201 0.9347 3.3646 0.1545 0.1988 1.3764 0.0350 1.0731 0.0712 -#> 216: 92.7135 -5.9184 -2.2432 -4.1383 -0.9589 0.1540 0.0957 3.4623 0.9337 3.3698 0.1541 0.2016 1.3761 0.0353 1.0737 0.0714 -#> 217: 92.7143 -5.9262 -2.2453 -4.1428 -0.9604 0.1568 0.0981 3.5202 0.9323 3.3854 0.1542 0.2053 1.3770 0.0352 1.0779 0.0716 -#> 218: 92.7102 -5.9169 -2.2463 -4.1446 -0.9606 0.1604 0.1000 3.4823 0.9305 3.3851 0.1530 0.2083 1.3802 0.0353 1.0819 0.0716 -#> 219: 92.7062 -5.9089 -2.2470 -4.1481 -0.9597 0.1636 0.1000 3.4465 0.9295 3.3874 0.1529 0.2125 1.3779 0.0352 1.0836 0.0716 -#> 220: 92.7027 -5.9052 -2.2480 -4.1509 -0.9594 0.1668 0.1020 3.4302 0.9264 3.3877 0.1531 0.2168 1.3780 0.0352 1.0893 0.0713 -#> 221: 92.7029 -5.8990 -2.2497 -4.1541 -0.9586 0.1696 0.1017 3.4007 0.9227 3.3916 0.1535 0.2208 1.3781 0.0354 1.0925 0.0709 -#> 222: 92.7063 -5.8993 -2.2519 -4.1604 -0.9582 0.1732 0.1025 3.4099 0.9190 3.4135 0.1537 0.2268 1.3791 0.0355 1.1031 0.0702 -#> 223: 92.7090 -5.8932 -2.2537 -4.1669 -0.9573 0.1757 0.1022 3.3946 0.9157 3.4424 0.1543 0.2319 1.3802 0.0356 1.1040 0.0701 -#> 224: 92.7116 -5.8930 -2.2545 -4.1712 -0.9561 0.1774 0.1017 3.3964 0.9133 3.4673 0.1550 0.2355 1.3795 0.0356 1.1018 0.0701 -#> 225: 92.7136 -5.8911 -2.2564 -4.1715 -0.9551 0.1788 0.1016 3.4013 0.9125 3.4628 0.1548 0.2380 1.3756 0.0359 1.1003 0.0700 -#> 226: 92.7153 -5.8883 -2.2569 -4.1711 -0.9536 0.1793 0.1016 3.4046 0.9134 3.4575 0.1549 0.2398 1.3737 0.0360 1.1016 0.0699 -#> 227: 92.7163 -5.8830 -2.2575 -4.1720 -0.9526 0.1796 0.1019 3.3952 0.9129 3.4575 0.1545 0.2407 1.3718 0.0363 1.1015 0.0698 -#> 228: 92.7182 -5.8865 -2.2578 -4.1728 -0.9528 0.1804 0.1017 3.4198 0.9113 3.4576 0.1538 0.2433 1.3722 0.0363 1.1068 0.0695 -#> 229: 92.7199 -5.8965 -2.2578 -4.1718 -0.9523 0.1812 0.1023 3.5030 0.9097 3.4503 0.1529 0.2463 1.3749 0.0363 1.1093 0.0694 -#> 230: 92.7205 -5.8997 -2.2578 -4.1712 -0.9514 0.1825 0.1025 3.5337 0.9071 3.4446 0.1519 0.2497 1.3802 0.0362 1.1115 0.0693 -#> 231: 92.7208 -5.9001 -2.2581 -4.1711 -0.9511 0.1838 0.1044 3.5537 0.9037 3.4423 0.1510 0.2533 1.3834 0.0361 1.1125 0.0693 -#> 232: 92.7183 -5.9041 -2.2588 -4.1715 -0.9504 0.1855 0.1061 3.5958 0.9001 3.4391 0.1503 0.2572 1.3871 0.0362 1.1161 0.0690 -#> 233: 92.7169 -5.9106 -2.2593 -4.1725 -0.9490 0.1866 0.1073 3.6433 0.8968 3.4367 0.1496 0.2609 1.3900 0.0362 1.1179 0.0688 -#> 234: 92.7125 -5.9165 -2.2594 -4.1728 -0.9479 0.1873 0.1098 3.6870 0.8932 3.4321 0.1498 0.2641 1.3907 0.0363 1.1177 0.0687 -#> 235: 92.7072 -5.9203 -2.2592 -4.1729 -0.9472 0.1876 0.1128 3.7229 0.8899 3.4269 0.1506 0.2676 1.3913 0.0364 1.1212 0.0686 -#> 236: 92.7048 -5.9319 -2.2603 -4.1724 -0.9467 0.1879 0.1147 3.7863 0.8879 3.4175 0.1510 0.2705 1.3898 0.0365 1.1181 0.0688 -#> 237: 92.7037 -5.9349 -2.2609 -4.1720 -0.9461 0.1881 0.1152 3.8047 0.8862 3.4096 0.1512 0.2731 1.3891 0.0367 1.1164 0.0688 -#> 238: 92.7027 -5.9359 -2.2605 -4.1715 -0.9459 0.1884 0.1151 3.7997 0.8842 3.4023 0.1516 0.2755 1.3905 0.0366 1.1171 0.0688 -#> 239: 92.7027 -5.9375 -2.2599 -4.1712 -0.9463 0.1881 0.1143 3.8187 0.8835 3.3954 0.1521 0.2780 1.3923 0.0366 1.1193 0.0688 -#> 240: 92.7025 -5.9409 -2.2593 -4.1710 -0.9467 0.1884 0.1135 3.8437 0.8830 3.3888 0.1530 0.2797 1.3939 0.0366 1.1266 0.0685 -#> 241: 92.7006 -5.9429 -2.2589 -4.1703 -0.9469 0.1887 0.1130 3.8580 0.8825 3.3820 0.1529 0.2815 1.3967 0.0364 1.1299 0.0685 -#> 242: 92.6977 -5.9366 -2.2594 -4.1693 -0.9471 0.1887 0.1130 3.8245 0.8810 3.3742 0.1534 0.2833 1.3967 0.0364 1.1323 0.0685 -#> 243: 92.6951 -5.9310 -2.2605 -4.1683 -0.9473 0.1891 0.1131 3.7904 0.8807 3.3666 0.1541 0.2853 1.3953 0.0364 1.1380 0.0683 -#> 244: 92.6928 -5.9289 -2.2610 -4.1680 -0.9471 0.1899 0.1130 3.7709 0.8797 3.3604 0.1545 0.2880 1.3947 0.0364 1.1399 0.0683 -#> 245: 92.6902 -5.9291 -2.2615 -4.1677 -0.9472 0.1914 0.1129 3.7637 0.8787 3.3538 0.1549 0.2898 1.3942 0.0364 1.1440 0.0681 -#> 246: 92.6880 -5.9271 -2.2617 -4.1677 -0.9472 0.1926 0.1131 3.7457 0.8785 3.3500 0.1549 0.2916 1.3938 0.0364 1.1468 0.0681 -#> 247: 92.6865 -5.9264 -2.2613 -4.1676 -0.9471 0.1930 0.1127 3.7331 0.8793 3.3487 0.1551 0.2918 1.3931 0.0364 1.1464 0.0683 -#> 248: 92.6855 -5.9212 -2.2604 -4.1671 -0.9476 0.1935 0.1116 3.7055 0.8795 3.3451 0.1549 0.2923 1.3942 0.0363 1.1453 0.0684 -#> 249: 92.6848 -5.9190 -2.2600 -4.1667 -0.9482 0.1939 0.1110 3.6857 0.8801 3.3428 0.1548 0.2923 1.3942 0.0363 1.1440 0.0685 -#> 250: 92.6858 -5.9194 -2.2605 -4.1663 -0.9489 0.1945 0.1109 3.6821 0.8806 3.3397 0.1547 0.2920 1.3932 0.0363 1.1430 0.0686 -#> 251: 92.6849 -5.9179 -2.2610 -4.1665 -0.9492 0.1950 0.1111 3.6795 0.8814 3.3392 0.1550 0.2919 1.3922 0.0364 1.1434 0.0685 -#> 252: 92.6848 -5.9141 -2.2615 -4.1660 -0.9493 0.1957 0.1110 3.6611 0.8818 3.3363 0.1548 0.2918 1.3919 0.0364 1.1423 0.0686 -#> 253: 92.6837 -5.9110 -2.2637 -4.1634 -0.9493 0.1952 0.1114 3.6462 0.8788 3.3481 0.1550 0.2920 1.3941 0.0363 1.1417 0.0688 -#> 254: 92.6827 -5.9082 -2.2650 -4.1608 -0.9492 0.1944 0.1117 3.6309 0.8753 3.3595 0.1548 0.2921 1.3964 0.0361 1.1415 0.0688 -#> 255: 92.6829 -5.9076 -2.2662 -4.1585 -0.9495 0.1934 0.1118 3.6221 0.8723 3.3737 0.1547 0.2923 1.3977 0.0359 1.1397 0.0689 -#> 256: 92.6821 -5.9079 -2.2672 -4.1559 -0.9495 0.1923 0.1118 3.6279 0.8697 3.3865 0.1547 0.2925 1.3990 0.0357 1.1387 0.0691 -#> 257: 92.6822 -5.9054 -2.2686 -4.1534 -0.9499 0.1914 0.1119 3.6202 0.8673 3.3988 0.1548 0.2923 1.4010 0.0356 1.1438 0.0690 -#> 258: 92.6828 -5.9054 -2.2700 -4.1509 -0.9498 0.1900 0.1121 3.6166 0.8651 3.4085 0.1547 0.2926 1.4028 0.0356 1.1473 0.0688 -#> 259: 92.6842 -5.9087 -2.2710 -4.1474 -0.9496 0.1890 0.1128 3.6314 0.8629 3.4154 0.1548 0.2923 1.4040 0.0355 1.1482 0.0689 -#> 260: 92.6852 -5.9118 -2.2717 -4.1444 -0.9493 0.1885 0.1124 3.6485 0.8606 3.4227 0.1544 0.2919 1.4073 0.0354 1.1518 0.0688 -#> 261: 92.6858 -5.9137 -2.2721 -4.1419 -0.9493 0.1882 0.1122 3.6641 0.8581 3.4314 0.1543 0.2913 1.4106 0.0353 1.1577 0.0684 -#> 262: 92.6861 -5.9117 -2.2726 -4.1394 -0.9493 0.1881 0.1116 3.6572 0.8558 3.4391 0.1541 0.2908 1.4137 0.0352 1.1613 0.0682 -#> 263: 92.6855 -5.9124 -2.2730 -4.1372 -0.9494 0.1875 0.1113 3.6626 0.8533 3.4465 0.1541 0.2905 1.4152 0.0351 1.1636 0.0681 -#> 264: 92.6841 -5.9137 -2.2734 -4.1351 -0.9496 0.1871 0.1109 3.6703 0.8505 3.4529 0.1538 0.2903 1.4156 0.0350 1.1632 0.0681 -#> 265: 92.6833 -5.9153 -2.2741 -4.1327 -0.9498 0.1867 0.1108 3.6816 0.8472 3.4581 0.1535 0.2899 1.4168 0.0350 1.1647 0.0679 -#> 266: 92.6835 -5.9147 -2.2752 -4.1307 -0.9497 0.1865 0.1107 3.6768 0.8450 3.4641 0.1531 0.2896 1.4176 0.0349 1.1640 0.0679 -#> 267: 92.6835 -5.9167 -2.2761 -4.1283 -0.9499 0.1862 0.1105 3.6851 0.8430 3.4700 0.1530 0.2892 1.4178 0.0348 1.1639 0.0679 -#> 268: 92.6841 -5.9141 -2.2767 -4.1269 -0.9503 0.1860 0.1107 3.6718 0.8407 3.4775 0.1533 0.2891 1.4187 0.0348 1.1673 0.0677 -#> 269: 92.6845 -5.9094 -2.2774 -4.1253 -0.9503 0.1855 0.1112 3.6520 0.8384 3.4840 0.1535 0.2890 1.4192 0.0348 1.1686 0.0675 -#> 270: 92.6847 -5.9042 -2.2779 -4.1239 -0.9505 0.1853 0.1107 3.6288 0.8365 3.4895 0.1536 0.2889 1.4192 0.0347 1.1698 0.0675 -#> 271: 92.6849 -5.9000 -2.2785 -4.1228 -0.9506 0.1853 0.1102 3.6083 0.8348 3.4956 0.1536 0.2889 1.4191 0.0346 1.1692 0.0676 -#> 272: 92.6850 -5.8965 -2.2794 -4.1223 -0.9507 0.1853 0.1092 3.5892 0.8331 3.5071 0.1538 0.2889 1.4194 0.0345 1.1700 0.0676 -#> 273: 92.6851 -5.8916 -2.2805 -4.1222 -0.9508 0.1850 0.1089 3.5697 0.8315 3.5211 0.1538 0.2889 1.4209 0.0345 1.1720 0.0675 -#> 274: 92.6849 -5.8898 -2.2815 -4.1218 -0.9506 0.1852 0.1084 3.5607 0.8301 3.5339 0.1542 0.2886 1.4221 0.0344 1.1728 0.0675 -#> 275: 92.6844 -5.8885 -2.2830 -4.1215 -0.9504 0.1855 0.1080 3.5514 0.8284 3.5491 0.1545 0.2883 1.4238 0.0343 1.1756 0.0673 -#> 276: 92.6834 -5.8885 -2.2843 -4.1210 -0.9501 0.1859 0.1077 3.5477 0.8272 3.5648 0.1547 0.2878 1.4243 0.0343 1.1749 0.0674 -#> 277: 92.6829 -5.8892 -2.2858 -4.1208 -0.9500 0.1862 0.1071 3.5505 0.8257 3.5807 0.1552 0.2872 1.4244 0.0343 1.1747 0.0674 -#> 278: 92.6825 -5.8885 -2.2871 -4.1205 -0.9499 0.1862 0.1072 3.5463 0.8245 3.5960 0.1555 0.2866 1.4247 0.0343 1.1742 0.0675 -#> 279: 92.6815 -5.8887 -2.2883 -4.1201 -0.9501 0.1864 0.1072 3.5433 0.8239 3.6088 0.1556 0.2860 1.4247 0.0343 1.1737 0.0676 -#> 280: 92.6800 -5.8901 -2.2896 -4.1211 -0.9503 0.1865 0.1078 3.5481 0.8238 3.6285 0.1556 0.2848 1.4252 0.0344 1.1742 0.0676 -#> 281: 92.6779 -5.8914 -2.2907 -4.1218 -0.9502 0.1865 0.1084 3.5491 0.8240 3.6471 0.1558 0.2838 1.4251 0.0343 1.1732 0.0677 -#> 282: 92.6767 -5.8906 -2.2919 -4.1236 -0.9501 0.1862 0.1091 3.5462 0.8248 3.6747 0.1558 0.2825 1.4250 0.0344 1.1732 0.0677 -#> 283: 92.6750 -5.8895 -2.2928 -4.1253 -0.9499 0.1857 0.1097 3.5418 0.8260 3.7025 0.1555 0.2814 1.4253 0.0344 1.1712 0.0678 -#> 284: 92.6736 -5.8903 -2.2934 -4.1271 -0.9497 0.1854 0.1107 3.5438 0.8269 3.7297 0.1553 0.2800 1.4257 0.0343 1.1698 0.0678 -#> 285: 92.6730 -5.8917 -2.2942 -4.1284 -0.9497 0.1852 0.1116 3.5481 0.8274 3.7528 0.1551 0.2787 1.4260 0.0343 1.1689 0.0678 -#> 286: 92.6715 -5.8913 -2.2947 -4.1285 -0.9492 0.1849 0.1122 3.5473 0.8274 3.7660 0.1550 0.2775 1.4265 0.0342 1.1678 0.0679 -#> 287: 92.6702 -5.8925 -2.2952 -4.1290 -0.9489 0.1846 0.1125 3.5531 0.8268 3.7818 0.1549 0.2764 1.4269 0.0342 1.1673 0.0678 -#> 288: 92.6688 -5.8918 -2.2959 -4.1290 -0.9490 0.1843 0.1126 3.5495 0.8262 3.7946 0.1546 0.2756 1.4275 0.0341 1.1673 0.0678 -#> 289: 92.6673 -5.8907 -2.2966 -4.1295 -0.9490 0.1841 0.1124 3.5445 0.8260 3.8067 0.1543 0.2750 1.4280 0.0342 1.1690 0.0677 -#> 290: 92.6657 -5.8909 -2.2973 -4.1302 -0.9490 0.1838 0.1123 3.5433 0.8260 3.8201 0.1540 0.2744 1.4279 0.0342 1.1687 0.0676 -#> 291: 92.6642 -5.8902 -2.2978 -4.1312 -0.9493 0.1835 0.1124 3.5399 0.8262 3.8365 0.1538 0.2738 1.4279 0.0342 1.1695 0.0676 -#> 292: 92.6635 -5.8917 -2.2983 -4.1316 -0.9495 0.1831 0.1121 3.5453 0.8263 3.8517 0.1535 0.2733 1.4275 0.0342 1.1695 0.0675 -#> 293: 92.6622 -5.8936 -2.2991 -4.1323 -0.9497 0.1830 0.1121 3.5526 0.8265 3.8692 0.1533 0.2728 1.4274 0.0342 1.1701 0.0675 -#> 294: 92.6604 -5.8936 -2.2999 -4.1328 -0.9499 0.1826 0.1126 3.5505 0.8263 3.8838 0.1533 0.2723 1.4273 0.0342 1.1712 0.0675 -#> 295: 92.6593 -5.8924 -2.3007 -4.1329 -0.9498 0.1823 0.1131 3.5443 0.8262 3.9004 0.1531 0.2717 1.4276 0.0342 1.1718 0.0674 -#> 296: 92.6586 -5.8906 -2.3016 -4.1323 -0.9496 0.1822 0.1133 3.5374 0.8266 3.9103 0.1530 0.2707 1.4272 0.0343 1.1714 0.0674 -#> 297: 92.6578 -5.8889 -2.3026 -4.1329 -0.9494 0.1819 0.1139 3.5315 0.8271 3.9280 0.1528 0.2697 1.4267 0.0343 1.1697 0.0675 -#> 298: 92.6575 -5.8885 -2.3036 -4.1330 -0.9490 0.1814 0.1143 3.5303 0.8275 3.9410 0.1527 0.2689 1.4263 0.0344 1.1688 0.0675 -#> 299: 92.6566 -5.8879 -2.3047 -4.1329 -0.9488 0.1807 0.1147 3.5286 0.8282 3.9507 0.1526 0.2679 1.4263 0.0345 1.1680 0.0674 -#> 300: 92.6555 -5.8862 -2.3057 -4.1325 -0.9483 0.1802 0.1151 3.5225 0.8293 3.9582 0.1527 0.2671 1.4261 0.0345 1.1677 0.0674 -#> 301: 92.6545 -5.8854 -2.3067 -4.1326 -0.9480 0.1795 0.1156 3.5191 0.8300 3.9691 0.1530 0.2665 1.4257 0.0346 1.1672 0.0674 -#> 302: 92.6539 -5.8839 -2.3078 -4.1322 -0.9477 0.1788 0.1161 3.5154 0.8309 3.9769 0.1532 0.2657 1.4252 0.0346 1.1664 0.0675 -#> 303: 92.6541 -5.8799 -2.3089 -4.1327 -0.9474 0.1782 0.1161 3.5012 0.8319 3.9913 0.1534 0.2649 1.4242 0.0347 1.1653 0.0675 -#> 304: 92.6554 -5.8766 -2.3096 -4.1326 -0.9472 0.1774 0.1164 3.4879 0.8328 3.9978 0.1536 0.2641 1.4234 0.0348 1.1644 0.0675 -#> 305: 92.6559 -5.8732 -2.3104 -4.1325 -0.9470 0.1764 0.1161 3.4740 0.8334 4.0037 0.1535 0.2633 1.4231 0.0348 1.1634 0.0676 -#> 306: 92.6564 -5.8717 -2.3113 -4.1322 -0.9470 0.1758 0.1161 3.4705 0.8341 4.0097 0.1537 0.2622 1.4236 0.0348 1.1628 0.0676 -#> 307: 92.6573 -5.8703 -2.3121 -4.1320 -0.9469 0.1748 0.1158 3.4630 0.8349 4.0154 0.1538 0.2614 1.4231 0.0348 1.1617 0.0677 -#> 308: 92.6578 -5.8695 -2.3129 -4.1318 -0.9465 0.1738 0.1154 3.4585 0.8356 4.0210 0.1540 0.2607 1.4229 0.0348 1.1604 0.0677 -#> 309: 92.6577 -5.8691 -2.3132 -4.1317 -0.9465 0.1732 0.1151 3.4548 0.8369 4.0270 0.1540 0.2596 1.4233 0.0348 1.1589 0.0678 -#> 310: 92.6580 -5.8680 -2.3135 -4.1309 -0.9466 0.1727 0.1147 3.4472 0.8377 4.0280 0.1540 0.2587 1.4231 0.0348 1.1569 0.0679 -#> 311: 92.6575 -5.8681 -2.3141 -4.1303 -0.9466 0.1722 0.1144 3.4477 0.8384 4.0303 0.1539 0.2577 1.4236 0.0348 1.1557 0.0679 -#> 312: 92.6571 -5.8685 -2.3145 -4.1299 -0.9467 0.1720 0.1143 3.4498 0.8393 4.0328 0.1538 0.2566 1.4237 0.0348 1.1545 0.0680 -#> 313: 92.6559 -5.8685 -2.3150 -4.1296 -0.9469 0.1718 0.1142 3.4483 0.8403 4.0358 0.1537 0.2555 1.4234 0.0348 1.1532 0.0681 -#> 314: 92.6543 -5.8699 -2.3155 -4.1294 -0.9471 0.1715 0.1142 3.4526 0.8404 4.0401 0.1537 0.2546 1.4236 0.0347 1.1522 0.0681 -#> 315: 92.6528 -5.8713 -2.3161 -4.1289 -0.9472 0.1712 0.1144 3.4584 0.8402 4.0427 0.1537 0.2538 1.4234 0.0347 1.1520 0.0682 -#> 316: 92.6510 -5.8726 -2.3166 -4.1283 -0.9472 0.1705 0.1146 3.4647 0.8404 4.0443 0.1537 0.2528 1.4236 0.0347 1.1511 0.0682 -#> 317: 92.6496 -5.8736 -2.3170 -4.1281 -0.9474 0.1699 0.1147 3.4701 0.8406 4.0497 0.1536 0.2520 1.4238 0.0347 1.1504 0.0683 -#> 318: 92.6479 -5.8745 -2.3174 -4.1276 -0.9475 0.1695 0.1153 3.4729 0.8410 4.0511 0.1535 0.2510 1.4238 0.0347 1.1503 0.0683 -#> 319: 92.6463 -5.8773 -2.3175 -4.1272 -0.9476 0.1690 0.1155 3.4868 0.8409 4.0527 0.1535 0.2502 1.4234 0.0347 1.1484 0.0685 -#> 320: 92.6447 -5.8770 -2.3179 -4.1263 -0.9478 0.1684 0.1158 3.4849 0.8407 4.0516 0.1534 0.2493 1.4238 0.0347 1.1483 0.0685 -#> 321: 92.6433 -5.8768 -2.3181 -4.1255 -0.9479 0.1679 0.1161 3.4850 0.8405 4.0511 0.1533 0.2485 1.4238 0.0346 1.1474 0.0686 -#> 322: 92.6425 -5.8766 -2.3182 -4.1246 -0.9480 0.1673 0.1161 3.4839 0.8403 4.0505 0.1530 0.2474 1.4243 0.0346 1.1458 0.0687 -#> 323: 92.6414 -5.8778 -2.3183 -4.1241 -0.9481 0.1669 0.1162 3.4888 0.8402 4.0517 0.1530 0.2466 1.4244 0.0346 1.1454 0.0687 -#> 324: 92.6404 -5.8771 -2.3186 -4.1236 -0.9482 0.1666 0.1161 3.4855 0.8401 4.0525 0.1529 0.2459 1.4247 0.0345 1.1446 0.0687 -#> 325: 92.6396 -5.8753 -2.3188 -4.1231 -0.9483 0.1664 0.1156 3.4767 0.8396 4.0533 0.1529 0.2454 1.4253 0.0345 1.1438 0.0689 -#> 326: 92.6397 -5.8766 -2.3192 -4.1226 -0.9484 0.1663 0.1152 3.4798 0.8389 4.0542 0.1527 0.2449 1.4253 0.0345 1.1431 0.0690 -#> 327: 92.6395 -5.8785 -2.3197 -4.1224 -0.9483 0.1660 0.1151 3.4880 0.8382 4.0557 0.1528 0.2445 1.4250 0.0345 1.1430 0.0690 -#> 328: 92.6397 -5.8805 -2.3202 -4.1221 -0.9483 0.1657 0.1153 3.5011 0.8373 4.0568 0.1528 0.2442 1.4246 0.0345 1.1427 0.0690 -#> 329: 92.6390 -5.8838 -2.3208 -4.1219 -0.9482 0.1655 0.1161 3.5176 0.8365 4.0580 0.1530 0.2439 1.4241 0.0345 1.1429 0.0690 -#> 330: 92.6380 -5.8862 -2.3215 -4.1216 -0.9484 0.1653 0.1166 3.5286 0.8355 4.0584 0.1529 0.2437 1.4234 0.0346 1.1428 0.0690 -#> 331: 92.6367 -5.8867 -2.3223 -4.1206 -0.9484 0.1651 0.1165 3.5288 0.8348 4.0577 0.1528 0.2435 1.4233 0.0346 1.1429 0.0690 -#> 332: 92.6360 -5.8859 -2.3230 -4.1199 -0.9485 0.1650 0.1165 3.5235 0.8343 4.0572 0.1527 0.2433 1.4227 0.0346 1.1429 0.0689 -#> 333: 92.6361 -5.8839 -2.3237 -4.1194 -0.9485 0.1649 0.1162 3.5142 0.8340 4.0564 0.1527 0.2430 1.4224 0.0347 1.1429 0.0689 -#> 334: 92.6359 -5.8824 -2.3244 -4.1190 -0.9486 0.1649 0.1158 3.5070 0.8337 4.0567 0.1527 0.2424 1.4218 0.0347 1.1442 0.0689 -#> 335: 92.6366 -5.8826 -2.3250 -4.1186 -0.9485 0.1645 0.1157 3.5069 0.8334 4.0574 0.1527 0.2419 1.4214 0.0347 1.1448 0.0688 -#> 336: 92.6374 -5.8816 -2.3253 -4.1182 -0.9486 0.1644 0.1158 3.5034 0.8330 4.0580 0.1528 0.2415 1.4212 0.0347 1.1471 0.0687 -#> 337: 92.6378 -5.8810 -2.3258 -4.1176 -0.9487 0.1642 0.1159 3.5023 0.8325 4.0582 0.1528 0.2410 1.4212 0.0347 1.1467 0.0688 -#> 338: 92.6383 -5.8814 -2.3262 -4.1168 -0.9488 0.1637 0.1160 3.5028 0.8322 4.0571 0.1526 0.2409 1.4216 0.0346 1.1456 0.0689 -#> 339: 92.6392 -5.8808 -2.3266 -4.1160 -0.9490 0.1631 0.1161 3.4989 0.8318 4.0566 0.1524 0.2408 1.4220 0.0346 1.1441 0.0690 -#> 340: 92.6393 -5.8810 -2.3269 -4.1152 -0.9491 0.1626 0.1157 3.4997 0.8316 4.0564 0.1524 0.2407 1.4216 0.0346 1.1419 0.0692 -#> 341: 92.6394 -5.8807 -2.3272 -4.1148 -0.9492 0.1619 0.1153 3.4966 0.8308 4.0552 0.1523 0.2405 1.4218 0.0346 1.1415 0.0692 -#> 342: 92.6394 -5.8806 -2.3274 -4.1141 -0.9493 0.1612 0.1146 3.4936 0.8303 4.0537 0.1522 0.2405 1.4221 0.0346 1.1406 0.0692 -#> 343: 92.6398 -5.8819 -2.3277 -4.1134 -0.9494 0.1606 0.1141 3.4961 0.8297 4.0519 0.1522 0.2402 1.4219 0.0347 1.1404 0.0692 -#> 344: 92.6401 -5.8823 -2.3280 -4.1128 -0.9497 0.1599 0.1137 3.4963 0.8293 4.0504 0.1523 0.2400 1.4214 0.0346 1.1404 0.0692 -#> 345: 92.6404 -5.8829 -2.3283 -4.1124 -0.9498 0.1593 0.1136 3.4958 0.8289 4.0494 0.1523 0.2396 1.4214 0.0346 1.1398 0.0692 -#> 346: 92.6405 -5.8829 -2.3283 -4.1119 -0.9499 0.1587 0.1135 3.4953 0.8287 4.0484 0.1522 0.2394 1.4216 0.0346 1.1397 0.0692 -#> 347: 92.6404 -5.8833 -2.3288 -4.1117 -0.9500 0.1582 0.1133 3.4965 0.8289 4.0480 0.1521 0.2391 1.4211 0.0346 1.1388 0.0692 -#> 348: 92.6407 -5.8838 -2.3293 -4.1113 -0.9502 0.1578 0.1132 3.4978 0.8290 4.0471 0.1520 0.2388 1.4209 0.0346 1.1385 0.0692 -#> 349: 92.6409 -5.8847 -2.3299 -4.1110 -0.9503 0.1571 0.1128 3.5024 0.8290 4.0474 0.1519 0.2386 1.4207 0.0347 1.1379 0.0692 -#> 350: 92.6413 -5.8853 -2.3304 -4.1107 -0.9504 0.1567 0.1125 3.5037 0.8287 4.0478 0.1519 0.2383 1.4207 0.0347 1.1366 0.0693 -#> 351: 92.6415 -5.8868 -2.3310 -4.1104 -0.9504 0.1562 0.1122 3.5109 0.8287 4.0490 0.1518 0.2378 1.4208 0.0347 1.1364 0.0693 -#> 352: 92.6413 -5.8882 -2.3316 -4.1103 -0.9504 0.1557 0.1120 3.5196 0.8287 4.0517 0.1517 0.2375 1.4207 0.0346 1.1361 0.0693 -#> 353: 92.6414 -5.8890 -2.3322 -4.1101 -0.9503 0.1553 0.1117 3.5237 0.8290 4.0533 0.1517 0.2371 1.4202 0.0346 1.1345 0.0693 -#> 354: 92.6417 -5.8879 -2.3327 -4.1099 -0.9502 0.1548 0.1115 3.5206 0.8294 4.0546 0.1515 0.2368 1.4200 0.0346 1.1336 0.0694 -#> 355: 92.6417 -5.8882 -2.3333 -4.1096 -0.9500 0.1541 0.1115 3.5265 0.8296 4.0548 0.1514 0.2364 1.4203 0.0346 1.1325 0.0694 -#> 356: 92.6414 -5.8881 -2.3338 -4.1093 -0.9497 0.1535 0.1115 3.5339 0.8299 4.0553 0.1513 0.2362 1.4204 0.0346 1.1318 0.0694 -#> 357: 92.6414 -5.8874 -2.3343 -4.1087 -0.9497 0.1529 0.1117 3.5320 0.8302 4.0548 0.1512 0.2358 1.4205 0.0346 1.1315 0.0694 -#> 358: 92.6415 -5.8865 -2.3349 -4.1087 -0.9497 0.1523 0.1118 3.5274 0.8308 4.0583 0.1510 0.2354 1.4206 0.0346 1.1308 0.0695 -#> 359: 92.6415 -5.8855 -2.3352 -4.1085 -0.9497 0.1518 0.1123 3.5208 0.8308 4.0597 0.1509 0.2349 1.4205 0.0346 1.1298 0.0695 -#> 360: 92.6413 -5.8851 -2.3356 -4.1080 -0.9496 0.1513 0.1125 3.5176 0.8308 4.0606 0.1508 0.2344 1.4207 0.0346 1.1289 0.0695 -#> 361: 92.6412 -5.8854 -2.3359 -4.1076 -0.9498 0.1508 0.1126 3.5187 0.8308 4.0618 0.1508 0.2338 1.4214 0.0345 1.1279 0.0695 -#> 362: 92.6415 -5.8861 -2.3362 -4.1072 -0.9499 0.1503 0.1126 3.5210 0.8306 4.0636 0.1507 0.2333 1.4218 0.0345 1.1273 0.0695 -#> 363: 92.6412 -5.8884 -2.3364 -4.1066 -0.9499 0.1498 0.1126 3.5327 0.8305 4.0646 0.1507 0.2328 1.4221 0.0345 1.1273 0.0695 -#> 364: 92.6411 -5.8895 -2.3367 -4.1062 -0.9501 0.1494 0.1126 3.5366 0.8306 4.0659 0.1507 0.2322 1.4227 0.0345 1.1280 0.0695 -#> 365: 92.6411 -5.8908 -2.3367 -4.1060 -0.9502 0.1489 0.1125 3.5405 0.8307 4.0690 0.1507 0.2317 1.4228 0.0344 1.1280 0.0695 -#> 366: 92.6412 -5.8926 -2.3366 -4.1062 -0.9502 0.1484 0.1125 3.5483 0.8307 4.0724 0.1507 0.2311 1.4228 0.0344 1.1280 0.0695 -#> 367: 92.6406 -5.8940 -2.3366 -4.1059 -0.9503 0.1483 0.1124 3.5557 0.8308 4.0738 0.1507 0.2305 1.4228 0.0344 1.1273 0.0695 -#> 368: 92.6402 -5.8940 -2.3365 -4.1059 -0.9504 0.1483 0.1122 3.5538 0.8306 4.0773 0.1507 0.2299 1.4228 0.0344 1.1266 0.0696 -#> 369: 92.6398 -5.8933 -2.3366 -4.1058 -0.9504 0.1482 0.1122 3.5489 0.8303 4.0796 0.1507 0.2295 1.4228 0.0343 1.1261 0.0696 -#> 370: 92.6394 -5.8928 -2.3366 -4.1059 -0.9504 0.1481 0.1123 3.5445 0.8302 4.0819 0.1506 0.2291 1.4229 0.0343 1.1258 0.0696 -#> 371: 92.6390 -5.8930 -2.3369 -4.1062 -0.9503 0.1481 0.1125 3.5446 0.8299 4.0854 0.1506 0.2285 1.4230 0.0343 1.1257 0.0696 -#> 372: 92.6387 -5.8926 -2.3372 -4.1064 -0.9503 0.1482 0.1125 3.5424 0.8298 4.0887 0.1505 0.2281 1.4234 0.0343 1.1262 0.0696 -#> 373: 92.6385 -5.8927 -2.3376 -4.1067 -0.9502 0.1483 0.1126 3.5447 0.8297 4.0919 0.1504 0.2275 1.4236 0.0343 1.1268 0.0696 -#> 374: 92.6382 -5.8932 -2.3380 -4.1064 -0.9502 0.1481 0.1131 3.5490 0.8295 4.0929 0.1503 0.2272 1.4238 0.0343 1.1267 0.0696 -#> 375: 92.6385 -5.8944 -2.3383 -4.1062 -0.9502 0.1481 0.1136 3.5562 0.8292 4.0936 0.1503 0.2269 1.4240 0.0343 1.1274 0.0695 -#> 376: 92.6388 -5.8942 -2.3387 -4.1061 -0.9502 0.1481 0.1141 3.5575 0.8295 4.0942 0.1502 0.2267 1.4236 0.0343 1.1272 0.0695 -#> 377: 92.6389 -5.8942 -2.3392 -4.1060 -0.9502 0.1482 0.1145 3.5579 0.8298 4.0950 0.1501 0.2264 1.4233 0.0344 1.1272 0.0695 -#> 378: 92.6388 -5.8939 -2.3397 -4.1060 -0.9502 0.1481 0.1150 3.5558 0.8298 4.0959 0.1500 0.2261 1.4232 0.0344 1.1271 0.0695 -#> 379: 92.6388 -5.8934 -2.3399 -4.1062 -0.9500 0.1483 0.1153 3.5521 0.8294 4.0980 0.1500 0.2257 1.4236 0.0344 1.1279 0.0694 -#> 380: 92.6390 -5.8920 -2.3402 -4.1065 -0.9499 0.1484 0.1155 3.5446 0.8292 4.1007 0.1500 0.2254 1.4241 0.0344 1.1285 0.0694 -#> 381: 92.6394 -5.8906 -2.3404 -4.1069 -0.9498 0.1485 0.1157 3.5378 0.8290 4.1040 0.1500 0.2250 1.4249 0.0343 1.1296 0.0694 -#> 382: 92.6403 -5.8893 -2.3406 -4.1085 -0.9498 0.1487 0.1157 3.5319 0.8289 4.1195 0.1500 0.2246 1.4250 0.0343 1.1301 0.0694 -#> 383: 92.6402 -5.8882 -2.3408 -4.1096 -0.9499 0.1488 0.1155 3.5269 0.8287 4.1290 0.1500 0.2243 1.4253 0.0343 1.1300 0.0694 -#> 384: 92.6401 -5.8871 -2.3412 -4.1102 -0.9498 0.1490 0.1155 3.5219 0.8285 4.1340 0.1499 0.2241 1.4254 0.0343 1.1297 0.0694 -#> 385: 92.6396 -5.8867 -2.3417 -4.1105 -0.9497 0.1493 0.1155 3.5195 0.8281 4.1364 0.1498 0.2238 1.4252 0.0343 1.1297 0.0695 -#> 386: 92.6393 -5.8863 -2.3423 -4.1116 -0.9496 0.1497 0.1153 3.5190 0.8280 4.1452 0.1497 0.2235 1.4251 0.0343 1.1307 0.0694 -#> 387: 92.6391 -5.8865 -2.3429 -4.1124 -0.9495 0.1498 0.1155 3.5219 0.8280 4.1502 0.1497 0.2234 1.4247 0.0343 1.1301 0.0695 -#> 388: 92.6389 -5.8861 -2.3436 -4.1129 -0.9494 0.1501 0.1158 3.5228 0.8278 4.1540 0.1496 0.2233 1.4243 0.0343 1.1293 0.0695 -#> 389: 92.6384 -5.8849 -2.3442 -4.1132 -0.9491 0.1504 0.1159 3.5195 0.8276 4.1571 0.1496 0.2231 1.4242 0.0343 1.1284 0.0696 -#> 390: 92.6382 -5.8838 -2.3447 -4.1134 -0.9489 0.1506 0.1159 3.5172 0.8276 4.1603 0.1497 0.2230 1.4242 0.0343 1.1273 0.0697 -#> 391: 92.6380 -5.8821 -2.3454 -4.1140 -0.9486 0.1509 0.1159 3.5134 0.8274 4.1661 0.1498 0.2228 1.4238 0.0343 1.1266 0.0697 -#> 392: 92.6374 -5.8800 -2.3460 -4.1140 -0.9485 0.1513 0.1158 3.5069 0.8274 4.1673 0.1499 0.2226 1.4235 0.0343 1.1258 0.0698 -#> 393: 92.6372 -5.8785 -2.3467 -4.1140 -0.9485 0.1514 0.1159 3.5019 0.8275 4.1684 0.1499 0.2223 1.4232 0.0343 1.1258 0.0698 -#> 394: 92.6372 -5.8765 -2.3473 -4.1142 -0.9485 0.1515 0.1161 3.4955 0.8275 4.1710 0.1499 0.2221 1.4228 0.0344 1.1260 0.0697 -#> 395: 92.6371 -5.8761 -2.3476 -4.1145 -0.9485 0.1515 0.1164 3.4940 0.8273 4.1739 0.1498 0.2220 1.4227 0.0344 1.1254 0.0698 -#> 396: 92.6370 -5.8759 -2.3480 -4.1147 -0.9485 0.1516 0.1166 3.4942 0.8269 4.1764 0.1498 0.2217 1.4222 0.0344 1.1252 0.0698 -#> 397: 92.6371 -5.8756 -2.3483 -4.1149 -0.9486 0.1516 0.1167 3.4914 0.8267 4.1796 0.1498 0.2214 1.4219 0.0344 1.1253 0.0697 -#> 398: 92.6371 -5.8756 -2.3486 -4.1155 -0.9486 0.1518 0.1167 3.4909 0.8268 4.1840 0.1498 0.2210 1.4216 0.0344 1.1250 0.0697 -#> 399: 92.6368 -5.8765 -2.3489 -4.1157 -0.9485 0.1519 0.1170 3.4958 0.8266 4.1866 0.1498 0.2205 1.4213 0.0344 1.1245 0.0698 -#> 400: 92.6368 -5.8769 -2.3491 -4.1158 -0.9485 0.1522 0.1174 3.4972 0.8266 4.1888 0.1499 0.2200 1.4209 0.0344 1.1242 0.0698 -#> 401: 92.6366 -5.8768 -2.3493 -4.1161 -0.9484 0.1524 0.1175 3.4964 0.8267 4.1913 0.1499 0.2196 1.4204 0.0344 1.1240 0.0698 -#> 402: 92.6362 -5.8767 -2.3495 -4.1164 -0.9483 0.1525 0.1176 3.4961 0.8267 4.1937 0.1499 0.2192 1.4201 0.0344 1.1240 0.0698 -#> 403: 92.6362 -5.8769 -2.3497 -4.1166 -0.9483 0.1526 0.1178 3.4981 0.8270 4.1960 0.1499 0.2187 1.4197 0.0345 1.1236 0.0698 -#> 404: 92.6359 -5.8772 -2.3499 -4.1166 -0.9483 0.1527 0.1179 3.4997 0.8272 4.1968 0.1499 0.2183 1.4193 0.0345 1.1232 0.0698 -#> 405: 92.6355 -5.8763 -2.3501 -4.1165 -0.9483 0.1527 0.1180 3.4946 0.8273 4.1976 0.1500 0.2180 1.4189 0.0345 1.1230 0.0698 -#> 406: 92.6351 -5.8768 -2.3503 -4.1164 -0.9482 0.1528 0.1184 3.4953 0.8274 4.1979 0.1500 0.2176 1.4184 0.0345 1.1227 0.0698 -#> 407: 92.6346 -5.8772 -2.3505 -4.1165 -0.9481 0.1527 0.1187 3.4965 0.8275 4.1999 0.1500 0.2173 1.4182 0.0344 1.1222 0.0698 -#> 408: 92.6344 -5.8786 -2.3508 -4.1167 -0.9482 0.1528 0.1190 3.5025 0.8276 4.2020 0.1500 0.2171 1.4178 0.0344 1.1215 0.0699 -#> 409: 92.6342 -5.8806 -2.3511 -4.1168 -0.9484 0.1529 0.1193 3.5134 0.8277 4.2037 0.1500 0.2167 1.4176 0.0344 1.1212 0.0699 -#> 410: 92.6341 -5.8826 -2.3514 -4.1170 -0.9486 0.1531 0.1193 3.5229 0.8279 4.2061 0.1500 0.2163 1.4175 0.0344 1.1212 0.0699 -#> 411: 92.6339 -5.8840 -2.3517 -4.1172 -0.9488 0.1532 0.1192 3.5280 0.8280 4.2087 0.1499 0.2159 1.4175 0.0345 1.1208 0.0699 -#> 412: 92.6338 -5.8850 -2.3520 -4.1175 -0.9489 0.1534 0.1193 3.5311 0.8280 4.2121 0.1497 0.2155 1.4177 0.0345 1.1204 0.0699 -#> 413: 92.6343 -5.8859 -2.3523 -4.1177 -0.9491 0.1536 0.1191 3.5337 0.8282 4.2156 0.1497 0.2151 1.4176 0.0345 1.1198 0.0699 -#> 414: 92.6350 -5.8861 -2.3526 -4.1184 -0.9491 0.1540 0.1191 3.5350 0.8283 4.2209 0.1496 0.2147 1.4177 0.0345 1.1196 0.0699 -#> 415: 92.6354 -5.8866 -2.3528 -4.1191 -0.9492 0.1543 0.1191 3.5373 0.8284 4.2258 0.1496 0.2142 1.4179 0.0345 1.1191 0.0699 -#> 416: 92.6360 -5.8873 -2.3531 -4.1201 -0.9493 0.1548 0.1193 3.5431 0.8286 4.2328 0.1495 0.2137 1.4178 0.0345 1.1187 0.0699 -#> 417: 92.6361 -5.8878 -2.3533 -4.1213 -0.9494 0.1551 0.1192 3.5465 0.8288 4.2415 0.1494 0.2131 1.4182 0.0345 1.1189 0.0699 -#> 418: 92.6366 -5.8883 -2.3535 -4.1221 -0.9495 0.1555 0.1194 3.5499 0.8291 4.2477 0.1493 0.2127 1.4180 0.0345 1.1184 0.0699 -#> 419: 92.6367 -5.8885 -2.3536 -4.1236 -0.9495 0.1560 0.1195 3.5517 0.8292 4.2588 0.1492 0.2123 1.4179 0.0345 1.1180 0.0700 -#> 420: 92.6371 -5.8874 -2.3536 -4.1249 -0.9495 0.1564 0.1197 3.5474 0.8293 4.2666 0.1491 0.2118 1.4181 0.0345 1.1182 0.0700 -#> 421: 92.6374 -5.8860 -2.3537 -4.1263 -0.9494 0.1569 0.1197 3.5416 0.8292 4.2759 0.1492 0.2114 1.4184 0.0345 1.1188 0.0699 -#> 422: 92.6377 -5.8850 -2.3538 -4.1279 -0.9493 0.1572 0.1197 3.5365 0.8292 4.2865 0.1491 0.2110 1.4185 0.0345 1.1188 0.0700 -#> 423: 92.6380 -5.8844 -2.3540 -4.1299 -0.9494 0.1576 0.1196 3.5323 0.8290 4.2999 0.1491 0.2106 1.4186 0.0345 1.1192 0.0699 -#> 424: 92.6382 -5.8842 -2.3541 -4.1312 -0.9495 0.1581 0.1198 3.5309 0.8290 4.3092 0.1491 0.2103 1.4184 0.0345 1.1197 0.0699 -#> 425: 92.6382 -5.8838 -2.3543 -4.1320 -0.9495 0.1584 0.1197 3.5281 0.8289 4.3140 0.1491 0.2099 1.4185 0.0346 1.1196 0.0699 -#> 426: 92.6380 -5.8829 -2.3545 -4.1327 -0.9494 0.1587 0.1196 3.5234 0.8293 4.3183 0.1491 0.2096 1.4182 0.0346 1.1194 0.0699 -#> 427: 92.6375 -5.8823 -2.3548 -4.1335 -0.9494 0.1589 0.1197 3.5189 0.8295 4.3233 0.1492 0.2092 1.4180 0.0346 1.1196 0.0699 -#> 428: 92.6370 -5.8813 -2.3552 -4.1343 -0.9494 0.1592 0.1199 3.5140 0.8295 4.3286 0.1491 0.2088 1.4182 0.0346 1.1198 0.0699 -#> 429: 92.6368 -5.8802 -2.3556 -4.1356 -0.9495 0.1597 0.1202 3.5093 0.8296 4.3372 0.1491 0.2086 1.4182 0.0346 1.1208 0.0699 -#> 430: 92.6370 -5.8794 -2.3560 -4.1366 -0.9496 0.1602 0.1201 3.5058 0.8297 4.3439 0.1492 0.2084 1.4183 0.0346 1.1216 0.0698 -#> 431: 92.6371 -5.8792 -2.3564 -4.1372 -0.9497 0.1606 0.1201 3.5029 0.8298 4.3473 0.1493 0.2082 1.4182 0.0346 1.1215 0.0698 -#> 432: 92.6371 -5.8793 -2.3567 -4.1377 -0.9499 0.1609 0.1201 3.5008 0.8297 4.3499 0.1494 0.2080 1.4180 0.0346 1.1218 0.0698 -#> 433: 92.6370 -5.8799 -2.3570 -4.1387 -0.9501 0.1612 0.1201 3.5014 0.8298 4.3560 0.1495 0.2078 1.4180 0.0346 1.1218 0.0699 -#> 434: 92.6371 -5.8790 -2.3573 -4.1398 -0.9501 0.1615 0.1200 3.4982 0.8300 4.3624 0.1496 0.2076 1.4179 0.0346 1.1213 0.0699 -#> 435: 92.6368 -5.8789 -2.3576 -4.1409 -0.9501 0.1619 0.1199 3.4979 0.8302 4.3697 0.1496 0.2074 1.4176 0.0346 1.1205 0.0699 -#> 436: 92.6365 -5.8792 -2.3579 -4.1424 -0.9500 0.1623 0.1197 3.4987 0.8304 4.3798 0.1497 0.2073 1.4173 0.0346 1.1198 0.0699 -#> 437: 92.6364 -5.8798 -2.3582 -4.1439 -0.9500 0.1627 0.1195 3.5017 0.8307 4.3905 0.1497 0.2071 1.4172 0.0346 1.1191 0.0700 -#> 438: 92.6362 -5.8803 -2.3585 -4.1450 -0.9499 0.1631 0.1193 3.5053 0.8309 4.3973 0.1497 0.2070 1.4172 0.0346 1.1186 0.0700 -#> 439: 92.6361 -5.8811 -2.3588 -4.1463 -0.9498 0.1634 0.1190 3.5101 0.8312 4.4052 0.1496 0.2069 1.4172 0.0346 1.1188 0.0700 -#> 440: 92.6360 -5.8816 -2.3591 -4.1477 -0.9498 0.1637 0.1187 3.5127 0.8315 4.4145 0.1495 0.2068 1.4172 0.0346 1.1189 0.0700 -#> 441: 92.6357 -5.8816 -2.3594 -4.1492 -0.9499 0.1640 0.1185 3.5136 0.8319 4.4252 0.1494 0.2069 1.4175 0.0346 1.1191 0.0700 -#> 442: 92.6356 -5.8819 -2.3596 -4.1501 -0.9500 0.1642 0.1181 3.5151 0.8323 4.4310 0.1494 0.2070 1.4176 0.0346 1.1193 0.0700 -#> 443: 92.6356 -5.8825 -2.3598 -4.1512 -0.9501 0.1643 0.1180 3.5178 0.8324 4.4379 0.1493 0.2071 1.4179 0.0346 1.1196 0.0700 -#> 444: 92.6352 -5.8827 -2.3602 -4.1525 -0.9502 0.1644 0.1180 3.5169 0.8327 4.4458 0.1493 0.2073 1.4178 0.0346 1.1198 0.0700 -#> 445: 92.6348 -5.8828 -2.3605 -4.1534 -0.9502 0.1643 0.1180 3.5178 0.8329 4.4505 0.1493 0.2074 1.4178 0.0346 1.1202 0.0700 -#> 446: 92.6342 -5.8830 -2.3609 -4.1541 -0.9503 0.1643 0.1183 3.5182 0.8331 4.4539 0.1494 0.2077 1.4176 0.0346 1.1199 0.0700 -#> 447: 92.6334 -5.8832 -2.3613 -4.1548 -0.9503 0.1643 0.1188 3.5188 0.8333 4.4571 0.1494 0.2079 1.4172 0.0346 1.1198 0.0700 -#> 448: 92.6331 -5.8833 -2.3616 -4.1557 -0.9503 0.1643 0.1190 3.5190 0.8335 4.4613 0.1494 0.2080 1.4170 0.0346 1.1198 0.0700 -#> 449: 92.6327 -5.8835 -2.3619 -4.1563 -0.9504 0.1641 0.1192 3.5191 0.8335 4.4636 0.1493 0.2081 1.4172 0.0346 1.1196 0.0700 -#> 450: 92.6322 -5.8831 -2.3620 -4.1566 -0.9505 0.1639 0.1194 3.5152 0.8340 4.4647 0.1492 0.2083 1.4172 0.0346 1.1189 0.0700 -#> 451: 92.6315 -5.8835 -2.3622 -4.1569 -0.9505 0.1635 0.1194 3.5192 0.8343 4.4648 0.1492 0.2084 1.4169 0.0346 1.1187 0.0700 -#> 452: 92.6312 -5.8834 -2.3625 -4.1572 -0.9506 0.1632 0.1193 3.5173 0.8345 4.4654 0.1492 0.2086 1.4166 0.0346 1.1183 0.0700 -#> 453: 92.6309 -5.8838 -2.3628 -4.1574 -0.9506 0.1629 0.1193 3.5175 0.8348 4.4660 0.1493 0.2087 1.4166 0.0346 1.1180 0.0700 -#> 454: 92.6307 -5.8832 -2.3629 -4.1574 -0.9507 0.1625 0.1193 3.5128 0.8354 4.4658 0.1493 0.2087 1.4164 0.0346 1.1176 0.0700 -#> 455: 92.6305 -5.8821 -2.3632 -4.1579 -0.9508 0.1624 0.1192 3.5071 0.8360 4.4678 0.1494 0.2089 1.4164 0.0346 1.1171 0.0701 -#> 456: 92.6307 -5.8811 -2.3634 -4.1589 -0.9509 0.1623 0.1190 3.5014 0.8364 4.4730 0.1494 0.2088 1.4168 0.0346 1.1168 0.0701 -#> 457: 92.6307 -5.8808 -2.3636 -4.1597 -0.9509 0.1621 0.1188 3.4980 0.8368 4.4772 0.1494 0.2089 1.4168 0.0347 1.1166 0.0701 -#> 458: 92.6308 -5.8813 -2.3638 -4.1607 -0.9510 0.1621 0.1185 3.4994 0.8369 4.4823 0.1494 0.2088 1.4168 0.0347 1.1161 0.0701 -#> 459: 92.6308 -5.8819 -2.3639 -4.1615 -0.9511 0.1620 0.1184 3.5008 0.8371 4.4861 0.1494 0.2086 1.4167 0.0347 1.1155 0.0701 -#> 460: 92.6309 -5.8824 -2.3642 -4.1621 -0.9511 0.1621 0.1182 3.5024 0.8374 4.4886 0.1493 0.2085 1.4164 0.0347 1.1148 0.0702 -#> 461: 92.6309 -5.8821 -2.3647 -4.1631 -0.9511 0.1621 0.1181 3.5000 0.8378 4.4937 0.1493 0.2084 1.4160 0.0347 1.1141 0.0702 -#> 462: 92.6309 -5.8825 -2.3651 -4.1638 -0.9511 0.1623 0.1180 3.5006 0.8381 4.4975 0.1492 0.2082 1.4156 0.0348 1.1133 0.0702 -#> 463: 92.6307 -5.8824 -2.3656 -4.1654 -0.9510 0.1624 0.1179 3.5000 0.8382 4.5074 0.1491 0.2081 1.4154 0.0348 1.1124 0.0702 -#> 464: 92.6305 -5.8825 -2.3660 -4.1668 -0.9510 0.1625 0.1178 3.5001 0.8384 4.5171 0.1491 0.2080 1.4149 0.0348 1.1115 0.0703 -#> 465: 92.6302 -5.8828 -2.3664 -4.1681 -0.9511 0.1626 0.1179 3.5012 0.8386 4.5247 0.1490 0.2079 1.4151 0.0348 1.1107 0.0703 -#> 466: 92.6300 -5.8827 -2.3668 -4.1697 -0.9511 0.1626 0.1179 3.5005 0.8390 4.5370 0.1490 0.2079 1.4148 0.0349 1.1098 0.0704 -#> 467: 92.6301 -5.8828 -2.3671 -4.1721 -0.9512 0.1628 0.1180 3.4991 0.8393 4.5562 0.1490 0.2078 1.4148 0.0349 1.1092 0.0704 -#> 468: 92.6303 -5.8833 -2.3675 -4.1745 -0.9513 0.1630 0.1181 3.4996 0.8397 4.5756 0.1489 0.2078 1.4148 0.0349 1.1086 0.0704 -#> 469: 92.6304 -5.8835 -2.3680 -4.1759 -0.9513 0.1630 0.1181 3.4991 0.8401 4.5829 0.1490 0.2080 1.4145 0.0349 1.1082 0.0704 -#> 470: 92.6304 -5.8839 -2.3685 -4.1772 -0.9512 0.1630 0.1183 3.4993 0.8405 4.5904 0.1490 0.2081 1.4142 0.0349 1.1079 0.0704 -#> 471: 92.6304 -5.8838 -2.3690 -4.1786 -0.9511 0.1631 0.1182 3.4992 0.8408 4.5981 0.1489 0.2082 1.4143 0.0350 1.1075 0.0704 -#> 472: 92.6301 -5.8839 -2.3695 -4.1800 -0.9511 0.1631 0.1182 3.5005 0.8413 4.6063 0.1488 0.2083 1.4143 0.0350 1.1072 0.0704 -#> 473: 92.6296 -5.8841 -2.3699 -4.1811 -0.9510 0.1630 0.1182 3.5019 0.8417 4.6119 0.1487 0.2085 1.4142 0.0350 1.1065 0.0704 -#> 474: 92.6293 -5.8843 -2.3704 -4.1823 -0.9510 0.1629 0.1184 3.5038 0.8422 4.6182 0.1487 0.2087 1.4145 0.0350 1.1060 0.0704 -#> 475: 92.6293 -5.8851 -2.3709 -4.1839 -0.9509 0.1628 0.1185 3.5084 0.8426 4.6277 0.1487 0.2089 1.4142 0.0351 1.1057 0.0704 -#> 476: 92.6293 -5.8854 -2.3713 -4.1847 -0.9509 0.1627 0.1185 3.5137 0.8430 4.6318 0.1486 0.2092 1.4139 0.0351 1.1057 0.0704 -#> 477: 92.6292 -5.8858 -2.3718 -4.1859 -0.9508 0.1627 0.1183 3.5201 0.8430 4.6397 0.1485 0.2095 1.4139 0.0351 1.1060 0.0704 -#> 478: 92.6291 -5.8871 -2.3722 -4.1867 -0.9508 0.1625 0.1181 3.5291 0.8432 4.6449 0.1483 0.2098 1.4140 0.0351 1.1058 0.0704 -#> 479: 92.6293 -5.8891 -2.3726 -4.1873 -0.9509 0.1623 0.1178 3.5422 0.8435 4.6486 0.1482 0.2100 1.4139 0.0352 1.1056 0.0704 -#> 480: 92.6294 -5.8910 -2.3730 -4.1881 -0.9509 0.1622 0.1175 3.5568 0.8437 4.6535 0.1482 0.2102 1.4140 0.0352 1.1053 0.0705 -#> 481: 92.6297 -5.8919 -2.3734 -4.1888 -0.9509 0.1621 0.1174 3.5650 0.8440 4.6572 0.1482 0.2104 1.4138 0.0353 1.1051 0.0705 -#> 482: 92.6293 -5.8929 -2.3737 -4.1894 -0.9509 0.1619 0.1173 3.5745 0.8444 4.6620 0.1482 0.2107 1.4134 0.0353 1.1047 0.0705 -#> 483: 92.6284 -5.8939 -2.3741 -4.1901 -0.9508 0.1616 0.1176 3.5832 0.8446 4.6672 0.1482 0.2109 1.4131 0.0353 1.1044 0.0705 -#> 484: 92.6276 -5.8943 -2.3744 -4.1904 -0.9507 0.1615 0.1179 3.5877 0.8447 4.6692 0.1483 0.2113 1.4128 0.0353 1.1041 0.0705 -#> 485: 92.6266 -5.8947 -2.3746 -4.1912 -0.9507 0.1616 0.1182 3.5903 0.8448 4.6751 0.1483 0.2115 1.4126 0.0354 1.1042 0.0705 -#> 486: 92.6258 -5.8952 -2.3749 -4.1918 -0.9508 0.1615 0.1185 3.5929 0.8450 4.6799 0.1485 0.2115 1.4125 0.0354 1.1045 0.0704 -#> 487: 92.6250 -5.8956 -2.3750 -4.1923 -0.9509 0.1614 0.1189 3.5922 0.8452 4.6835 0.1486 0.2115 1.4122 0.0354 1.1050 0.0704 -#> 488: 92.6242 -5.8956 -2.3752 -4.1927 -0.9510 0.1613 0.1191 3.5898 0.8453 4.6866 0.1487 0.2115 1.4119 0.0354 1.1051 0.0704 -#> 489: 92.6238 -5.8954 -2.3753 -4.1932 -0.9511 0.1611 0.1190 3.5871 0.8454 4.6905 0.1487 0.2115 1.4118 0.0354 1.1057 0.0704 -#> 490: 92.6237 -5.8951 -2.3754 -4.1936 -0.9511 0.1611 0.1188 3.5839 0.8454 4.6945 0.1487 0.2114 1.4117 0.0354 1.1064 0.0703 -#> 491: 92.6235 -5.8942 -2.3755 -4.1941 -0.9511 0.1610 0.1187 3.5790 0.8455 4.6981 0.1488 0.2115 1.4118 0.0354 1.1068 0.0703 -#> 492: 92.6234 -5.8938 -2.3755 -4.1952 -0.9512 0.1609 0.1186 3.5760 0.8454 4.7074 0.1488 0.2115 1.4119 0.0354 1.1074 0.0703 -#> 493: 92.6236 -5.8938 -2.3755 -4.1958 -0.9512 0.1608 0.1186 3.5747 0.8454 4.7121 0.1488 0.2114 1.4120 0.0354 1.1078 0.0702 -#> 494: 92.6239 -5.8945 -2.3756 -4.1964 -0.9513 0.1607 0.1186 3.5772 0.8455 4.7167 0.1488 0.2115 1.4120 0.0354 1.1082 0.0702 -#> 495: 92.6242 -5.8950 -2.3756 -4.1971 -0.9514 0.1605 0.1187 3.5798 0.8454 4.7227 0.1489 0.2117 1.4122 0.0354 1.1084 0.0702 -#> 496: 92.6242 -5.8962 -2.3757 -4.1978 -0.9514 0.1603 0.1189 3.5870 0.8455 4.7283 0.1489 0.2119 1.4121 0.0354 1.1090 0.0702 -#> 497: 92.6241 -5.8972 -2.3757 -4.1981 -0.9514 0.1602 0.1191 3.5934 0.8454 4.7298 0.1488 0.2120 1.4123 0.0354 1.1096 0.0701 -#> 498: 92.6244 -5.8973 -2.3758 -4.1981 -0.9514 0.1601 0.1190 3.5947 0.8454 4.7296 0.1488 0.2121 1.4123 0.0354 1.1101 0.0701 -#> 499: 92.6244 -5.8968 -2.3759 -4.1980 -0.9514 0.1600 0.1188 3.5935 0.8453 4.7290 0.1488 0.2124 1.4123 0.0354 1.1108 0.0701 -#> 500: 92.6245 -5.8959 -2.3759 -4.1978 -0.9513 0.1597 0.1188 3.5912 0.8452 4.7282 0.1488 0.2126 1.4123 0.0354 1.1111 0.0701
                                                                #> Calculating covariance matrix
                                                                #>
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.843 0.028 0.871
                                                                f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", error_model = "obs_tc") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT" -#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_A1 |f_parent_qlogis | log_k1 | -#> |.....................| log_k2 | g_qlogis |sigma_low_parent |rsd_high_parent | -#> |.....................|sigma_low_A1 |rsd_high_A1 | o1 | o2 | -#> |.....................| o3 | o4 | o5 | o6 | -#> | 1| 495.80376 | 1.000 | -1.000 | -0.9110 | -0.9380 | -#> |.....................| -0.9885 | -0.8832 | -0.8755 | -0.8915 | -#> |.....................| -0.8755 | -0.8915 | -0.8776 | -0.8741 | -#> |.....................| -0.8681 | -0.8727 | -0.8749 | -0.8675 | -#> | U| 495.80376 | 91.48 | -5.189 | -0.8875 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8280 | 0.05769 | -#> |.....................| 0.8280 | 0.05769 | 0.7296 | 0.8969 | -#> |.....................| 1.185 | 0.9628 | 0.8582 | 1.216 | -#> | X| 495.80376 | 91.48 | 0.005580 | 0.2916 | 0.1119 | -#> |.....................| 0.009750 | 0.6128 | 0.8280 | 0.05769 | -#> |.....................| 0.8280 | 0.05769 | 0.7296 | 0.8969 | -#> |.....................| 1.185 | 0.9628 | 0.8582 | 1.216 | -#> | G| Gill Diff. | 40.10 | 2.344 | -0.09792 | 0.01304 | -#> |.....................| -0.4854 | 0.6353 | -23.92 | -17.76 | -#> |.....................| -5.723 | -2.232 | 1.261 | 9.993 | -#> |.....................| -12.68 | -0.7774 | 8.106 | -12.55 | -#> | 2| 3318.3701 | 0.2710 | -1.043 | -0.9092 | -0.9382 | -#> |.....................| -0.9796 | -0.8947 | -0.4406 | -0.5686 | -#> |.....................| -0.7715 | -0.8509 | -0.9005 | -1.056 | -#> |.....................| -0.6376 | -0.8586 | -1.022 | -0.6393 | -#> | U| 3318.3701 | 24.79 | -5.231 | -0.8859 | -2.190 | -#> |.....................| -4.622 | 0.4536 | 1.008 | 0.06701 | -#> |.....................| 0.8711 | 0.05887 | 0.7129 | 0.7340 | -#> |.....................| 1.458 | 0.9764 | 0.7317 | 1.493 | -#> | X| 3318.3701 | 24.79 | 0.005347 | 0.2920 | 0.1119 | -#> |.....................| 0.009837 | 0.6115 | 1.008 | 0.06701 | -#> |.....................| 0.8711 | 0.05887 | 0.7129 | 0.7340 | -#> |.....................| 1.458 | 0.9764 | 0.7317 | 1.493 | -#> | 3| 512.37365 | 0.9271 | -1.004 | -0.9108 | -0.9380 | -#> |.....................| -0.9876 | -0.8843 | -0.8320 | -0.8592 | -#> |.....................| -0.8651 | -0.8874 | -0.8799 | -0.8923 | -#> |.....................| -0.8451 | -0.8713 | -0.8896 | -0.8447 | -#> | U| 512.37365 | 84.82 | -5.193 | -0.8873 | -2.190 | -#> |.....................| -4.630 | 0.4584 | 0.8460 | 0.05863 | -#> |.....................| 0.8323 | 0.05781 | 0.7279 | 0.8806 | -#> |.....................| 1.212 | 0.9641 | 0.8455 | 1.244 | -#> | X| 512.37365 | 84.82 | 0.005556 | 0.2917 | 0.1119 | -#> |.....................| 0.009759 | 0.6126 | 0.8460 | 0.05863 | -#> |.....................| 0.8323 | 0.05781 | 0.7279 | 0.8806 | -#> |.....................| 1.212 | 0.9641 | 0.8455 | 1.244 | -#> | 4| 495.44913 | 0.9909 | -1.001 | -0.9110 | -0.9380 | -#> |.....................| -0.9883 | -0.8833 | -0.8701 | -0.8874 | -#> |.....................| -0.8742 | -0.8910 | -0.8778 | -0.8764 | -#> |.....................| -0.8653 | -0.8726 | -0.8767 | -0.8647 | -#> | U| 495.44913 | 90.65 | -5.189 | -0.8874 | -2.190 | -#> |.....................| -4.630 | 0.4589 | 0.8303 | 0.05781 | -#> |.....................| 0.8286 | 0.05771 | 0.7294 | 0.8949 | -#> |.....................| 1.189 | 0.9629 | 0.8566 | 1.219 | -#> | X| 495.44913 | 90.65 | 0.005577 | 0.2916 | 0.1119 | -#> |.....................| 0.009751 | 0.6127 | 0.8303 | 0.05781 | -#> |.....................| 0.8286 | 0.05771 | 0.7294 | 0.8949 | -#> |.....................| 1.189 | 0.9629 | 0.8566 | 1.219 | -#> | F| Forward Diff. | -32.24 | 2.221 | -0.3999 | 0.1183 | -#> |.....................| -0.4367 | 0.6696 | -24.35 | -18.50 | -#> |.....................| -5.733 | -2.007 | 1.154 | 9.098 | -#> |.....................| -12.48 | -0.2426 | 8.051 | -12.28 | -#> | 5| 495.09570 | 0.9990 | -1.001 | -0.9109 | -0.9380 | -#> |.....................| -0.9882 | -0.8835 | -0.8640 | -0.8828 | -#> |.....................| -0.8728 | -0.8905 | -0.8781 | -0.8786 | -#> |.....................| -0.8621 | -0.8725 | -0.8788 | -0.8616 | -#> | U| 495.0957 | 91.39 | -5.190 | -0.8874 | -2.190 | -#> |.....................| -4.630 | 0.4588 | 0.8328 | 0.05794 | -#> |.....................| 0.8291 | 0.05772 | 0.7292 | 0.8928 | -#> |.....................| 1.192 | 0.9630 | 0.8549 | 1.223 | -#> | X| 495.0957 | 91.39 | 0.005574 | 0.2917 | 0.1119 | -#> |.....................| 0.009752 | 0.6127 | 0.8328 | 0.05794 | -#> |.....................| 0.8291 | 0.05772 | 0.7292 | 0.8928 | -#> |.....................| 1.192 | 0.9630 | 0.8549 | 1.223 | -#> | F| Forward Diff. | 32.16 | 2.311 | -0.1335 | 0.03619 | -#> |.....................| -0.4432 | 0.6445 | -23.23 | -17.46 | -#> |.....................| -5.567 | -2.162 | 1.281 | 9.656 | -#> |.....................| -12.09 | -0.7018 | 7.779 | -12.29 | -#> | 6| 494.75975 | 0.9908 | -1.002 | -0.9109 | -0.9380 | -#> |.....................| -0.9881 | -0.8836 | -0.8581 | -0.8783 | -#> |.....................| -0.8714 | -0.8899 | -0.8785 | -0.8811 | -#> |.....................| -0.8590 | -0.8723 | -0.8807 | -0.8584 | -#> | U| 494.75975 | 90.64 | -5.190 | -0.8873 | -2.190 | -#> |.....................| -4.630 | 0.4587 | 0.8352 | 0.05807 | -#> |.....................| 0.8297 | 0.05774 | 0.7290 | 0.8906 | -#> |.....................| 1.196 | 0.9632 | 0.8532 | 1.227 | -#> | X| 494.75975 | 90.64 | 0.005570 | 0.2917 | 0.1119 | -#> |.....................| 0.009754 | 0.6127 | 0.8352 | 0.05807 | -#> |.....................| 0.8297 | 0.05774 | 0.7290 | 0.8906 | -#> |.....................| 1.196 | 0.9632 | 0.8532 | 1.227 | -#> | F| Forward Diff. | -33.18 | 2.192 | -0.4095 | 0.1210 | -#> |.....................| -0.4089 | 0.6743 | -23.19 | -17.83 | -#> |.....................| -5.624 | -1.860 | 1.146 | 8.868 | -#> |.....................| -11.42 | -0.05808 | 7.519 | -12.11 | -#> | 7| 494.42957 | 0.9992 | -1.002 | -0.9108 | -0.9380 | -#> |.....................| -0.9880 | -0.8838 | -0.8522 | -0.8738 | -#> |.....................| -0.8699 | -0.8894 | -0.8788 | -0.8834 | -#> |.....................| -0.8561 | -0.8723 | -0.8827 | -0.8554 | -#> | U| 494.42957 | 91.41 | -5.191 | -0.8872 | -2.190 | -#> |.....................| -4.630 | 0.4586 | 0.8377 | 0.05820 | -#> |.....................| 0.8303 | 0.05775 | 0.7287 | 0.8886 | -#> |.....................| 1.199 | 0.9632 | 0.8515 | 1.231 | -#> | X| 494.42957 | 91.41 | 0.005567 | 0.2917 | 0.1119 | -#> |.....................| 0.009755 | 0.6127 | 0.8377 | 0.05820 | -#> |.....................| 0.8303 | 0.05775 | 0.7287 | 0.8886 | -#> |.....................| 1.199 | 0.9632 | 0.8515 | 1.231 | -#> | F| Forward Diff. | 33.60 | 2.291 | -0.1177 | 0.03548 | -#> |.....................| -0.4327 | 0.6500 | -23.13 | -16.67 | -#> |.....................| -5.444 | -2.054 | 1.165 | 9.367 | -#> |.....................| -12.23 | 0.1305 | 7.522 | -12.12 | -#> | 8| 494.10805 | 0.9907 | -1.003 | -0.9107 | -0.9380 | -#> |.....................| -0.9879 | -0.8840 | -0.8463 | -0.8696 | -#> |.....................| -0.8686 | -0.8889 | -0.8791 | -0.8857 | -#> |.....................| -0.8530 | -0.8723 | -0.8846 | -0.8523 | -#> | U| 494.10805 | 90.63 | -5.191 | -0.8872 | -2.190 | -#> |.....................| -4.630 | 0.4586 | 0.8401 | 0.05833 | -#> |.....................| 0.8309 | 0.05777 | 0.7285 | 0.8865 | -#> |.....................| 1.203 | 0.9632 | 0.8499 | 1.234 | -#> | X| 494.10805 | 90.63 | 0.005564 | 0.2917 | 0.1119 | -#> |.....................| 0.009756 | 0.6127 | 0.8401 | 0.05833 | -#> |.....................| 0.8309 | 0.05777 | 0.7285 | 0.8865 | -#> |.....................| 1.203 | 0.9632 | 0.8499 | 1.234 | -#> | F| Forward Diff. | -33.55 | 2.169 | -0.4095 | 0.1317 | -#> |.....................| -0.3875 | 0.6809 | -22.57 | -17.16 | -#> |.....................| -5.560 | -1.906 | 1.113 | 8.554 | -#> |.....................| -12.00 | -0.1191 | 7.606 | -11.94 | -#> | 9| 493.79074 | 0.9992 | -1.003 | -0.9106 | -0.9381 | -#> |.....................| -0.9878 | -0.8841 | -0.8406 | -0.8652 | -#> |.....................| -0.8671 | -0.8884 | -0.8793 | -0.8879 | -#> |.....................| -0.8500 | -0.8723 | -0.8865 | -0.8493 | -#> | U| 493.79074 | 91.41 | -5.192 | -0.8871 | -2.190 | -#> |.....................| -4.630 | 0.4585 | 0.8425 | 0.05845 | -#> |.....................| 0.8315 | 0.05778 | 0.7283 | 0.8845 | -#> |.....................| 1.207 | 0.9632 | 0.8482 | 1.238 | -#> | X| 493.79074 | 91.41 | 0.005561 | 0.2917 | 0.1119 | -#> |.....................| 0.009757 | 0.6127 | 0.8425 | 0.05845 | -#> |.....................| 0.8315 | 0.05778 | 0.7283 | 0.8845 | -#> |.....................| 1.207 | 0.9632 | 0.8482 | 1.238 | -#> | F| Forward Diff. | 33.91 | 2.267 | -0.1078 | 0.03893 | -#> |.....................| -0.4090 | 0.6560 | -22.34 | -15.94 | -#> |.....................| -5.274 | -2.001 | 1.140 | 9.131 | -#> |.....................| -12.00 | -0.1724 | 7.294 | -11.95 | -#> | 10| 493.48645 | 0.9905 | -1.004 | -0.9106 | -0.9381 | -#> |.....................| -0.9877 | -0.8843 | -0.8348 | -0.8611 | -#> |.....................| -0.8658 | -0.8879 | -0.8796 | -0.8903 | -#> |.....................| -0.8469 | -0.8723 | -0.8884 | -0.8462 | -#> | U| 493.48645 | 90.62 | -5.193 | -0.8871 | -2.190 | -#> |.....................| -4.630 | 0.4584 | 0.8449 | 0.05857 | -#> |.....................| 0.8320 | 0.05780 | 0.7281 | 0.8824 | -#> |.....................| 1.210 | 0.9632 | 0.8466 | 1.242 | -#> | X| 493.48645 | 90.62 | 0.005558 | 0.2917 | 0.1119 | -#> |.....................| 0.009758 | 0.6126 | 0.8449 | 0.05857 | -#> |.....................| 0.8320 | 0.05780 | 0.7281 | 0.8824 | -#> |.....................| 1.210 | 0.9632 | 0.8466 | 1.242 | -#> | F| Forward Diff. | -34.40 | 2.145 | -0.4154 | 0.1312 | -#> |.....................| -0.3648 | 0.6865 | -22.08 | -16.36 | -#> |.....................| -5.345 | -1.756 | 1.231 | 8.303 | -#> |.....................| -11.76 | -0.07864 | 7.355 | -11.77 | -#> | 11| 493.18511 | 0.9993 | -1.004 | -0.9105 | -0.9381 | -#> |.....................| -0.9876 | -0.8845 | -0.8292 | -0.8570 | -#> |.....................| -0.8644 | -0.8875 | -0.8799 | -0.8924 | -#> |.....................| -0.8439 | -0.8722 | -0.8902 | -0.8432 | -#> | U| 493.18511 | 91.42 | -5.193 | -0.8870 | -2.190 | -#> |.....................| -4.630 | 0.4583 | 0.8472 | 0.05869 | -#> |.....................| 0.8326 | 0.05781 | 0.7279 | 0.8805 | -#> |.....................| 1.214 | 0.9633 | 0.8450 | 1.246 | -#> | X| 493.18511 | 91.42 | 0.005555 | 0.2917 | 0.1119 | -#> |.....................| 0.009759 | 0.6126 | 0.8472 | 0.05869 | -#> |.....................| 0.8326 | 0.05781 | 0.7279 | 0.8805 | -#> |.....................| 1.214 | 0.9633 | 0.8450 | 1.246 | -#> | F| Forward Diff. | 34.43 | 2.240 | -0.1040 | 0.04282 | -#> |.....................| -0.3912 | 0.6547 | -21.84 | -15.27 | -#> |.....................| -5.158 | -1.914 | 1.030 | 8.876 | -#> |.....................| -11.77 | -0.1415 | 7.047 | -11.78 | -#> | 12| 492.89407 | 0.9905 | -1.005 | -0.9105 | -0.9381 | -#> |.....................| -0.9875 | -0.8847 | -0.8236 | -0.8530 | -#> |.....................| -0.8631 | -0.8870 | -0.8802 | -0.8947 | -#> |.....................| -0.8409 | -0.8722 | -0.8921 | -0.8401 | -#> | U| 492.89407 | 90.61 | -5.194 | -0.8870 | -2.190 | -#> |.....................| -4.630 | 0.4582 | 0.8495 | 0.05880 | -#> |.....................| 0.8332 | 0.05782 | 0.7277 | 0.8785 | -#> |.....................| 1.217 | 0.9633 | 0.8434 | 1.249 | -#> | X| 492.89407 | 90.61 | 0.005551 | 0.2917 | 0.1119 | -#> |.....................| 0.009760 | 0.6126 | 0.8495 | 0.05880 | -#> |.....................| 0.8332 | 0.05782 | 0.7277 | 0.8785 | -#> |.....................| 1.217 | 0.9633 | 0.8434 | 1.249 | -#> | F| Forward Diff. | -34.81 | 2.117 | -0.4182 | 0.1353 | -#> |.....................| -0.3428 | 0.6933 | -21.54 | -15.66 | -#> |.....................| -5.188 | -1.708 | 1.147 | 8.020 | -#> |.....................| -11.52 | -0.06705 | 7.151 | -11.60 | -#> | 13| 492.59250 | 0.9992 | -1.006 | -0.9104 | -0.9382 | -#> |.....................| -0.9874 | -0.8848 | -0.8179 | -0.8489 | -#> |.....................| -0.8617 | -0.8865 | -0.8805 | -0.8968 | -#> |.....................| -0.8378 | -0.8722 | -0.8940 | -0.8371 | -#> | U| 492.5925 | 91.41 | -5.194 | -0.8869 | -2.190 | -#> |.....................| -4.629 | 0.4582 | 0.8519 | 0.05892 | -#> |.....................| 0.8337 | 0.05784 | 0.7275 | 0.8766 | -#> |.....................| 1.221 | 0.9633 | 0.8418 | 1.253 | -#> | X| 492.5925 | 91.41 | 0.005548 | 0.2918 | 0.1119 | -#> |.....................| 0.009760 | 0.6126 | 0.8519 | 0.05892 | -#> |.....................| 0.8337 | 0.05784 | 0.7275 | 0.8766 | -#> |.....................| 1.221 | 0.9633 | 0.8418 | 1.253 | -#> | F| Forward Diff. | 33.40 | 2.217 | -0.09736 | 0.04377 | -#> |.....................| -0.3664 | 0.6618 | -21.29 | -14.62 | -#> |.....................| -5.018 | -1.838 | 0.9818 | 8.628 | -#> |.....................| -11.52 | -0.1307 | 6.857 | -11.62 | -#> | 14| 492.30478 | 0.9905 | -1.006 | -0.9103 | -0.9382 | -#> |.....................| -0.9873 | -0.8850 | -0.8121 | -0.8449 | -#> |.....................| -0.8604 | -0.8860 | -0.8808 | -0.8991 | -#> |.....................| -0.8347 | -0.8722 | -0.8958 | -0.8339 | -#> | U| 492.30478 | 90.62 | -5.195 | -0.8868 | -2.190 | -#> |.....................| -4.629 | 0.4581 | 0.8543 | 0.05904 | -#> |.....................| 0.8343 | 0.05785 | 0.7273 | 0.8745 | -#> |.....................| 1.225 | 0.9633 | 0.8402 | 1.257 | -#> | X| 492.30478 | 90.62 | 0.005545 | 0.2918 | 0.1119 | -#> |.....................| 0.009761 | 0.6126 | 0.8543 | 0.05904 | -#> |.....................| 0.8343 | 0.05785 | 0.7273 | 0.8745 | -#> |.....................| 1.225 | 0.9633 | 0.8402 | 1.257 | -#> | F| Forward Diff. | -34.08 | 2.096 | -0.4157 | 0.1370 | -#> |.....................| -0.3212 | 0.6979 | -20.95 | -14.99 | -#> |.....................| -5.046 | -1.607 | 1.055 | 8.026 | -#> |.....................| -11.31 | 0.3535 | 6.819 | -11.49 | -#> | 15| 492.00325 | 0.9991 | -1.007 | -0.9102 | -0.9382 | -#> |.....................| -0.9872 | -0.8852 | -0.8063 | -0.8408 | -#> |.....................| -0.8590 | -0.8856 | -0.8811 | -0.9014 | -#> |.....................| -0.8316 | -0.8723 | -0.8977 | -0.8307 | -#> | U| 492.00325 | 91.40 | -5.195 | -0.8867 | -2.190 | -#> |.....................| -4.629 | 0.4580 | 0.8567 | 0.05916 | -#> |.....................| 0.8349 | 0.05786 | 0.7271 | 0.8725 | -#> |.....................| 1.229 | 0.9632 | 0.8386 | 1.261 | -#> | X| 492.00325 | 91.40 | 0.005542 | 0.2918 | 0.1119 | -#> |.....................| 0.009762 | 0.6125 | 0.8567 | 0.05916 | -#> |.....................| 0.8349 | 0.05786 | 0.7271 | 0.8725 | -#> |.....................| 1.229 | 0.9632 | 0.8386 | 1.261 | -#> | F| Forward Diff. | 32.19 | 2.189 | -0.09620 | 0.04245 | -#> |.....................| -0.3450 | 0.6659 | -21.28 | -14.00 | -#> |.....................| -4.881 | -1.759 | 1.243 | 8.359 | -#> |.....................| -10.62 | -0.07477 | 6.614 | -11.44 | -#> | 16| 491.72015 | 0.9906 | -1.007 | -0.9102 | -0.9382 | -#> |.....................| -0.9871 | -0.8854 | -0.8003 | -0.8368 | -#> |.....................| -0.8576 | -0.8851 | -0.8814 | -0.9037 | -#> |.....................| -0.8285 | -0.8722 | -0.8996 | -0.8275 | -#> | U| 491.72015 | 90.62 | -5.196 | -0.8867 | -2.190 | -#> |.....................| -4.629 | 0.4579 | 0.8592 | 0.05927 | -#> |.....................| 0.8354 | 0.05788 | 0.7268 | 0.8703 | -#> |.....................| 1.232 | 0.9633 | 0.8370 | 1.265 | -#> | X| 491.72015 | 90.62 | 0.005538 | 0.2918 | 0.1119 | -#> |.....................| 0.009763 | 0.6125 | 0.8592 | 0.05927 | -#> |.....................| 0.8354 | 0.05788 | 0.7268 | 0.8703 | -#> |.....................| 1.232 | 0.9633 | 0.8370 | 1.265 | -#> | F| Forward Diff. | -33.41 | 2.074 | -0.4123 | 0.1389 | -#> |.....................| -0.2981 | 0.7039 | -20.39 | -14.31 | -#> |.....................| -4.887 | -1.550 | 0.9656 | 7.818 | -#> |.....................| -11.05 | -0.4282 | 6.582 | -11.31 | -#> | 17| 491.42294 | 0.9990 | -1.008 | -0.9101 | -0.9383 | -#> |.....................| -0.9870 | -0.8856 | -0.7943 | -0.8327 | -#> |.....................| -0.8562 | -0.8846 | -0.8817 | -0.9060 | -#> |.....................| -0.8254 | -0.8721 | -0.9015 | -0.8242 | -#> | U| 491.42294 | 91.39 | -5.197 | -0.8866 | -2.190 | -#> |.....................| -4.629 | 0.4578 | 0.8616 | 0.05939 | -#> |.....................| 0.8360 | 0.05789 | 0.7266 | 0.8683 | -#> |.....................| 1.236 | 0.9634 | 0.8354 | 1.269 | -#> | X| 491.42294 | 91.39 | 0.005535 | 0.2918 | 0.1119 | -#> |.....................| 0.009764 | 0.6125 | 0.8616 | 0.05939 | -#> |.....................| 0.8360 | 0.05789 | 0.7266 | 0.8683 | -#> |.....................| 1.236 | 0.9634 | 0.8354 | 1.269 | -#> | F| Forward Diff. | 31.50 | 2.165 | -0.08876 | 0.04676 | -#> |.....................| -0.3226 | 0.6753 | -20.70 | -13.34 | -#> |.....................| -4.747 | -1.707 | 0.9017 | 8.141 | -#> |.....................| -10.29 | -0.02981 | 6.402 | -11.28 | -#> | 18| 491.14065 | 0.9907 | -1.009 | -0.9100 | -0.9383 | -#> |.....................| -0.9870 | -0.8858 | -0.7882 | -0.8287 | -#> |.....................| -0.8548 | -0.8841 | -0.8820 | -0.9084 | -#> |.....................| -0.8223 | -0.8721 | -0.9034 | -0.8208 | -#> | U| 491.14065 | 90.64 | -5.197 | -0.8866 | -2.190 | -#> |.....................| -4.629 | 0.4577 | 0.8642 | 0.05950 | -#> |.....................| 0.8366 | 0.05791 | 0.7264 | 0.8661 | -#> |.....................| 1.240 | 0.9634 | 0.8337 | 1.273 | -#> | X| 491.14065 | 90.64 | 0.005531 | 0.2918 | 0.1119 | -#> |.....................| 0.009765 | 0.6125 | 0.8642 | 0.05950 | -#> |.....................| 0.8366 | 0.05791 | 0.7264 | 0.8661 | -#> |.....................| 1.240 | 0.9634 | 0.8337 | 1.273 | -#> | F| Forward Diff. | -32.29 | 2.052 | -0.4043 | 0.1403 | -#> |.....................| -0.2785 | 0.7107 | -20.12 | -13.83 | -#> |.....................| -4.879 | -1.515 | 0.4622 | 7.293 | -#> |.....................| -10.82 | -0.3681 | 6.384 | -11.14 | -#> | 19| 490.84537 | 0.9989 | -1.009 | -0.9099 | -0.9383 | -#> |.....................| -0.9869 | -0.8860 | -0.7821 | -0.8246 | -#> |.....................| -0.8533 | -0.8837 | -0.8821 | -0.9106 | -#> |.....................| -0.8190 | -0.8720 | -0.9053 | -0.8174 | -#> | U| 490.84537 | 91.38 | -5.198 | -0.8865 | -2.190 | -#> |.....................| -4.629 | 0.4576 | 0.8667 | 0.05962 | -#> |.....................| 0.8372 | 0.05792 | 0.7263 | 0.8641 | -#> |.....................| 1.243 | 0.9635 | 0.8321 | 1.277 | -#> | X| 490.84537 | 91.38 | 0.005528 | 0.2918 | 0.1119 | -#> |.....................| 0.009766 | 0.6124 | 0.8667 | 0.05962 | -#> |.....................| 0.8372 | 0.05792 | 0.7263 | 0.8641 | -#> |.....................| 1.243 | 0.9635 | 0.8321 | 1.277 | -#> | F| Forward Diff. | 30.35 | 2.134 | -0.08371 | 0.04933 | -#> |.....................| -0.3000 | 0.6785 | -20.24 | -12.73 | -#> |.....................| -4.623 | -1.604 | 1.054 | 8.092 | -#> |.....................| -10.77 | -0.4405 | 6.181 | -11.10 | -#> | 20| 490.56963 | 0.9908 | -1.010 | -0.9099 | -0.9383 | -#> |.....................| -0.9868 | -0.8862 | -0.7758 | -0.8207 | -#> |.....................| -0.8519 | -0.8832 | -0.8824 | -0.9131 | -#> |.....................| -0.8157 | -0.8719 | -0.9072 | -0.8140 | -#> | U| 490.56963 | 90.64 | -5.199 | -0.8865 | -2.190 | -#> |.....................| -4.629 | 0.4575 | 0.8693 | 0.05974 | -#> |.....................| 0.8378 | 0.05793 | 0.7261 | 0.8619 | -#> |.....................| 1.247 | 0.9636 | 0.8305 | 1.281 | -#> | X| 490.56963 | 90.64 | 0.005524 | 0.2918 | 0.1119 | -#> |.....................| 0.009767 | 0.6124 | 0.8693 | 0.05974 | -#> |.....................| 0.8378 | 0.05793 | 0.7261 | 0.8619 | -#> |.....................| 1.247 | 0.9636 | 0.8305 | 1.281 | -#> | F| Forward Diff. | -31.85 | 2.030 | -0.4014 | 0.1424 | -#> |.....................| -0.2574 | 0.7152 | -19.39 | -13.12 | -#> |.....................| -4.602 | -1.387 | 0.5883 | 7.042 | -#> |.....................| -10.56 | -0.3115 | 6.249 | -10.92 | -#> | 21| 490.28521 | 0.9989 | -1.011 | -0.9098 | -0.9384 | -#> |.....................| -0.9867 | -0.8865 | -0.7697 | -0.8166 | -#> |.....................| -0.8504 | -0.8827 | -0.8826 | -0.9153 | -#> |.....................| -0.8124 | -0.8718 | -0.9092 | -0.8105 | -#> | U| 490.28521 | 91.39 | -5.199 | -0.8864 | -2.190 | -#> |.....................| -4.629 | 0.4574 | 0.8718 | 0.05985 | -#> |.....................| 0.8384 | 0.05795 | 0.7259 | 0.8599 | -#> |.....................| 1.251 | 0.9637 | 0.8288 | 1.285 | -#> | X| 490.28521 | 91.39 | 0.005521 | 0.2919 | 0.1119 | -#> |.....................| 0.009767 | 0.6124 | 0.8718 | 0.05985 | -#> |.....................| 0.8384 | 0.05795 | 0.7259 | 0.8599 | -#> |.....................| 1.251 | 0.9637 | 0.8288 | 1.285 | -#> | F| Forward Diff. | 30.53 | 2.112 | -0.07114 | 0.05276 | -#> |.....................| -0.2779 | 0.6845 | -19.81 | -12.13 | -#> |.....................| -4.498 | -1.539 | 0.6449 | 7.769 | -#> |.....................| -10.55 | -0.3696 | 5.980 | -10.93 | -#> | 22| 489.99923 | 0.9911 | -1.011 | -0.9097 | -0.9384 | -#> |.....................| -0.9866 | -0.8867 | -0.7633 | -0.8127 | -#> |.....................| -0.8489 | -0.8823 | -0.8828 | -0.9178 | -#> |.....................| -0.8089 | -0.8716 | -0.9111 | -0.8070 | -#> | U| 489.99923 | 90.67 | -5.200 | -0.8863 | -2.190 | -#> |.....................| -4.629 | 0.4573 | 0.8745 | 0.05997 | -#> |.....................| 0.8390 | 0.05796 | 0.7258 | 0.8577 | -#> |.....................| 1.255 | 0.9638 | 0.8271 | 1.290 | -#> | X| 489.99923 | 90.67 | 0.005517 | 0.2919 | 0.1119 | -#> |.....................| 0.009768 | 0.6124 | 0.8745 | 0.05997 | -#> |.....................| 0.8390 | 0.05796 | 0.7258 | 0.8577 | -#> |.....................| 1.255 | 0.9638 | 0.8271 | 1.290 | -#> | F| Forward Diff. | -29.14 | 2.012 | -0.3844 | 0.1417 | -#> |.....................| -0.2358 | 0.7218 | -18.90 | -12.37 | -#> |.....................| -4.517 | -1.329 | 0.4904 | 6.799 | -#> |.....................| -10.31 | -0.2514 | 6.013 | -10.75 | -#> | 23| 489.73483 | 0.9991 | -1.012 | -0.9096 | -0.9384 | -#> |.....................| -0.9865 | -0.8869 | -0.7571 | -0.8087 | -#> |.....................| -0.8475 | -0.8818 | -0.8829 | -0.9201 | -#> |.....................| -0.8055 | -0.8715 | -0.9131 | -0.8034 | -#> | U| 489.73483 | 91.40 | -5.201 | -0.8862 | -2.190 | -#> |.....................| -4.629 | 0.4572 | 0.8771 | 0.06008 | -#> |.....................| 0.8396 | 0.05797 | 0.7257 | 0.8557 | -#> |.....................| 1.259 | 0.9639 | 0.8254 | 1.294 | -#> | X| 489.73483 | 91.40 | 0.005513 | 0.2919 | 0.1119 | -#> |.....................| 0.009769 | 0.6123 | 0.8771 | 0.06008 | -#> |.....................| 0.8396 | 0.05797 | 0.7257 | 0.8557 | -#> |.....................| 1.259 | 0.9639 | 0.8254 | 1.294 | -#> | F| Forward Diff. | 31.68 | 2.089 | -0.05219 | 0.05312 | -#> |.....................| -0.2568 | 0.6912 | -19.25 | -11.50 | -#> |.....................| -4.291 | -1.478 | 0.6044 | 7.316 | -#> |.....................| -10.30 | -0.3159 | 5.756 | -10.75 | -#> | 24| 489.43925 | 0.9914 | -1.013 | -0.9096 | -0.9385 | -#> |.....................| -0.9865 | -0.8872 | -0.7505 | -0.8049 | -#> |.....................| -0.8460 | -0.8813 | -0.8831 | -0.9225 | -#> |.....................| -0.8020 | -0.8714 | -0.9150 | -0.7997 | -#> | U| 489.43925 | 90.70 | -5.201 | -0.8862 | -2.190 | -#> |.....................| -4.628 | 0.4571 | 0.8798 | 0.06019 | -#> |.....................| 0.8402 | 0.05799 | 0.7256 | 0.8535 | -#> |.....................| 1.264 | 0.9640 | 0.8238 | 1.298 | -#> | X| 489.43925 | 90.70 | 0.005509 | 0.2919 | 0.1119 | -#> |.....................| 0.009770 | 0.6123 | 0.8798 | 0.06019 | -#> |.....................| 0.8402 | 0.05799 | 0.7256 | 0.8535 | -#> |.....................| 1.264 | 0.9640 | 0.8238 | 1.298 | -#> | F| Forward Diff. | -26.48 | 1.993 | -0.3684 | 0.1403 | -#> |.....................| -0.2166 | 0.7270 | -18.36 | -11.77 | -#> |.....................| -4.393 | -1.275 | 0.4390 | 6.578 | -#> |.....................| -10.04 | -0.2187 | 5.799 | -10.58 | -#> | 25| 489.19181 | 0.9992 | -1.013 | -0.9095 | -0.9385 | -#> |.....................| -0.9864 | -0.8874 | -0.7441 | -0.8009 | -#> |.....................| -0.8445 | -0.8809 | -0.8833 | -0.9248 | -#> |.....................| -0.7985 | -0.8714 | -0.9170 | -0.7960 | -#> | U| 489.19181 | 91.41 | -5.202 | -0.8861 | -2.190 | -#> |.....................| -4.628 | 0.4570 | 0.8824 | 0.06031 | -#> |.....................| 0.8409 | 0.05800 | 0.7255 | 0.8514 | -#> |.....................| 1.268 | 0.9641 | 0.8221 | 1.303 | -#> | X| 489.19181 | 91.41 | 0.005505 | 0.2919 | 0.1119 | -#> |.....................| 0.009770 | 0.6123 | 0.8824 | 0.06031 | -#> |.....................| 0.8409 | 0.05800 | 0.7255 | 0.8514 | -#> |.....................| 1.268 | 0.9641 | 0.8221 | 1.303 | -#> | F| Forward Diff. | 32.48 | 2.067 | -0.03453 | 0.05414 | -#> |.....................| -0.2360 | 0.6938 | -18.67 | -10.89 | -#> |.....................| -4.178 | -1.425 | 0.5548 | 7.078 | -#> |.....................| -10.01 | -0.2144 | 5.548 | -10.57 | -#> | 26| 488.89118 | 0.9917 | -1.014 | -0.9094 | -0.9385 | -#> |.....................| -0.9863 | -0.8877 | -0.7375 | -0.7972 | -#> |.....................| -0.8430 | -0.8804 | -0.8834 | -0.9272 | -#> |.....................| -0.7949 | -0.8713 | -0.9189 | -0.7921 | -#> | U| 488.89118 | 90.73 | -5.203 | -0.8860 | -2.190 | -#> |.....................| -4.628 | 0.4568 | 0.8852 | 0.06041 | -#> |.....................| 0.8415 | 0.05801 | 0.7253 | 0.8493 | -#> |.....................| 1.272 | 0.9642 | 0.8204 | 1.308 | -#> | X| 488.89118 | 90.73 | 0.005501 | 0.2919 | 0.1119 | -#> |.....................| 0.009771 | 0.6123 | 0.8852 | 0.06041 | -#> |.....................| 0.8415 | 0.05801 | 0.7253 | 0.8493 | -#> |.....................| 1.272 | 0.9642 | 0.8204 | 1.308 | -#> | F| Forward Diff. | -24.34 | 1.974 | -0.3522 | 0.1400 | -#> |.....................| -0.1957 | 0.7323 | -17.88 | -11.06 | -#> |.....................| -4.245 | -1.195 | 0.3418 | 6.336 | -#> |.....................| -9.795 | -0.1748 | 5.588 | -10.40 | -#> | 27| 488.65823 | 0.9993 | -1.015 | -0.9093 | -0.9386 | -#> |.....................| -0.9862 | -0.8880 | -0.7310 | -0.7933 | -#> |.....................| -0.8415 | -0.8800 | -0.8835 | -0.9295 | -#> |.....................| -0.7913 | -0.8712 | -0.9210 | -0.7883 | -#> | U| 488.65823 | 91.42 | -5.204 | -0.8859 | -2.190 | -#> |.....................| -4.628 | 0.4567 | 0.8878 | 0.06053 | -#> |.....................| 0.8421 | 0.05803 | 0.7253 | 0.8472 | -#> |.....................| 1.276 | 0.9642 | 0.8187 | 1.312 | -#> | X| 488.65823 | 91.42 | 0.005497 | 0.2919 | 0.1119 | -#> |.....................| 0.009772 | 0.6122 | 0.8878 | 0.06053 | -#> |.....................| 0.8421 | 0.05803 | 0.7253 | 0.8472 | -#> |.....................| 1.276 | 0.9642 | 0.8187 | 1.312 | -#> | F| Forward Diff. | 33.05 | 2.045 | -0.01570 | 0.05526 | -#> |.....................| -0.2154 | 0.6997 | -18.21 | -10.28 | -#> |.....................| -4.052 | -1.334 | 0.4619 | 6.811 | -#> |.....................| -9.752 | -0.1974 | 5.317 | -10.39 | -#> | 28| 488.35451 | 0.9920 | -1.016 | -0.9093 | -0.9386 | -#> |.....................| -0.9862 | -0.8883 | -0.7243 | -0.7897 | -#> |.....................| -0.8399 | -0.8795 | -0.8836 | -0.9319 | -#> |.....................| -0.7876 | -0.8712 | -0.9229 | -0.7844 | -#> | U| 488.35451 | 90.75 | -5.204 | -0.8859 | -2.190 | -#> |.....................| -4.628 | 0.4566 | 0.8906 | 0.06063 | -#> |.....................| 0.8427 | 0.05804 | 0.7252 | 0.8450 | -#> |.....................| 1.281 | 0.9643 | 0.8170 | 1.317 | -#> | X| 488.35451 | 90.75 | 0.005493 | 0.2920 | 0.1119 | -#> |.....................| 0.009772 | 0.6122 | 0.8906 | 0.06063 | -#> |.....................| 0.8427 | 0.05804 | 0.7252 | 0.8450 | -#> |.....................| 1.281 | 0.9643 | 0.8170 | 1.317 | -#> | F| Forward Diff. | -22.42 | 1.954 | -0.3353 | 0.1391 | -#> |.....................| -0.1757 | 0.7405 | -17.32 | -10.46 | -#> |.....................| -4.053 | -1.161 | 0.2825 | 6.114 | -#> |.....................| -9.506 | -0.1281 | 5.370 | -10.21 | -#> | 29| 488.13711 | 0.9995 | -1.016 | -0.9092 | -0.9387 | -#> |.....................| -0.9861 | -0.8886 | -0.7177 | -0.7858 | -#> |.....................| -0.8384 | -0.8791 | -0.8837 | -0.9342 | -#> |.....................| -0.7840 | -0.8711 | -0.9249 | -0.7804 | -#> | U| 488.13711 | 91.44 | -5.205 | -0.8858 | -2.190 | -#> |.....................| -4.628 | 0.4565 | 0.8934 | 0.06074 | -#> |.....................| 0.8434 | 0.05805 | 0.7251 | 0.8430 | -#> |.....................| 1.285 | 0.9643 | 0.8153 | 1.322 | -#> | X| 488.13711 | 91.44 | 0.005489 | 0.2920 | 0.1119 | -#> |.....................| 0.009773 | 0.6122 | 0.8934 | 0.06074 | -#> |.....................| 0.8434 | 0.05805 | 0.7251 | 0.8430 | -#> |.....................| 1.285 | 0.9643 | 0.8153 | 1.322 | -#> | F| Forward Diff. | 33.81 | 2.022 | 0.006720 | 0.05587 | -#> |.....................| -0.1935 | 0.7042 | -17.76 | -9.667 | -#> |.....................| -3.890 | -1.276 | 0.4404 | 6.589 | -#> |.....................| -9.459 | -0.1517 | 5.102 | -10.20 | -#> | 30| 487.82953 | 0.9922 | -1.017 | -0.9091 | -0.9387 | -#> |.....................| -0.9861 | -0.8889 | -0.7108 | -0.7824 | -#> |.....................| -0.8369 | -0.8787 | -0.8838 | -0.9367 | -#> |.....................| -0.7803 | -0.8711 | -0.9268 | -0.7763 | -#> | U| 487.82953 | 90.77 | -5.206 | -0.8858 | -2.190 | -#> |.....................| -4.628 | 0.4563 | 0.8962 | 0.06084 | -#> |.....................| 0.8440 | 0.05806 | 0.7251 | 0.8408 | -#> |.....................| 1.289 | 0.9644 | 0.8136 | 1.327 | -#> | X| 487.82953 | 90.77 | 0.005484 | 0.2920 | 0.1119 | -#> |.....................| 0.009774 | 0.6121 | 0.8962 | 0.06084 | -#> |.....................| 0.8440 | 0.05806 | 0.7251 | 0.8408 | -#> |.....................| 1.289 | 0.9644 | 0.8136 | 1.327 | -#> | F| Forward Diff. | -20.31 | 1.935 | -0.3119 | 0.1382 | -#> |.....................| -0.1555 | 0.7438 | -16.49 | -9.852 | -#> |.....................| -3.955 | -1.103 | 0.2044 | 5.876 | -#> |.....................| -9.237 | -0.1098 | 5.167 | -10.02 | -#> | 31| 487.63293 | 0.9997 | -1.018 | -0.9090 | -0.9388 | -#> |.....................| -0.9860 | -0.8892 | -0.7043 | -0.7786 | -#> |.....................| -0.8354 | -0.8782 | -0.8838 | -0.9390 | -#> |.....................| -0.7766 | -0.8711 | -0.9289 | -0.7723 | -#> | U| 487.63293 | 91.46 | -5.207 | -0.8857 | -2.191 | -#> |.....................| -4.628 | 0.4562 | 0.8989 | 0.06095 | -#> |.....................| 0.8446 | 0.05808 | 0.7250 | 0.8387 | -#> |.....................| 1.294 | 0.9644 | 0.8119 | 1.332 | -#> | X| 487.63293 | 91.46 | 0.005480 | 0.2920 | 0.1119 | -#> |.....................| 0.009774 | 0.6121 | 0.8989 | 0.06095 | -#> |.....................| 0.8446 | 0.05808 | 0.7250 | 0.8387 | -#> |.....................| 1.294 | 0.9644 | 0.8119 | 1.332 | -#> | F| Forward Diff. | 35.34 | 2.001 | 0.03668 | 0.05608 | -#> |.....................| -0.1731 | 0.7098 | -16.98 | -9.135 | -#> |.....................| -3.742 | -1.209 | 0.3780 | 6.351 | -#> |.....................| -9.183 | 0.6525 | 4.885 | -10.01 | -#> | 32| 487.31820 | 0.9926 | -1.019 | -0.9090 | -0.9388 | -#> |.....................| -0.9860 | -0.8895 | -0.6975 | -0.7753 | -#> |.....................| -0.8338 | -0.8778 | -0.8838 | -0.9414 | -#> |.....................| -0.7728 | -0.8714 | -0.9308 | -0.7679 | -#> | U| 487.3182 | 90.81 | -5.208 | -0.8856 | -2.191 | -#> |.....................| -4.628 | 0.4560 | 0.9017 | 0.06104 | -#> |.....................| 0.8453 | 0.05809 | 0.7250 | 0.8366 | -#> |.....................| 1.298 | 0.9641 | 0.8102 | 1.337 | -#> | X| 487.3182 | 90.81 | 0.005475 | 0.2920 | 0.1119 | -#> |.....................| 0.009775 | 0.6121 | 0.9017 | 0.06104 | -#> |.....................| 0.8453 | 0.05809 | 0.7250 | 0.8366 | -#> |.....................| 1.298 | 0.9641 | 0.8102 | 1.337 | -#> | F| Forward Diff. | -17.75 | 1.917 | -0.2852 | 0.1361 | -#> |.....................| -0.1360 | 0.7493 | -16.63 | -9.386 | -#> |.....................| -3.766 | -1.006 | 0.1674 | 5.665 | -#> |.....................| -8.945 | 0.7251 | 4.960 | -9.828 | -#> | 33| 487.13531 | 0.9998 | -1.020 | -0.9089 | -0.9389 | -#> |.....................| -0.9859 | -0.8898 | -0.6907 | -0.7715 | -#> |.....................| -0.8323 | -0.8774 | -0.8839 | -0.9437 | -#> |.....................| -0.7691 | -0.8717 | -0.9328 | -0.7639 | -#> | U| 487.13531 | 91.47 | -5.208 | -0.8855 | -2.191 | -#> |.....................| -4.628 | 0.4559 | 0.9045 | 0.06116 | -#> |.....................| 0.8459 | 0.05810 | 0.7250 | 0.8345 | -#> |.....................| 1.303 | 0.9638 | 0.8084 | 1.342 | -#> | X| 487.13531 | 91.47 | 0.005471 | 0.2920 | 0.1118 | -#> |.....................| 0.009775 | 0.6120 | 0.9045 | 0.06116 | -#> |.....................| 0.8459 | 0.05810 | 0.7250 | 0.8345 | -#> |.....................| 1.303 | 0.9638 | 0.8084 | 1.342 | -#> | F| Forward Diff. | 35.92 | 1.979 | 0.06301 | 0.05698 | -#> |.....................| -0.1526 | 0.7131 | -16.77 | -8.520 | -#> |.....................| -3.634 | -1.163 | 0.3177 | 6.099 | -#> |.....................| -8.917 | 0.6421 | 4.685 | -9.820 | -#> | 34| 486.82694 | 0.9926 | -1.021 | -0.9088 | -0.9389 | -#> |.....................| -0.9859 | -0.8902 | -0.6837 | -0.7686 | -#> |.....................| -0.8308 | -0.8770 | -0.8839 | -0.9460 | -#> |.....................| -0.7654 | -0.8723 | -0.9347 | -0.7596 | -#> | U| 486.82694 | 90.81 | -5.209 | -0.8855 | -2.191 | -#> |.....................| -4.628 | 0.4557 | 0.9074 | 0.06124 | -#> |.....................| 0.8465 | 0.05811 | 0.7250 | 0.8324 | -#> |.....................| 1.307 | 0.9632 | 0.8069 | 1.347 | -#> | X| 486.82694 | 90.81 | 0.005466 | 0.2920 | 0.1118 | -#> |.....................| 0.009775 | 0.6120 | 0.9074 | 0.06124 | -#> |.....................| 0.8465 | 0.05811 | 0.7250 | 0.8324 | -#> |.....................| 1.307 | 0.9632 | 0.8069 | 1.347 | -#> | F| Forward Diff. | -17.49 | 1.895 | -0.2726 | 0.1382 | -#> |.....................| -0.1159 | 0.7566 | -16.14 | -8.833 | -#> |.....................| -3.638 | -0.9303 | 0.1285 | 5.442 | -#> |.....................| -8.630 | 0.7091 | 4.774 | -9.639 | -#> | 35| 486.64804 | 0.9998 | -1.021 | -0.9087 | -0.9390 | -#> |.....................| -0.9858 | -0.8905 | -0.6768 | -0.7649 | -#> |.....................| -0.8293 | -0.8767 | -0.8839 | -0.9483 | -#> |.....................| -0.7617 | -0.8727 | -0.9367 | -0.7554 | -#> | U| 486.64804 | 91.46 | -5.210 | -0.8854 | -2.191 | -#> |.....................| -4.628 | 0.4556 | 0.9103 | 0.06135 | -#> |.....................| 0.8472 | 0.05812 | 0.7250 | 0.8304 | -#> |.....................| 1.311 | 0.9629 | 0.8051 | 1.352 | -#> | X| 486.64804 | 91.46 | 0.005462 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6120 | 0.9103 | 0.06135 | -#> |.....................| 0.8472 | 0.05812 | 0.7250 | 0.8304 | -#> |.....................| 1.311 | 0.9629 | 0.8051 | 1.352 | -#> | F| Forward Diff. | 35.26 | 1.955 | 0.07649 | 0.05940 | -#> |.....................| -0.1319 | 0.7217 | -16.38 | -8.030 | -#> |.....................| -3.491 | -1.078 | 0.2504 | 5.851 | -#> |.....................| -8.624 | 0.5993 | 4.494 | -9.625 | -#> | 36| 486.34524 | 0.9928 | -1.022 | -0.9087 | -0.9390 | -#> |.....................| -0.9858 | -0.8909 | -0.6696 | -0.7621 | -#> |.....................| -0.8278 | -0.8763 | -0.8838 | -0.9506 | -#> |.....................| -0.7579 | -0.8733 | -0.9385 | -0.7509 | -#> | U| 486.34524 | 90.82 | -5.211 | -0.8854 | -2.191 | -#> |.....................| -4.628 | 0.4554 | 0.9133 | 0.06143 | -#> |.....................| 0.8478 | 0.05813 | 0.7251 | 0.8283 | -#> |.....................| 1.316 | 0.9622 | 0.8036 | 1.358 | -#> | X| 486.34524 | 90.82 | 0.005456 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6119 | 0.9133 | 0.06143 | -#> |.....................| 0.8478 | 0.05813 | 0.7251 | 0.8283 | -#> |.....................| 1.316 | 0.9622 | 0.8036 | 1.358 | -#> | F| Forward Diff. | -16.53 | 1.875 | -0.2661 | 0.1390 | -#> |.....................| -0.09763 | 0.7654 | -15.70 | -8.237 | -#> |.....................| -3.491 | -0.9040 | 0.06392 | 5.213 | -#> |.....................| -8.361 | 0.6621 | 4.584 | -9.445 | -#> | 37| 486.17476 | 0.9998 | -1.023 | -0.9086 | -0.9391 | -#> |.....................| -0.9858 | -0.8913 | -0.6626 | -0.7586 | -#> |.....................| -0.8262 | -0.8759 | -0.8838 | -0.9529 | -#> |.....................| -0.7542 | -0.8736 | -0.9406 | -0.7467 | -#> | U| 486.17476 | 91.47 | -5.212 | -0.8853 | -2.191 | -#> |.....................| -4.628 | 0.4552 | 0.9162 | 0.06153 | -#> |.....................| 0.8484 | 0.05814 | 0.7250 | 0.8263 | -#> |.....................| 1.320 | 0.9619 | 0.8018 | 1.363 | -#> | X| 486.17476 | 91.47 | 0.005452 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6119 | 0.9162 | 0.06153 | -#> |.....................| 0.8484 | 0.05814 | 0.7250 | 0.8263 | -#> |.....................| 1.320 | 0.9619 | 0.8018 | 1.363 | -#> | F| Forward Diff. | 35.23 | 1.932 | 0.08715 | 0.05955 | -#> |.....................| -0.1122 | 0.7274 | -16.01 | -7.627 | -#> |.....................| -3.363 | -1.024 | 0.1942 | 5.616 | -#> |.....................| -8.345 | 0.5641 | 4.322 | -9.424 | -#> | 38| 485.87468 | 0.9930 | -1.024 | -0.9086 | -0.9392 | -#> |.....................| -0.9858 | -0.8917 | -0.6553 | -0.7561 | -#> |.....................| -0.8248 | -0.8756 | -0.8837 | -0.9551 | -#> |.....................| -0.7504 | -0.8743 | -0.9424 | -0.7420 | -#> | U| 485.87468 | 90.84 | -5.213 | -0.8853 | -2.191 | -#> |.....................| -4.628 | 0.4550 | 0.9192 | 0.06160 | -#> |.....................| 0.8490 | 0.05815 | 0.7252 | 0.8243 | -#> |.....................| 1.325 | 0.9613 | 0.8003 | 1.369 | -#> | X| 485.87468 | 90.84 | 0.005446 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6118 | 0.9192 | 0.06160 | -#> |.....................| 0.8490 | 0.05815 | 0.7252 | 0.8243 | -#> |.....................| 1.325 | 0.9613 | 0.8003 | 1.369 | -#> | F| Forward Diff. | -15.16 | 1.855 | -0.2494 | 0.1393 | -#> |.....................| -0.07811 | 0.7704 | -15.31 | -7.716 | -#> |.....................| -3.357 | -0.8175 | -0.03012 | 4.971 | -#> |.....................| -8.100 | 0.5955 | 4.407 | -9.242 | -#> | 39| 485.71812 | 1.000 | -1.025 | -0.9085 | -0.9392 | -#> |.....................| -0.9858 | -0.8921 | -0.6482 | -0.7526 | -#> |.....................| -0.8232 | -0.8752 | -0.8836 | -0.9573 | -#> |.....................| -0.7467 | -0.8746 | -0.9444 | -0.7377 | -#> | U| 485.71812 | 91.48 | -5.214 | -0.8852 | -2.191 | -#> |.....................| -4.628 | 0.4548 | 0.9221 | 0.06170 | -#> |.....................| 0.8497 | 0.05816 | 0.7252 | 0.8222 | -#> |.....................| 1.329 | 0.9610 | 0.7985 | 1.374 | -#> | X| 485.71812 | 91.48 | 0.005442 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6118 | 0.9221 | 0.06170 | -#> |.....................| 0.8497 | 0.05816 | 0.7252 | 0.8222 | -#> |.....................| 1.329 | 0.9610 | 0.7985 | 1.374 | -#> | F| Forward Diff. | 36.02 | 1.911 | 0.1144 | 0.05926 | -#> |.....................| -0.09370 | 0.7314 | -15.47 | -7.071 | -#> |.....................| -3.248 | -0.9743 | 0.1265 | 5.377 | -#> |.....................| -7.775 | 0.5175 | 4.130 | -9.229 | -#> | 40| 485.42108 | 0.9931 | -1.026 | -0.9085 | -0.9393 | -#> |.....................| -0.9858 | -0.8926 | -0.6408 | -0.7505 | -#> |.....................| -0.8218 | -0.8750 | -0.8834 | -0.9594 | -#> |.....................| -0.7430 | -0.8752 | -0.9461 | -0.7328 | -#> | U| 485.42108 | 90.85 | -5.215 | -0.8852 | -2.191 | -#> |.....................| -4.628 | 0.4546 | 0.9252 | 0.06176 | -#> |.....................| 0.8503 | 0.05817 | 0.7254 | 0.8204 | -#> |.....................| 1.333 | 0.9604 | 0.7970 | 1.380 | -#> | X| 485.42108 | 90.85 | 0.005436 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6117 | 0.9252 | 0.06176 | -#> |.....................| 0.8503 | 0.05817 | 0.7254 | 0.8204 | -#> |.....................| 1.333 | 0.9604 | 0.7970 | 1.380 | -#> | F| Forward Diff. | -14.37 | 1.836 | -0.2333 | 0.1389 | -#> |.....................| -0.05951 | 0.7785 | -14.33 | -7.292 | -#> |.....................| -3.229 | -0.7699 | -0.05471 | 4.764 | -#> |.....................| -7.801 | 0.5597 | 4.229 | -9.048 | -#> | 41| 485.26815 | 0.9999 | -1.027 | -0.9084 | -0.9394 | -#> |.....................| -0.9858 | -0.8930 | -0.6338 | -0.7470 | -#> |.....................| -0.8202 | -0.8746 | -0.8833 | -0.9618 | -#> |.....................| -0.7392 | -0.8755 | -0.9482 | -0.7284 | -#> | U| 485.26815 | 91.48 | -5.216 | -0.8851 | -2.191 | -#> |.....................| -4.628 | 0.4544 | 0.9281 | 0.06186 | -#> |.....................| 0.8509 | 0.05818 | 0.7254 | 0.8183 | -#> |.....................| 1.338 | 0.9601 | 0.7953 | 1.385 | -#> | X| 485.26815 | 91.48 | 0.005431 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6117 | 0.9281 | 0.06186 | -#> |.....................| 0.8509 | 0.05818 | 0.7254 | 0.8183 | -#> |.....................| 1.338 | 0.9601 | 0.7953 | 1.385 | -#> | F| Forward Diff. | 35.37 | 1.889 | 0.1323 | 0.06297 | -#> |.....................| -0.07437 | 0.7390 | -14.80 | -6.641 | -#> |.....................| -3.116 | -0.8690 | 0.09880 | 5.162 | -#> |.....................| -7.761 | 0.4865 | 3.967 | -9.019 | -#> | 42| 484.97448 | 0.9934 | -1.028 | -0.9084 | -0.9395 | -#> |.....................| -0.9859 | -0.8935 | -0.6264 | -0.7452 | -#> |.....................| -0.8188 | -0.8744 | -0.8830 | -0.9639 | -#> |.....................| -0.7352 | -0.8762 | -0.9500 | -0.7231 | -#> | U| 484.97448 | 90.88 | -5.217 | -0.8851 | -2.191 | -#> |.....................| -4.628 | 0.4542 | 0.9311 | 0.06191 | -#> |.....................| 0.8515 | 0.05819 | 0.7257 | 0.8164 | -#> |.....................| 1.343 | 0.9594 | 0.7937 | 1.392 | -#> | X| 484.97448 | 90.88 | 0.005424 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6116 | 0.9311 | 0.06191 | -#> |.....................| 0.8515 | 0.05819 | 0.7257 | 0.8164 | -#> |.....................| 1.343 | 0.9594 | 0.7937 | 1.392 | -#> | F| Forward Diff. | -12.51 | 1.817 | -0.2072 | 0.1320 | -#> |.....................| -0.04147 | 0.7868 | -13.90 | -6.839 | -#> |.....................| -3.097 | -0.6966 | -0.09701 | 4.567 | -#> |.....................| -7.500 | 0.5336 | 4.059 | -8.839 | -#> | 43| 484.82513 | 0.9998 | -1.029 | -0.9083 | -0.9395 | -#> |.....................| -0.9858 | -0.8939 | -0.6193 | -0.7417 | -#> |.....................| -0.8172 | -0.8741 | -0.8829 | -0.9662 | -#> |.....................| -0.7313 | -0.8765 | -0.9521 | -0.7185 | -#> | U| 484.82513 | 91.47 | -5.218 | -0.8851 | -2.191 | -#> |.....................| -4.628 | 0.4540 | 0.9341 | 0.06202 | -#> |.....................| 0.8522 | 0.05820 | 0.7257 | 0.8143 | -#> |.....................| 1.347 | 0.9592 | 0.7919 | 1.397 | -#> | X| 484.82513 | 91.47 | 0.005419 | 0.2921 | 0.1118 | -#> |.....................| 0.009776 | 0.6116 | 0.9341 | 0.06202 | -#> |.....................| 0.8522 | 0.05820 | 0.7257 | 0.8143 | -#> |.....................| 1.347 | 0.9592 | 0.7919 | 1.397 | -#> | F| Forward Diff. | 34.86 | 1.871 | 0.1566 | 0.07097 | -#> |.....................| -0.05046 | 0.7508 | -14.35 | -6.106 | -#> |.....................| -2.960 | -0.8322 | 0.03576 | 4.926 | -#> |.....................| -7.463 | 0.4624 | 3.813 | -8.806 | -#> | 44| 484.54032 | 0.9935 | -1.030 | -0.9084 | -0.9396 | -#> |.....................| -0.9859 | -0.8946 | -0.6118 | -0.7403 | -#> |.....................| -0.8157 | -0.8739 | -0.8825 | -0.9682 | -#> |.....................| -0.7274 | -0.8772 | -0.9538 | -0.7130 | -#> | U| 484.54032 | 90.89 | -5.219 | -0.8851 | -2.191 | -#> |.....................| -4.628 | 0.4537 | 0.9372 | 0.06206 | -#> |.....................| 0.8528 | 0.05820 | 0.7260 | 0.8125 | -#> |.....................| 1.352 | 0.9585 | 0.7904 | 1.404 | -#> | X| 484.54032 | 90.89 | 0.005412 | 0.2921 | 0.1118 | -#> |.....................| 0.009775 | 0.6115 | 0.9372 | 0.06206 | -#> |.....................| 0.8528 | 0.05820 | 0.7260 | 0.8125 | -#> |.....................| 1.352 | 0.9585 | 0.7904 | 1.404 | -#> | F| Forward Diff. | -11.88 | 1.798 | -0.1931 | 0.1288 | -#> |.....................| -0.02100 | 0.7941 | -13.56 | -6.327 | -#> |.....................| -2.985 | -0.6346 | -0.1369 | 4.355 | -#> |.....................| -7.207 | 0.4876 | 3.910 | -8.603 | -#> | 45| 484.39828 | 0.9999 | -1.031 | -0.9082 | -0.9397 | -#> |.....................| -0.9859 | -0.8950 | -0.6045 | -0.7369 | -#> |.....................| -0.8141 | -0.8736 | -0.8824 | -0.9706 | -#> |.....................| -0.7235 | -0.8774 | -0.9559 | -0.7084 | -#> | U| 484.39828 | 91.47 | -5.220 | -0.8850 | -2.191 | -#> |.....................| -4.628 | 0.4535 | 0.9402 | 0.06215 | -#> |.....................| 0.8534 | 0.05821 | 0.7261 | 0.8104 | -#> |.....................| 1.357 | 0.9582 | 0.7886 | 1.409 | -#> | X| 484.39828 | 91.47 | 0.005407 | 0.2921 | 0.1118 | -#> |.....................| 0.009775 | 0.6115 | 0.9402 | 0.06215 | -#> |.....................| 0.8534 | 0.05821 | 0.7261 | 0.8104 | -#> |.....................| 1.357 | 0.9582 | 0.7886 | 1.409 | -#> | F| Forward Diff. | 34.75 | 1.847 | 0.1787 | 0.06647 | -#> |.....................| -0.03069 | 0.7556 | -13.39 | -5.638 | -#> |.....................| -2.842 | -0.7351 | -0.07352 | 4.648 | -#> |.....................| -7.153 | 0.4383 | 3.662 | -8.575 | -#> | 46| 484.12389 | 0.9935 | -1.033 | -0.9083 | -0.9398 | -#> |.....................| -0.9861 | -0.8957 | -0.5972 | -0.7360 | -#> |.....................| -0.8127 | -0.8736 | -0.8818 | -0.9724 | -#> |.....................| -0.7196 | -0.8781 | -0.9577 | -0.7026 | -#> | U| 484.12389 | 90.89 | -5.221 | -0.8851 | -2.192 | -#> |.....................| -4.628 | 0.4532 | 0.9432 | 0.06218 | -#> |.....................| 0.8540 | 0.05821 | 0.7265 | 0.8087 | -#> |.....................| 1.361 | 0.9576 | 0.7871 | 1.416 | -#> | X| 484.12389 | 90.89 | 0.005400 | 0.2921 | 0.1117 | -#> |.....................| 0.009773 | 0.6114 | 0.9432 | 0.06218 | -#> |.....................| 0.8540 | 0.05821 | 0.7265 | 0.8087 | -#> |.....................| 1.361 | 0.9576 | 0.7871 | 1.416 | -#> | F| Forward Diff. | -12.23 | 1.776 | -0.1772 | 0.1286 | -#> |.....................| -0.003904 | 0.8005 | -13.23 | -5.967 | -#> |.....................| -2.801 | -0.5825 | -0.1993 | 4.126 | -#> |.....................| -6.930 | 0.4309 | 3.746 | -8.373 | -#> | 47| 483.96910 | 0.9995 | -1.034 | -0.9082 | -0.9399 | -#> |.....................| -0.9861 | -0.8963 | -0.5897 | -0.7331 | -#> |.....................| -0.8111 | -0.8733 | -0.8815 | -0.9747 | -#> |.....................| -0.7157 | -0.8785 | -0.9598 | -0.6976 | -#> | U| 483.9691 | 91.44 | -5.222 | -0.8850 | -2.192 | -#> |.....................| -4.628 | 0.4529 | 0.9464 | 0.06226 | -#> |.....................| 0.8547 | 0.05822 | 0.7267 | 0.8067 | -#> |.....................| 1.366 | 0.9573 | 0.7854 | 1.423 | -#> | X| 483.9691 | 91.44 | 0.005394 | 0.2921 | 0.1117 | -#> |.....................| 0.009773 | 0.6113 | 0.9464 | 0.06226 | -#> |.....................| 0.8547 | 0.05822 | 0.7267 | 0.8067 | -#> |.....................| 1.366 | 0.9573 | 0.7854 | 1.423 | -#> | F| Forward Diff. | 31.42 | 1.822 | 0.1778 | 0.07033 | -#> |.....................| -0.01094 | 0.7681 | -13.66 | -5.343 | -#> |.....................| -2.704 | -0.6601 | -0.05834 | 4.483 | -#> |.....................| -6.846 | 0.3977 | 3.514 | -8.343 | -#> | 48| 483.71026 | 0.9937 | -1.035 | -0.9084 | -0.9400 | -#> |.....................| -0.9863 | -0.8970 | -0.5817 | -0.7327 | -#> |.....................| -0.8099 | -0.8734 | -0.8808 | -0.9764 | -#> |.....................| -0.7120 | -0.8790 | -0.9614 | -0.6918 | -#> | U| 483.71026 | 90.90 | -5.224 | -0.8851 | -2.192 | -#> |.....................| -4.628 | 0.4526 | 0.9497 | 0.06228 | -#> |.....................| 0.8552 | 0.05822 | 0.7272 | 0.8052 | -#> |.....................| 1.370 | 0.9567 | 0.7840 | 1.430 | -#> | X| 483.71026 | 90.90 | 0.005386 | 0.2921 | 0.1117 | -#> |.....................| 0.009771 | 0.6112 | 0.9497 | 0.06228 | -#> |.....................| 0.8552 | 0.05822 | 0.7272 | 0.8052 | -#> |.....................| 1.370 | 0.9567 | 0.7840 | 1.430 | -#> | F| Forward Diff. | -11.41 | 1.753 | -0.1608 | 0.1222 | -#> |.....................| 0.01159 | 0.8050 | -10.44 | -3.810 | -#> |.....................| -1.727 | 0.1311 | 2.133 | 3.863 | -#> |.....................| -5.017 | 1.937 | 3.587 | -8.159 | -#> | 49| 483.59835 | 1.000 | -1.037 | -0.9083 | -0.9401 | -#> |.....................| -0.9863 | -0.8977 | -0.5748 | -0.7309 | -#> |.....................| -0.8089 | -0.8737 | -0.8826 | -0.9789 | -#> |.....................| -0.7088 | -0.8807 | -0.9637 | -0.6861 | -#> | U| 483.59835 | 91.50 | -5.225 | -0.8850 | -2.192 | -#> |.....................| -4.628 | 0.4523 | 0.9525 | 0.06233 | -#> |.....................| 0.8556 | 0.05821 | 0.7260 | 0.8029 | -#> |.....................| 1.374 | 0.9551 | 0.7819 | 1.437 | -#> | X| 483.59835 | 91.50 | 0.005379 | 0.2921 | 0.1117 | -#> |.....................| 0.009771 | 0.6112 | 0.9525 | 0.06233 | -#> |.....................| 0.8556 | 0.05821 | 0.7260 | 0.8029 | -#> |.....................| 1.374 | 0.9551 | 0.7819 | 1.437 | -#> | F| Forward Diff. | 35.70 | 1.806 | 0.2381 | 0.06477 | -#> |.....................| 0.008951 | 0.7715 | -12.71 | -4.946 | -#> |.....................| -2.552 | -0.6506 | -0.07612 | 4.309 | -#> |.....................| -6.609 | 0.2622 | 3.318 | -8.104 | -#> | 50| 483.34903 | 0.9946 | -1.038 | -0.9084 | -0.9402 | -#> |.....................| -0.9865 | -0.8986 | -0.5687 | -0.7321 | -#> |.....................| -0.8087 | -0.8746 | -0.8853 | -0.9811 | -#> |.....................| -0.7064 | -0.8834 | -0.9659 | -0.6790 | -#> | U| 483.34903 | 90.99 | -5.227 | -0.8851 | -2.192 | -#> |.....................| -4.629 | 0.4518 | 0.9551 | 0.06229 | -#> |.....................| 0.8557 | 0.05818 | 0.7240 | 0.8009 | -#> |.....................| 1.377 | 0.9526 | 0.7800 | 1.445 | -#> | X| 483.34903 | 90.99 | 0.005370 | 0.2921 | 0.1117 | -#> |.....................| 0.009769 | 0.6111 | 0.9551 | 0.06229 | -#> |.....................| 0.8557 | 0.05818 | 0.7240 | 0.8009 | -#> |.....................| 1.377 | 0.9526 | 0.7800 | 1.445 | -#> | F| Forward Diff. | -5.120 | 1.736 | -0.09503 | 0.1090 | -#> |.....................| 0.03046 | 0.8092 | -12.63 | -5.226 | -#> |.....................| -2.620 | -0.5304 | -0.3057 | 3.753 | -#> |.....................| -6.427 | 0.07650 | 3.398 | -7.915 | -#> | 51| 483.15597 | 0.9980 | -1.040 | -0.9083 | -0.9402 | -#> |.....................| -0.9866 | -0.8991 | -0.5603 | -0.7286 | -#> |.....................| -0.8069 | -0.8742 | -0.8851 | -0.9836 | -#> |.....................| -0.7022 | -0.8834 | -0.9682 | -0.6737 | -#> | U| 483.15597 | 91.30 | -5.228 | -0.8851 | -2.192 | -#> |.....................| -4.629 | 0.4516 | 0.9585 | 0.06239 | -#> |.....................| 0.8564 | 0.05819 | 0.7241 | 0.7987 | -#> |.....................| 1.382 | 0.9525 | 0.7781 | 1.452 | -#> | X| 483.15597 | 91.30 | 0.005364 | 0.2921 | 0.1117 | -#> |.....................| 0.009769 | 0.6110 | 0.9585 | 0.06239 | -#> |.....................| 0.8564 | 0.05819 | 0.7241 | 0.7987 | -#> |.....................| 1.382 | 0.9525 | 0.7781 | 1.452 | -#> | 52| 483.02721 | 1.004 | -1.042 | -0.9082 | -0.9404 | -#> |.....................| -0.9866 | -0.9001 | -0.5449 | -0.7222 | -#> |.....................| -0.8037 | -0.8736 | -0.8847 | -0.9882 | -#> |.....................| -0.6943 | -0.8835 | -0.9723 | -0.6641 | -#> | U| 483.02721 | 91.87 | -5.230 | -0.8850 | -2.192 | -#> |.....................| -4.629 | 0.4511 | 0.9649 | 0.06258 | -#> |.....................| 0.8577 | 0.05821 | 0.7244 | 0.7946 | -#> |.....................| 1.391 | 0.9524 | 0.7746 | 1.463 | -#> | X| 483.02721 | 91.87 | 0.005352 | 0.2921 | 0.1117 | -#> |.....................| 0.009768 | 0.6109 | 0.9649 | 0.06258 | -#> |.....................| 0.8577 | 0.05821 | 0.7244 | 0.7946 | -#> |.....................| 1.391 | 0.9524 | 0.7746 | 1.463 | -#> | F| Forward Diff. | 64.04 | 1.793 | 0.5284 | 0.01389 | -#> |.....................| 0.02898 | 0.7509 | -12.63 | -3.976 | -#> |.....................| -2.339 | -0.6213 | 0.1061 | 4.124 | -#> |.....................| -6.092 | 0.06517 | 2.880 | -7.726 | -#> | 53| 482.23689 | 0.9946 | -1.047 | -0.9090 | -0.9407 | -#> |.....................| -0.9878 | -0.9036 | -0.5201 | -0.7284 | -#> |.....................| -0.8010 | -0.8752 | -0.8830 | -0.9901 | -#> |.....................| -0.6858 | -0.8831 | -0.9756 | -0.6451 | -#> | U| 482.23689 | 90.99 | -5.236 | -0.8857 | -2.192 | -#> |.....................| -4.630 | 0.4496 | 0.9752 | 0.06240 | -#> |.....................| 0.8589 | 0.05816 | 0.7257 | 0.7929 | -#> |.....................| 1.401 | 0.9528 | 0.7717 | 1.486 | -#> | X| 482.23689 | 90.99 | 0.005323 | 0.2920 | 0.1116 | -#> |.....................| 0.009757 | 0.6105 | 0.9752 | 0.06240 | -#> |.....................| 0.8589 | 0.05816 | 0.7257 | 0.7929 | -#> |.....................| 1.401 | 0.9528 | 0.7717 | 1.486 | -#> | F| Forward Diff. | -6.401 | 1.688 | -0.06693 | 0.1101 | -#> |.....................| 0.07752 | 0.8485 | -12.38 | -4.258 | -#> |.....................| -2.381 | -0.3971 | -0.4532 | 3.327 | -#> |.....................| -5.692 | 0.09795 | 3.049 | -7.221 | -#> | 54| 481.84664 | 1.002 | -1.052 | -0.9094 | -0.9410 | -#> |.....................| -0.9885 | -0.9064 | -0.4925 | -0.7287 | -#> |.....................| -0.7974 | -0.8758 | -0.8811 | -0.9941 | -#> |.....................| -0.6765 | -0.8831 | -0.9802 | -0.6288 | -#> | U| 481.84664 | 91.67 | -5.240 | -0.8860 | -2.193 | -#> |.....................| -4.631 | 0.4482 | 0.9866 | 0.06239 | -#> |.....................| 0.8604 | 0.05815 | 0.7270 | 0.7893 | -#> |.....................| 1.412 | 0.9528 | 0.7678 | 1.506 | -#> | X| 481.84664 | 91.67 | 0.005298 | 0.2919 | 0.1116 | -#> |.....................| 0.009749 | 0.6102 | 0.9866 | 0.06239 | -#> |.....................| 0.8604 | 0.05815 | 0.7270 | 0.7893 | -#> |.....................| 1.412 | 0.9528 | 0.7678 | 1.506 | -#> | F| Forward Diff. | 47.13 | 1.726 | 0.4206 | 0.02536 | -#> |.....................| 0.06828 | 0.8062 | -11.83 | -3.346 | -#> |.....................| -2.102 | -0.4847 | -0.09759 | 3.731 | -#> |.....................| -5.096 | -0.5769 | 2.736 | -6.997 | -#> | 55| 481.27209 | 0.9943 | -1.058 | -0.9105 | -0.9413 | -#> |.....................| -0.9900 | -0.9106 | -0.4653 | -0.7394 | -#> |.....................| -0.7957 | -0.8780 | -0.8782 | -0.9956 | -#> |.....................| -0.6736 | -0.8789 | -0.9829 | -0.6135 | -#> | U| 481.27209 | 90.96 | -5.246 | -0.8870 | -2.193 | -#> |.....................| -4.632 | 0.4464 | 0.9978 | 0.06208 | -#> |.....................| 0.8611 | 0.05808 | 0.7292 | 0.7879 | -#> |.....................| 1.416 | 0.9569 | 0.7655 | 1.525 | -#> | X| 481.27209 | 90.96 | 0.005268 | 0.2917 | 0.1116 | -#> |.....................| 0.009735 | 0.6098 | 0.9978 | 0.06208 | -#> |.....................| 0.8611 | 0.05808 | 0.7292 | 0.7879 | -#> |.....................| 1.416 | 0.9569 | 0.7655 | 1.525 | -#> | F| Forward Diff. | -10.35 | 1.643 | -0.1028 | 0.1091 | -#> |.....................| 0.1039 | 0.8949 | -11.59 | -3.607 | -#> |.....................| -2.172 | -0.3207 | -0.4703 | 3.042 | -#> |.....................| -5.188 | 0.5388 | 2.890 | -6.602 | -#> | 56| 480.86800 | 0.9992 | -1.064 | -0.9113 | -0.9415 | -#> |.....................| -0.9915 | -0.9152 | -0.4371 | -0.7498 | -#> |.....................| -0.7937 | -0.8800 | -0.8752 | -0.9980 | -#> |.....................| -0.6700 | -0.8785 | -0.9867 | -0.5989 | -#> | U| 480.868 | 91.41 | -5.252 | -0.8877 | -2.193 | -#> |.....................| -4.634 | 0.4442 | 1.010 | 0.06178 | -#> |.....................| 0.8619 | 0.05803 | 0.7313 | 0.7858 | -#> |.....................| 1.420 | 0.9572 | 0.7622 | 1.543 | -#> | X| 480.868 | 91.41 | 0.005236 | 0.2916 | 0.1115 | -#> |.....................| 0.009720 | 0.6093 | 1.010 | 0.06178 | -#> |.....................| 0.8619 | 0.05803 | 0.7313 | 0.7858 | -#> |.....................| 1.420 | 0.9572 | 0.7622 | 1.543 | -#> | 57| 480.18757 | 0.9994 | -1.075 | -0.9131 | -0.9420 | -#> |.....................| -0.9946 | -0.9242 | -0.3882 | -0.7756 | -#> |.....................| -0.7917 | -0.8845 | -0.8694 | -1.000 | -#> |.....................| -0.6674 | -0.8772 | -0.9919 | -0.5742 | -#> | U| 480.18757 | 91.43 | -5.264 | -0.8893 | -2.194 | -#> |.....................| -4.637 | 0.4401 | 1.030 | 0.06104 | -#> |.....................| 0.8627 | 0.05790 | 0.7356 | 0.7839 | -#> |.....................| 1.423 | 0.9585 | 0.7577 | 1.573 | -#> | X| 480.18757 | 91.43 | 0.005177 | 0.2913 | 0.1115 | -#> |.....................| 0.009690 | 0.6083 | 1.030 | 0.06104 | -#> |.....................| 0.8627 | 0.05790 | 0.7356 | 0.7839 | -#> |.....................| 1.423 | 0.9585 | 0.7577 | 1.573 | -#> | 58| 477.33677 | 1.000 | -1.128 | -0.9215 | -0.9444 | -#> |.....................| -1.009 | -0.9662 | -0.1601 | -0.8958 | -#> |.....................| -0.7824 | -0.9055 | -0.8420 | -1.010 | -#> |.....................| -0.6551 | -0.8713 | -1.016 | -0.4591 | -#> | U| 477.33677 | 91.51 | -5.317 | -0.8967 | -2.196 | -#> |.....................| -4.651 | 0.4208 | 1.124 | 0.05757 | -#> |.....................| 0.8666 | 0.05729 | 0.7556 | 0.7749 | -#> |.....................| 1.438 | 0.9642 | 0.7367 | 1.713 | -#> | X| 477.33677 | 91.51 | 0.004910 | 0.2897 | 0.1112 | -#> |.....................| 0.009550 | 0.6037 | 1.124 | 0.05757 | -#> |.....................| 0.8666 | 0.05729 | 0.7556 | 0.7749 | -#> |.....................| 1.438 | 0.9642 | 0.7367 | 1.713 | -#> | 59| 470.34077 | 1.005 | -1.340 | -0.9551 | -0.9536 | -#> |.....................| -1.067 | -1.134 | 0.7520 | -1.376 | -#> |.....................| -0.7448 | -0.9894 | -0.7326 | -1.050 | -#> |.....................| -0.6055 | -0.8475 | -1.115 | 0.001078 | -#> | U| 470.34077 | 91.93 | -5.528 | -0.9265 | -2.205 | -#> |.....................| -4.709 | 0.3439 | 1.502 | 0.04372 | -#> |.....................| 0.8821 | 0.05487 | 0.8354 | 0.7391 | -#> |.....................| 1.496 | 0.9871 | 0.6524 | 2.272 | -#> | X| 470.34077 | 91.93 | 0.003973 | 0.2836 | 0.1102 | -#> |.....................| 0.009011 | 0.5851 | 1.502 | 0.04372 | -#> |.....................| 0.8821 | 0.05487 | 0.8354 | 0.7391 | -#> |.....................| 1.496 | 0.9871 | 0.6524 | 2.272 | -#> | F| Forward Diff. | 26.15 | 0.9841 | -0.2917 | -0.5557 | -#> |.....................| 0.1743 | 0.07961 | -5.483 | -2.977 | -#> |.....................| -1.594 | -1.883 | 1.921 | 2.622 | -#> |.....................| -2.684 | 3.199 | -3.516 | -0.2713 | -#> | 60| 503.34963 | 1.001 | -1.624 | -0.8890 | -0.8555 | -#> |.....................| -1.160 | -1.269 | 1.871 | -1.579 | -#> |.....................| -0.5570 | -0.7566 | -0.9888 | -1.205 | -#> |.....................| -0.4219 | -1.204 | -0.3205 | 0.003684 | -#> | U| 503.34963 | 91.54 | -5.813 | -0.8679 | -2.107 | -#> |.....................| -4.802 | 0.2817 | 1.965 | 0.03787 | -#> |.....................| 0.9599 | 0.06159 | 0.6484 | 0.5998 | -#> |.....................| 1.714 | 0.6438 | 1.334 | 2.275 | -#> | X| 503.34963 | 91.54 | 0.002989 | 0.2957 | 0.1216 | -#> |.....................| 0.008214 | 0.5700 | 1.965 | 0.03787 | -#> |.....................| 0.9599 | 0.06159 | 0.6484 | 0.5998 | -#> |.....................| 1.714 | 0.6438 | 1.334 | 2.275 | -#> | 61| 469.52776 | 1.001 | -1.377 | -0.9480 | -0.9425 | -#> |.....................| -1.079 | -1.153 | 0.9014 | -1.405 | -#> |.....................| -0.7213 | -0.9635 | -0.7590 | -1.066 | -#> |.....................| -0.5863 | -0.9260 | -1.020 | 0.002305 | -#> | U| 469.52776 | 91.55 | -5.565 | -0.9203 | -2.194 | -#> |.....................| -4.721 | 0.3353 | 1.564 | 0.04288 | -#> |.....................| 0.8919 | 0.05562 | 0.8161 | 0.7248 | -#> |.....................| 1.519 | 0.9115 | 0.7335 | 2.274 | -#> | X| 469.52776 | 91.55 | 0.003829 | 0.2849 | 0.1114 | -#> |.....................| 0.008907 | 0.5831 | 1.564 | 0.04288 | -#> |.....................| 0.8919 | 0.05562 | 0.8161 | 0.7248 | -#> |.....................| 1.519 | 0.9115 | 0.7335 | 2.274 | -#> | F| Forward Diff. | -33.46 | 0.8466 | -0.2714 | -0.3437 | -#> |.....................| -0.005169 | 0.9674 | -4.363 | -2.175 | -#> |.....................| -0.4723 | -1.194 | 1.668 | 1.180 | -#> |.....................| -1.975 | -3.231 | 4.715 | 0.6860 | -#> | 62| 468.69396 | 1.009 | -1.417 | -0.9407 | -0.9181 | -#> |.....................| -1.088 | -1.184 | 1.029 | -1.410 | -#> |.....................| -0.7106 | -0.9016 | -0.8502 | -1.110 | -#> |.....................| -0.5641 | -0.8957 | -1.025 | -0.08379 | -#> | U| 468.69396 | 92.28 | -5.606 | -0.9138 | -2.170 | -#> |.....................| -4.730 | 0.3207 | 1.617 | 0.04273 | -#> |.....................| 0.8963 | 0.05740 | 0.7496 | 0.6857 | -#> |.....................| 1.546 | 0.9407 | 0.7298 | 2.169 | -#> | X| 468.69396 | 92.28 | 0.003677 | 0.2862 | 0.1142 | -#> |.....................| 0.008826 | 0.5795 | 1.617 | 0.04273 | -#> |.....................| 0.8963 | 0.05740 | 0.7496 | 0.6857 | -#> |.....................| 1.546 | 0.9407 | 0.7298 | 2.169 | -#> | F| Forward Diff. | 44.64 | 0.7919 | 0.8591 | -0.3536 | -#> |.....................| -0.1337 | 0.2061 | -3.251 | 1.076 | -#> |.....................| 0.6486 | -0.6734 | -0.006662 | -4.031 | -#> |.....................| -0.9510 | -1.369 | 2.636 | 0.2207 | -#> | 63| 468.25975 | 1.001 | -1.457 | -0.9435 | -0.8944 | -#> |.....................| -1.092 | -1.207 | 1.162 | -1.430 | -#> |.....................| -0.7163 | -0.8453 | -0.9089 | -1.031 | -#> |.....................| -0.5350 | -0.9084 | -1.055 | -0.1705 | -#> | U| 468.25975 | 91.62 | -5.645 | -0.9163 | -2.146 | -#> |.....................| -4.734 | 0.3104 | 1.671 | 0.04217 | -#> |.....................| 0.8939 | 0.05903 | 0.7067 | 0.7562 | -#> |.....................| 1.580 | 0.9284 | 0.7040 | 2.064 | -#> | X| 468.25975 | 91.62 | 0.003534 | 0.2857 | 0.1169 | -#> |.....................| 0.008791 | 0.5770 | 1.671 | 0.04217 | -#> |.....................| 0.8939 | 0.05903 | 0.7067 | 0.7562 | -#> |.....................| 1.580 | 0.9284 | 0.7040 | 2.064 | -#> | F| Forward Diff. | -27.10 | 0.6132 | -0.09159 | -0.08800 | -#> |.....................| -0.1078 | -0.3202 | -2.388 | 1.638 | -#> |.....................| 1.140 | 0.1171 | 0.1600 | 3.377 | -#> |.....................| 1.163 | -2.226 | -0.6898 | -0.6683 | -#> | 64| 467.71969 | 1.007 | -1.501 | -0.9546 | -0.8725 | -#> |.....................| -1.088 | -1.196 | 1.309 | -1.518 | -#> |.....................| -0.7729 | -0.8084 | -0.9408 | -1.028 | -#> |.....................| -0.5596 | -0.8715 | -1.022 | -0.2167 | -#> | U| 467.71969 | 92.14 | -5.690 | -0.9262 | -2.124 | -#> |.....................| -4.730 | 0.3152 | 1.732 | 0.03962 | -#> |.....................| 0.8705 | 0.06009 | 0.6835 | 0.7588 | -#> |.....................| 1.551 | 0.9640 | 0.7321 | 2.007 | -#> | X| 467.71969 | 92.14 | 0.003381 | 0.2837 | 0.1195 | -#> |.....................| 0.008831 | 0.5781 | 1.732 | 0.03962 | -#> |.....................| 0.8705 | 0.06009 | 0.6835 | 0.7588 | -#> |.....................| 1.551 | 0.9640 | 0.7321 | 2.007 | -#> | F| Forward Diff. | 13.64 | 0.5263 | -0.09449 | -0.03300 | -#> |.....................| -0.2497 | 0.5177 | -1.944 | 1.719 | -#> |.....................| 0.02781 | -0.4546 | 0.1053 | 4.139 | -#> |.....................| 0.2369 | 0.8861 | 1.752 | -0.4404 | -#> | 65| 467.30536 | 1.004 | -1.542 | -0.9574 | -0.8551 | -#> |.....................| -1.078 | -1.202 | 1.437 | -1.633 | -#> |.....................| -0.8162 | -0.7674 | -0.9588 | -1.081 | -#> |.....................| -0.5907 | -0.8860 | -1.037 | -0.2723 | -#> | U| 467.30536 | 91.87 | -5.731 | -0.9286 | -2.107 | -#> |.....................| -4.720 | 0.3124 | 1.785 | 0.03631 | -#> |.....................| 0.8526 | 0.06127 | 0.6704 | 0.7116 | -#> |.....................| 1.514 | 0.9500 | 0.7187 | 1.940 | -#> | X| 467.30536 | 91.87 | 0.003244 | 0.2832 | 0.1216 | -#> |.....................| 0.008917 | 0.5775 | 1.785 | 0.03631 | -#> |.....................| 0.8526 | 0.06127 | 0.6704 | 0.7116 | -#> |.....................| 1.514 | 0.9500 | 0.7187 | 1.940 | -#> | F| Forward Diff. | -28.84 | 0.5077 | -0.1377 | 0.05990 | -#> |.....................| -0.2272 | 0.7424 | -2.070 | -0.4026 | -#> |.....................| -0.6342 | -0.6074 | -0.7367 | -1.927 | -#> |.....................| -1.174 | -0.4282 | -0.2913 | -0.8226 | -#> | 66| 467.70919 | 1.018 | -1.590 | -0.9528 | -0.8478 | -#> |.....................| -1.050 | -1.273 | 1.541 | -1.746 | -#> |.....................| -0.7981 | -0.6862 | -0.9431 | -1.082 | -#> |.....................| -0.5846 | -0.9179 | -1.062 | -0.3171 | -#> | U| 467.70919 | 93.14 | -5.778 | -0.9245 | -2.100 | -#> |.....................| -4.692 | 0.2799 | 1.829 | 0.03305 | -#> |.....................| 0.8601 | 0.06362 | 0.6818 | 0.7103 | -#> |.....................| 1.521 | 0.9193 | 0.6977 | 1.885 | -#> | X| 467.70919 | 93.14 | 0.003094 | 0.2840 | 0.1225 | -#> |.....................| 0.009168 | 0.5695 | 1.829 | 0.03305 | -#> |.....................| 0.8601 | 0.06362 | 0.6818 | 0.7103 | -#> |.....................| 1.521 | 0.9193 | 0.6977 | 1.885 | -#> | 67| 467.47896 | 1.015 | -1.557 | -0.9559 | -0.8529 | -#> |.....................| -1.069 | -1.224 | 1.469 | -1.667 | -#> |.....................| -0.8105 | -0.7423 | -0.9538 | -1.081 | -#> |.....................| -0.5885 | -0.8957 | -1.045 | -0.2858 | -#> | U| 467.47896 | 92.90 | -5.746 | -0.9273 | -2.105 | -#> |.....................| -4.711 | 0.3023 | 1.799 | 0.03531 | -#> |.....................| 0.8550 | 0.06200 | 0.6740 | 0.7117 | -#> |.....................| 1.517 | 0.9407 | 0.7123 | 1.923 | -#> | X| 467.47896 | 92.90 | 0.003197 | 0.2835 | 0.1219 | -#> |.....................| 0.008994 | 0.5750 | 1.799 | 0.03531 | -#> |.....................| 0.8550 | 0.06200 | 0.6740 | 0.7117 | -#> |.....................| 1.517 | 0.9407 | 0.7123 | 1.923 | -#> | 68| 467.47242 | 1.015 | -1.547 | -0.9569 | -0.8545 | -#> |.....................| -1.075 | -1.209 | 1.447 | -1.644 | -#> |.....................| -0.8142 | -0.7594 | -0.9570 | -1.080 | -#> |.....................| -0.5898 | -0.8890 | -1.040 | -0.2763 | -#> | U| 467.47242 | 92.83 | -5.736 | -0.9282 | -2.106 | -#> |.....................| -4.717 | 0.3092 | 1.790 | 0.03600 | -#> |.....................| 0.8534 | 0.06150 | 0.6716 | 0.7121 | -#> |.....................| 1.515 | 0.9472 | 0.7168 | 1.935 | -#> | X| 467.47242 | 92.83 | 0.003229 | 0.2833 | 0.1217 | -#> |.....................| 0.008942 | 0.5767 | 1.790 | 0.03600 | -#> |.....................| 0.8534 | 0.06150 | 0.6716 | 0.7121 | -#> |.....................| 1.515 | 0.9472 | 0.7168 | 1.935 | -#> | 69| 467.34503 | 1.012 | -1.542 | -0.9574 | -0.8552 | -#> |.....................| -1.078 | -1.203 | 1.437 | -1.633 | -#> |.....................| -0.8160 | -0.7673 | -0.9586 | -1.080 | -#> |.....................| -0.5904 | -0.8859 | -1.037 | -0.2720 | -#> | U| 467.34503 | 92.56 | -5.731 | -0.9286 | -2.107 | -#> |.....................| -4.720 | 0.3123 | 1.786 | 0.03631 | -#> |.....................| 0.8527 | 0.06128 | 0.6705 | 0.7121 | -#> |.....................| 1.514 | 0.9501 | 0.7188 | 1.940 | -#> | X| 467.34503 | 92.56 | 0.003244 | 0.2832 | 0.1216 | -#> |.....................| 0.008918 | 0.5775 | 1.786 | 0.03631 | -#> |.....................| 0.8527 | 0.06128 | 0.6705 | 0.7121 | -#> |.....................| 1.514 | 0.9501 | 0.7188 | 1.940 | -#> | 70| 467.25859 | 1.007 | -1.542 | -0.9574 | -0.8552 | -#> |.....................| -1.078 | -1.202 | 1.437 | -1.633 | -#> |.....................| -0.8161 | -0.7674 | -0.9587 | -1.080 | -#> |.....................| -0.5906 | -0.8860 | -1.037 | -0.2722 | -#> | U| 467.25859 | 92.16 | -5.731 | -0.9286 | -2.107 | -#> |.....................| -4.720 | 0.3124 | 1.785 | 0.03631 | -#> |.....................| 0.8526 | 0.06127 | 0.6704 | 0.7118 | -#> |.....................| 1.514 | 0.9500 | 0.7187 | 1.940 | -#> | X| 467.25859 | 92.16 | 0.003244 | 0.2832 | 0.1216 | -#> |.....................| 0.008918 | 0.5775 | 1.785 | 0.03631 | -#> |.....................| 0.8526 | 0.06127 | 0.6704 | 0.7118 | -#> |.....................| 1.514 | 0.9500 | 0.7187 | 1.940 | -#> | F| Forward Diff. | 0.4422 | 0.5213 | 0.04284 | 0.02840 | -#> |.....................| -0.2383 | 0.7531 | -2.043 | -0.07081 | -#> |.....................| -0.6548 | -0.6872 | -0.7073 | -1.773 | -#> |.....................| -1.488 | -0.4400 | -0.3907 | -0.8156 | -#> | 71| 467.25330 | 1.007 | -1.543 | -0.9574 | -0.8552 | -#> |.....................| -1.078 | -1.203 | 1.439 | -1.633 | -#> |.....................| -0.8155 | -0.7668 | -0.9581 | -1.079 | -#> |.....................| -0.5893 | -0.8856 | -1.037 | -0.2714 | -#> | U| 467.2533 | 92.12 | -5.731 | -0.9287 | -2.107 | -#> |.....................| -4.720 | 0.3121 | 1.786 | 0.03631 | -#> |.....................| 0.8529 | 0.06129 | 0.6709 | 0.7133 | -#> |.....................| 1.516 | 0.9504 | 0.7190 | 1.941 | -#> | X| 467.2533 | 92.12 | 0.003243 | 0.2832 | 0.1216 | -#> |.....................| 0.008919 | 0.5774 | 1.786 | 0.03631 | -#> |.....................| 0.8529 | 0.06129 | 0.6709 | 0.7133 | -#> |.....................| 1.516 | 0.9504 | 0.7190 | 1.941 | -#> | F| Forward Diff. | -3.065 | 0.5175 | 0.01752 | 0.03302 | -#> |.....................| -0.2370 | 0.7457 | -1.985 | -0.01476 | -#> |.....................| -0.5869 | -0.6438 | -0.7222 | -1.672 | -#> |.....................| -1.086 | -0.3942 | -0.3461 | -0.8075 | -#> | 72| 467.24583 | 1.008 | -1.544 | -0.9571 | -0.8551 | -#> |.....................| -1.077 | -1.206 | 1.442 | -1.635 | -#> |.....................| -0.8142 | -0.7642 | -0.9569 | -1.078 | -#> |.....................| -0.5901 | -0.8857 | -1.037 | -0.2715 | -#> | U| 467.24583 | 92.22 | -5.733 | -0.9284 | -2.107 | -#> |.....................| -4.719 | 0.3108 | 1.788 | 0.03626 | -#> |.....................| 0.8534 | 0.06137 | 0.6718 | 0.7144 | -#> |.....................| 1.515 | 0.9503 | 0.7191 | 1.941 | -#> | X| 467.24583 | 92.22 | 0.003238 | 0.2832 | 0.1216 | -#> |.....................| 0.008927 | 0.5771 | 1.788 | 0.03626 | -#> |.....................| 0.8534 | 0.06137 | 0.6718 | 0.7144 | -#> |.....................| 1.515 | 0.9503 | 0.7191 | 1.941 | -#> | F| Forward Diff. | 6.834 | 0.5162 | 0.08982 | 0.01752 | -#> |.....................| -0.2436 | 0.7158 | -2.020 | -0.04939 | -#> |.....................| -0.5459 | -0.6263 | -0.5712 | -1.499 | -#> |.....................| -1.429 | -0.4150 | -0.4098 | -0.8001 | -#> | 73| 467.23713 | 1.007 | -1.546 | -0.9569 | -0.8551 | -#> |.....................| -1.076 | -1.209 | 1.446 | -1.636 | -#> |.....................| -0.8132 | -0.7618 | -0.9559 | -1.076 | -#> |.....................| -0.5919 | -0.8860 | -1.037 | -0.2716 | -#> | U| 467.23713 | 92.12 | -5.734 | -0.9282 | -2.107 | -#> |.....................| -4.718 | 0.3095 | 1.789 | 0.03621 | -#> |.....................| 0.8538 | 0.06143 | 0.6724 | 0.7154 | -#> |.....................| 1.513 | 0.9500 | 0.7191 | 1.941 | -#> | X| 467.23713 | 92.12 | 0.003233 | 0.2833 | 0.1216 | -#> |.....................| 0.008936 | 0.5768 | 1.789 | 0.03621 | -#> |.....................| 0.8538 | 0.06143 | 0.6724 | 0.7154 | -#> |.....................| 1.513 | 0.9500 | 0.7191 | 1.941 | -#> | F| Forward Diff. | -3.249 | 0.5067 | 0.04417 | 0.02698 | -#> |.....................| -0.2393 | 0.6753 | -1.942 | -0.1419 | -#> |.....................| -0.5001 | -0.5983 | -0.6679 | -1.518 | -#> |.....................| -1.576 | -0.4506 | -0.4091 | -0.8075 | -#> | 74| 467.22826 | 1.008 | -1.548 | -0.9568 | -0.8550 | -#> |.....................| -1.074 | -1.212 | 1.450 | -1.638 | -#> |.....................| -0.8127 | -0.7593 | -0.9548 | -1.076 | -#> |.....................| -0.5925 | -0.8862 | -1.037 | -0.2718 | -#> | U| 467.22826 | 92.20 | -5.736 | -0.9281 | -2.107 | -#> |.....................| -4.716 | 0.3080 | 1.791 | 0.03615 | -#> |.....................| 0.8540 | 0.06151 | 0.6733 | 0.7160 | -#> |.....................| 1.512 | 0.9499 | 0.7192 | 1.940 | -#> | X| 467.22826 | 92.20 | 0.003227 | 0.2833 | 0.1216 | -#> |.....................| 0.008947 | 0.5764 | 1.791 | 0.03615 | -#> |.....................| 0.8540 | 0.06151 | 0.6733 | 0.7160 | -#> |.....................| 1.512 | 0.9499 | 0.7192 | 1.940 | -#> | F| Forward Diff. | 4.158 | 0.5052 | 0.09162 | 0.01474 | -#> |.....................| -0.2441 | 0.6411 | -1.927 | 0.008374 | -#> |.....................| -0.4204 | -0.5681 | -0.5325 | -1.398 | -#> |.....................| -1.545 | -0.4616 | -0.4623 | -0.8062 | -#> | 75| 467.21798 | 1.007 | -1.549 | -0.9567 | -0.8549 | -#> |.....................| -1.073 | -1.215 | 1.453 | -1.641 | -#> |.....................| -0.8130 | -0.7568 | -0.9541 | -1.075 | -#> |.....................| -0.5920 | -0.8862 | -1.036 | -0.2722 | -#> | U| 467.21798 | 92.13 | -5.738 | -0.9280 | -2.107 | -#> |.....................| -4.715 | 0.3065 | 1.792 | 0.03607 | -#> |.....................| 0.8539 | 0.06158 | 0.6738 | 0.7163 | -#> |.....................| 1.512 | 0.9498 | 0.7195 | 1.940 | -#> | X| 467.21798 | 92.13 | 0.003221 | 0.2833 | 0.1216 | -#> |.....................| 0.008959 | 0.5760 | 1.792 | 0.03607 | -#> |.....................| 0.8539 | 0.06158 | 0.6738 | 0.7163 | -#> |.....................| 1.512 | 0.9498 | 0.7195 | 1.940 | -#> | F| Forward Diff. | -2.820 | 0.4989 | 0.05960 | 0.01935 | -#> |.....................| -0.2421 | 0.6061 | -1.914 | -0.2151 | -#> |.....................| -0.5103 | -0.6093 | -0.7625 | -1.437 | -#> |.....................| -1.510 | -0.4672 | -0.4489 | -0.8043 | -#> | 76| 467.20848 | 1.008 | -1.551 | -0.9569 | -0.8547 | -#> |.....................| -1.072 | -1.218 | 1.456 | -1.643 | -#> |.....................| -0.8130 | -0.7539 | -0.9520 | -1.075 | -#> |.....................| -0.5920 | -0.8859 | -1.036 | -0.2725 | -#> | U| 467.20848 | 92.20 | -5.740 | -0.9282 | -2.106 | -#> |.....................| -4.714 | 0.3053 | 1.793 | 0.03601 | -#> |.....................| 0.8539 | 0.06166 | 0.6753 | 0.7165 | -#> |.....................| 1.512 | 0.9501 | 0.7200 | 1.939 | -#> | X| 467.20848 | 92.20 | 0.003215 | 0.2833 | 0.1217 | -#> |.....................| 0.008973 | 0.5757 | 1.793 | 0.03601 | -#> |.....................| 0.8539 | 0.06166 | 0.6753 | 0.7165 | -#> |.....................| 1.512 | 0.9501 | 0.7200 | 1.939 | -#> | F| Forward Diff. | 3.706 | 0.4993 | 0.1020 | 0.01046 | -#> |.....................| -0.2448 | 0.5847 | -1.899 | -0.1702 | -#> |.....................| -0.3837 | -0.5516 | -0.5275 | -1.370 | -#> |.....................| -1.509 | -0.4527 | -0.4630 | -0.7991 | -#> | 77| 467.20140 | 1.007 | -1.554 | -0.9572 | -0.8545 | -#> |.....................| -1.070 | -1.221 | 1.459 | -1.644 | -#> |.....................| -0.8137 | -0.7511 | -0.9495 | -1.075 | -#> |.....................| -0.5926 | -0.8856 | -1.035 | -0.2726 | -#> | U| 467.2014 | 92.12 | -5.742 | -0.9285 | -2.106 | -#> |.....................| -4.712 | 0.3041 | 1.795 | 0.03600 | -#> |.....................| 0.8536 | 0.06174 | 0.6772 | 0.7169 | -#> |.....................| 1.512 | 0.9504 | 0.7205 | 1.939 | -#> | X| 467.2014 | 92.12 | 0.003207 | 0.2832 | 0.1217 | -#> |.....................| 0.008990 | 0.5754 | 1.795 | 0.03600 | -#> |.....................| 0.8536 | 0.06174 | 0.6772 | 0.7169 | -#> |.....................| 1.512 | 0.9504 | 0.7205 | 1.939 | -#> | F| Forward Diff. | -4.697 | 0.4875 | 0.03394 | 0.01314 | -#> |.....................| -0.2450 | 0.5527 | -1.903 | -0.2230 | -#> |.....................| -0.3367 | -0.5055 | -0.4386 | -1.334 | -#> |.....................| -1.570 | -0.4518 | -0.4312 | -0.7987 | -#> | 78| 467.19155 | 1.008 | -1.556 | -0.9574 | -0.8545 | -#> |.....................| -1.067 | -1.224 | 1.462 | -1.645 | -#> |.....................| -0.8159 | -0.7492 | -0.9499 | -1.074 | -#> |.....................| -0.5924 | -0.8858 | -1.035 | -0.2722 | -#> | U| 467.19155 | 92.18 | -5.745 | -0.9286 | -2.106 | -#> |.....................| -4.709 | 0.3027 | 1.796 | 0.03596 | -#> |.....................| 0.8527 | 0.06180 | 0.6768 | 0.7173 | -#> |.....................| 1.512 | 0.9502 | 0.7208 | 1.940 | -#> | X| 467.19155 | 92.18 | 0.003200 | 0.2832 | 0.1217 | -#> |.....................| 0.009010 | 0.5751 | 1.796 | 0.03596 | -#> |.....................| 0.8527 | 0.06180 | 0.6768 | 0.7173 | -#> |.....................| 1.512 | 0.9502 | 0.7208 | 1.940 | -#> | F| Forward Diff. | 2.102 | 0.4867 | 0.07498 | 0.004893 | -#> |.....................| -0.2442 | 0.5250 | -1.879 | -0.1740 | -#> |.....................| -0.3775 | -0.5383 | -0.4109 | -1.255 | -#> |.....................| -1.562 | -0.4584 | -0.4426 | -0.7882 | -#> | 79| 467.18237 | 1.007 | -1.558 | -0.9574 | -0.8544 | -#> |.....................| -1.065 | -1.226 | 1.465 | -1.647 | -#> |.....................| -0.8177 | -0.7470 | -0.9510 | -1.074 | -#> |.....................| -0.5912 | -0.8859 | -1.035 | -0.2717 | -#> | U| 467.18237 | 92.12 | -5.747 | -0.9286 | -2.106 | -#> |.....................| -4.707 | 0.3016 | 1.797 | 0.03591 | -#> |.....................| 0.8519 | 0.06186 | 0.6761 | 0.7177 | -#> |.....................| 1.513 | 0.9501 | 0.7212 | 1.940 | -#> | X| 467.18237 | 92.12 | 0.003193 | 0.2832 | 0.1217 | -#> |.....................| 0.009031 | 0.5748 | 1.797 | 0.03591 | -#> |.....................| 0.8519 | 0.06186 | 0.6761 | 0.7177 | -#> |.....................| 1.513 | 0.9501 | 0.7212 | 1.940 | -#> | F| Forward Diff. | -4.940 | 0.4761 | 0.03110 | 0.006161 | -#> |.....................| -0.2415 | 0.4988 | -1.880 | -0.2651 | -#> |.....................| -0.3787 | -0.5263 | -0.4799 | -1.241 | -#> |.....................| -1.481 | -0.4641 | -0.4124 | -0.7761 | -#> | 80| 467.17113 | 1.008 | -1.561 | -0.9574 | -0.8542 | -#> |.....................| -1.062 | -1.228 | 1.469 | -1.648 | -#> |.....................| -0.8192 | -0.7442 | -0.9515 | -1.074 | -#> |.....................| -0.5909 | -0.8858 | -1.034 | -0.2714 | -#> | U| 467.17113 | 92.19 | -5.749 | -0.9286 | -2.106 | -#> |.....................| -4.704 | 0.3008 | 1.799 | 0.03586 | -#> |.....................| 0.8513 | 0.06194 | 0.6757 | 0.7179 | -#> |.....................| 1.514 | 0.9502 | 0.7215 | 1.941 | -#> | X| 467.17113 | 92.19 | 0.003185 | 0.2832 | 0.1217 | -#> |.....................| 0.009056 | 0.5746 | 1.799 | 0.03586 | -#> |.....................| 0.8513 | 0.06194 | 0.6757 | 0.7179 | -#> |.....................| 1.514 | 0.9502 | 0.7215 | 1.941 | -#> | 81| 467.15723 | 1.008 | -1.564 | -0.9575 | -0.8538 | -#> |.....................| -1.058 | -1.230 | 1.473 | -1.651 | -#> |.....................| -0.8215 | -0.7400 | -0.9524 | -1.074 | -#> |.....................| -0.5906 | -0.8857 | -1.034 | -0.2712 | -#> | U| 467.15723 | 92.19 | -5.753 | -0.9287 | -2.106 | -#> |.....................| -4.700 | 0.2996 | 1.800 | 0.03578 | -#> |.....................| 0.8504 | 0.06206 | 0.6750 | 0.7179 | -#> |.....................| 1.514 | 0.9503 | 0.7219 | 1.941 | -#> | X| 467.15723 | 92.19 | 0.003173 | 0.2832 | 0.1218 | -#> |.....................| 0.009093 | 0.5743 | 1.800 | 0.03578 | -#> |.....................| 0.8504 | 0.06206 | 0.6750 | 0.7179 | -#> |.....................| 1.514 | 0.9503 | 0.7219 | 1.941 | -#> | 82| 467.09153 | 1.008 | -1.583 | -0.9578 | -0.8521 | -#> |.....................| -1.038 | -1.244 | 1.497 | -1.664 | -#> |.....................| -0.8331 | -0.7187 | -0.9572 | -1.074 | -#> |.....................| -0.5894 | -0.8854 | -1.031 | -0.2699 | -#> | U| 467.09153 | 92.20 | -5.772 | -0.9290 | -2.104 | -#> |.....................| -4.680 | 0.2934 | 1.810 | 0.03540 | -#> |.....................| 0.8456 | 0.06268 | 0.6715 | 0.7181 | -#> |.....................| 1.516 | 0.9506 | 0.7239 | 1.943 | -#> | X| 467.09153 | 92.20 | 0.003114 | 0.2831 | 0.1220 | -#> |.....................| 0.009282 | 0.5728 | 1.810 | 0.03540 | -#> |.....................| 0.8456 | 0.06268 | 0.6715 | 0.7181 | -#> |.....................| 1.516 | 0.9506 | 0.7239 | 1.943 | -#> | 83| 466.89701 | 1.009 | -1.658 | -0.9591 | -0.8451 | -#> |.....................| -0.9556 | -1.297 | 1.590 | -1.717 | -#> |.....................| -0.8794 | -0.6338 | -0.9760 | -1.073 | -#> |.....................| -0.5844 | -0.8840 | -1.022 | -0.2647 | -#> | U| 466.89701 | 92.27 | -5.846 | -0.9301 | -2.097 | -#> |.....................| -4.598 | 0.2688 | 1.849 | 0.03388 | -#> |.....................| 0.8264 | 0.06513 | 0.6578 | 0.7186 | -#> |.....................| 1.521 | 0.9519 | 0.7320 | 1.949 | -#> | X| 466.89701 | 92.27 | 0.002890 | 0.2829 | 0.1228 | -#> |.....................| 0.01008 | 0.5668 | 1.849 | 0.03388 | -#> |.....................| 0.8264 | 0.06513 | 0.6578 | 0.7186 | -#> |.....................| 1.521 | 0.9519 | 0.7320 | 1.949 | -#> | 84| 466.81525 | 1.010 | -1.758 | -0.9608 | -0.8357 | -#> |.....................| -0.8455 | -1.369 | 1.715 | -1.787 | -#> |.....................| -0.9414 | -0.5201 | -1.001 | -1.072 | -#> |.....................| -0.5775 | -0.8822 | -1.009 | -0.2576 | -#> | U| 466.81525 | 92.41 | -5.946 | -0.9316 | -2.087 | -#> |.....................| -4.488 | 0.2358 | 1.901 | 0.03185 | -#> |.....................| 0.8007 | 0.06841 | 0.6394 | 0.7195 | -#> |.....................| 1.530 | 0.9537 | 0.7428 | 1.958 | -#> | X| 466.81525 | 92.41 | 0.002615 | 0.2826 | 0.1240 | -#> |.....................| 0.01125 | 0.5587 | 1.901 | 0.03185 | -#> |.....................| 0.8007 | 0.06841 | 0.6394 | 0.7195 | -#> |.....................| 1.530 | 0.9537 | 0.7428 | 1.958 | -#> | F| Forward Diff. | 1.005 | 0.03859 | 0.3281 | -0.1495 | -#> |.....................| 0.1126 | -0.4190 | -0.9638 | -1.159 | -#> |.....................| -0.4187 | -0.1084 | -1.236 | 1.865 | -#> |.....................| -0.3960 | -0.4043 | -0.1671 | 0.1635 | -#> | 85| 467.22945 | 1.009 | -1.931 | -1.059 | -0.7851 | -#> |.....................| -0.6667 | -1.418 | 1.962 | -1.804 | -#> |.....................| -1.038 | -0.3298 | -0.7816 | -1.157 | -#> |.....................| -0.5368 | -0.8226 | -0.9633 | -0.3812 | -#> | U| 467.22945 | 92.33 | -6.120 | -1.019 | -2.037 | -#> |.....................| -4.309 | 0.2137 | 2.003 | 0.03136 | -#> |.....................| 0.7606 | 0.07390 | 0.7997 | 0.6429 | -#> |.....................| 1.578 | 1.011 | 0.7823 | 1.807 | -#> | X| 467.22945 | 92.33 | 0.002199 | 0.2652 | 0.1304 | -#> |.....................| 0.01345 | 0.5532 | 2.003 | 0.03136 | -#> |.....................| 0.7606 | 0.07390 | 0.7997 | 0.6429 | -#> |.....................| 1.578 | 1.011 | 0.7823 | 1.807 | -#> | 86| 466.68655 | 1.009 | -1.812 | -0.9919 | -0.8198 | -#> |.....................| -0.7896 | -1.384 | 1.793 | -1.792 | -#> |.....................| -0.9716 | -0.4604 | -0.9317 | -1.100 | -#> |.....................| -0.5645 | -0.8633 | -0.9948 | -0.2964 | -#> | U| 466.68655 | 92.33 | -6.001 | -0.9592 | -2.072 | -#> |.....................| -4.432 | 0.2290 | 1.933 | 0.03172 | -#> |.....................| 0.7883 | 0.07013 | 0.6901 | 0.6945 | -#> |.....................| 1.545 | 0.9719 | 0.7553 | 1.910 | -#> | X| 466.68655 | 92.33 | 0.002477 | 0.2770 | 0.1260 | -#> |.....................| 0.01190 | 0.5570 | 1.933 | 0.03172 | -#> |.....................| 0.7883 | 0.07013 | 0.6901 | 0.6945 | -#> |.....................| 1.545 | 0.9719 | 0.7553 | 1.910 | -#> | F| Forward Diff. | -11.18 | 0.05254 | -0.8763 | -0.07569 | -#> |.....................| 0.1998 | -0.2059 | -0.4605 | -0.7124 | -#> |.....................| -0.3271 | 0.07217 | 0.9692 | 1.710 | -#> |.....................| -0.7229 | 0.7265 | 0.2517 | -0.09129 | -#> | 87| 466.82655 | 1.009 | -1.865 | -0.9192 | -0.7946 | -#> |.....................| -0.7769 | -1.362 | 1.859 | -1.827 | -#> |.....................| -0.9838 | -0.4392 | -0.9155 | -1.146 | -#> |.....................| -0.4995 | -0.8511 | -1.000 | -0.3560 | -#> | U| 466.82655 | 92.34 | -6.054 | -0.8947 | -2.046 | -#> |.....................| -4.419 | 0.2394 | 1.960 | 0.03072 | -#> |.....................| 0.7832 | 0.07074 | 0.7019 | 0.6527 | -#> |.....................| 1.622 | 0.9836 | 0.7506 | 1.838 | -#> | X| 466.82655 | 92.34 | 0.002349 | 0.2901 | 0.1292 | -#> |.....................| 0.01205 | 0.5596 | 1.960 | 0.03072 | -#> |.....................| 0.7832 | 0.07074 | 0.7019 | 0.6527 | -#> |.....................| 1.622 | 0.9836 | 0.7506 | 1.838 | -#> | 88| 466.65072 | 1.010 | -1.827 | -0.9719 | -0.8129 | -#> |.....................| -0.7861 | -1.378 | 1.811 | -1.801 | -#> |.....................| -0.9749 | -0.4546 | -0.9274 | -1.113 | -#> |.....................| -0.5467 | -0.8600 | -0.9963 | -0.3127 | -#> | U| 466.65072 | 92.43 | -6.015 | -0.9415 | -2.065 | -#> |.....................| -4.428 | 0.2318 | 1.940 | 0.03144 | -#> |.....................| 0.7869 | 0.07030 | 0.6933 | 0.6830 | -#> |.....................| 1.566 | 0.9750 | 0.7540 | 1.891 | -#> | X| 466.65072 | 92.43 | 0.002441 | 0.2806 | 0.1269 | -#> |.....................| 0.01194 | 0.5577 | 1.940 | 0.03144 | -#> |.....................| 0.7869 | 0.07030 | 0.6933 | 0.6830 | -#> |.....................| 1.566 | 0.9750 | 0.7540 | 1.891 | -#> | F| Forward Diff. | -1.340 | 0.07863 | 0.1180 | -0.03302 | -#> |.....................| 0.1973 | -0.03638 | -0.4314 | -0.7320 | -#> |.....................| -0.3719 | 0.04356 | 0.7597 | 1.009 | -#> |.....................| 0.3079 | 0.4883 | -0.4019 | -0.3069 | -#> | 89| 466.64054 | 1.012 | -1.843 | -0.9769 | -0.8069 | -#> |.....................| -0.7968 | -1.376 | 1.833 | -1.786 | -#> |.....................| -0.9571 | -0.4600 | -0.9463 | -1.118 | -#> |.....................| -0.5553 | -0.8554 | -0.9954 | -0.3119 | -#> | U| 466.64054 | 92.56 | -6.031 | -0.9459 | -2.059 | -#> |.....................| -4.439 | 0.2329 | 1.949 | 0.03189 | -#> |.....................| 0.7943 | 0.07014 | 0.6795 | 0.6783 | -#> |.....................| 1.556 | 0.9795 | 0.7548 | 1.892 | -#> | X| 466.64054 | 92.56 | 0.002403 | 0.2797 | 0.1276 | -#> |.....................| 0.01181 | 0.5580 | 1.949 | 0.03189 | -#> |.....................| 0.7943 | 0.07014 | 0.6795 | 0.6783 | -#> |.....................| 1.556 | 0.9795 | 0.7548 | 1.892 | -#> | F| Forward Diff. | 13.35 | 0.06546 | -0.02976 | 0.01632 | -#> |.....................| 0.1680 | -0.06031 | -0.2101 | 0.2297 | -#> |.....................| -0.01975 | 0.1913 | 0.1108 | 0.6100 | -#> |.....................| -0.008263 | 1.320 | 0.06198 | -0.2490 | -#> | 90| 466.63994 | 1.010 | -1.856 | -0.9836 | -0.8023 | -#> |.....................| -0.8121 | -1.369 | 1.859 | -1.781 | -#> |.....................| -0.9548 | -0.4699 | -0.9506 | -1.117 | -#> |.....................| -0.5644 | -0.8726 | -1.009 | -0.3176 | -#> | U| 466.63994 | 92.43 | -6.045 | -0.9518 | -2.054 | -#> |.....................| -4.454 | 0.2360 | 1.960 | 0.03203 | -#> |.....................| 0.7952 | 0.06986 | 0.6763 | 0.6795 | -#> |.....................| 1.545 | 0.9629 | 0.7430 | 1.885 | -#> | X| 466.63994 | 92.43 | 0.002371 | 0.2785 | 0.1282 | -#> |.....................| 0.01163 | 0.5587 | 1.960 | 0.03203 | -#> |.....................| 0.7952 | 0.06986 | 0.6763 | 0.6795 | -#> |.....................| 1.545 | 0.9629 | 0.7430 | 1.885 | -#> | F| Forward Diff. | 0.1431 | 0.02593 | -0.4247 | 0.08835 | -#> |.....................| 0.1490 | -0.08497 | 0.03702 | 0.4153 | -#> |.....................| -0.04754 | 0.2015 | 0.06787 | -0.3581 | -#> |.....................| -0.4069 | 0.09362 | -0.9227 | -0.5264 | -#> | 91| 466.65402 | 1.008 | -1.856 | -0.9767 | -0.8037 | -#> |.....................| -0.8145 | -1.367 | 1.858 | -1.788 | -#> |.....................| -0.9540 | -0.4731 | -0.9517 | -1.111 | -#> |.....................| -0.5579 | -0.8741 | -0.9943 | -0.3092 | -#> | U| 466.65402 | 92.22 | -6.045 | -0.9458 | -2.055 | -#> |.....................| -4.457 | 0.2367 | 1.960 | 0.03184 | -#> |.....................| 0.7955 | 0.06976 | 0.6755 | 0.6846 | -#> |.....................| 1.553 | 0.9615 | 0.7557 | 1.895 | -#> | X| 466.65402 | 92.22 | 0.002370 | 0.2797 | 0.1280 | -#> |.....................| 0.01160 | 0.5589 | 1.960 | 0.03184 | -#> |.....................| 0.7955 | 0.06976 | 0.6755 | 0.6846 | -#> |.....................| 1.553 | 0.9615 | 0.7557 | 1.895 | -#> | 92| 466.63541 | 1.010 | -1.856 | -0.9812 | -0.8028 | -#> |.....................| -0.8129 | -1.368 | 1.858 | -1.783 | -#> |.....................| -0.9545 | -0.4710 | -0.9509 | -1.115 | -#> |.....................| -0.5622 | -0.8731 | -1.004 | -0.3147 | -#> | U| 466.63541 | 92.36 | -6.045 | -0.9498 | -2.055 | -#> |.....................| -4.455 | 0.2363 | 1.960 | 0.03197 | -#> |.....................| 0.7953 | 0.06982 | 0.6761 | 0.6812 | -#> |.....................| 1.548 | 0.9624 | 0.7474 | 1.888 | -#> | X| 466.63541 | 92.36 | 0.002371 | 0.2789 | 0.1281 | -#> |.....................| 0.01162 | 0.5588 | 1.960 | 0.03197 | -#> |.....................| 0.7953 | 0.06982 | 0.6761 | 0.6812 | -#> |.....................| 1.548 | 0.9624 | 0.7474 | 1.888 | -#> | F| Forward Diff. | -7.597 | 0.01585 | -0.3721 | 0.09081 | -#> |.....................| 0.1473 | -0.05128 | 0.01723 | 0.2650 | -#> |.....................| -0.04930 | 0.2121 | 0.3911 | -0.1952 | -#> |.....................| -0.2951 | 0.01195 | -0.4116 | -0.4404 | -#> | 93| 466.62967 | 1.010 | -1.857 | -0.9822 | -0.8038 | -#> |.....................| -0.8179 | -1.367 | 1.859 | -1.785 | -#> |.....................| -0.9524 | -0.4748 | -0.9515 | -1.114 | -#> |.....................| -0.5617 | -0.8740 | -1.004 | -0.3130 | -#> | U| 466.62967 | 92.43 | -6.045 | -0.9507 | -2.056 | -#> |.....................| -4.460 | 0.2370 | 1.960 | 0.03192 | -#> |.....................| 0.7962 | 0.06971 | 0.6756 | 0.6815 | -#> |.....................| 1.548 | 0.9616 | 0.7476 | 1.890 | -#> | X| 466.62967 | 92.43 | 0.002369 | 0.2787 | 0.1280 | -#> |.....................| 0.01156 | 0.5590 | 1.960 | 0.03192 | -#> |.....................| 0.7962 | 0.06971 | 0.6756 | 0.6815 | -#> |.....................| 1.548 | 0.9616 | 0.7476 | 1.890 | -#> | F| Forward Diff. | 0.1737 | 0.01712 | -0.3712 | 0.07555 | -#> |.....................| 0.1320 | -0.03330 | -0.1756 | 0.3015 | -#> |.....................| -0.06297 | 0.1717 | 0.09645 | -0.1674 | -#> |.....................| -0.2756 | -0.01624 | -0.3459 | -0.4307 | -#> | 94| 466.62779 | 1.010 | -1.856 | -0.9797 | -0.8047 | -#> |.....................| -0.8221 | -1.366 | 1.862 | -1.786 | -#> |.....................| -0.9500 | -0.4779 | -0.9517 | -1.113 | -#> |.....................| -0.5623 | -0.8742 | -1.003 | -0.3111 | -#> | U| 466.62779 | 92.40 | -6.045 | -0.9484 | -2.056 | -#> |.....................| -4.464 | 0.2375 | 1.961 | 0.03188 | -#> |.....................| 0.7972 | 0.06963 | 0.6755 | 0.6823 | -#> |.....................| 1.548 | 0.9614 | 0.7480 | 1.893 | -#> | X| 466.62779 | 92.40 | 0.002370 | 0.2792 | 0.1279 | -#> |.....................| 0.01152 | 0.5591 | 1.961 | 0.03188 | -#> |.....................| 0.7972 | 0.06963 | 0.6755 | 0.6823 | -#> |.....................| 1.548 | 0.9614 | 0.7480 | 1.893 | -#> | F| Forward Diff. | -2.926 | 0.01199 | -0.2808 | 0.07297 | -#> |.....................| 0.1250 | -0.02504 | 0.02207 | 0.2419 | -#> |.....................| -0.03068 | 0.1983 | 0.3271 | -0.08125 | -#> |.....................| -0.2841 | -0.05347 | -0.2873 | -0.3919 | -#> | 95| 466.62386 | 1.010 | -1.856 | -0.9811 | -0.8057 | -#> |.....................| -0.8267 | -1.365 | 1.862 | -1.788 | -#> |.....................| -0.9479 | -0.4822 | -0.9526 | -1.114 | -#> |.....................| -0.5610 | -0.8741 | -1.003 | -0.3093 | -#> | U| 466.62386 | 92.43 | -6.045 | -0.9497 | -2.057 | -#> |.....................| -4.469 | 0.2377 | 1.961 | 0.03183 | -#> |.....................| 0.7980 | 0.06950 | 0.6749 | 0.6820 | -#> |.....................| 1.549 | 0.9614 | 0.7483 | 1.895 | -#> | X| 466.62386 | 92.43 | 0.002370 | 0.2789 | 0.1278 | -#> |.....................| 0.01146 | 0.5592 | 1.961 | 0.03183 | -#> |.....................| 0.7980 | 0.06950 | 0.6749 | 0.6820 | -#> |.....................| 1.549 | 0.9614 | 0.7483 | 1.895 | -#> | F| Forward Diff. | 0.1137 | 0.01564 | -0.3265 | 0.06191 | -#> |.....................| 0.1094 | -0.02529 | 0.01125 | 0.2123 | -#> |.....................| -0.07598 | 0.1365 | 0.2003 | -0.1363 | -#> |.....................| -0.2276 | -0.05501 | -0.2526 | -0.4116 | -#> | 96| 466.62386 | 1.010 | -1.856 | -0.9811 | -0.8057 | -#> |.....................| -0.8267 | -1.365 | 1.862 | -1.788 | -#> |.....................| -0.9479 | -0.4822 | -0.9526 | -1.114 | -#> |.....................| -0.5610 | -0.8741 | -1.003 | -0.3093 | -#> | U| 466.62386 | 92.43 | -6.045 | -0.9497 | -2.057 | -#> |.....................| -4.469 | 0.2377 | 1.961 | 0.03183 | -#> |.....................| 0.7980 | 0.06950 | 0.6749 | 0.6820 | -#> |.....................| 1.549 | 0.9614 | 0.7483 | 1.895 | -#> | X| 466.62386 | 92.43 | 0.002370 | 0.2789 | 0.1278 | -#> |.....................| 0.01146 | 0.5592 | 1.961 | 0.03183 | -#> |.....................| 0.7980 | 0.06950 | 0.6749 | 0.6820 | -#> |.....................| 1.549 | 0.9614 | 0.7483 | 1.895 | -#> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); +#> cmt(A1); +#> rx_expr_6~ETA[1]+THETA[1]; +#> parent(0)=rx_expr_6; +#> rx_expr_7~ETA[4]+THETA[4]; +#> rx_expr_8~ETA[6]+THETA[6]; +#> rx_expr_9~ETA[5]+THETA[5]; +#> rx_expr_12~exp(rx_expr_7); +#> rx_expr_13~exp(rx_expr_9); +#> rx_expr_15~t*rx_expr_12; +#> rx_expr_16~t*rx_expr_13; +#> rx_expr_17~exp(-(rx_expr_8)); +#> rx_expr_19~1+rx_expr_17; +#> rx_expr_24~1/(rx_expr_19); +#> rx_expr_26~(rx_expr_24); +#> rx_expr_27~1-rx_expr_26; +#> d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27)); +#> rx_expr_10~ETA[2]+THETA[2]; +#> rx_expr_14~exp(rx_expr_10); +#> d/dt(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27)); +#> rx_expr_0~CMT==2; +#> rx_expr_1~CMT==1; +#> rx_expr_2~1-(rx_expr_0); +#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0); +#> rx_expr_3~(rx_expr_0); +#> rx_expr_5~(rx_expr_2); +#> rx_expr_18~rx_expr_5*(rx_expr_1); +#> rx_lambda_~rx_expr_18+rx_expr_3; +#> rx_hi_~rx_expr_18+rx_expr_3; +#> rx_low_~0; +#> rx_expr_4~A1*(rx_expr_0); +#> rx_expr_11~parent*(rx_expr_2); +#> rx_expr_22~rx_expr_11*(rx_expr_1); +#> rx_pred_=(rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1); +#> rx_r_=(rx_expr_0)*(Rx_pow_di(((rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1)),2)*Rx_pow_di(THETA[10],2)+Rx_pow_di(THETA[9],2))+(Rx_pow_di(THETA[8],2)*Rx_pow_di(((rx_expr_4+rx_expr_22)*(rx_expr_1)),2)+Rx_pow_di(THETA[7],2))*(rx_expr_2)*(rx_expr_1); +#> parent_0=THETA[1]; +#> log_k_A1=THETA[2]; +#> f_parent_qlogis=THETA[3]; +#> log_k1=THETA[4]; +#> log_k2=THETA[5]; +#> g_qlogis=THETA[6]; +#> sigma_low_parent=THETA[7]; +#> rsd_high_parent=THETA[8]; +#> sigma_low_A1=THETA[9]; +#> rsd_high_A1=THETA[10]; +#> eta.parent_0=ETA[1]; +#> eta.log_k_A1=ETA[2]; +#> eta.f_parent_qlogis=ETA[3]; +#> eta.log_k1=ETA[4]; +#> eta.log_k2=ETA[5]; +#> eta.g_qlogis=ETA[6]; +#> parent_0_model=rx_expr_6; +#> k_A1=rx_expr_14; +#> k1=rx_expr_12; +#> k2=rx_expr_13; +#> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); +#> g=1/(rx_expr_19); +#> tad=tad(); +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 17.73 0.411 18.14
                                                                AIC( f_nlmixr_sfo_sfo_focei_const$nm, f_nlmixr_fomc_sfo_focei_const$nm, @@ -13656,110 +4965,11 @@ obtained by fitting the same model to a list of datasets using f_nlmixr_dfop_sfo_saem_obs_tc$nm, f_nlmixr_dfop_sfo_focei_obs_tc$nm ) -
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #>
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #>
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #>
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #>
                                                                #> df AIC -#> f_nlmixr_sfo_sfo_focei_const$nm 9 1082.4868 -#> f_nlmixr_fomc_sfo_focei_const$nm 11 814.4317 -#> f_nlmixr_dfop_sfo_focei_const$nm 13 866.0485 -#> f_nlmixr_fomc_sfo_saem_obs$nm 12 791.7256 -#> f_nlmixr_fomc_sfo_focei_obs$nm 12 794.5998 -#> f_nlmixr_dfop_sfo_saem_obs$nm 14 812.0463 -#> f_nlmixr_dfop_sfo_focei_obs$nm 14 846.9228 -#> f_nlmixr_fomc_sfo_focei_tc$nm 12 812.3585 -#> f_nlmixr_dfop_sfo_focei_tc$nm 14 842.3479 -#> f_nlmixr_fomc_sfo_saem_obs_tc$nm 14 817.1261 -#> f_nlmixr_fomc_sfo_focei_obs_tc$nm 14 787.4863 -#> f_nlmixr_dfop_sfo_saem_obs_tc$nm 16 858.3213 -#> f_nlmixr_dfop_sfo_focei_obs_tc$nm 16 811.0630
                                                                # Currently, FOMC-SFO with two-component error by variable fitted by focei gives the +
                                                                #> Error in AIC(f_nlmixr_sfo_sfo_focei_const$nm, f_nlmixr_fomc_sfo_focei_const$nm, f_nlmixr_dfop_sfo_focei_const$nm, f_nlmixr_fomc_sfo_saem_obs$nm, f_nlmixr_fomc_sfo_focei_obs$nm, f_nlmixr_dfop_sfo_saem_obs$nm, f_nlmixr_dfop_sfo_focei_obs$nm, f_nlmixr_fomc_sfo_focei_tc$nm, f_nlmixr_dfop_sfo_focei_tc$nm, f_nlmixr_fomc_sfo_saem_obs_tc$nm, f_nlmixr_fomc_sfo_focei_obs_tc$nm, f_nlmixr_dfop_sfo_saem_obs_tc$nm, f_nlmixr_dfop_sfo_focei_obs_tc$nm): object 'f_nlmixr_sfo_sfo_focei_const' not found
                                                                # Currently, FOMC-SFO with two-component error by variable fitted by focei gives the # lowest AIC plot(f_nlmixr_fomc_sfo_focei_obs_tc) -
                                                                summary(f_nlmixr_fomc_sfo_focei_obs_tc) -
                                                                #> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.0.5 -#> R version used for fitting: 4.1.0 -#> Date of fit: Fri Jun 11 10:54:54 2021 -#> Date of summary: Fri Jun 11 10:56:12 2021 -#> -#> Equations: -#> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent -#> d_A1/dt = + f_parent_to_A1 * (alpha/beta) * 1/((time/beta) + 1) * -#> parent - k_A1 * A1 -#> -#> Data: -#> 170 observations of 2 variable(s) grouped in 5 datasets -#> -#> Degradation model predictions using RxODE -#> -#> Fitted in 23.28 s -#> -#> Variance model: Two-component variance unique to each observed variable -#> -#> Mean of starting values for individual parameters: -#> parent_0 log_k_A1 f_parent_qlogis log_alpha log_beta -#> 93.1168 -5.3034 -0.9442 -0.1065 2.2909 -#> -#> Mean of starting values for error model parameters: -#> sigma_low_parent rsd_high_parent sigma_low_A1 rsd_high_A1 -#> 1.15958 0.03005 1.15958 0.03005 -#> -#> Fixed degradation parameter values: -#> None -#> -#> Results: -#> -#> Likelihood calculated by focei -#> AIC BIC logLik -#> 787.5 831.4 -379.7 -#> -#> Optimised parameters: -#> est. lower upper -#> parent_0 93.6898 91.2681 96.1114 -#> log_k_A1 -6.2923 -8.3662 -4.2185 -#> f_parent_qlogis -1.0019 -1.3760 -0.6278 -#> log_alpha -0.1639 -0.6641 0.3363 -#> log_beta 2.2031 1.6723 2.7340 -#> -#> Correlation: -#> prnt_0 lg__A1 f_prn_ lg_lph -#> log_k_A1 0.368 -#> f_parent_qlogis -0.788 -0.401 -#> log_alpha 0.338 0.942 -0.307 -#> log_beta -0.401 -0.761 0.253 -0.555 -#> -#> Random effects (omega): -#> eta.parent_0 eta.log_k_A1 eta.f_parent_qlogis eta.log_alpha -#> eta.parent_0 4.74 0.00 0.0000 0.0000 -#> eta.log_k_A1 0.00 5.57 0.0000 0.0000 -#> eta.f_parent_qlogis 0.00 0.00 0.1646 0.0000 -#> eta.log_alpha 0.00 0.00 0.0000 0.3312 -#> eta.log_beta 0.00 0.00 0.0000 0.0000 -#> eta.log_beta -#> eta.parent_0 0.0000 -#> eta.log_k_A1 0.0000 -#> eta.f_parent_qlogis 0.0000 -#> eta.log_alpha 0.0000 -#> eta.log_beta 0.3438 -#> -#> Variance model: -#> sigma_low_parent rsd_high_parent sigma_low_A1 rsd_high_A1 -#> 2.35467 0.00261 0.64525 0.08456 -#> -#> Backtransformed parameters: -#> est. lower upper -#> parent_0 93.68976 9.127e+01 96.11140 -#> k_A1 0.00185 2.326e-04 0.01472 -#> f_parent_to_A1 0.26857 2.017e-01 0.34801 -#> alpha 0.84879 5.147e-01 1.39971 -#> beta 9.05342 5.325e+00 15.39359 -#> -#> Resulting formation fractions: -#> ff -#> parent_A1 0.2686 -#> parent_sink 0.7314 -#> -#> Estimated disappearance times: -#> DT50 DT90 DT50back -#> parent 11.43 127.4 38.35 -#> A1 374.59 1244.4 NA
                                                                # } +
                                                                #> Error in plot(f_nlmixr_fomc_sfo_focei_obs_tc): object 'f_nlmixr_fomc_sfo_focei_obs_tc' not found
                                                                summary(f_nlmixr_fomc_sfo_focei_obs_tc) +
                                                                #> Error in summary(f_nlmixr_fomc_sfo_focei_obs_tc): object 'f_nlmixr_fomc_sfo_focei_obs_tc' not found
                                                                # }
                                                                diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 3896120a..c0338df8 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 @@ -161,6 +161,8 @@
                                                                Performance benefit by using compiled model definitions in mkin
                                                                +
                                                                Example evaluations of the dimethenamid data from 2018
                                                                +
                                                                diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html new file mode 100644 index 00000000..7648f75a --- /dev/null +++ b/docs/dev/articles/web_only/dimethenamid_2018.html @@ -0,0 +1,420 @@ + + + + + + + +Example evaluations of the dimethenamid data from 2018 • mkin + + + + + + + + + + + +
                                                                +
                                                                + + + + +
                                                                +
                                                                +
                                                                +

                                                                Example evaluations of the dimethenamid data from 2018

                                                                +

                                                                Johannes Ranke

                                                                + +

                                                                Last change 27 July 2021, built on 27 Jul 2021

                                                                + + Source: vignettes/web_only/dimethenamid_2018.rmd + + +
                                                                + + + +

                                                                Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany
                                                                Privatdozent at the University of Bremen

                                                                +
                                                                +

                                                                +Introduction

                                                                +

                                                                During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics (submitted) and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified.

                                                                +

                                                                This vignette is an attempt to satisfy this need.

                                                                +
                                                                +
                                                                +

                                                                +Data

                                                                +

                                                                Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 (EFSA 2018) were transcribed from the risk assessment report (Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018) which can be downloaded from the EFSA register of questions.

                                                                +

                                                                The data are available in the mkin package. The following code (hidden by default, please use the button to the right to show it) treats the data available for the racemic mixture dimethenamid (DMTA) and its enantiomer dimethenamid-P (DMTAP) in the same way, as no difference between their degradation behaviour was identified in the EU risk assessment. The observation times of each dataset are multiplied with the corresponding normalisation factor also available in the dataset, in order to make it possible to describe all datasets with a single set of parameters.

                                                                +

                                                                Also, datasets observed in the same soil are merged, resulting in dimethenamid (DMTA) data from six soils.

                                                                +
                                                                +library(mkin)
+dmta_ds <- lapply(1:8, function(i) {
+  ds_i <- dimethenamid_2018$ds[[i]]$data
+  ds_i[ds_i$name == "DMTAP", "name"] <-  "DMTA"
+  ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
+  ds_i
+})
+names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
+dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]])
+dmta_ds[["Borstel 1"]] <- NULL
+dmta_ds[["Borstel 2"]] <- NULL
+dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
+dmta_ds[["Elliot 1"]] <- NULL
+dmta_ds[["Elliot 2"]] <- NULL
                                                                +
                                                                +
                                                                +

                                                                +Parent degradation

                                                                +

                                                                We evaluate the observed degradation of the parent compound using simple exponential decline (SFO) and biexponential decline (DFOP), using constant variance (const) and a two-component variance (tc) as error models.

                                                                +
                                                                +

                                                                +Separate evaluations

                                                                +

                                                                As a first step, to get a visual impression of the fit of the different models, we do separate evaluations for each soil using the mmkin function from the mkin package:

                                                                +
                                                                +f_parent_mkin_const <- mmkin(c("SFO", "DFOP"), dmta_ds,
+  error_model = "const", quiet = TRUE)
+f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds,
+  error_model = "tc", quiet = TRUE)
                                                                +

                                                                The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):

                                                                +
                                                                +plot(mixed(f_parent_mkin_const["SFO", ]))
                                                                +

                                                                +

                                                                Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:

                                                                +
                                                                +plot(mixed(f_parent_mkin_const["DFOP", ]))
                                                                +

                                                                +

                                                                The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:

                                                                +
                                                                +plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)
                                                                +

                                                                +

                                                                While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).

                                                                +

                                                                The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:

                                                                +
                                                                +plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)
                                                                +

                                                                +
                                                                +
                                                                +

                                                                +Nonlinear mixed-effects models

                                                                +

                                                                Instead of taking a model selection decision for each of the individual fits, we fit nonlinear mixed-effects models (using different fitting algorithms as implemented in different packages) and do model selection using all available data at the same time. In order to make sure that these decisions are not unduly influenced by the type of algorithm used, by implementation details or by the use of wrong control parameters, we compare the model selection results obtained with different R packages, with different algorithms and checking control parameters.

                                                                +
                                                                +

                                                                +nlme

                                                                +

                                                                The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We use would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. However, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.

                                                                +
                                                                +library(nlme)
+f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ])
+#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ])
+# maxIter = 50 reached
+f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ])
+f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ])
                                                                +

                                                                Note that overparameterisation is also indicated by warnings obtained when fitting SFO or DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in some iterations). In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.

                                                                +
                                                                +f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ],
+  random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1)))
+anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better
+#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc,
+#  random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)))
+# using log Cholesky parameterisation for random effects (nlme default) does
+# not converge here and gives lots of warnings about the LME step not converging
                                                                +

                                                                The model comparison function of the nlme package can directly be applied to these fits showing a similar goodness-of-fit of the SFO model, but a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant. as the p-value is below 0.0001.

                                                                +
                                                                +anova(
+  f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
+)
                                                                +
                                                                                        Model df    AIC    BIC  logLik   Test L.Ratio p-value
+f_parent_nlme_sfo_const     1  5 818.63 834.00 -404.31                       
+f_parent_nlme_sfo_tc        2  6 820.61 839.06 -404.31 1 vs 2   0.014  0.9049
+f_parent_nlme_dfop_tc       3 10 687.84 718.59 -333.92 2 vs 3 140.771  <.0001
                                                                +

                                                                The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.

                                                                +
                                                                +plot(f_parent_nlme_dfop_tc)
                                                                +

                                                                +
                                                                +
                                                                +

                                                                +saemix

                                                                +

                                                                The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be performed using an interface to the saemix package available in current development versions of the mkin package.

                                                                +

                                                                The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit.

                                                                +

                                                                The convergence plot for the SFO model using constant variance is shown below.

                                                                +
                                                                +library(saemix)
+f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
+  transformations = "saemix")
+plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")
                                                                +

                                                                +

                                                                Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.

                                                                +
                                                                +f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
+  transformations = "saemix")
+plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")
                                                                +

                                                                +

                                                                When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous (see the failure of nlme with the default number of iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.

                                                                +
                                                                +f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
+  control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE,
+    save = FALSE, save.graphs = FALSE, displayProgress = FALSE),
+  transformations = "saemix")
+plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")
                                                                +

                                                                +

                                                                The same applies to the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced by using the two-component error, it remains more or less stable already after 200 iterations of the first phase.

                                                                +
                                                                +f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
+  control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE,
+    save = FALSE, save.graphs = FALSE, displayProgress = FALSE),
+  transformations = "saemix")
+plot(f_parent_saemix_dfop_tc_moreiter$so, plot.type = "convergence")
                                                                +

                                                                +

                                                                The four combinations can be compared using the model comparison function from the saemix package:

                                                                +
                                                                +compare.saemix(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
+  f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so)
                                                                +
                                                                Likelihoods calculated by importance sampling
                                                                +
                                                                     AIC    BIC
+1 818.37 817.33
+2 820.38 819.14
+3 725.91 724.04
+4 683.64 681.55
                                                                +

                                                                As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. The numeric values are reasonably close to the ones obtained using nlme, considering that the algorithms for fitting the model and for the likelihood calculation are quite different.

                                                                +

                                                                In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.

                                                                +
                                                                +f_parent_saemix_dfop_tc_moreiter$so <-
+  llgq.saemix(f_parent_saemix_dfop_tc_moreiter$so)
+AIC(f_parent_saemix_dfop_tc_moreiter$so)
                                                                +
                                                                [1] 683.64
                                                                +
                                                                +AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "gq")
                                                                +
                                                                [1] 683.7
                                                                +
                                                                +AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "lin")
                                                                +
                                                                [1] 683.17
                                                                +

                                                                The AIC values based on importance sampling and Gaussian quadrature are quite similar. Using linearisation is less accurate, but still gives a similar value.

                                                                +
                                                                +
                                                                +

                                                                +nlmixr

                                                                +

                                                                In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.

                                                                +

                                                                First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared.

                                                                +
                                                                +library(nlmixr)
+f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei")
+f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei")
+f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei")
+f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei")
                                                                +
                                                                +AIC(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm,
+  f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm)
                                                                +
                                                                                                    df    AIC
+f_parent_nlmixr_focei_sfo_const$nm   5 818.63
+f_parent_nlmixr_focei_sfo_tc$nm      6 820.61
+f_parent_nlmixr_focei_dfop_const$nm  9 728.11
+f_parent_nlmixr_focei_dfop_tc$nm    10 687.82
                                                                +

                                                                The AIC values are very close to the ones obtained with nlme which are repeated below for convenience.

                                                                +
                                                                +AIC(
+  f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
+)
                                                                +
                                                                                        df    AIC
+f_parent_nlme_sfo_const  5 818.63
+f_parent_nlme_sfo_tc     6 820.61
+f_parent_nlme_dfop_tc   10 687.84
                                                                +

                                                                Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance

                                                                +
                                                                +f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem",
+  control = nlmixr::saemControl(logLik = TRUE))
+traceplot(f_parent_nlmixr_saem_sfo_const$nm)
                                                                +

                                                                +

                                                                for SFO with two-component error

                                                                +
                                                                +f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem",
+  control = nlmixr::saemControl(logLik = TRUE))
+traceplot(f_parent_nlmixr_saem_sfo_tc$nm)
                                                                +

                                                                +

                                                                For DFOP with constant variance, the convergence plots show considerable instability of the fit, which can be alleviated by increasing the number of iterations and the number of parallel chains for the first phase of algorithm.

                                                                +
                                                                +f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem",
+  control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15)
+traceplot(f_parent_nlmixr_saem_dfop_const$nm)
                                                                +

                                                                +

                                                                For DFOP with two-component error, the same increase in iterations and parallel chains was used, but using the two-component error appears to lead to a less erratic convergence, so this may not be necessary to this degree.

                                                                +
                                                                +f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem",
+  control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15))
+traceplot(f_parent_nlmixr_saem_dfop_tc$nm)
                                                                +

                                                                +

                                                                The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using the two-component error model is given as Infinity.

                                                                +
                                                                +AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
+  f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm)
                                                                +
                                                                                                   df    AIC
+f_parent_nlmixr_saem_sfo_const$nm   5 820.54
+f_parent_nlmixr_saem_sfo_tc$nm      6 835.26
+f_parent_nlmixr_saem_dfop_const$nm  9 842.84
+f_parent_nlmixr_saem_dfop_tc$nm    10 684.51
                                                                +

                                                                The following table gives the AIC values obtained with the three packages.

                                                                +
                                                                +AIC_all <- data.frame(
+  nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),
+  nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm,
+  f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC),
+  saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
+    f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so), AIC),
+  nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
+  f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC)
+)
+kable(AIC_all)
                                                                + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                nlmenlmixr_foceisaemixnlmixr_saem
                                                                818.63818.63818.37820.54
                                                                820.61820.61820.38835.26
                                                                NA728.11725.91842.84
                                                                687.84687.82683.64684.51
                                                                +
                                                                +
                                                                +
                                                                +
                                                                +

                                                                +References

                                                                + +
                                                                +
                                                                +

                                                                EFSA. 2018. “Peer Review of the Pesticide Risk Assessment of the Active Substance Dimethenamid-P.” EFSA Journal 16 (4): 5211.

                                                                +
                                                                +
                                                                +

                                                                Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria. 2018. “Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - November 2017.” https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716.

                                                                +
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                                                                +

                                                                Developed by Johannes Ranke.

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                                                                +

                                                                Site built with pkgdown 1.6.1.

                                                                +
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We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/authors.html b/docs/dev/authors.html index 4208dc24..943cba1b 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/index.html b/docs/dev/index.html index 6e3fa6e1..8049b3a1 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -38,7 +38,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index 234ba02f..cfe577cf 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index b2c50e79..16f7f0d6 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,8 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-06-17T12:41Z + web_only/dimethenamid_2018: dimethenamid_2018.html +last_built: 2021-07-27T15:54Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png index 55aa7eec..92c7cc2d 100644 Binary files a/docs/dev/reference/Rplot005.png and b/docs/dev/reference/Rplot005.png differ diff --git a/docs/dev/reference/dimethenamid_2018-2.png b/docs/dev/reference/dimethenamid_2018-2.png new file mode 100644 index 00000000..a81b2aaf Binary files /dev/null and b/docs/dev/reference/dimethenamid_2018-2.png differ diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index e255765e..160dcaa3 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> mkin - 1.0.5 + 1.1.0 @@ -168,7 +168,7 @@ constrained by data protection regulations.

                                                                Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour Rev. 2 - November 2017 -http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211

                                                                +https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716

                                                                Details

                                                                The R code used to create this data object is installed with this package @@ -295,8 +295,11 @@ specific pieces of information in the comments.

                                                                #> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) #> }) #> } -#> <environment: 0x555559c2bd78>
                                                                f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", - control = nlmixr::foceiControl(print = 500)) +#> <environment: 0x555559c00ce8>
                                                                # The focei fit takes about four minutes on my system +system.time( + f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", + control = nlmixr::foceiControl(print = 500)) +)
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:02 #>
                                                                #> → calculate sensitivities
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:04 #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:01 @@ -320,12 +323,13 @@ specific pieces of information in the comments.

                                                                #> |.....................| o5 | o6 | o7 | o8 | #> |.....................| o9 | o10 |...........|...........| #> calculating covariance matrix -#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: S matrix non-positive definite
                                                                #> Warning: using R matrix to calculate covariance
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                summary(f_dmta_nlmixr_focei) +#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: S matrix non-positive definite
                                                                #> Warning: using R matrix to calculate covariance
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                #> user system elapsed +#> 227.879 9.742 237.728
                                                                summary(f_dmta_nlmixr_focei)
                                                                #> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.0.5 +#> mkin version used for pre-fitting: 1.1.0 #> R version used for fitting: 4.1.0 -#> Date of fit: Thu Jun 17 14:04:58 2021 -#> Date of summary: Thu Jun 17 14:04:58 2021 +#> Date of fit: Tue Jul 27 16:02:33 2021 +#> Date of summary: Tue Jul 27 16:02:34 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -346,7 +350,7 @@ specific pieces of information in the comments.

                                                                #> #> Degradation model predictions using RxODE #> -#> Fitted in 242.937 s +#> Fitted in 237.547 s #> #> Variance model: Two-component variance function #> @@ -480,13 +484,194 @@ specific pieces of information in the comments.

                                                                #> M23 34.99 116.24 NA NA NA #> M27 53.05 176.23 NA NA NA #> M31 48.48 161.05 NA NA NA
                                                                plot(f_dmta_nlmixr_focei) -
                                                                # saem has a problem with this model/data combination, maybe because of the -# overparameterised error model, to be investigated -#f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", -# control = saemControl(print = 500)) -#summary(f_dmta_nlmixr_saem) -#plot(f_dmta_nlmixr_saem) -# } +
                                                                # Using saemix takes about 18 minutes +system.time( + f_dmta_saemix <- saem(f_dmta_mkin_tc, test_log_parms = TRUE) +) +
                                                                #> Running main SAEM algorithm +#> [1] "Tue Jul 27 16:02:34 2021" +#> .... +#> Minimisation finished +#> [1] "Tue Jul 27 16:21:39 2021"
                                                                #> user system elapsed +#> 1213.394 0.087 1213.578
                                                                +# nlmixr with est = "saem" is pretty fast with default iteration numbers, most +# of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end +# The likelihood calculated for the nlmixr fit is much lower than that found by saemix +# Also, the trace plot and the plot of the individual predictions is not +# convincing for the parent. It seems we are fitting an overparameterised +# model, so the result we get strongly depends on starting parameters and control settings. +system.time( + f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", + control = nlmixr::saemControl(print = 500, logLik = TRUE, nmc = 9)) +) +
                                                                #> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1: 98.3427 -3.5148 -3.3187 -3.7728 -2.1163 -2.8457 0.9482 -2.8064 -2.7412 -2.8745 2.7912 0.6805 0.8213 0.8055 0.8578 1.4980 2.9309 0.2850 0.2854 0.2850 4.0990 0.3821 3.5349 0.6537 5.4143 0.0002 4.5093 0.1905 +#> 500: 97.8277 -4.3506 -4.0318 -4.1520 -3.0553 -3.5843 1.1326 -2.0873 -2.0421 -2.0751 0.2960 1.2515 0.2531 0.3807 0.7928 0.8863 6.5211 0.1433 0.1082 0.3353 0.8960 0.0470 0.7501 0.0475 0.9527 0.0281 0.7321 0.0594
                                                                #> Calculating covariance matrix
                                                                #>
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #>
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> user system elapsed +#> 818.782 3.808 154.926
                                                                traceplot(f_dmta_nlmixr_saem$nm) +
                                                                #> Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"
                                                                summary(f_dmta_nlmixr_saem) +
                                                                #> nlmixr version used for fitting: 2.0.4 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.0 +#> Date of fit: Tue Jul 27 16:25:23 2021 +#> Date of summary: Tue Jul 27 16:25:23 2021 +#> +#> Equations: +#> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) +#> * DMTA +#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 +#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M31 * M31 +#> +#> Data: +#> 568 observations of 4 variable(s) grouped in 6 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 154.632 s +#> +#> Variance model: Two-component variance function +#> +#> Mean of starting values for individual parameters: +#> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 +#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis +#> -1.7571 -2.2341 -3.7763 0.4502 +#> +#> Mean of starting values for error model parameters: +#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 +#> 0.69793 0.02577 0.69793 0.02577 0.69793 +#> rsd_high_M27 sigma_low_M31 rsd_high_M31 +#> 0.02577 0.69793 0.02577 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 2036 2157 -989.8 +#> +#> Optimised parameters: +#> est. lower upper +#> DMTA_0 97.828 96.121 99.535 +#> log_k_M23 -4.351 -5.300 -3.401 +#> log_k_M27 -4.032 -4.470 -3.594 +#> log_k_M31 -4.152 -4.689 -3.615 +#> log_k1 -3.055 -3.785 -2.325 +#> log_k2 -3.584 -4.517 -2.651 +#> g_qlogis 1.133 -2.165 4.430 +#> f_DMTA_tffm0_1_qlogis -2.087 -2.407 -1.768 +#> f_DMTA_tffm0_2_qlogis -2.042 -2.336 -1.748 +#> f_DMTA_tffm0_3_qlogis -2.075 -2.557 -1.593 +#> +#> Correlation: +#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs +#> log_k_M23 -0.031 +#> log_k_M27 -0.050 0.004 +#> log_k_M31 -0.032 0.003 0.078 +#> log_k1 0.014 -0.002 -0.002 -0.001 +#> log_k2 0.059 0.006 -0.001 0.002 -0.037 +#> g_qlogis -0.077 0.005 0.009 0.004 0.035 -0.201 +#> f_DMTA_tffm0_1_qlogis -0.104 0.066 0.009 0.006 0.000 -0.011 0.014 +#> f_DMTA_tffm0_2_qlogis -0.120 0.013 0.081 -0.033 -0.002 -0.013 0.017 +#> f_DMTA_tffm0_3_qlogis -0.086 0.010 0.060 0.078 -0.002 -0.005 0.010 +#> f_DMTA_0_1 f_DMTA_0_2 +#> log_k_M23 +#> log_k_M27 +#> log_k_M31 +#> log_k1 +#> log_k2 +#> g_qlogis +#> f_DMTA_tffm0_1_qlogis +#> f_DMTA_tffm0_2_qlogis 0.026 +#> f_DMTA_tffm0_3_qlogis 0.019 0.002 +#> +#> Random effects (omega): +#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 +#> eta.DMTA_0 0.296 0.000 0.0000 0.0000 +#> eta.log_k_M23 0.000 1.252 0.0000 0.0000 +#> eta.log_k_M27 0.000 0.000 0.2531 0.0000 +#> eta.log_k_M31 0.000 0.000 0.0000 0.3807 +#> eta.log_k1 0.000 0.000 0.0000 0.0000 +#> eta.log_k2 0.000 0.000 0.0000 0.0000 +#> eta.g_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.log_k1 eta.log_k2 eta.g_qlogis +#> eta.DMTA_0 0.0000 0.0000 0.000 +#> eta.log_k_M23 0.0000 0.0000 0.000 +#> eta.log_k_M27 0.0000 0.0000 0.000 +#> eta.log_k_M31 0.0000 0.0000 0.000 +#> eta.log_k1 0.7928 0.0000 0.000 +#> eta.log_k2 0.0000 0.8863 0.000 +#> eta.g_qlogis 0.0000 0.0000 6.521 +#> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis +#> eta.DMTA_0 0.0000 0.0000 +#> eta.log_k_M23 0.0000 0.0000 +#> eta.log_k_M27 0.0000 0.0000 +#> eta.log_k_M31 0.0000 0.0000 +#> eta.log_k1 0.0000 0.0000 +#> eta.log_k2 0.0000 0.0000 +#> eta.g_qlogis 0.0000 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.1433 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1082 +#> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis +#> eta.DMTA_0 0.0000 +#> eta.log_k_M23 0.0000 +#> eta.log_k_M27 0.0000 +#> eta.log_k_M31 0.0000 +#> eta.log_k1 0.0000 +#> eta.log_k2 0.0000 +#> eta.g_qlogis 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.3353 +#> +#> Variance model: +#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 +#> 0.89603 0.04704 0.75015 0.04753 0.95265 +#> rsd_high_M27 sigma_low_M31 rsd_high_M31 +#> 0.02810 0.73212 0.05942 +#> +#> Backtransformed parameters: +#> est. lower upper +#> DMTA_0 97.82774 96.120503 99.53498 +#> k_M23 0.01290 0.004991 0.03334 +#> k_M27 0.01774 0.011451 0.02749 +#> k_M31 0.01573 0.009195 0.02692 +#> f_DMTA_to_M23 0.11033 NA NA +#> f_DMTA_to_M27 0.10218 NA NA +#> f_DMTA_to_M31 0.08784 NA NA +#> k1 0.04711 0.022707 0.09773 +#> k2 0.02775 0.010918 0.07056 +#> g 0.75632 0.102960 0.98823 +#> +#> Resulting formation fractions: +#> ff +#> DMTA_M23 0.11033 +#> DMTA_M27 0.10218 +#> DMTA_M31 0.08784 +#> DMTA_sink 0.69965 +#> +#> Estimated disappearance times: +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> DMTA 16.59 57.44 17.29 14.71 24.97 +#> M23 53.74 178.51 NA NA NA +#> M27 39.07 129.78 NA NA NA +#> M31 44.06 146.36 NA NA NA
                                                                plot(f_dmta_nlmixr_saem) +
                                                                # }
                                                                diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index bb030605..d5ec387a 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html index f63dbc31..5981c625 100644 --- a/docs/dev/reference/mean_degparms.html +++ b/docs/dev/reference/mean_degparms.html @@ -72,7 +72,7 @@ mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html index 413f0ae1..e57e7062 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -44,9 +44,7 @@ variable, specifying the corresponding submodel as well as outgoing pathways (see examples). Print mkinmod objects in a way that the user finds his way to get to its -components. -This is a convenience function to set up the lists used as arguments for -mkinmod." /> +components." /> @@ -78,7 +76,7 @@ mkinmod." /> mkin - 1.0.3.9000 + 1.1.0 @@ -155,8 +153,6 @@ variable, specifying the corresponding submodel as well as outgoing pathways (see examples).

                                                                Print mkinmod objects in a way that the user finds his way to get to its components.

                                                                -

                                                                This is a convenience function to set up the lists used as arguments for -mkinmod.

                                                                mkinmod(
@@ -348,7 +344,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media
                                                                
    parent = mkinsub("SFO", "m1", full_name = "Test compound"),
    m1 = mkinsub("SFO", full_name = "Metabolite M1"),
    name = "SFO_SFO", dll_dir = DLL_dir, unload = TRUE, overwrite = TRUE)
-
                                                                #> Copied DLL from /tmp/Rtmp92fCb2/file133ad522561845.so to /home/jranke/.local/share/mkin/SFO_SFO.so
                                                                # Now we can save the model and restore it in a new session
+
                                                                #> Copied DLL from /tmp/RtmpPWWdbW/fileccff46a6d9773.so to /home/jranke/.local/share/mkin/SFO_SFO.so
                                                                # Now we can save the model and restore it in a new session
 saveRDS(SFO_SFO.2, file = "~/SFO_SFO.rds")
 # Terminate the R session here if you would like to check, and then do
 library(mkin)
@@ -397,7 +393,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media
                                                                
 #>     })
 #>     return(predicted)
 #> }
-#> <environment: 0x5555572517f8>
                                                                
+#> <environment: 0x5555645abab8>
                                                                
 # If we have several parallel metabolites
 # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)
 m_synth_DFOP_par <- mkinmod(
diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png
index 365aaef0..f739089a 100644
Binary files a/docs/dev/reference/nlme-1.png and b/docs/dev/reference/nlme-1.png differ
diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png
index 40841404..d3b29bb0 100644
Binary files a/docs/dev/reference/nlme-2.png and b/docs/dev/reference/nlme-2.png differ
diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html
index 55a94443..184585df 100644
--- a/docs/dev/reference/nlme.html
+++ b/docs/dev/reference/nlme.html
@@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." />
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
                                                                
 
@@ -216,28 +216,28 @@ datasets. They are used internally by the nlme.m
 #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) 
 #>   Data: grouped_data 
 #>        AIC      BIC    logLik
-#>   300.6824 310.2426 -145.3412
+#>   278.1355 287.7946 -134.0677
 #> 
 #> Random effects:
 #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
 #>         parent_0 log_k_parent_sink Residual
-#> StdDev: 1.697361         0.6801209 3.666073
+#> StdDev: 3.406042         0.6437579 2.620833
 #> 
 #> Fixed effects:  parent_0 + log_k_parent_sink ~ 1 
 #>                       Value Std.Error DF  t-value p-value
-#> parent_0          100.99378 1.3890416 46 72.70753       0
-#> log_k_parent_sink  -3.07521 0.4018589 46 -7.65246       0
+#> parent_0          101.50173  2.123709 47 47.79457       0
+#> log_k_parent_sink  -3.07597  0.379775 47 -8.09945       0
 #>  Correlation: 
 #>                   prnt_0
-#> log_k_parent_sink 0.027 
+#> log_k_parent_sink 0.01  
 #> 
 #> Standardized Within-Group Residuals:
-#>        Min         Q1        Med         Q3        Max 
-#> -1.9942823 -0.5622565  0.1791579  0.7165038  2.0704781 
+#>         Min          Q1         Med          Q3         Max 
+#> -2.06889303 -0.50100169 -0.06268253  0.62557544  2.19922001 
 #> 
-#> Number of Observations: 50
+#> Number of Observations: 51
 #> Number of Groups: 3 
                                                                plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
 
                                                                # augPred does not work on fits with more than one state
 # variable
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html
index 2bbf4f80..866091ca 100644
--- a/docs/dev/reference/nlme.mmkin.html
+++ b/docs/dev/reference/nlme.mmkin.html
@@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." />
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
                                                                
 
@@ -194,10 +194,9 @@ mmkin model are used as fixed parameters
                                                                
     
     
       random
-      
                                                                If not specified, correlated random effects are set up
-for all optimised degradation model parameters using the log-Cholesky
-parameterization nlme::pdLogChol that is also the default of
-the generic nlme method.
                                                                
+      
                                                                If not specified, no correlations between random effects are
+set up for the optimised degradation model parameters. This is
+achieved by using the nlme::pdDiag method.
                                                                
     
     
       groups
diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html
index d09f2ad4..328bad43 100644
--- a/docs/dev/reference/nlmixr.mmkin.html
+++ b/docs/dev/reference/nlmixr.mmkin.html
@@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." />
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
 
@@ -4501,7 +4501,7 @@ obtained by fitting the same model to a list of datasets using k_A1=rx_expr_11;
 #> f_parent=1/(1+exp(-(ETA[4]+THETA[4])));
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 5.607 0.474 6.078
                                                                f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei")
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 5.548 0.415 5.961
                                                                f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei")
 
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4550,7 +4550,7 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8);
 #> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.853 0.393 7.242
                                                                f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei")
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.895 0.416 7.309
                                                                f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei")
 
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4607,10 +4607,10 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> g=1/(rx_expr_20);
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 15.18 0.414 15.6
                                                                
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 15.03 0.478 15.51
                                                                
 # Variance by variable is supported by 'saem' and 'focei'
 f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem")
-
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.22 0.089 1.31
                                                                f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.294 0.134 1.427
                                                                f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei")
 
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4659,8 +4659,8 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8);
 #> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.784 0.418 7.2
                                                                f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem")
-
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.357 0.096 1.452
                                                                f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei")
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.584 0.393 6.976
                                                                f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem")
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.302 0.142 1.443
                                                                f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei")
 
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4717,7 +4717,7 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> g=1/(rx_expr_19);
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 15.17 0.353 15.52
                                                                
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 14.58 0.482 15.06
                                                                
 # Identical two-component error for all variables is only possible with
 # est = 'focei' in nlmixr
 f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei")
@@ -4771,7 +4771,7 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8);
 #> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.708 0.429 9.135
                                                                f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei")
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.484 0.401 8.883
                                                                f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei")
 
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent);
 #> cmt(A1);
 #> rx_expr_6~ETA[1]+THETA[1];
@@ -4830,12 +4830,12 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> g=1/(rx_expr_21);
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 18.05 0.446 18.5
                                                                
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 18.44 0.438 18.87
                                                                
 # Two-component error by variable is possible with both estimation methods
 # Variance by variable is supported by 'saem' and 'focei'
 f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem",
   error_model = "obs_tc")
-
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.763 0.036 0.799
                                                                f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei",
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.784 0.028 0.812
                                                                f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei",
   error_model = "obs_tc")
 
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent);
 #> cmt(A1);
@@ -4887,9 +4887,9 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8);
 #> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.196 0.388 8.584
                                                                f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem",
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.157 0.51 8.664
                                                                f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem",
   error_model = "obs_tc")
-
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.843 0.028 0.871
                                                                f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei",
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.81 0.045 0.854
                                                                f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei",
   error_model = "obs_tc")
 
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent);
 #> cmt(A1);
@@ -4949,7 +4949,7 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
 #> g=1/(rx_expr_19);
 #> tad=tad();
-#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 17.73 0.411 18.14
                                                                
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 17.34 0.397 17.73
                                                                
 AIC(
   f_nlmixr_sfo_sfo_focei_const$nm,
   f_nlmixr_fomc_sfo_focei_const$nm,
diff --git a/docs/dev/reference/plot.mixed.mmkin-3.png b/docs/dev/reference/plot.mixed.mmkin-3.png
index a9b96726..7e2876b3 100644
Binary files a/docs/dev/reference/plot.mixed.mmkin-3.png and b/docs/dev/reference/plot.mixed.mmkin-3.png differ
diff --git a/docs/dev/reference/plot.mixed.mmkin-4.png b/docs/dev/reference/plot.mixed.mmkin-4.png
index 22219e5e..945c4d41 100644
Binary files a/docs/dev/reference/plot.mixed.mmkin-4.png and b/docs/dev/reference/plot.mixed.mmkin-4.png differ
diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html
index a4222991..7f3faa90 100644
--- a/docs/dev/reference/plot.mixed.mmkin.html
+++ b/docs/dev/reference/plot.mixed.mmkin.html
@@ -72,7 +72,7 @@
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
                                                                
 
@@ -296,10 +296,10 @@ corresponding model prediction lines for the different datasets.
                                                                
 
                                                                
 f_saem <- saem(f, transformations = "saemix")
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:37 2021"
+#> [1] "Tue Jul 27 16:30:50 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:56:44 2021"
                                                                plot(f_saem)
+#> [1] "Tue Jul 27 16:30:58 2021"
                                                                plot(f_saem)
 
                                                                
 f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs")
 f_nlmix <- nlmix(f_obs)
diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html
index f5ace044..ac4fa4d9 100644
--- a/docs/dev/reference/reexports.html
+++ b/docs/dev/reference/reexports.html
@@ -81,7 +81,7 @@ below to see their documentation.
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
                                                                
 
diff --git a/docs/dev/reference/saem-5.png b/docs/dev/reference/saem-5.png
index 8212ec67..d22e7285 100644
Binary files a/docs/dev/reference/saem-5.png and b/docs/dev/reference/saem-5.png differ
diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html
index 98faad6f..15271c8a 100644
--- a/docs/dev/reference/saem.html
+++ b/docs/dev/reference/saem.html
@@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." />
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
 
@@ -158,7 +158,7 @@ Expectation Maximisation algorithm (SAEM).
                                                                
   object,
   transformations = c("mkin", "saemix"),
   degparms_start = numeric(),
-  test_log_parms = FALSE,
+  test_log_parms = TRUE,
   conf.level = 0.6,
   solution_type = "auto",
   nbiter.saemix = c(300, 100),
@@ -288,27 +288,27 @@ using mmkin.
                                                                
   state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
 f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:49 2021"
+#> [1] "Tue Jul 27 16:31:02 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:56:51 2021"
                                                                
+#> [1] "Tue Jul 27 16:31:04 2021"
                                                                
 f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
 f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:53 2021"
+#> [1] "Tue Jul 27 16:31:06 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:56:54 2021"
                                                                f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
+#> [1] "Tue Jul 27 16:31:07 2021"
                                                                f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:54 2021"
+#> [1] "Tue Jul 27 16:31:07 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:56:57 2021"
                                                                f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
+#> [1] "Tue Jul 27 16:31:09 2021"
                                                                f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:56:57 2021"
+#> [1] "Tue Jul 27 16:31:10 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:00 2021"
                                                                
+#> [1] "Tue Jul 27 16:31:12 2021"
                                                                
 # The returned saem.mmkin object contains an SaemixObject, therefore we can use
 # functions from saemix
 library(saemix)
@@ -357,10 +357,10 @@ using mmkin.
                                                                
 f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
 f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:57:03 2021"
+#> [1] "Tue Jul 27 16:31:16 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:09 2021"
                                                                compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
+#> [1] "Tue Jul 27 16:31:20 2021"
                                                                compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
 
                                                                #> Likelihoods calculated by importance sampling
                                                                #>        AIC      BIC
 #> 1 467.7096 464.9757
 #> 2 469.6831 466.5586
                                                                
@@ -381,15 +381,15 @@ using mmkin.
                                                                
 # four minutes
 f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:57:12 2021"
+#> [1] "Tue Jul 27 16:31:24 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:17 2021"
                                                                f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
+#> [1] "Tue Jul 27 16:31:29 2021"
                                                                f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:57:17 2021"
+#> [1] "Tue Jul 27 16:31:30 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:26 2021"
                                                                # We can use print, plot and summary methods to check the results
+#> [1] "Tue Jul 27 16:31:38 2021"
                                                                # We can use print, plot and summary methods to check the results
 print(f_saem_dfop_sfo)
 
                                                                #> Kinetic nonlinear mixed-effects model fit by SAEM
 #> Structural model:
@@ -405,35 +405,35 @@ using mmkin.
                                                                
 #> 
 #> Likelihood computed by importance sampling
 #>     AIC   BIC logLik
-#>   841.6 836.5 -407.8
+#>   839.6 834.6 -406.8
 #> 
 #> Fitted parameters:
 #>                     estimate    lower   upper
-#> parent_0            93.76647 91.15312 96.3798
-#> log_k_A1            -6.13235 -8.45788 -3.8068
-#> f_parent_qlogis     -0.97364 -1.36940 -0.5779
-#> log_k1              -2.53176 -3.80372 -1.2598
-#> log_k2              -3.58667 -5.29524 -1.8781
-#> g_qlogis             0.01238 -1.07968  1.1044
-#> Var.parent_0         7.61106 -3.34955 18.5717
-#> Var.log_k_A1         4.64679 -2.73133 12.0249
-#> Var.f_parent_qlogis  0.19693 -0.05498  0.4488
-#> Var.log_k1           2.01717 -0.51980  4.5542
-#> Var.log_k2           3.63412 -0.92964  8.1979
-#> Var.g_qlogis         0.20045 -0.97425  1.3751
-#> a.1                  1.88335  1.66636  2.1004
-#> SD.parent_0          2.75881  0.77234  4.7453
-#> SD.log_k_A1          2.15564  0.44429  3.8670
-#> SD.f_parent_qlogis   0.44377  0.15994  0.7276
-#> SD.log_k1            1.42027  0.52714  2.3134
-#> SD.log_k2            1.90634  0.70934  3.1033
-#> SD.g_qlogis          0.44771 -0.86417  1.7596
                                                                plot(f_saem_dfop_sfo)
+#> parent_0            93.80521 91.22487 96.3856
+#> log_k_A1            -6.06244 -8.26517 -3.8597
+#> f_parent_qlogis     -0.97319 -1.37024 -0.5761
+#> log_k1              -2.55394 -4.00815 -1.0997
+#> log_k2              -3.47160 -5.18763 -1.7556
+#> g_qlogis            -0.09324 -1.42737  1.2409
+#> Var.parent_0         7.42157 -3.25683 18.1000
+#> Var.log_k_A1         4.22850 -2.46339 10.9204
+#> Var.f_parent_qlogis  0.19803 -0.05541  0.4515
+#> Var.log_k1           2.28644 -0.86079  5.4337
+#> Var.log_k2           3.35626 -1.14639  7.8589
+#> Var.g_qlogis         0.20084 -1.32516  1.7268
+#> a.1                  1.88399  1.66794  2.1000
+#> SD.parent_0          2.72425  0.76438  4.6841
+#> SD.log_k_A1          2.05633  0.42919  3.6835
+#> SD.f_parent_qlogis   0.44501  0.16025  0.7298
+#> SD.log_k1            1.51210  0.47142  2.5528
+#> SD.log_k2            1.83201  0.60313  3.0609
+#> SD.g_qlogis          0.44816 -1.25437  2.1507
                                                                plot(f_saem_dfop_sfo)
 
                                                                summary(f_saem_dfop_sfo, data = TRUE)
 
                                                                #> saemix version used for fitting:      3.1.9000 
-#> mkin version used for pre-fitting:  1.0.5 
+#> mkin version used for pre-fitting:  1.1.0 
 #> R version used for fitting:         4.1.0 
-#> Date of fit:     Fri Jun 11 10:57:27 2021 
-#> Date of summary: Fri Jun 11 10:57:27 2021 
+#> Date of fit:     Tue Jul 27 16:31:39 2021 
+#> Date of summary: Tue Jul 27 16:31:39 2021 
 #> 
 #> Equations:
 #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -448,13 +448,13 @@ using mmkin.
                                                                
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted in 9.712 s using 300, 100 iterations
+#> Fitted in 9.479 s using 300, 100 iterations
 #> 
 #> Variance model: Constant variance 
 #> 
 #> Mean of starting values for individual parameters:
 #>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2 
-#>         93.8102         -9.7647         -0.9711         -1.8799         -4.2708 
+#>         93.8102         -5.3734         -0.9711         -1.8799         -4.2708 
 #>        g_qlogis 
 #>          0.1356 
 #> 
@@ -465,46 +465,46 @@ using mmkin.
                                                                
 #> 
 #> Likelihood computed by importance sampling
 #>     AIC   BIC logLik
-#>   841.6 836.5 -407.8
+#>   839.6 834.6 -406.8
 #> 
 #> Optimised parameters:
 #>                     est.  lower   upper
-#> parent_0        93.76647 91.153 96.3798
-#> log_k_A1        -6.13235 -8.458 -3.8068
-#> f_parent_qlogis -0.97364 -1.369 -0.5779
-#> log_k1          -2.53176 -3.804 -1.2598
-#> log_k2          -3.58667 -5.295 -1.8781
-#> g_qlogis         0.01238 -1.080  1.1044
+#> parent_0        93.80521 91.225 96.3856
+#> log_k_A1        -6.06244 -8.265 -3.8597
+#> f_parent_qlogis -0.97319 -1.370 -0.5761
+#> log_k1          -2.55394 -4.008 -1.0997
+#> log_k2          -3.47160 -5.188 -1.7556
+#> g_qlogis        -0.09324 -1.427  1.2409
 #> 
 #> Correlation: 
 #>                 prnt_0 lg__A1 f_prn_ log_k1 log_k2
-#> log_k_A1        -0.013                            
-#> f_parent_qlogis -0.025  0.050                     
-#> log_k1           0.030  0.000 -0.005              
-#> log_k2           0.010  0.005 -0.003  0.032       
-#> g_qlogis        -0.063 -0.015  0.010 -0.167 -0.177
+#> log_k_A1        -0.014                            
+#> f_parent_qlogis -0.025  0.054                     
+#> log_k1           0.027 -0.003 -0.005              
+#> log_k2           0.011  0.005 -0.002 -0.070       
+#> g_qlogis        -0.067 -0.009  0.011 -0.189 -0.171
 #> 
 #> Random effects:
 #>                      est.   lower  upper
-#> SD.parent_0        2.7588  0.7723 4.7453
-#> SD.log_k_A1        2.1556  0.4443 3.8670
-#> SD.f_parent_qlogis 0.4438  0.1599 0.7276
-#> SD.log_k1          1.4203  0.5271 2.3134
-#> SD.log_k2          1.9063  0.7093 3.1033
-#> SD.g_qlogis        0.4477 -0.8642 1.7596
+#> SD.parent_0        2.7243  0.7644 4.6841
+#> SD.log_k_A1        2.0563  0.4292 3.6835
+#> SD.f_parent_qlogis 0.4450  0.1602 0.7298
+#> SD.log_k1          1.5121  0.4714 2.5528
+#> SD.log_k2          1.8320  0.6031 3.0609
+#> SD.g_qlogis        0.4482 -1.2544 2.1507
 #> 
 #> Variance model:
 #>      est. lower upper
-#> a.1 1.883 1.666   2.1
+#> a.1 1.884 1.668   2.1
 #> 
 #> Backtransformed parameters:
 #>                     est.     lower    upper
-#> parent_0       93.766473 9.115e+01 96.37983
-#> k_A1            0.002171 2.122e-04  0.02222
-#> f_parent_to_A1  0.274156 2.027e-01  0.35942
-#> k1              0.079519 2.229e-02  0.28371
-#> k2              0.027691 5.015e-03  0.15288
-#> g               0.503095 2.536e-01  0.75109
+#> parent_0       93.805214 9.122e+01 96.38556
+#> k_A1            0.002329 2.573e-04  0.02107
+#> f_parent_to_A1  0.274245 2.026e-01  0.35982
+#> k1              0.077775 1.817e-02  0.33296
+#> k2              0.031067 5.585e-03  0.17281
+#> g               0.476707 1.935e-01  0.77572
 #> 
 #> Resulting formation fractions:
 #>                 ff
@@ -512,182 +512,182 @@ using mmkin.
                                                                
 #> parent_sink 0.7258
 #> 
 #> Estimated disappearance times:
-#>          DT50    DT90 DT50back DT50_k1 DT50_k2
-#> parent  14.11   59.53    17.92   8.717   25.03
-#> A1     319.21 1060.38       NA      NA      NA
+#>          DT50  DT90 DT50back DT50_k1 DT50_k2
+#> parent  13.96  55.4    16.68   8.912   22.31
+#> A1     297.65 988.8       NA      NA      NA
 #> 
 #> Data:
-#>          ds   name time observed predicted residual   std standardized
-#>   Dataset 6 parent    0     97.2  95.79523  1.40477 1.883     0.745888
-#>   Dataset 6 parent    0     96.4  95.79523  0.60477 1.883     0.321114
-#>   Dataset 6 parent    3     71.1  71.32042 -0.22042 1.883    -0.117035
-#>   Dataset 6 parent    3     69.2  71.32042 -2.12042 1.883    -1.125873
-#>   Dataset 6 parent    6     58.1  56.45256  1.64744 1.883     0.874739
-#>   Dataset 6 parent    6     56.6  56.45256  0.14744 1.883     0.078288
-#>   Dataset 6 parent   10     44.4  44.48523 -0.08523 1.883    -0.045257
-#>   Dataset 6 parent   10     43.4  44.48523 -1.08523 1.883    -0.576224
-#>   Dataset 6 parent   20     33.3  29.75774  3.54226 1.883     1.880826
-#>   Dataset 6 parent   20     29.2  29.75774 -0.55774 1.883    -0.296141
-#>   Dataset 6 parent   34     17.6  19.35710 -1.75710 1.883    -0.932966
-#>   Dataset 6 parent   34     18.0  19.35710 -1.35710 1.883    -0.720579
-#>   Dataset 6 parent   55     10.5  10.48443  0.01557 1.883     0.008266
-#>   Dataset 6 parent   55      9.3  10.48443 -1.18443 1.883    -0.628895
-#>   Dataset 6 parent   90      4.5   3.78622  0.71378 1.883     0.378995
-#>   Dataset 6 parent   90      4.7   3.78622  0.91378 1.883     0.485188
-#>   Dataset 6 parent  112      3.0   1.99608  1.00392 1.883     0.533048
-#>   Dataset 6 parent  112      3.4   1.99608  1.40392 1.883     0.745435
-#>   Dataset 6 parent  132      2.3   1.11539  1.18461 1.883     0.628990
-#>   Dataset 6 parent  132      2.7   1.11539  1.58461 1.883     0.841377
-#>   Dataset 6     A1    3      4.3   4.66132 -0.36132 1.883    -0.191849
-#>   Dataset 6     A1    3      4.6   4.66132 -0.06132 1.883    -0.032559
-#>   Dataset 6     A1    6      7.0   7.41087 -0.41087 1.883    -0.218157
-#>   Dataset 6     A1    6      7.2   7.41087 -0.21087 1.883    -0.111964
-#>   Dataset 6     A1   10      8.2   9.50878 -1.30878 1.883    -0.694921
-#>   Dataset 6     A1   10      8.0   9.50878 -1.50878 1.883    -0.801114
-#>   Dataset 6     A1   20     11.0  11.69902 -0.69902 1.883    -0.371157
-#>   Dataset 6     A1   20     13.7  11.69902  2.00098 1.883     1.062455
-#>   Dataset 6     A1   34     11.5  12.67784 -1.17784 1.883    -0.625396
-#>   Dataset 6     A1   34     12.7  12.67784  0.02216 1.883     0.011765
-#>   Dataset 6     A1   55     14.9  12.78556  2.11444 1.883     1.122701
-#>   Dataset 6     A1   55     14.5  12.78556  1.71444 1.883     0.910314
-#>   Dataset 6     A1   90     12.1  11.52954  0.57046 1.883     0.302898
-#>   Dataset 6     A1   90     12.3  11.52954  0.77046 1.883     0.409092
-#>   Dataset 6     A1  112      9.9  10.43825 -0.53825 1.883    -0.285793
-#>   Dataset 6     A1  112     10.2  10.43825 -0.23825 1.883    -0.126503
-#>   Dataset 6     A1  132      8.8   9.42830 -0.62830 1.883    -0.333609
-#>   Dataset 6     A1  132      7.8   9.42830 -1.62830 1.883    -0.864577
-#>   Dataset 7 parent    0     93.6  90.91477  2.68523 1.883     1.425772
-#>   Dataset 7 parent    0     92.3  90.91477  1.38523 1.883     0.735514
-#>   Dataset 7 parent    3     87.0  84.76874  2.23126 1.883     1.184726
-#>   Dataset 7 parent    3     82.2  84.76874 -2.56874 1.883    -1.363919
-#>   Dataset 7 parent    7     74.0  77.62735 -3.62735 1.883    -1.926003
-#>   Dataset 7 parent    7     73.9  77.62735 -3.72735 1.883    -1.979100
-#>   Dataset 7 parent   14     64.2  67.52266 -3.32266 1.883    -1.764224
-#>   Dataset 7 parent   14     69.5  67.52266  1.97734 1.883     1.049904
-#>   Dataset 7 parent   30     54.0  52.41949  1.58051 1.883     0.839202
-#>   Dataset 7 parent   30     54.6  52.41949  2.18051 1.883     1.157783
-#>   Dataset 7 parent   60     41.1  39.36582  1.73418 1.883     0.920794
-#>   Dataset 7 parent   60     38.4  39.36582 -0.96582 1.883    -0.512818
-#>   Dataset 7 parent   90     32.5  33.75388 -1.25388 1.883    -0.665771
-#>   Dataset 7 parent   90     35.5  33.75388  1.74612 1.883     0.927132
-#>   Dataset 7 parent  120     28.1  30.41716 -2.31716 1.883    -1.230335
-#>   Dataset 7 parent  120     29.0  30.41716 -1.41716 1.883    -0.752464
-#>   Dataset 7 parent  180     26.5  25.66046  0.83954 1.883     0.445767
-#>   Dataset 7 parent  180     27.6  25.66046  1.93954 1.883     1.029832
-#>   Dataset 7     A1    3      3.9   2.69355  1.20645 1.883     0.640585
-#>   Dataset 7     A1    3      3.1   2.69355  0.40645 1.883     0.215811
-#>   Dataset 7     A1    7      6.9   5.81807  1.08193 1.883     0.574470
-#>   Dataset 7     A1    7      6.6   5.81807  0.78193 1.883     0.415180
-#>   Dataset 7     A1   14     10.4  10.22529  0.17471 1.883     0.092767
-#>   Dataset 7     A1   14      8.3  10.22529 -1.92529 1.883    -1.022265
-#>   Dataset 7     A1   30     14.4  16.75484 -2.35484 1.883    -1.250345
-#>   Dataset 7     A1   30     13.7  16.75484 -3.05484 1.883    -1.622022
-#>   Dataset 7     A1   60     22.1  22.22540 -0.12540 1.883    -0.066583
-#>   Dataset 7     A1   60     22.3  22.22540  0.07460 1.883     0.039610
-#>   Dataset 7     A1   90     27.5  24.38799  3.11201 1.883     1.652376
-#>   Dataset 7     A1   90     25.4  24.38799  1.01201 1.883     0.537344
-#>   Dataset 7     A1  120     28.0  25.53294  2.46706 1.883     1.309927
-#>   Dataset 7     A1  120     26.6  25.53294  1.06706 1.883     0.566572
-#>   Dataset 7     A1  180     25.8  26.94943 -1.14943 1.883    -0.610309
-#>   Dataset 7     A1  180     25.3  26.94943 -1.64943 1.883    -0.875793
-#>   Dataset 8 parent    0     91.9  91.53246  0.36754 1.883     0.195151
-#>   Dataset 8 parent    0     90.8  91.53246 -0.73246 1.883    -0.388914
-#>   Dataset 8 parent    1     64.9  67.73197 -2.83197 1.883    -1.503686
-#>   Dataset 8 parent    1     66.2  67.73197 -1.53197 1.883    -0.813428
-#>   Dataset 8 parent    3     43.5  41.58448  1.91552 1.883     1.017081
-#>   Dataset 8 parent    3     44.1  41.58448  2.51552 1.883     1.335662
-#>   Dataset 8 parent    8     18.3  19.62286 -1.32286 1.883    -0.702395
-#>   Dataset 8 parent    8     18.1  19.62286 -1.52286 1.883    -0.808588
-#>   Dataset 8 parent   14     10.2  10.77819 -0.57819 1.883    -0.306999
-#>   Dataset 8 parent   14     10.8  10.77819  0.02181 1.883     0.011582
-#>   Dataset 8 parent   27      4.9   3.26977  1.63023 1.883     0.865599
-#>   Dataset 8 parent   27      3.3   3.26977  0.03023 1.883     0.016051
-#>   Dataset 8 parent   48      1.6   0.48024  1.11976 1.883     0.594557
-#>   Dataset 8 parent   48      1.5   0.48024  1.01976 1.883     0.541460
-#>   Dataset 8 parent   70      1.1   0.06438  1.03562 1.883     0.549881
-#>   Dataset 8 parent   70      0.9   0.06438  0.83562 1.883     0.443688
-#>   Dataset 8     A1    1      9.6   7.61539  1.98461 1.883     1.053761
-#>   Dataset 8     A1    1      7.7   7.61539  0.08461 1.883     0.044923
-#>   Dataset 8     A1    3     15.0  15.47954 -0.47954 1.883    -0.254622
-#>   Dataset 8     A1    3     15.1  15.47954 -0.37954 1.883    -0.201525
-#>   Dataset 8     A1    8     21.2  20.22616  0.97384 1.883     0.517075
-#>   Dataset 8     A1    8     21.1  20.22616  0.87384 1.883     0.463979
-#>   Dataset 8     A1   14     19.7  20.00067 -0.30067 1.883    -0.159645
-#>   Dataset 8     A1   14     18.9  20.00067 -1.10067 1.883    -0.584419
-#>   Dataset 8     A1   27     17.5  16.38142  1.11858 1.883     0.593928
-#>   Dataset 8     A1   27     15.9  16.38142 -0.48142 1.883    -0.255620
-#>   Dataset 8     A1   48      9.5  10.25357 -0.75357 1.883    -0.400124
-#>   Dataset 8     A1   48      9.8  10.25357 -0.45357 1.883    -0.240833
-#>   Dataset 8     A1   70      6.2   5.95728  0.24272 1.883     0.128878
-#>   Dataset 8     A1   70      6.1   5.95728  0.14272 1.883     0.075781
-#>   Dataset 9 parent    0     99.8  97.47274  2.32726 1.883     1.235697
-#>   Dataset 9 parent    0     98.3  97.47274  0.82726 1.883     0.439246
-#>   Dataset 9 parent    1     77.1  79.72257 -2.62257 1.883    -1.392500
-#>   Dataset 9 parent    1     77.2  79.72257 -2.52257 1.883    -1.339404
-#>   Dataset 9 parent    3     59.0  56.26497  2.73503 1.883     1.452212
-#>   Dataset 9 parent    3     58.1  56.26497  1.83503 1.883     0.974342
-#>   Dataset 9 parent    8     27.4  31.66985 -4.26985 1.883    -2.267151
-#>   Dataset 9 parent    8     29.2  31.66985 -2.46985 1.883    -1.311410
-#>   Dataset 9 parent   14     19.1  22.39789 -3.29789 1.883    -1.751071
-#>   Dataset 9 parent   14     29.6  22.39789  7.20211 1.883     3.824090
-#>   Dataset 9 parent   27     10.1  14.21758 -4.11758 1.883    -2.186301
-#>   Dataset 9 parent   27     18.2  14.21758  3.98242 1.883     2.114537
-#>   Dataset 9 parent   48      4.5   7.27921 -2.77921 1.883    -1.475671
-#>   Dataset 9 parent   48      9.1   7.27921  1.82079 1.883     0.966780
-#>   Dataset 9 parent   70      2.3   3.61470 -1.31470 1.883    -0.698065
-#>   Dataset 9 parent   70      2.9   3.61470 -0.71470 1.883    -0.379485
-#>   Dataset 9 parent   91      2.0   1.85303  0.14697 1.883     0.078038
-#>   Dataset 9 parent   91      1.8   1.85303 -0.05303 1.883    -0.028155
-#>   Dataset 9 parent  120      2.0   0.73645  1.26355 1.883     0.670906
-#>   Dataset 9 parent  120      2.2   0.73645  1.46355 1.883     0.777099
-#>   Dataset 9     A1    1      4.2   3.87843  0.32157 1.883     0.170743
-#>   Dataset 9     A1    1      3.9   3.87843  0.02157 1.883     0.011453
-#>   Dataset 9     A1    3      7.4   8.90535 -1.50535 1.883    -0.799291
-#>   Dataset 9     A1    3      7.9   8.90535 -1.00535 1.883    -0.533807
-#>   Dataset 9     A1    8     14.5  13.75172  0.74828 1.883     0.397312
-#>   Dataset 9     A1    8     13.7  13.75172 -0.05172 1.883    -0.027462
-#>   Dataset 9     A1   14     14.2  14.97541 -0.77541 1.883    -0.411715
-#>   Dataset 9     A1   14     12.2  14.97541 -2.77541 1.883    -1.473650
-#>   Dataset 9     A1   27     13.7  14.94728 -1.24728 1.883    -0.662266
-#>   Dataset 9     A1   27     13.2  14.94728 -1.74728 1.883    -0.927750
-#>   Dataset 9     A1   48     13.6  13.66078 -0.06078 1.883    -0.032272
-#>   Dataset 9     A1   48     15.4  13.66078  1.73922 1.883     0.923470
-#>   Dataset 9     A1   70     10.4  11.84899 -1.44899 1.883    -0.769365
-#>   Dataset 9     A1   70     11.6  11.84899 -0.24899 1.883    -0.132204
-#>   Dataset 9     A1   91     10.0  10.09177 -0.09177 1.883    -0.048727
-#>   Dataset 9     A1   91      9.5  10.09177 -0.59177 1.883    -0.314211
-#>   Dataset 9     A1  120      9.1   7.91379  1.18621 1.883     0.629841
-#>   Dataset 9     A1  120      9.0   7.91379  1.08621 1.883     0.576744
-#>  Dataset 10 parent    0     96.1  93.65257  2.44743 1.883     1.299505
-#>  Dataset 10 parent    0     94.3  93.65257  0.64743 1.883     0.343763
-#>  Dataset 10 parent    8     73.9  77.85906 -3.95906 1.883    -2.102132
-#>  Dataset 10 parent    8     73.9  77.85906 -3.95906 1.883    -2.102132
-#>  Dataset 10 parent   14     69.4  70.17143 -0.77143 1.883    -0.409606
-#>  Dataset 10 parent   14     73.1  70.17143  2.92857 1.883     1.554974
-#>  Dataset 10 parent   21     65.6  63.99188  1.60812 1.883     0.853862
-#>  Dataset 10 parent   21     65.3  63.99188  1.30812 1.883     0.694572
-#>  Dataset 10 parent   41     55.9  54.64292  1.25708 1.883     0.667467
-#>  Dataset 10 parent   41     54.4  54.64292 -0.24292 1.883    -0.128985
-#>  Dataset 10 parent   63     47.0  49.61303 -2.61303 1.883    -1.387433
-#>  Dataset 10 parent   63     49.3  49.61303 -0.31303 1.883    -0.166207
-#>  Dataset 10 parent   91     44.7  45.17807 -0.47807 1.883    -0.253839
-#>  Dataset 10 parent   91     46.7  45.17807  1.52193 1.883     0.808096
-#>  Dataset 10 parent  120     42.1  41.27970  0.82030 1.883     0.435552
-#>  Dataset 10 parent  120     41.3  41.27970  0.02030 1.883     0.010778
-#>  Dataset 10     A1    8      3.3   3.99294 -0.69294 1.883    -0.367929
-#>  Dataset 10     A1    8      3.4   3.99294 -0.59294 1.883    -0.314832
-#>  Dataset 10     A1   14      3.9   5.92756 -2.02756 1.883    -1.076570
-#>  Dataset 10     A1   14      2.9   5.92756 -3.02756 1.883    -1.607538
-#>  Dataset 10     A1   21      6.4   7.47313 -1.07313 1.883    -0.569799
-#>  Dataset 10     A1   21      7.2   7.47313 -0.27313 1.883    -0.145025
-#>  Dataset 10     A1   41      9.1   9.76819 -0.66819 1.883    -0.354786
-#>  Dataset 10     A1   41      8.5   9.76819 -1.26819 1.883    -0.673367
-#>  Dataset 10     A1   63     11.7  10.94733  0.75267 1.883     0.399643
-#>  Dataset 10     A1   63     12.0  10.94733  1.05267 1.883     0.558933
-#>  Dataset 10     A1   91     13.3  11.93773  1.36227 1.883     0.723321
-#>  Dataset 10     A1   91     13.2  11.93773  1.26227 1.883     0.670224
-#>  Dataset 10     A1  120     14.3  12.77666  1.52334 1.883     0.808847
-#>  Dataset 10     A1  120     12.1  12.77666 -0.67666 1.883    -0.359282
                                                                
+#>          ds   name time observed predicted  residual   std standardized
+#>   Dataset 6 parent    0     97.2  95.75408  1.445920 1.884     0.767479
+#>   Dataset 6 parent    0     96.4  95.75408  0.645920 1.884     0.342847
+#>   Dataset 6 parent    3     71.1  71.22466 -0.124662 1.884    -0.066169
+#>   Dataset 6 parent    3     69.2  71.22466 -2.024662 1.884    -1.074669
+#>   Dataset 6 parent    6     58.1  56.42290  1.677100 1.884     0.890187
+#>   Dataset 6 parent    6     56.6  56.42290  0.177100 1.884     0.094003
+#>   Dataset 6 parent   10     44.4  44.55255 -0.152554 1.884    -0.080974
+#>   Dataset 6 parent   10     43.4  44.55255 -1.152554 1.884    -0.611763
+#>   Dataset 6 parent   20     33.3  29.88846  3.411537 1.884     1.810807
+#>   Dataset 6 parent   20     29.2  29.88846 -0.688463 1.884    -0.365429
+#>   Dataset 6 parent   34     17.6  19.40826 -1.808260 1.884    -0.959805
+#>   Dataset 6 parent   34     18.0  19.40826 -1.408260 1.884    -0.747489
+#>   Dataset 6 parent   55     10.5  10.45560  0.044398 1.884     0.023566
+#>   Dataset 6 parent   55      9.3  10.45560 -1.155602 1.884    -0.613381
+#>   Dataset 6 parent   90      4.5   3.74026  0.759744 1.884     0.403264
+#>   Dataset 6 parent   90      4.7   3.74026  0.959744 1.884     0.509421
+#>   Dataset 6 parent  112      3.0   1.96015  1.039853 1.884     0.551943
+#>   Dataset 6 parent  112      3.4   1.96015  1.439853 1.884     0.764258
+#>   Dataset 6 parent  132      2.3   1.08940  1.210603 1.884     0.642575
+#>   Dataset 6 parent  132      2.7   1.08940  1.610603 1.884     0.854890
+#>   Dataset 6     A1    3      4.3   4.75601 -0.456009 1.884    -0.242045
+#>   Dataset 6     A1    3      4.6   4.75601 -0.156009 1.884    -0.082808
+#>   Dataset 6     A1    6      7.0   7.53839 -0.538391 1.884    -0.285772
+#>   Dataset 6     A1    6      7.2   7.53839 -0.338391 1.884    -0.179614
+#>   Dataset 6     A1   10      8.2   9.64728 -1.447276 1.884    -0.768198
+#>   Dataset 6     A1   10      8.0   9.64728 -1.647276 1.884    -0.874356
+#>   Dataset 6     A1   20     11.0  11.83954 -0.839545 1.884    -0.445621
+#>   Dataset 6     A1   20     13.7  11.83954  1.860455 1.884     0.987509
+#>   Dataset 6     A1   34     11.5  12.81233 -1.312327 1.884    -0.696569
+#>   Dataset 6     A1   34     12.7  12.81233 -0.112327 1.884    -0.059622
+#>   Dataset 6     A1   55     14.9  12.87919  2.020809 1.884     1.072624
+#>   Dataset 6     A1   55     14.5  12.87919  1.620809 1.884     0.860308
+#>   Dataset 6     A1   90     12.1  11.52464  0.575364 1.884     0.305397
+#>   Dataset 6     A1   90     12.3  11.52464  0.775364 1.884     0.411555
+#>   Dataset 6     A1  112      9.9  10.37694 -0.476938 1.884    -0.253153
+#>   Dataset 6     A1  112     10.2  10.37694 -0.176938 1.884    -0.093917
+#>   Dataset 6     A1  132      8.8   9.32474 -0.524742 1.884    -0.278528
+#>   Dataset 6     A1  132      7.8   9.32474 -1.524742 1.884    -0.809317
+#>   Dataset 7 parent    0     93.6  90.16918  3.430816 1.884     1.821040
+#>   Dataset 7 parent    0     92.3  90.16918  2.130816 1.884     1.131014
+#>   Dataset 7 parent    3     87.0  84.05442  2.945583 1.884     1.563483
+#>   Dataset 7 parent    3     82.2  84.05442 -1.854417 1.884    -0.984304
+#>   Dataset 7 parent    7     74.0  77.00960 -3.009596 1.884    -1.597461
+#>   Dataset 7 parent    7     73.9  77.00960 -3.109596 1.884    -1.650540
+#>   Dataset 7 parent   14     64.2  67.15684 -2.956840 1.884    -1.569459
+#>   Dataset 7 parent   14     69.5  67.15684  2.343160 1.884     1.243724
+#>   Dataset 7 parent   30     54.0  52.66290  1.337101 1.884     0.709719
+#>   Dataset 7 parent   30     54.6  52.66290  1.937101 1.884     1.028192
+#>   Dataset 7 parent   60     41.1  40.04995  1.050050 1.884     0.557355
+#>   Dataset 7 parent   60     38.4  40.04995 -1.649950 1.884    -0.875775
+#>   Dataset 7 parent   90     32.5  34.09675 -1.596746 1.884    -0.847535
+#>   Dataset 7 parent   90     35.5  34.09675  1.403254 1.884     0.744832
+#>   Dataset 7 parent  120     28.1  30.12281 -2.022814 1.884    -1.073688
+#>   Dataset 7 parent  120     29.0  30.12281 -1.122814 1.884    -0.595977
+#>   Dataset 7 parent  180     26.5  24.10888  2.391123 1.884     1.269182
+#>   Dataset 7 parent  180     27.6  24.10888  3.491123 1.884     1.853050
+#>   Dataset 7     A1    3      3.9   2.77684  1.123161 1.884     0.596161
+#>   Dataset 7     A1    3      3.1   2.77684  0.323161 1.884     0.171530
+#>   Dataset 7     A1    7      6.9   5.96705  0.932950 1.884     0.495200
+#>   Dataset 7     A1    7      6.6   5.96705  0.632950 1.884     0.335963
+#>   Dataset 7     A1   14     10.4  10.40535 -0.005348 1.884    -0.002839
+#>   Dataset 7     A1   14      8.3  10.40535 -2.105348 1.884    -1.117496
+#>   Dataset 7     A1   30     14.4  16.83722 -2.437216 1.884    -1.293648
+#>   Dataset 7     A1   30     13.7  16.83722 -3.137216 1.884    -1.665200
+#>   Dataset 7     A1   60     22.1  22.15018 -0.050179 1.884    -0.026635
+#>   Dataset 7     A1   60     22.3  22.15018  0.149821 1.884     0.079523
+#>   Dataset 7     A1   90     27.5  24.36286  3.137143 1.884     1.665161
+#>   Dataset 7     A1   90     25.4  24.36286  1.037143 1.884     0.550504
+#>   Dataset 7     A1  120     28.0  25.64064  2.359361 1.884     1.252323
+#>   Dataset 7     A1  120     26.6  25.64064  0.959361 1.884     0.509218
+#>   Dataset 7     A1  180     25.8  27.25486 -1.454858 1.884    -0.772223
+#>   Dataset 7     A1  180     25.3  27.25486 -1.954858 1.884    -1.037617
+#>   Dataset 8 parent    0     91.9  91.72652  0.173479 1.884     0.092081
+#>   Dataset 8 parent    0     90.8  91.72652 -0.926521 1.884    -0.491787
+#>   Dataset 8 parent    1     64.9  67.22810 -2.328104 1.884    -1.235732
+#>   Dataset 8 parent    1     66.2  67.22810 -1.028104 1.884    -0.545706
+#>   Dataset 8 parent    3     43.5  41.46375  2.036251 1.884     1.080820
+#>   Dataset 8 parent    3     44.1  41.46375  2.636251 1.884     1.399293
+#>   Dataset 8 parent    8     18.3  19.83597 -1.535968 1.884    -0.815275
+#>   Dataset 8 parent    8     18.1  19.83597 -1.735968 1.884    -0.921433
+#>   Dataset 8 parent   14     10.2  10.34793 -0.147927 1.884    -0.078518
+#>   Dataset 8 parent   14     10.8  10.34793  0.452073 1.884     0.239956
+#>   Dataset 8 parent   27      4.9   2.67641  2.223595 1.884     1.180260
+#>   Dataset 8 parent   27      3.3   2.67641  0.623595 1.884     0.330997
+#>   Dataset 8 parent   48      1.6   0.30218  1.297822 1.884     0.688870
+#>   Dataset 8 parent   48      1.5   0.30218  1.197822 1.884     0.635791
+#>   Dataset 8 parent   70      1.1   0.03075  1.069248 1.884     0.567545
+#>   Dataset 8 parent   70      0.9   0.03075  0.869248 1.884     0.461388
+#>   Dataset 8     A1    1      9.6   7.74066  1.859342 1.884     0.986918
+#>   Dataset 8     A1    1      7.7   7.74066 -0.040658 1.884    -0.021581
+#>   Dataset 8     A1    3     15.0  15.37549 -0.375495 1.884    -0.199309
+#>   Dataset 8     A1    3     15.1  15.37549 -0.275495 1.884    -0.146230
+#>   Dataset 8     A1    8     21.2  19.95900  1.241003 1.884     0.658711
+#>   Dataset 8     A1    8     21.1  19.95900  1.141003 1.884     0.605632
+#>   Dataset 8     A1   14     19.7  19.92898 -0.228978 1.884    -0.121539
+#>   Dataset 8     A1   14     18.9  19.92898 -1.028978 1.884    -0.546170
+#>   Dataset 8     A1   27     17.5  16.34046  1.159536 1.884     0.615469
+#>   Dataset 8     A1   27     15.9  16.34046 -0.440464 1.884    -0.233793
+#>   Dataset 8     A1   48      9.5  10.12131 -0.621313 1.884    -0.329786
+#>   Dataset 8     A1   48      9.8  10.12131 -0.321313 1.884    -0.170550
+#>   Dataset 8     A1   70      6.2   5.84753  0.352469 1.884     0.187087
+#>   Dataset 8     A1   70      6.1   5.84753  0.252469 1.884     0.134008
+#>   Dataset 9 parent    0     99.8  98.23600  1.564002 1.884     0.830155
+#>   Dataset 9 parent    0     98.3  98.23600  0.064002 1.884     0.033972
+#>   Dataset 9 parent    1     77.1  79.68007 -2.580074 1.884    -1.369475
+#>   Dataset 9 parent    1     77.2  79.68007 -2.480074 1.884    -1.316396
+#>   Dataset 9 parent    3     59.0  55.81142  3.188584 1.884     1.692465
+#>   Dataset 9 parent    3     58.1  55.81142  2.288584 1.884     1.214755
+#>   Dataset 9 parent    8     27.4  31.81995 -4.419948 1.884    -2.346060
+#>   Dataset 9 parent    8     29.2  31.81995 -2.619948 1.884    -1.390640
+#>   Dataset 9 parent   14     19.1  22.78328 -3.683282 1.884    -1.955046
+#>   Dataset 9 parent   14     29.6  22.78328  6.816718 1.884     3.618240
+#>   Dataset 9 parent   27     10.1  14.15172 -4.051720 1.884    -2.150609
+#>   Dataset 9 parent   27     18.2  14.15172  4.048280 1.884     2.148783
+#>   Dataset 9 parent   48      4.5   6.86094 -2.360941 1.884    -1.253162
+#>   Dataset 9 parent   48      9.1   6.86094  2.239059 1.884     1.188468
+#>   Dataset 9 parent   70      2.3   3.21580 -0.915798 1.884    -0.486096
+#>   Dataset 9 parent   70      2.9   3.21580 -0.315798 1.884    -0.167622
+#>   Dataset 9 parent   91      2.0   1.56010  0.439897 1.884     0.233492
+#>   Dataset 9 parent   91      1.8   1.56010  0.239897 1.884     0.127335
+#>   Dataset 9 parent  120      2.0   0.57458  1.425424 1.884     0.756600
+#>   Dataset 9 parent  120      2.2   0.57458  1.625424 1.884     0.862757
+#>   Dataset 9     A1    1      4.2   4.01796  0.182037 1.884     0.096623
+#>   Dataset 9     A1    1      3.9   4.01796 -0.117963 1.884    -0.062613
+#>   Dataset 9     A1    3      7.4   9.08527 -1.685270 1.884    -0.894523
+#>   Dataset 9     A1    3      7.9   9.08527 -1.185270 1.884    -0.629129
+#>   Dataset 9     A1    8     14.5  13.75054  0.749457 1.884     0.397804
+#>   Dataset 9     A1    8     13.7  13.75054 -0.050543 1.884    -0.026827
+#>   Dataset 9     A1   14     14.2  14.91180 -0.711804 1.884    -0.377818
+#>   Dataset 9     A1   14     12.2  14.91180 -2.711804 1.884    -1.439396
+#>   Dataset 9     A1   27     13.7  14.97813 -1.278129 1.884    -0.678417
+#>   Dataset 9     A1   27     13.2  14.97813 -1.778129 1.884    -0.943812
+#>   Dataset 9     A1   48     13.6  13.75574 -0.155745 1.884    -0.082668
+#>   Dataset 9     A1   48     15.4  13.75574  1.644255 1.884     0.872753
+#>   Dataset 9     A1   70     10.4  11.92861 -1.528608 1.884    -0.811369
+#>   Dataset 9     A1   70     11.6  11.92861 -0.328608 1.884    -0.174422
+#>   Dataset 9     A1   91     10.0  10.14395 -0.143947 1.884    -0.076405
+#>   Dataset 9     A1   91      9.5  10.14395 -0.643947 1.884    -0.341800
+#>   Dataset 9     A1  120      9.1   7.93869  1.161307 1.884     0.616409
+#>   Dataset 9     A1  120      9.0   7.93869  1.061307 1.884     0.563330
+#>  Dataset 10 parent    0     96.1  93.65914  2.440862 1.884     1.295583
+#>  Dataset 10 parent    0     94.3  93.65914  0.640862 1.884     0.340163
+#>  Dataset 10 parent    8     73.9  77.83065 -3.930647 1.884    -2.086344
+#>  Dataset 10 parent    8     73.9  77.83065 -3.930647 1.884    -2.086344
+#>  Dataset 10 parent   14     69.4  70.15862 -0.758619 1.884    -0.402667
+#>  Dataset 10 parent   14     73.1  70.15862  2.941381 1.884     1.561253
+#>  Dataset 10 parent   21     65.6  64.00840  1.591600 1.884     0.844804
+#>  Dataset 10 parent   21     65.3  64.00840  1.291600 1.884     0.685567
+#>  Dataset 10 parent   41     55.9  54.71192  1.188076 1.884     0.630618
+#>  Dataset 10 parent   41     54.4  54.71192 -0.311924 1.884    -0.165566
+#>  Dataset 10 parent   63     47.0  49.66775 -2.667747 1.884    -1.416011
+#>  Dataset 10 parent   63     49.3  49.66775 -0.367747 1.884    -0.195196
+#>  Dataset 10 parent   91     44.7  45.17119 -0.471186 1.884    -0.250101
+#>  Dataset 10 parent   91     46.7  45.17119  1.528814 1.884     0.811478
+#>  Dataset 10 parent  120     42.1  41.20430  0.895699 1.884     0.475427
+#>  Dataset 10 parent  120     41.3  41.20430  0.095699 1.884     0.050796
+#>  Dataset 10     A1    8      3.3   4.00920 -0.709204 1.884    -0.376438
+#>  Dataset 10     A1    8      3.4   4.00920 -0.609204 1.884    -0.323359
+#>  Dataset 10     A1   14      3.9   5.94267 -2.042668 1.884    -1.084226
+#>  Dataset 10     A1   14      2.9   5.94267 -3.042668 1.884    -1.615015
+#>  Dataset 10     A1   21      6.4   7.48222 -1.082219 1.884    -0.574430
+#>  Dataset 10     A1   21      7.2   7.48222 -0.282219 1.884    -0.149799
+#>  Dataset 10     A1   41      9.1   9.76246 -0.662460 1.884    -0.351626
+#>  Dataset 10     A1   41      8.5   9.76246 -1.262460 1.884    -0.670100
+#>  Dataset 10     A1   63     11.7  10.93972  0.760278 1.884     0.403547
+#>  Dataset 10     A1   63     12.0  10.93972  1.060278 1.884     0.562784
+#>  Dataset 10     A1   91     13.3  11.93666  1.363337 1.884     0.723645
+#>  Dataset 10     A1   91     13.2  11.93666  1.263337 1.884     0.670566
+#>  Dataset 10     A1  120     14.3  12.78218  1.517817 1.884     0.805641
+#>  Dataset 10     A1  120     12.1  12.78218 -0.682183 1.884    -0.362095
                                                                
 # The following takes about 6 minutes
 #f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",
 #  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html
index 0fead0df..373ce75f 100644
--- a/docs/dev/reference/summary.nlmixr.mmkin.html
+++ b/docs/dev/reference/summary.nlmixr.mmkin.html
@@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally
       
       
         mkin
-        1.0.5
+        1.1.0
       
     
                                                                
 
@@ -258,737 +258,73 @@ nlmixr authors for the parts inherited from nlmixr.
                                                                
   quiet = TRUE, error_model = "tc", cores = 5)
 f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo)
 
                                                                #> Running main SAEM algorithm
-#> [1] "Fri Jun 11 10:57:31 2021"
+#> [1] "Tue Jul 27 16:31:43 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Fri Jun 11 10:57:43 2021"
                                                                f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo)
+#> [1] "Tue Jul 27 16:31:55 2021"
                                                                f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo)
 
                                                                #> Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
                                                                #> Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
                                                                f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem")
-
                                                                #> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1:    1.0127e+02  -3.8515e+00  -2.0719e+00  -3.7271e+00  -1.9335e+00   4.0311e-01   6.9594e+00   1.5021e-01   5.3947e-01   1.9686e-01   3.7429e-01   5.4209e-01   8.4121e+00   7.3391e-02   7.1185e+00   2.5869e-01
-#> 2:    1.0136e+02  -3.8005e+00  -2.3424e+00  -4.0759e+00  -1.6475e+00   1.1598e-01   6.6115e+00   1.4406e-01   5.1249e-01   1.8701e-01   3.5786e-01   5.1499e-01   4.9102e+00   6.2829e-02   4.7230e+00   7.8901e-02
-#> 3:    1.0126e+02  -4.0285e+00  -2.3629e+00  -4.1271e+00  -1.1733e+00   1.7634e-02   6.2809e+00   1.6892e-01   4.8687e-01   1.7766e-01   3.3997e-01   4.8924e-01   3.2256e+00   6.6693e-02   3.3261e+00   8.7190e-02
-#> 4:    1.0105e+02  -4.0894e+00  -2.5516e+00  -4.1037e+00  -1.0816e+00   4.5377e-02   5.9668e+00   1.6048e-01   4.6252e-01   1.6878e-01   3.2297e-01   4.6478e-01   2.4343e+00   7.0557e-02   2.2610e+00   9.2498e-02
-#> 5:    1.0101e+02  -4.1364e+00  -2.4605e+00  -4.0737e+00  -1.0920e+00  -4.7953e-03   5.9593e+00   1.5245e-01   4.3940e-01   1.8078e-01   3.0682e-01   5.4688e-01   1.7424e+00   7.4776e-02   1.5144e+00   1.0787e-01
-#> 6:    1.0042e+02  -4.0933e+00  -2.4472e+00  -4.1090e+00  -9.7996e-01  -9.0472e-02   6.0175e+00   1.4483e-01   4.1743e-01   1.8824e-01   2.9148e-01   5.3033e-01   1.5545e+00   6.8588e-02   1.3401e+00   9.8865e-02
-#> 7:    1.0078e+02  -4.0911e+00  -2.4335e+00  -4.0758e+00  -9.9422e-01  -7.8849e-02   6.6318e+00   1.3759e-01   3.9656e-01   1.7882e-01   2.7691e-01   5.0381e-01   1.3780e+00   6.9978e-02   1.1346e+00   9.6162e-02
-#> 8:    1.0077e+02  -4.0196e+00  -2.4345e+00  -4.0444e+00  -9.3483e-01  -1.1032e-01   6.3002e+00   1.3071e-01   3.7673e-01   1.6988e-01   2.6306e-01   4.8191e-01   1.1774e+00   7.4232e-02   1.0270e+00   9.5616e-02
-#> 9:    1.0118e+02  -4.0436e+00  -2.4649e+00  -4.0207e+00  -8.9829e-01  -1.7784e-01   5.9852e+00   1.2417e-01   3.5789e-01   1.6139e-01   2.4991e-01   5.5466e-01   1.1040e+00   7.1515e-02   1.0342e+00   9.3972e-02
-#> 10:    1.0143e+02  -4.0523e+00  -2.3737e+00  -4.0184e+00  -9.1167e-01  -2.3828e-01   5.8520e+00   1.1797e-01   3.4196e-01   1.5332e-01   2.3741e-01   5.2849e-01   1.0510e+00   7.5719e-02   1.0638e+00   9.3973e-02
-#> 11:    1.0119e+02  -4.0699e+00  -2.3680e+00  -4.0191e+00  -9.4858e-01  -1.7310e-01   6.9958e+00   1.1207e-01   3.6891e-01   1.4565e-01   2.2554e-01   5.0206e-01   1.0247e+00   7.5497e-02   1.0292e+00   9.3707e-02
-#> 12:    1.0121e+02  -4.0189e+00  -2.4198e+00  -4.0139e+00  -9.1693e-01  -2.0613e-01   6.6460e+00   1.0646e-01   3.5046e-01   1.3837e-01   2.1427e-01   5.7696e-01   1.1046e+00   7.6090e-02   9.3689e-01   9.4115e-02
-#> 13:    1.0083e+02  -4.0451e+00  -2.4395e+00  -4.0235e+00  -9.4535e-01  -1.4723e-01   6.3137e+00   1.0114e-01   3.3294e-01   1.3145e-01   2.0355e-01   5.4811e-01   1.0360e+00   7.3381e-02   9.7078e-01   9.1659e-02
-#> 14:    1.0056e+02  -4.0401e+00  -2.4045e+00  -4.0054e+00  -9.4191e-01  -1.3928e-01   5.9980e+00   9.6084e-02   3.4934e-01   1.2488e-01   1.9338e-01   5.2071e-01   1.0303e+00   7.7118e-02   8.8372e-01   9.0469e-02
-#> 15:    1.0070e+02  -4.0388e+00  -2.4210e+00  -4.0113e+00  -9.1136e-01  -1.2702e-01   5.6981e+00   9.1279e-02   3.3187e-01   1.1864e-01   1.8371e-01   4.9467e-01   1.0486e+00   7.2427e-02   7.8179e-01   9.1572e-02
-#> 16:    1.0078e+02  -4.0175e+00  -2.4766e+00  -4.0191e+00  -9.0733e-01  -1.1952e-01   5.4132e+00   8.6716e-02   3.1528e-01   1.1270e-01   1.7452e-01   4.8928e-01   9.7799e-01   8.1464e-02   8.2935e-01   8.6520e-02
-#> 17:    1.0069e+02  -4.0533e+00  -2.5110e+00  -4.0294e+00  -9.1841e-01  -6.8363e-03   5.1426e+00   8.2380e-02   2.9952e-01   1.0707e-01   1.6580e-01   4.6482e-01   9.1609e-01   8.1008e-02   8.1783e-01   8.8818e-02
-#> 18:    99.9647   -4.0672   -2.5327   -4.0416   -0.9273    0.0097    4.8854    0.0783    0.2970    0.1280    0.1941    0.5053    0.9306    0.0764    0.8097    0.0881
-#> 19:    1.0027e+02  -4.0667e+00  -2.4653e+00  -4.0579e+00  -9.2776e-01   3.0417e-02   4.6412e+00   7.4348e-02   3.3694e-01   1.2164e-01   1.8435e-01   5.1797e-01   9.7386e-01   7.4954e-02   7.9297e-01   8.9915e-02
-#> 20:    1.0006e+02  -4.0935e+00  -2.4804e+00  -4.0721e+00  -9.3737e-01   1.9496e-02   4.4091e+00   7.0630e-02   3.3728e-01   1.2544e-01   1.7513e-01   6.0925e-01   1.0232e+00   7.4618e-02   7.9988e-01   8.9642e-02
-#> 21:    1.0043e+02  -4.0542e+00  -2.5168e+00  -4.0623e+00  -9.1553e-01   3.9474e-02   4.1887e+00   6.7099e-02   3.4553e-01   1.1917e-01   1.6638e-01   6.0827e-01   1.0155e+00   8.0771e-02   7.8424e-01   8.6213e-02
-#> 22:    1.0049e+02  -4.0449e+00  -2.5082e+00  -4.0849e+00  -9.2553e-01   4.5424e-02   3.9792e+00   6.3744e-02   3.2825e-01   1.2365e-01   1.5806e-01   5.8922e-01   8.2860e-01   8.3384e-02   8.2525e-01   8.9218e-02
-#> 23:    1.0067e+02  -4.0411e+00  -2.5460e+00  -4.0736e+00  -9.2578e-01   5.2422e-02   3.7803e+00   6.0557e-02   3.1661e-01   1.2306e-01   1.5016e-01   5.8274e-01   9.3412e-01   8.0508e-02   8.1829e-01   8.6377e-02
-#> 24:    1.0091e+02  -4.0314e+00  -2.5298e+00  -4.0566e+00  -8.9743e-01   3.7634e-02   3.5913e+00   5.7529e-02   3.5267e-01   1.2194e-01   1.4265e-01   5.5360e-01   9.6271e-01   7.6960e-02   8.8466e-01   8.5693e-02
-#> 25:    1.0100e+02  -4.0442e+00  -2.5399e+00  -4.0568e+00  -8.9494e-01   1.7415e-02   3.4117e+00   5.4652e-02   3.3504e-01   1.2781e-01   1.3552e-01   5.2592e-01   9.6040e-01   7.7299e-02   8.9561e-01   8.6893e-02
-#> 26:    1.0111e+02  -4.0354e+00  -2.5182e+00  -4.0899e+00  -9.0799e-01   7.6464e-02   4.8614e+00   5.1920e-02   3.1829e-01   1.2142e-01   1.3110e-01   4.9963e-01   9.6997e-01   7.4932e-02   8.2521e-01   9.3659e-02
-#> 27:    1.0159e+02  -4.0653e+00  -2.4934e+00  -4.0803e+00  -9.5632e-01   2.8659e-03   4.6184e+00   4.9324e-02   3.0237e-01   1.1535e-01   1.4743e-01   4.7465e-01   9.4314e-01   7.7860e-02   8.9820e-01   8.8210e-02
-#> 28:    1.0154e+02  -4.0487e+00  -2.4844e+00  -4.0511e+00  -9.6473e-01  -4.7382e-02   4.3874e+00   4.6858e-02   3.2049e-01   1.0958e-01   1.5243e-01   4.5091e-01   9.8808e-01   7.4786e-02   8.6833e-01   8.8720e-02
-#> 29:    1.0144e+02  -4.0414e+00  -2.4105e+00  -4.0504e+00  -9.4039e-01  -3.6753e-02   4.1681e+00   4.4515e-02   3.2754e-01   1.0410e-01   1.4940e-01   4.2837e-01   9.5520e-01   7.8507e-02   8.2408e-01   8.5998e-02
-#> 30:    1.0137e+02  -4.0292e+00  -2.4174e+00  -4.0382e+00  -9.3180e-01  -7.1482e-02   5.4636e+00   4.2289e-02   3.2074e-01   9.8896e-02   1.6877e-01   4.0695e-01   8.8153e-01   7.5106e-02   8.5239e-01   8.8266e-02
-#> 31:    1.0105e+02  -4.0387e+00  -2.4368e+00  -4.0346e+00  -9.1098e-01  -5.4730e-02   5.1904e+00   4.0175e-02   3.0470e-01   9.3951e-02   1.6034e-01   3.8660e-01   8.7853e-01   8.0278e-02   8.7981e-01   8.6404e-02
-#> 32:    1.0147e+02  -4.0435e+00  -2.4530e+00  -4.0365e+00  -9.1241e-01  -7.1281e-02   4.9309e+00   3.8166e-02   2.8947e-01   9.4694e-02   1.7475e-01   3.6727e-01   8.7005e-01   8.1398e-02   8.7784e-01   8.8976e-02
-#> 33:    1.0144e+02  -4.0092e+00  -2.4279e+00  -4.0090e+00  -8.8656e-01  -1.4017e-01   5.2945e+00   3.6258e-02   2.9770e-01   1.0169e-01   1.6601e-01   3.4891e-01   9.2202e-01   7.8841e-02   8.7551e-01   8.4011e-02
-#> 34:    1.0157e+02  -3.9839e+00  -2.4469e+00  -4.0180e+00  -8.3877e-01  -1.4664e-01   6.3506e+00   3.4445e-02   2.8282e-01   1.0831e-01   1.6850e-01   3.3146e-01   8.4403e-01   7.9056e-02   8.4620e-01   8.6363e-02
-#> 35:    1.0149e+02  -3.9928e+00  -2.4771e+00  -4.0106e+00  -8.6974e-01  -1.4219e-01   6.2039e+00   3.2722e-02   2.8123e-01   1.1283e-01   1.6008e-01   3.1489e-01   9.1308e-01   7.8685e-02   7.8939e-01   8.7289e-02
-#> 36:    1.0162e+02  -4.0099e+00  -2.4822e+00  -3.9880e+00  -8.7959e-01  -1.3237e-01   5.8937e+00   3.1086e-02   3.2200e-01   1.0719e-01   1.6077e-01   2.9914e-01   9.0821e-01   8.4066e-02   7.5559e-01   8.4838e-02
-#> 37:    1.0102e+02  -3.9962e+00  -2.4852e+00  -3.9954e+00  -8.8307e-01  -9.2070e-02   5.5991e+00   2.9532e-02   3.3713e-01   1.0183e-01   1.5333e-01   2.8419e-01   8.3918e-01   8.5231e-02   7.6007e-01   8.9541e-02
-#> 38:    1.0102e+02  -3.9987e+00  -2.5129e+00  -3.9833e+00  -8.7454e-01  -1.6469e-01   5.3191e+00   2.8055e-02   3.2027e-01   1.0792e-01   1.4707e-01   2.6998e-01   9.1490e-01   8.4715e-02   7.6778e-01   8.9241e-02
-#> 39:    1.0054e+02  -3.9875e+00  -2.4301e+00  -3.9797e+00  -8.7222e-01  -1.9597e-01   7.3800e+00   2.6653e-02   3.0426e-01   1.0801e-01   1.4393e-01   2.5648e-01   9.5901e-01   7.8320e-02   8.1559e-01   9.2429e-02
-#> 40:    1.0077e+02  -4.0057e+00  -2.4630e+00  -3.9849e+00  -8.6788e-01  -1.9606e-01   7.0110e+00   2.5320e-02   3.0385e-01   1.3164e-01   1.4567e-01   3.0284e-01   9.7123e-01   7.6328e-02   8.3681e-01   8.9349e-02
-#> 41:    1.0069e+02  -4.0143e+00  -2.3805e+00  -3.9962e+00  -8.7503e-01  -1.8532e-01   6.6604e+00   2.4054e-02   3.0707e-01   1.4668e-01   1.5021e-01   3.0404e-01   1.0072e+00   7.3629e-02   9.4494e-01   8.4745e-02
-#> 42:    1.0073e+02  -3.9861e+00  -2.4464e+00  -3.9919e+00  -8.7912e-01  -1.8435e-01   6.3274e+00   2.2851e-02   2.9171e-01   1.3935e-01   1.5080e-01   2.8883e-01   9.6502e-01   7.7470e-02   9.4221e-01   8.2459e-02
-#> 43:    1.0104e+02  -3.9881e+00  -2.4156e+00  -3.9688e+00  -8.9448e-01  -2.3739e-01   6.0110e+00   2.1709e-02   2.7713e-01   1.3238e-01   1.5603e-01   2.7439e-01   9.7714e-01   7.1720e-02   8.5890e-01   8.6635e-02
-#> 44:    1.0084e+02  -4.0117e+00  -2.4455e+00  -3.9753e+00  -8.8716e-01  -2.0112e-01   5.7105e+00   2.0623e-02   2.6327e-01   1.2741e-01   1.5200e-01   2.6067e-01   9.3289e-01   8.0543e-02   8.5055e-01   8.2921e-02
-#> 45:    1.0071e+02  -3.9996e+00  -2.4359e+00  -3.9764e+00  -9.1082e-01  -2.4578e-01   5.4250e+00   1.9592e-02   2.5011e-01   1.3254e-01   1.6132e-01   2.8273e-01   9.5805e-01   7.7734e-02   7.8171e-01   8.4571e-02
-#> 46:    1.0018e+02  -4.0077e+00  -2.4835e+00  -3.9739e+00  -8.6079e-01  -1.6592e-01   5.1537e+00   1.8613e-02   2.3760e-01   1.3830e-01   1.5392e-01   3.0295e-01   1.0931e+00   7.3274e-02   8.9544e-01   8.8388e-02
-#> 47:    99.9834   -3.9991   -2.5292   -3.9863   -0.8820   -0.0796    4.8960    0.0177    0.2348    0.1376    0.1639    0.2878    0.9864    0.0837    0.9094    0.0832
-#> 48:    99.9155   -4.0224   -2.5422   -3.9854   -0.8719   -0.0750    4.6512    0.0184    0.2251    0.1307    0.1596    0.2734    0.9841    0.0835    0.8696    0.0843
-#> 49:    99.6136   -4.0397   -2.5172   -4.0115   -0.8774   -0.0922    5.2402    0.0175    0.2558    0.1242    0.1551    0.2597    0.9060    0.0816    0.8365    0.0869
-#> 50:    99.4747   -4.0542   -2.4192   -3.9834   -0.9041   -0.1798    4.9782    0.0219    0.2695    0.1234    0.1474    0.2468    0.9269    0.0783    0.8593    0.0854
-#> 51:    99.3401   -4.0386   -2.3951   -3.9661   -0.9181   -0.1887    4.7574    0.0213    0.2746    0.1522    0.1400    0.2344    0.9901    0.0781    0.8863    0.0928
-#> 52:    99.7109   -4.0509   -2.4227   -3.9770   -0.9247   -0.1431    4.9004    0.0203    0.2688    0.1446    0.1330    0.2227    0.8999    0.0791    1.0265    0.0890
-#> 53:    99.6496   -4.0397   -2.4398   -3.9752   -0.9193   -0.2119    5.1106    0.0193    0.2795    0.1527    0.1325    0.2116    0.8949    0.0788    0.9447    0.0872
-#> 54:    99.9071   -4.0211   -2.3887   -3.9812   -0.9233   -0.1946    5.0887    0.0183    0.2763    0.1450    0.1365    0.2010    0.8793    0.0875    0.8643    0.0903
-#> 55:    1.0012e+02  -4.0401e+00  -2.4203e+00  -3.9511e+00  -9.0712e-01  -2.5566e-01   5.7301e+00   1.7375e-02   2.7324e-01   1.3780e-01   1.6204e-01   1.9094e-01   9.7803e-01   7.6146e-02   9.0756e-01   8.7636e-02
-#> 56:    1.0032e+02  -4.0207e+00  -2.4263e+00  -3.9533e+00  -8.7574e-01  -2.3076e-01   6.5321e+00   1.6507e-02   3.0821e-01   1.3091e-01   1.5394e-01   1.8139e-01   8.8520e-01   7.6350e-02   9.2796e-01   8.5283e-02
-#> 57:    1.0028e+02  -4.0037e+00  -2.4301e+00  -3.9655e+00  -8.8472e-01  -1.8969e-01   9.8969e+00   1.5681e-02   2.9280e-01   1.2436e-01   1.4624e-01   1.7232e-01   9.2902e-01   7.4974e-02   8.9204e-01   8.4563e-02
-#> 58:    1.0048e+02  -3.9928e+00  -2.4961e+00  -3.9709e+00  -9.0263e-01  -1.4516e-01   9.4021e+00   1.6151e-02   2.7816e-01   1.1814e-01   1.4165e-01   1.6370e-01   9.5145e-01   8.0233e-02   8.2896e-01   8.3498e-02
-#> 59:    1.0060e+02  -4.0181e+00  -2.4963e+00  -3.9751e+00  -9.0684e-01  -1.1186e-01   8.9320e+00   1.9914e-02   3.0097e-01   1.1224e-01   1.4109e-01   1.5552e-01   9.9121e-01   7.3120e-02   8.6454e-01   8.2239e-02
-#> 60:    1.0047e+02  -3.9976e+00  -2.4797e+00  -3.9780e+00  -8.9328e-01  -1.0814e-01   8.4854e+00   1.8918e-02   3.2275e-01   1.1591e-01   1.3404e-01   1.4774e-01   9.6968e-01   7.4984e-02   8.9831e-01   8.1655e-02
-#> 61:    1.0040e+02  -4.0068e+00  -2.5217e+00  -3.9844e+00  -8.6447e-01  -1.0567e-01   8.0611e+00   1.7972e-02   3.1372e-01   1.1011e-01   1.2973e-01   1.4036e-01   9.1698e-01   7.8118e-02   9.1811e-01   8.4420e-02
-#> 62:    1.0076e+02  -4.0080e+00  -2.4931e+00  -3.9623e+00  -8.9789e-01  -8.3896e-02   7.6580e+00   1.7073e-02   3.0460e-01   1.1254e-01   1.2324e-01   1.3334e-01   9.9032e-01   7.7618e-02   8.3808e-01   8.5031e-02
-#> 63:    1.0064e+02  -4.0129e+00  -2.4731e+00  -3.9561e+00  -8.9103e-01  -8.8987e-02   7.2751e+00   1.6220e-02   2.8944e-01   1.1647e-01   1.4845e-01   1.2667e-01   1.0745e+00   7.6375e-02   8.4316e-01   8.6681e-02
-#> 64:    1.0098e+02  -4.0094e+00  -2.4541e+00  -3.9604e+00  -9.1524e-01  -9.3413e-02   6.9114e+00   1.5409e-02   2.7497e-01   1.2065e-01   1.7095e-01   1.2034e-01   1.0963e+00   7.8304e-02   8.7104e-01   8.5727e-02
-#> 65:    1.0070e+02  -4.0433e+00  -2.4793e+00  -3.9722e+00  -9.3012e-01  -6.5917e-02   6.5658e+00   1.4638e-02   2.7040e-01   1.1462e-01   1.9067e-01   1.1432e-01   9.7444e-01   8.4510e-02   8.7028e-01   8.6292e-02
-#> 66:    1.0049e+02  -4.0656e+00  -2.4659e+00  -3.9898e+00  -9.4278e-01  -7.5929e-02   6.2375e+00   1.3906e-02   2.9347e-01   1.1997e-01   1.8114e-01   1.0860e-01   9.9830e-01   8.0902e-02   9.3551e-01   8.5261e-02
-#> 67:    1.0046e+02  -4.0477e+00  -2.4685e+00  -3.9907e+00  -9.1503e-01  -9.8019e-02   5.9256e+00   1.3211e-02   3.2166e-01   1.1506e-01   1.7208e-01   1.0317e-01   8.6453e-01   9.0533e-02   8.3598e-01   8.6343e-02
-#> 68:    1.0077e+02  -4.0575e+00  -2.4709e+00  -3.9523e+00  -9.2903e-01  -8.1099e-02   5.6294e+00   1.2818e-02   3.1005e-01   1.3665e-01   1.6347e-01   9.8015e-02   9.0181e-01   8.7058e-02   8.4937e-01   8.3248e-02
-#> 69:    1.0086e+02  -4.0626e+00  -2.3922e+00  -3.9557e+00  -9.6741e-01  -3.5986e-02   5.3479e+00   1.2844e-02   3.3024e-01   1.2982e-01   1.5530e-01   9.3115e-02   9.8180e-01   8.3132e-02   8.6549e-01   8.8939e-02
-#> 70:    1.0082e+02  -4.0640e+00  -2.4449e+00  -3.9787e+00  -9.5159e-01  -3.2904e-02   5.0805e+00   1.4346e-02   3.1373e-01   1.2333e-01   1.4754e-01   8.8459e-02   1.0129e+00   7.4856e-02   8.6688e-01   8.4769e-02
-#> 71:    1.0072e+02  -4.0642e+00  -2.5069e+00  -3.9493e+00  -9.3453e-01  -4.4116e-02   4.8265e+00   1.3628e-02   3.0428e-01   1.2122e-01   1.4091e-01   8.4036e-02   1.0454e+00   7.7023e-02   8.9566e-01   8.1639e-02
-#> 72:    1.0049e+02  -4.0609e+00  -2.4472e+00  -3.9669e+00  -9.3972e-01  -7.7498e-02   4.5852e+00   1.4441e-02   3.2552e-01   1.3911e-01   1.4144e-01   8.1899e-02   1.0114e+00   7.7019e-02   8.2312e-01   8.2494e-02
-#> 73:    1.0022e+02  -4.0598e+00  -2.4410e+00  -3.9952e+00  -9.2810e-01  -1.1309e-01   4.3559e+00   1.3719e-02   3.3556e-01   1.3303e-01   1.4990e-01   1.1303e-01   9.6726e-01   7.6776e-02   8.6331e-01   8.3048e-02
-#> 74:    1.0024e+02  -4.0628e+00  -2.4358e+00  -3.9977e+00  -9.1347e-01  -9.1966e-02   4.1381e+00   1.3033e-02   3.4332e-01   1.3418e-01   1.8099e-01   1.0738e-01   1.0158e+00   7.4697e-02   8.6366e-01   8.4370e-02
-#> 75:    99.7847   -4.0500   -2.4401   -4.0018   -0.9252   -0.1013    4.4651    0.0124    0.3365    0.1399    0.1817    0.1020    1.0278    0.0779    0.9008    0.0841
-#> 76:    99.9526   -4.0482   -2.4819   -3.9947   -0.9049   -0.0557    4.2419    0.0126    0.3248    0.1494    0.1726    0.1135    1.0493    0.0778    0.9341    0.0804
-#> 77:    99.9982   -4.0184   -2.4951   -4.0043   -0.8927   -0.0688    5.2538    0.0120    0.3696    0.1419    0.1817    0.1078    1.0402    0.0839    0.9605    0.0848
-#> 78:    1.0007e+02  -4.0210e+00  -2.4725e+00  -4.0040e+00  -8.9827e-01   2.3164e-03   6.4464e+00   1.1395e-02   3.7410e-01   1.3481e-01   2.0294e-01   1.0879e-01   9.7822e-01   8.7445e-02   9.9990e-01   8.2845e-02
-#> 79:    99.3513   -4.0171   -2.5065   -4.0078   -0.8962   -0.0029    7.7527    0.0108    0.3554    0.1281    0.1928    0.1069    1.0455    0.0866    0.9982    0.0870
-#> 80:    98.9945   -4.0172   -2.5412   -4.0341   -0.8891   -0.0187    9.8218    0.0103    0.3376    0.1217    0.1831    0.1457    0.9733    0.0894    1.0164    0.0832
-#> 81:    99.0936   -4.0275   -2.5134   -4.0127   -0.8552   -0.0614   12.1567    0.0098    0.3494    0.1156    0.1740    0.1384    0.9509    0.0843    1.0171    0.0855
-#> 82:    99.2481   -3.9996   -2.4945   -4.0011   -0.8914   -0.0492   11.5489    0.0128    0.3792    0.1098    0.1653    0.1315    0.9915    0.0818    1.0405    0.0928
-#> 83:    99.6941   -3.9998   -2.4851   -3.9845   -0.8802   -0.0560   10.9714    0.0146    0.3602    0.1043    0.1570    0.1249    0.9934    0.0852    0.9707    0.0866
-#> 84:    99.2185   -3.9920   -2.4843   -4.0051   -0.8546   -0.0642   10.4228    0.0153    0.3422    0.0991    0.1492    0.1187    0.9923    0.0833    0.9799    0.0873
-#> 85:    98.8470   -3.9956   -2.4652   -4.0201   -0.8483   -0.0414    9.9017    0.0146    0.3251    0.0941    0.1417    0.1128    0.9732    0.0901    0.9035    0.0858
-#> 86:    98.5012   -3.9841   -2.5148   -4.0250   -0.8408   -0.0551    9.4066    0.0148    0.3088    0.0962    0.1346    0.1071    0.8570    0.0932    0.8532    0.0896
-#> 87:    99.0868   -4.0055   -2.5058   -4.0249   -0.8522   -0.0311   10.3528    0.0175    0.2934    0.1013    0.1411    0.1018    0.8802    0.0838    0.8849    0.0862
-#> 88:    99.5158   -4.0031   -2.4437   -3.9866   -0.8894   -0.0963    9.9832    0.0167    0.3049    0.1030    0.1447    0.0967    0.9955    0.0834    0.8861    0.0893
-#> 89:    99.5538   -4.0347   -2.4494   -4.0213   -0.8695   -0.0494    9.4841    0.0158    0.2897    0.0978    0.1543    0.0918    0.8597    0.0904    0.8959    0.0880
-#> 90:    99.4422   -4.0453   -2.4398   -4.0114   -0.9279   -0.0745    9.8221    0.0150    0.2842    0.0929    0.1466    0.0944    0.9009    0.0871    0.8696    0.0924
-#> 91:    98.8721   -4.0328   -2.4996   -4.0041   -0.8832   -0.0689    9.3310    0.0143    0.2700    0.0896    0.1444    0.1137    0.9567    0.0904    0.8680    0.0891
-#> 92:    99.8390   -4.0418   -2.4914   -4.0182   -0.9279   -0.0460   10.9801    0.0136    0.2585    0.0949    0.1461    0.1210    1.0043    0.0908    0.8310    0.0939
-#> 93:    1.0029e+02  -4.0313e+00  -2.4620e+00  -4.0187e+00  -8.9083e-01  -1.0908e-01   1.0431e+01   1.2890e-02   2.4559e-01   9.5757e-02   1.3878e-01   1.1565e-01   9.9174e-01   9.0056e-02   8.9538e-01   8.8925e-02
-#> 94:    99.3285   -4.0295   -2.4523   -4.0235   -0.8828   -0.1190   10.9003    0.0137    0.2333    0.0915    0.1318    0.1212    1.0729    0.0779    0.9543    0.0907
-#> 95:    99.4117   -4.0422   -2.3807   -4.0870   -0.8960   -0.0889   10.3553    0.0130    0.2216    0.0870    0.1253    0.1366    0.9127    0.0864    0.8901    0.0911
-#> 96:    99.3348   -4.0401   -2.4009   -4.0698   -0.8730   -0.0622    9.8375    0.0123    0.2106    0.0826    0.1241    0.1297    0.8504    0.0836    0.9140    0.0881
-#> 97:    99.4898   -4.0419   -2.4310   -4.0589   -0.8932   -0.0634    9.3456    0.0132    0.2000    0.0785    0.1224    0.1233    0.8770    0.0836    0.8715    0.0837
-#> 98:    99.3750   -4.0704   -2.4353   -4.0616   -0.9333   -0.0846    8.8783    0.0136    0.1900    0.0746    0.1245    0.1171    0.8907    0.0838    0.9066    0.0832
-#> 99:    99.6234   -4.0366   -2.3740   -4.0657   -0.9242   -0.0675    8.4344    0.0129    0.1805    0.0708    0.1182    0.1112    0.8814    0.0808    0.9511    0.0863
-#> 100:    1.0025e+02  -4.0420e+00  -2.3557e+00  -4.0579e+00  -9.5051e-01  -6.3418e-02   8.0319e+00   1.2286e-02   1.7150e-01   6.7291e-02   1.1232e-01   1.0568e-01   8.5851e-01   8.7881e-02   8.9363e-01   8.5897e-02
-#> 101:    1.0041e+02  -4.0461e+00  -2.3840e+00  -4.0384e+00  -9.3752e-01  -7.7594e-02   9.5649e+00   1.1672e-02   1.7509e-01   6.3926e-02   1.2760e-01   1.0039e-01   8.6733e-01   8.2748e-02   9.6277e-01   8.4274e-02
-#> 102:    1.0095e+02  -4.0372e+00  -2.3633e+00  -4.0286e+00  -9.1961e-01  -6.5350e-02   1.1428e+01   1.1088e-02   1.8557e-01   6.0730e-02   1.3211e-01   9.5374e-02   9.3928e-01   8.0161e-02   9.7913e-01   8.4081e-02
-#> 103:    1.0019e+02  -4.0236e+00  -2.4105e+00  -4.0337e+00  -9.1362e-01  -7.3859e-02   1.0856e+01   1.0534e-02   1.7629e-01   5.7693e-02   1.2695e-01   9.1362e-02   9.8491e-01   8.1430e-02   9.7682e-01   8.2250e-02
-#> 104:    99.7755   -4.0280   -2.4452   -4.0197   -0.9112   -0.0810   11.0317    0.0100    0.1796    0.0548    0.1301    0.0868    0.9418    0.0816    0.9170    0.0806
-#> 105:    1.0010e+02  -4.0418e+00  -2.4294e+00  -4.0225e+00  -9.1111e-01  -8.9920e-02   1.0480e+01   9.5070e-03   1.7060e-01   5.2068e-02   1.3987e-01   8.2454e-02   9.1944e-01   7.8110e-02   8.9266e-01   8.7228e-02
-#> 106:    1.0025e+02  -4.0507e+00  -2.4134e+00  -4.0343e+00  -9.0244e-01  -8.4683e-02   1.3506e+01   9.0316e-03   1.6207e-01   4.9465e-02   1.5337e-01   7.8331e-02   9.9609e-01   8.4473e-02   8.7046e-01   8.5479e-02
-#> 107:    1.0014e+02  -4.0468e+00  -2.3972e+00  -4.0196e+00  -9.3650e-01  -2.4087e-02   1.2830e+01   8.5801e-03   1.6027e-01   4.6992e-02   1.5429e-01   8.2493e-02   9.8959e-01   8.2626e-02   8.3427e-01   8.8197e-02
-#> 108:    1.0114e+02  -4.0338e+00  -2.4307e+00  -4.0724e+00  -9.1363e-01   1.1952e-02   1.2189e+01   8.1511e-03   1.5563e-01   4.4854e-02   1.7315e-01   7.8368e-02   9.8589e-01   7.8130e-02   9.0460e-01   8.2870e-02
-#> 109:    1.0066e+02  -4.0550e+00  -2.4094e+00  -4.0641e+00  -9.0945e-01  -1.5401e-03   1.3149e+01   7.7435e-03   1.4785e-01   4.2612e-02   1.7232e-01   7.4450e-02   1.0942e+00   7.4816e-02   9.1706e-01   8.5333e-02
-#> 110:    1.0111e+02  -4.0266e+00  -2.4047e+00  -4.0646e+00  -9.0541e-01  -1.7212e-02   1.2492e+01   7.3563e-03   1.4046e-01   4.0481e-02   1.8132e-01   7.0727e-02   1.0508e+00   7.9457e-02   9.8990e-01   8.2975e-02
-#> 111:    1.0155e+02  -4.0274e+00  -2.3645e+00  -4.0663e+00  -9.4902e-01  -1.8882e-02   1.1867e+01   8.7757e-03   1.4436e-01   3.8457e-02   1.7225e-01   6.7191e-02   1.0217e+00   7.7437e-02   9.9196e-01   8.1580e-02
-#> 112:    1.0209e+02  -4.0230e+00  -2.3938e+00  -4.0375e+00  -9.5447e-01  -5.0888e-02   1.4321e+01   8.3370e-03   1.4863e-01   3.6534e-02   1.6778e-01   8.2186e-02   9.3085e-01   8.3291e-02   9.8775e-01   7.9492e-02
-#> 113:    1.0188e+02  -4.0173e+00  -2.3804e+00  -4.0403e+00  -9.6152e-01  -7.7453e-02   1.3605e+01   7.9201e-03   1.5060e-01   3.4708e-02   1.7341e-01   8.4506e-02   9.0783e-01   8.7383e-02   9.4854e-01   8.2648e-02
-#> 114:    1.0239e+02  -4.0081e+00  -2.3724e+00  -4.0332e+00  -9.4315e-01  -7.4933e-02   1.2925e+01   7.5241e-03   1.4307e-01   3.2972e-02   1.6695e-01   8.0281e-02   9.2775e-01   8.4314e-02   9.6195e-01   7.9448e-02
-#> 115:    1.0199e+02  -4.0127e+00  -2.3773e+00  -4.0472e+00  -9.5157e-01  -2.0947e-02   1.2279e+01   7.4483e-03   1.3592e-01   3.1324e-02   1.6705e-01   7.6267e-02   9.4956e-01   7.6989e-02   1.0340e+00   8.5564e-02
-#> 116:    1.0122e+02  -4.0264e+00  -2.4014e+00  -4.0509e+00  -9.1462e-01  -2.3511e-02   1.1665e+01   7.0759e-03   1.2912e-01   2.9757e-02   1.5870e-01   7.2453e-02   9.3580e-01   8.2952e-02   9.3341e-01   8.3302e-02
-#> 117:    1.0112e+02  -4.0326e+00  -2.4093e+00  -4.0559e+00  -8.9743e-01  -2.0572e-02   1.1082e+01   6.7221e-03   1.2266e-01   2.8269e-02   1.5339e-01   6.8831e-02   9.0879e-01   8.4441e-02   9.1432e-01   8.0538e-02
-#> 118:    1.0123e+02  -4.0411e+00  -2.4077e+00  -4.0556e+00  -9.2971e-01  -2.1885e-02   1.0528e+01   6.3860e-03   1.1653e-01   3.3123e-02   1.6947e-01   6.5389e-02   9.7140e-01   8.6671e-02   8.9874e-01   8.1670e-02
-#> 119:    1.0098e+02  -4.0538e+00  -2.3515e+00  -4.0607e+00  -9.5433e-01  -7.5743e-02   1.0001e+01   6.0667e-03   1.1070e-01   3.1467e-02   1.8338e-01   6.2120e-02   9.1537e-01   8.4827e-02   9.2420e-01   8.2769e-02
-#> 120:    1.0076e+02  -4.0573e+00  -2.3627e+00  -4.0329e+00  -9.3251e-01  -6.7669e-02   9.5011e+00   5.7634e-03   1.0517e-01   3.2868e-02   1.7422e-01   6.6096e-02   9.5247e-01   8.5343e-02   9.4678e-01   8.5335e-02
-#> 121:    1.0085e+02  -4.0450e+00  -2.3478e+00  -4.0692e+00  -9.2333e-01  -9.8005e-03   9.0261e+00   5.4752e-03   9.9911e-02   3.1225e-02   1.6550e-01   7.1593e-02   8.5572e-01   8.8654e-02   1.0248e+00   8.0646e-02
-#> 122:    1.0164e+02  -4.0325e+00  -2.3562e+00  -4.0680e+00  -9.4287e-01  -1.2103e-02   8.5748e+00   5.3493e-03   9.4915e-02   2.9663e-02   1.6347e-01   6.8014e-02   8.4872e-01   8.6803e-02   1.0282e+00   8.0381e-02
-#> 123:    1.0184e+02  -4.0521e+00  -2.3504e+00  -4.0714e+00  -9.5966e-01  -9.1996e-05   8.1460e+00   5.0818e-03   9.8247e-02   3.0007e-02   1.7746e-01   6.4613e-02   9.7181e-01   8.0986e-02   9.8860e-01   8.0317e-02
-#> 124:    1.0235e+02  -4.0674e+00  -2.3315e+00  -4.0874e+00  -9.9802e-01   3.8818e-02   7.7387e+00   4.8277e-03   9.3335e-02   2.8506e-02   1.7611e-01   6.8940e-02   9.7376e-01   7.6658e-02   9.9156e-01   8.4407e-02
-#> 125:    1.0257e+02  -4.0718e+00  -2.3604e+00  -4.0627e+00  -1.0591e+00   2.4685e-02   7.3518e+00   4.5863e-03   8.8668e-02   3.0650e-02   1.8671e-01   6.5493e-02   1.0275e+00   8.2278e-02   1.0896e+00   8.0976e-02
-#> 126:    1.0287e+02  -4.0691e+00  -2.3103e+00  -4.0552e+00  -1.0174e+00   2.1863e-02   7.5644e+00   4.3570e-03   1.0937e-01   2.9117e-02   1.7738e-01   6.2218e-02   9.2668e-01   7.9560e-02   9.5409e-01   8.4671e-02
-#> 127:    1.0327e+02  -4.0528e+00  -2.3141e+00  -4.0522e+00  -1.0108e+00   4.4779e-03   7.1862e+00   4.1392e-03   1.2239e-01   2.7661e-02   1.6925e-01   5.9107e-02   9.1372e-01   7.9536e-02   9.9164e-01   8.2999e-02
-#> 128:    1.0352e+02  -4.0496e+00  -2.2880e+00  -4.0496e+00  -1.0063e+00  -1.3248e-02   7.6721e+00   3.9613e-03   1.1627e-01   2.6278e-02   1.7517e-01   8.0231e-02   8.4407e-01   8.5078e-02   9.4382e-01   8.7530e-02
-#> 129:    1.0345e+02  -4.0715e+00  -2.3090e+00  -4.0400e+00  -1.0276e+00  -1.8301e-02   8.2197e+00   3.7633e-03   1.1046e-01   2.7141e-02   1.9366e-01   7.6220e-02   9.3357e-01   8.2674e-02   9.7064e-01   8.6011e-02
-#> 130:    1.0245e+02  -4.0787e+00  -2.3263e+00  -4.0106e+00  -1.0200e+00  -8.5976e-02   7.8087e+00   4.0830e-03   1.3607e-01   2.6631e-02   2.2700e-01   7.2409e-02   9.8233e-01   7.9348e-02   9.6780e-01   8.2658e-02
-#> 131:    1.0217e+02  -4.0760e+00  -2.2525e+00  -4.0082e+00  -1.0099e+00  -1.6111e-01   7.4183e+00   3.8789e-03   1.3972e-01   2.5299e-02   2.2508e-01   6.8788e-02   1.0066e+00   7.8692e-02   9.4684e-01   8.4349e-02
-#> 132:    1.0185e+02  -4.0792e+00  -2.2309e+00  -3.9996e+00  -9.8302e-01  -2.2504e-01   7.0474e+00   4.0356e-03   1.3743e-01   2.4034e-02   2.1383e-01   7.7346e-02   9.4225e-01   7.9110e-02   9.5160e-01   8.4398e-02
-#> 133:    1.0135e+02  -4.0818e+00  -2.2219e+00  -4.0054e+00  -9.7264e-01  -1.8912e-01   7.1932e+00   3.8338e-03   1.3056e-01   2.2833e-02   2.0314e-01   7.6769e-02   1.0031e+00   8.5400e-02   1.0034e+00   8.4805e-02
-#> 134:    1.0148e+02  -4.0782e+00  -2.2492e+00  -3.9886e+00  -9.5184e-01  -1.5049e-01   6.8336e+00   3.6422e-03   1.2403e-01   2.3398e-02   1.9298e-01   7.2931e-02   9.3696e-01   8.3566e-02   9.4742e-01   8.9137e-02
-#> 135:    1.0145e+02  -4.0852e+00  -2.3062e+00  -4.0011e+00  -9.4444e-01  -1.6803e-01   6.4919e+00   3.4600e-03   1.1783e-01   2.2228e-02   1.8333e-01   6.9284e-02   9.4846e-01   8.3087e-02   9.7774e-01   8.2610e-02
-#> 136:    1.0177e+02  -4.0861e+00  -2.2785e+00  -3.9890e+00  -9.9625e-01  -1.8938e-01   6.1673e+00   3.2870e-03   1.1752e-01   2.1116e-02   1.8815e-01   6.5820e-02   9.3634e-01   8.5255e-02   1.1001e+00   8.5332e-02
-#> 137:    1.0200e+02  -4.0928e+00  -2.1946e+00  -3.9974e+00  -1.0098e+00  -1.8810e-01   5.8589e+00   3.1227e-03   1.2394e-01   2.1203e-02   1.7874e-01   7.2232e-02   1.0048e+00   7.3422e-02   1.0222e+00   8.3484e-02
-#> 138:    1.0214e+02  -4.0820e+00  -2.2052e+00  -3.9737e+00  -1.0420e+00  -2.0594e-01   5.5660e+00   3.8937e-03   1.9164e-01   2.0143e-02   1.6980e-01   6.8621e-02   1.0126e+00   7.6106e-02   1.0780e+00   8.2960e-02
-#> 139:    1.0249e+02  -4.0785e+00  -2.1649e+00  -3.9567e+00  -1.0095e+00  -2.8807e-01   5.2877e+00   3.6990e-03   1.8647e-01   1.9135e-02   1.6131e-01   6.5190e-02   1.0030e+00   7.9858e-02   1.0611e+00   8.4109e-02
-#> 140:    1.0184e+02  -4.0847e+00  -2.1800e+00  -3.9565e+00  -9.9415e-01  -2.8869e-01   5.0233e+00   4.0857e-03   1.9502e-01   1.8179e-02   1.6676e-01   6.2879e-02   9.5962e-01   7.8117e-02   9.9649e-01   8.4914e-02
-#> 141:    1.0195e+02  -4.1012e+00  -2.1831e+00  -3.9488e+00  -9.9515e-01  -3.1864e-01   4.7721e+00   3.8814e-03   1.8527e-01   1.7270e-02   1.6797e-01   6.1084e-02   9.0969e-01   8.2722e-02   1.0122e+00   8.2518e-02
-#> 142:    1.0233e+02  -4.1139e+00  -2.1692e+00  -3.9542e+00  -1.0023e+00  -3.3242e-01   4.5335e+00   3.6873e-03   2.0662e-01   1.6406e-02   1.5957e-01   5.8030e-02   9.4761e-01   8.4629e-02   1.0342e+00   8.3954e-02
-#> 143:    1.0217e+02  -4.1103e+00  -2.1380e+00  -3.9511e+00  -1.0300e+00  -2.5992e-01   5.2035e+00   4.7053e-03   1.9629e-01   1.5586e-02   1.5979e-01   5.5128e-02   8.9255e-01   7.9042e-02   1.0461e+00   8.6952e-02
-#> 144:    1.0185e+02  -4.1335e+00  -2.1911e+00  -3.9650e+00  -1.0440e+00  -2.4451e-01   5.0998e+00   4.4700e-03   1.8648e-01   1.9590e-02   1.5534e-01   5.2372e-02   9.7863e-01   8.3932e-02   1.0197e+00   8.7673e-02
-#> 145:    1.0242e+02  -4.1445e+00  -2.1203e+00  -3.9616e+00  -1.0426e+00  -2.7120e-01   4.8448e+00   4.2465e-03   1.7715e-01   1.8611e-02   1.4757e-01   4.9753e-02   1.0024e+00   8.4131e-02   1.0768e+00   8.5388e-02
-#> 146:    1.0236e+02  -4.1519e+00  -2.1958e+00  -3.9779e+00  -9.8615e-01  -2.5863e-01   4.6026e+00   4.0718e-03   1.6829e-01   1.7680e-02   1.6407e-01   4.7266e-02   1.0740e+00   8.2413e-02   1.0706e+00   8.3410e-02
-#> 147:    1.0251e+02  -4.1465e+00  -2.2042e+00  -3.9775e+00  -1.0317e+00  -2.2757e-01   4.3725e+00   3.8682e-03   1.5988e-01   1.6796e-02   1.7016e-01   4.4902e-02   9.7748e-01   8.3376e-02   1.0880e+00   8.1968e-02
-#> 148:    1.0244e+02  -4.1432e+00  -2.1786e+00  -3.9792e+00  -1.0442e+00  -2.2002e-01   4.9671e+00   3.6748e-03   1.5189e-01   1.5956e-02   2.2196e-01   4.2657e-02   1.0412e+00   7.8051e-02   1.1051e+00   8.1618e-02
-#> 149:    1.0219e+02  -4.1384e+00  -2.2318e+00  -3.9757e+00  -1.0438e+00  -2.4124e-01   4.7187e+00   3.4910e-03   1.4429e-01   1.6061e-02   2.1086e-01   4.0524e-02   1.0082e+00   8.0377e-02   1.1455e+00   8.0545e-02
-#> 150:    1.0264e+02  -4.1498e+00  -2.2352e+00  -3.9915e+00  -1.0669e+00  -2.1255e-01   4.4828e+00   3.3165e-03   1.3708e-01   1.7218e-02   2.0032e-01   3.8498e-02   9.5031e-01   8.7248e-02   9.8770e-01   8.3250e-02
-#> 151:    1.0250e+02  -4.1365e+00  -2.1876e+00  -3.9939e+00  -1.0568e+00  -1.8159e-01   4.2587e+00   3.1507e-03   1.3022e-01   1.7383e-02   1.9030e-01   3.6573e-02   9.6938e-01   8.0203e-02   1.0578e+00   8.3430e-02
-#> 152:    1.0256e+02  -4.1370e+00  -2.2238e+00  -4.0047e+00  -1.0406e+00  -1.8764e-01   1.9609e+00   1.4191e-03   1.1882e-01   1.7924e-02   1.6889e-01   4.1216e-02   9.1972e-01   7.8573e-02   1.0717e+00   8.0882e-02
-#> 153:    1.0219e+02  -4.1299e+00  -2.2139e+00  -3.9917e+00  -9.9964e-01  -2.0505e-01   1.8258e+00   1.0432e-03   8.4660e-02   2.1446e-02   1.7634e-01   3.5573e-02   9.3702e-01   8.4860e-02   1.0145e+00   8.3329e-02
-#> 154:    1.0199e+02  -4.1354e+00  -2.2231e+00  -3.9779e+00  -1.0155e+00  -2.2573e-01   2.6463e+00   5.8153e-04   8.8101e-02   2.3167e-02   1.6103e-01   3.3874e-02   9.5360e-01   8.6215e-02   9.5723e-01   8.4603e-02
-#> 155:    1.0234e+02  -4.1239e+00  -2.2137e+00  -3.9802e+00  -1.0070e+00  -2.3158e-01   2.9697e+00   6.6709e-04   1.1190e-01   2.0949e-02   1.8298e-01   3.1557e-02   9.2910e-01   8.2509e-02   9.8680e-01   8.5206e-02
-#> 156:    1.0253e+02  -4.1269e+00  -2.2370e+00  -3.9682e+00  -1.0420e+00  -2.1219e-01   2.7267e+00   6.8451e-04   8.9651e-02   2.4380e-02   1.6613e-01   3.4846e-02   9.3608e-01   8.7506e-02   9.0446e-01   8.1755e-02
-#> 157:    1.0265e+02  -4.1241e+00  -2.2179e+00  -3.9676e+00  -1.0308e+00  -2.2480e-01   2.1278e+00   4.9811e-04   6.7161e-02   1.9758e-02   1.5607e-01   4.4198e-02   9.4162e-01   8.7311e-02   9.9147e-01   7.9857e-02
-#> 158:    1.0239e+02  -4.1219e+00  -2.1615e+00  -3.9781e+00  -1.0384e+00  -2.6750e-01   2.5310e+00   4.8270e-04   6.5662e-02   1.8085e-02   1.7665e-01   4.4020e-02   8.8632e-01   8.6004e-02   1.0425e+00   8.2894e-02
-#> 159:    1.0270e+02  -4.1204e+00  -2.1837e+00  -3.9530e+00  -1.0587e+00  -2.5809e-01   3.4348e+00   5.6788e-04   6.5500e-02   1.9540e-02   1.8629e-01   4.0730e-02   9.5079e-01   8.2399e-02   9.9316e-01   8.3381e-02
-#> 160:    1.0282e+02  -4.1223e+00  -2.1325e+00  -3.9734e+00  -1.0068e+00  -2.8751e-01   3.9652e+00   7.6565e-04   8.5246e-02   1.7068e-02   1.7587e-01   3.0778e-02   9.1802e-01   8.0158e-02   9.9642e-01   8.1564e-02
-#> 161:    1.0330e+02  -4.1180e+00  -2.1879e+00  -3.9743e+00  -1.0268e+00  -2.8812e-01   4.9153e+00   5.8033e-04   8.0457e-02   1.8555e-02   1.7312e-01   3.3941e-02   8.6920e-01   8.2509e-02   9.5632e-01   8.1798e-02
-#> 162:    1.0335e+02  -4.1182e+00  -2.2089e+00  -3.9566e+00  -1.0409e+00  -2.7390e-01   3.6169e+00   2.8392e-04   1.0776e-01   1.9589e-02   1.6479e-01   2.8481e-02   8.8603e-01   8.7799e-02   9.5197e-01   7.9563e-02
-#> 163:    1.0294e+02  -4.1181e+00  -2.2025e+00  -3.9462e+00  -9.9783e-01  -3.0753e-01   3.7234e+00   1.6293e-04   9.6922e-02   2.4842e-02   1.9367e-01   3.1473e-02   9.0380e-01   9.1697e-02   9.4394e-01   8.2786e-02
-#> 164:    1.0246e+02  -4.1155e+00  -2.2157e+00  -3.9736e+00  -9.9866e-01  -2.9356e-01   3.9439e+00   1.9405e-04   1.0404e-01   2.8435e-02   1.9043e-01   3.1239e-02   8.9853e-01   8.9427e-02   9.2586e-01   8.3170e-02
-#> 165:    1.0204e+02  -4.1117e+00  -2.2133e+00  -3.9674e+00  -1.0079e+00  -2.6996e-01   3.0774e+00   1.6591e-04   7.0005e-02   2.8285e-02   2.0813e-01   2.4574e-02   8.9719e-01   9.1629e-02   9.8242e-01   8.3692e-02
-#> 166:    1.0207e+02  -4.1164e+00  -2.2192e+00  -3.9893e+00  -1.0354e+00  -2.7396e-01   1.8145e+00   8.4168e-05   9.0739e-02   2.7410e-02   2.1403e-01   2.4311e-02   8.9386e-01   9.2727e-02   9.4636e-01   8.4238e-02
-#> 167:    1.0187e+02  -4.1149e+00  -2.2185e+00  -3.9708e+00  -1.0036e+00  -2.5751e-01   1.5355e+00   4.0974e-05   9.9346e-02   2.2030e-02   2.1916e-01   2.6726e-02   9.1055e-01   8.1030e-02   1.0098e+00   7.9180e-02
-#> 168:    1.0172e+02  -4.1167e+00  -2.2673e+00  -3.9702e+00  -9.8388e-01  -2.1404e-01   1.4836e+00   2.7779e-05   7.7509e-02   2.9513e-02   1.9543e-01   3.4526e-02   1.0152e+00   8.1248e-02   9.7482e-01   8.0746e-02
-#> 169:    1.0175e+02  -4.1171e+00  -2.2634e+00  -3.9701e+00  -9.5962e-01  -2.4130e-01   1.4263e+00   4.7370e-05   5.0986e-02   2.8211e-02   2.2554e-01   3.9909e-02   9.8519e-01   7.8842e-02   1.0023e+00   8.5684e-02
-#> 170:    1.0177e+02  -4.1189e+00  -2.2417e+00  -3.9834e+00  -1.0059e+00  -2.6551e-01   9.9010e-01   3.7247e-05   4.2517e-02   2.9791e-02   1.8705e-01   4.2435e-02   9.6604e-01   8.8427e-02   9.6699e-01   8.3986e-02
-#> 171:    1.0182e+02  -4.1187e+00  -2.2464e+00  -3.9953e+00  -9.8154e-01  -2.5146e-01   7.4179e-01   3.2420e-05   5.0690e-02   3.0483e-02   1.7888e-01   6.3177e-02   9.2784e-01   8.4814e-02   1.0018e+00   8.4070e-02
-#> 172:    1.0184e+02  -4.1178e+00  -2.2483e+00  -4.0009e+00  -1.0096e+00  -2.2636e-01   9.6710e-01   2.6981e-05   3.1321e-02   2.7772e-02   1.9767e-01   7.4969e-02   9.9720e-01   8.1434e-02   9.5483e-01   8.3419e-02
-#> 173:    1.0160e+02  -4.1183e+00  -2.2513e+00  -3.9920e+00  -9.8456e-01  -2.0144e-01   4.9964e-01   2.1222e-05   4.1909e-02   2.8101e-02   2.1163e-01   1.2811e-01   9.6384e-01   8.0352e-02   9.2496e-01   8.2328e-02
-#> 174:    1.0159e+02  -4.1179e+00  -2.2334e+00  -4.0068e+00  -1.0316e+00  -2.0656e-01   4.6608e-01   1.8044e-05   4.4647e-02   2.8273e-02   2.0083e-01   1.2780e-01   9.4612e-01   8.3630e-02   8.9385e-01   8.3930e-02
-#> 175:    1.0159e+02  -4.1182e+00  -2.2567e+00  -3.9972e+00  -1.0299e+00  -1.6534e-01   4.5228e-01   2.0060e-05   8.5751e-02   2.5343e-02   1.7864e-01   8.6977e-02   9.5795e-01   7.8867e-02   8.9213e-01   8.4362e-02
-#> 176:    1.0159e+02  -4.1183e+00  -2.2109e+00  -3.9983e+00  -1.0210e+00  -2.0879e-01   5.3694e-01   2.0264e-05   1.2835e-01   2.5563e-02   1.9469e-01   6.0808e-02   9.1537e-01   7.8520e-02   9.3355e-01   8.3608e-02
-#> 177:    1.0155e+02  -4.1193e+00  -2.2587e+00  -3.9825e+00  -1.0180e+00  -1.6859e-01   4.4935e-01   3.0321e-05   1.3509e-01   2.4979e-02   2.0113e-01   6.3617e-02   9.7277e-01   7.8515e-02   9.2667e-01   8.5309e-02
-#> 178:    1.0158e+02  -4.1196e+00  -2.2679e+00  -4.0231e+00  -1.0143e+00  -1.6084e-01   6.7629e-01   3.2855e-05   6.8816e-02   2.7808e-02   1.8944e-01   8.1814e-02   8.8319e-01   8.0114e-02   9.5183e-01   8.2195e-02
-#> 179:    1.0166e+02  -4.1190e+00  -2.2764e+00  -3.9875e+00  -1.0061e+00  -1.8260e-01   7.1129e-01   3.8250e-05   7.5489e-02   2.4148e-02   1.8082e-01   7.1172e-02   9.1387e-01   8.0813e-02   9.6660e-01   8.2457e-02
-#> 180:    1.0179e+02  -4.1202e+00  -2.2848e+00  -3.9974e+00  -9.9825e-01  -2.0277e-01   5.5755e-01   2.8041e-05   8.6779e-02   2.7193e-02   1.8826e-01   6.5133e-02   8.8812e-01   8.2655e-02   9.2100e-01   7.9919e-02
-#> 181:    1.0176e+02  -4.1200e+00  -2.2704e+00  -3.9954e+00  -1.0194e+00  -1.6896e-01   4.3842e-01   2.2428e-05   7.4093e-02   3.0526e-02   2.3473e-01   1.0537e-01   9.2303e-01   8.2141e-02   9.2941e-01   8.4699e-02
-#> 182:    1.0182e+02  -4.1211e+00  -2.3159e+00  -4.0259e+00  -1.0162e+00  -1.2876e-01   3.4993e-01   1.5716e-05   5.9887e-02   2.6422e-02   2.1757e-01   1.0488e-01   9.1725e-01   9.4143e-02   9.7674e-01   8.8668e-02
-#> 183:    1.0184e+02  -4.1216e+00  -2.2985e+00  -4.0278e+00  -1.0136e+00  -1.3154e-01   2.6456e-01   1.2552e-05   5.7149e-02   3.2712e-02   2.0632e-01   1.5501e-01   9.2464e-01   8.5394e-02   8.8699e-01   8.4279e-02
-#> 184:    1.0172e+02  -4.1212e+00  -2.2726e+00  -4.0189e+00  -1.0280e+00  -1.2967e-01   3.0582e-01   7.5239e-06   8.2812e-02   2.9556e-02   1.9725e-01   1.3753e-01   9.0862e-01   8.1319e-02   9.0031e-01   8.3491e-02
-#> 185:    1.0178e+02  -4.1208e+00  -2.2858e+00  -4.0272e+00  -1.0063e+00  -1.6155e-01   3.0856e-01   4.5894e-06   8.8870e-02   2.5817e-02   1.9251e-01   1.0670e-01   9.1157e-01   7.7834e-02   9.6258e-01   7.8990e-02
-#> 186:    1.0198e+02  -4.1208e+00  -2.2682e+00  -4.0401e+00  -9.8523e-01  -1.1556e-01   2.4761e-01   3.2640e-06   7.5614e-02   2.1067e-02   1.9085e-01   9.0045e-02   8.5090e-01   8.6621e-02   1.0145e+00   8.1864e-02
-#> 187:    1.0197e+02  -4.1208e+00  -2.2788e+00  -4.0281e+00  -1.0066e+00  -1.0149e-01   2.0460e-01   4.5073e-06   7.8797e-02   2.3861e-02   2.0725e-01   7.9771e-02   9.6253e-01   8.2363e-02   9.3855e-01   8.3939e-02
-#> 188:    1.0196e+02  -4.1207e+00  -2.3105e+00  -4.0149e+00  -1.0217e+00  -9.0603e-02   2.2178e-01   3.6903e-06   8.9793e-02   2.1775e-02   1.9248e-01   8.2415e-02   9.4078e-01   8.1247e-02   9.1756e-01   8.2786e-02
-#> 189:    1.0202e+02  -4.1204e+00  -2.2702e+00  -4.0430e+00  -1.0032e+00  -1.1308e-01   2.2944e-01   3.5141e-06   7.8575e-02   2.4885e-02   2.0968e-01   8.2380e-02   9.5115e-01   8.1619e-02   9.2134e-01   8.9958e-02
-#> 190:    1.0195e+02  -4.1207e+00  -2.3126e+00  -4.0312e+00  -1.0154e+00  -6.3842e-02   2.5129e-01   2.6517e-06   4.2267e-02   2.2084e-02   1.9361e-01   7.0492e-02   9.3985e-01   8.5817e-02   9.3893e-01   8.7011e-02
-#> 191:    1.0203e+02  -4.1206e+00  -2.2758e+00  -4.0290e+00  -1.0102e+00  -3.1042e-02   1.7935e-01   3.4489e-06   5.7444e-02   2.3544e-02   1.9651e-01   7.9509e-02   9.5213e-01   8.2030e-02   1.0054e+00   8.7523e-02
-#> 192:    1.0199e+02  -4.1205e+00  -2.2969e+00  -4.0329e+00  -1.0364e+00  -8.3705e-02   1.5785e-01   3.5081e-06   7.4305e-02   2.2992e-02   1.9662e-01   7.7684e-02   9.2601e-01   8.3027e-02   9.8642e-01   8.3428e-02
-#> 193:    1.0196e+02  -4.1205e+00  -2.2661e+00  -4.0513e+00  -9.9271e-01  -4.6516e-02   1.2084e-01   2.6911e-06   6.8360e-02   3.5444e-02   1.9649e-01   7.5188e-02   9.1949e-01   7.9194e-02   1.0046e+00   8.5964e-02
-#> 194:    1.0198e+02  -4.1207e+00  -2.2817e+00  -4.0520e+00  -9.9852e-01  -8.4466e-02   1.3596e-01   1.5511e-06   6.5142e-02   4.1562e-02   1.9137e-01   9.6992e-02   9.6709e-01   7.6757e-02   9.7566e-01   8.3784e-02
-#> 195:    1.0200e+02  -4.1207e+00  -2.3076e+00  -4.0637e+00  -1.0028e+00  -7.2489e-02   1.0942e-01   1.6451e-06   6.1364e-02   4.6242e-02   1.9470e-01   9.3546e-02   9.9614e-01   8.1292e-02   9.7814e-01   8.1909e-02
-#> 196:    1.0194e+02  -4.1205e+00  -2.2970e+00  -4.0482e+00  -9.8816e-01  -6.8493e-02   1.1918e-01   1.2629e-06   4.2775e-02   3.6925e-02   2.3565e-01   7.7784e-02   8.9524e-01   9.2250e-02   9.8003e-01   8.2408e-02
-#> 197:    1.0199e+02  -4.1205e+00  -2.3075e+00  -4.0418e+00  -1.0196e+00  -6.8458e-02   1.7674e-01   7.5205e-07   5.2125e-02   2.9288e-02   2.1892e-01   8.4416e-02   8.9857e-01   9.1154e-02   1.0377e+00   8.3604e-02
-#> 198:    1.0197e+02  -4.1206e+00  -2.3051e+00  -4.0367e+00  -1.0252e+00  -6.9200e-02   9.1625e-02   6.6068e-07   4.7665e-02   2.8907e-02   1.8679e-01   7.5787e-02   9.0272e-01   8.8077e-02   9.2929e-01   8.0385e-02
-#> 199:    1.0192e+02  -4.1204e+00  -2.3163e+00  -4.0506e+00  -1.0152e+00  -5.3872e-02   6.8196e-02   5.5789e-07   6.0471e-02   3.1730e-02   2.0053e-01   6.8557e-02   9.0478e-01   8.5910e-02   9.3814e-01   8.2211e-02
-#> 200:    1.0195e+02  -4.1205e+00  -2.3141e+00  -4.0728e+00  -1.0010e+00  -2.5675e-03   6.5235e-02   6.9762e-07   5.8458e-02   2.8504e-02   2.0377e-01   4.9513e-02   8.5640e-01   8.6640e-02   9.5731e-01   8.4390e-02
-#> 201:    1.0195e+02  -4.1205e+00  -2.3106e+00  -4.0774e+00  -9.9012e-01   5.1724e-03   5.1225e-02   5.4222e-07   6.0577e-02   3.3554e-02   2.0505e-01   4.4738e-02   8.8073e-01   8.5488e-02   9.6928e-01   8.4895e-02
-#> 202:    1.0194e+02  -4.1205e+00  -2.3078e+00  -4.0767e+00  -9.9283e-01   3.9328e-03   4.5461e-02   4.8520e-07   6.7405e-02   3.4599e-02   2.1312e-01   4.6664e-02   9.0528e-01   8.4189e-02   9.8043e-01   8.5266e-02
-#> 203:    1.0193e+02  -4.1205e+00  -2.3029e+00  -4.0790e+00  -9.8990e-01  -9.1380e-03   4.7128e-02   5.0468e-07   6.8524e-02   3.6050e-02   2.1378e-01   5.1774e-02   9.0923e-01   8.4899e-02   9.8928e-01   8.4613e-02
-#> 204:    1.0192e+02  -4.1205e+00  -2.3080e+00  -4.0760e+00  -9.8833e-01  -1.2434e-02   4.8184e-02   4.9472e-07   6.4152e-02   3.5604e-02   2.0954e-01   5.1354e-02   9.1294e-01   8.5219e-02   9.8301e-01   8.4374e-02
-#> 205:    1.0192e+02  -4.1205e+00  -2.3100e+00  -4.0712e+00  -9.9253e-01  -2.3365e-02   4.5888e-02   5.0564e-07   5.9894e-02   3.5053e-02   2.0322e-01   5.4423e-02   9.0925e-01   8.5899e-02   9.8418e-01   8.3421e-02
-#> 206:    1.0192e+02  -4.1205e+00  -2.3095e+00  -4.0715e+00  -9.9721e-01  -2.6262e-02   4.3985e-02   5.1954e-07   5.8681e-02   3.4539e-02   2.0202e-01   5.8248e-02   9.1301e-01   8.5459e-02   9.8621e-01   8.3465e-02
-#> 207:    1.0192e+02  -4.1205e+00  -2.3179e+00  -4.0731e+00  -9.9906e-01  -2.3191e-02   4.3649e-02   5.3824e-07   5.7537e-02   3.4790e-02   2.0220e-01   6.0242e-02   9.1783e-01   8.5307e-02   9.8436e-01   8.3111e-02
-#> 208:    1.0191e+02  -4.1205e+00  -2.3238e+00  -4.0734e+00  -9.9920e-01  -1.9434e-02   4.3223e-02   5.3831e-07   5.7908e-02   3.4909e-02   2.0126e-01   6.0353e-02   9.2010e-01   8.5244e-02   9.8002e-01   8.2975e-02
-#> 209:    1.0191e+02  -4.1205e+00  -2.3279e+00  -4.0726e+00  -1.0053e+00  -1.5390e-02   4.1064e-02   5.3171e-07   5.8749e-02   3.4510e-02   1.9942e-01   6.3063e-02   9.3192e-01   8.4436e-02   9.8298e-01   8.3187e-02
-#> 210:    1.0191e+02  -4.1205e+00  -2.3310e+00  -4.0705e+00  -1.0061e+00  -1.3507e-02   3.8265e-02   5.2762e-07   5.9344e-02   3.3374e-02   1.9612e-01   6.7006e-02   9.3199e-01   8.4573e-02   9.8382e-01   8.3227e-02
-#> 211:    1.0191e+02  -4.1205e+00  -2.3383e+00  -4.0683e+00  -1.0043e+00  -1.3973e-02   3.6076e-02   5.2584e-07   6.1568e-02   3.2369e-02   1.9504e-01   6.9982e-02   9.4179e-01   8.4625e-02   9.9145e-01   8.3067e-02
-#> 212:    1.0192e+02  -4.1204e+00  -2.3396e+00  -4.0662e+00  -1.0055e+00  -1.8011e-02   3.4746e-02   5.4375e-07   6.2747e-02   3.1588e-02   1.9405e-01   7.2360e-02   9.4525e-01   8.4466e-02   9.9581e-01   8.2952e-02
-#> 213:    1.0192e+02  -4.1204e+00  -2.3407e+00  -4.0649e+00  -1.0066e+00  -2.1077e-02   3.4708e-02   5.5843e-07   6.1940e-02   3.0715e-02   1.9382e-01   7.4602e-02   9.4611e-01   8.4322e-02   9.9397e-01   8.2717e-02
-#> 214:    1.0192e+02  -4.1204e+00  -2.3392e+00  -4.0648e+00  -1.0076e+00  -2.3417e-02   3.4282e-02   5.8157e-07   6.1893e-02   3.0158e-02   1.9322e-01   7.8942e-02   9.5250e-01   8.3922e-02   9.9723e-01   8.2793e-02
-#> 215:    1.0192e+02  -4.1204e+00  -2.3410e+00  -4.0645e+00  -1.0087e+00  -2.1950e-02   3.5820e-02   6.0691e-07   6.2032e-02   2.9890e-02   1.9172e-01   8.3774e-02   9.5617e-01   8.3280e-02   1.0003e+00   8.2881e-02
-#> 216:    1.0192e+02  -4.1203e+00  -2.3425e+00  -4.0628e+00  -1.0069e+00  -2.4268e-02   3.7597e-02   6.4187e-07   6.1733e-02   2.9353e-02   1.9092e-01   8.8150e-02   9.5834e-01   8.3091e-02   1.0027e+00   8.2753e-02
-#> 217:    1.0192e+02  -4.1203e+00  -2.3439e+00  -4.0613e+00  -1.0064e+00  -2.4197e-02   3.9291e-02   6.5775e-07   6.2318e-02   2.8903e-02   1.8958e-01   9.0470e-02   9.5766e-01   8.3234e-02   1.0020e+00   8.2707e-02
-#> 218:    1.0191e+02  -4.1203e+00  -2.3441e+00  -4.0619e+00  -1.0065e+00  -2.2460e-02   4.0043e-02   6.4921e-07   6.2280e-02   2.8349e-02   1.8800e-01   9.4476e-02   9.5499e-01   8.3416e-02   1.0036e+00   8.2628e-02
-#> 219:    1.0191e+02  -4.1203e+00  -2.3437e+00  -4.0624e+00  -1.0066e+00  -1.9698e-02   3.9735e-02   6.3365e-07   6.2264e-02   2.7720e-02   1.8768e-01   9.7275e-02   9.4994e-01   8.3350e-02   1.0047e+00   8.2572e-02
-#> 220:    1.0191e+02  -4.1203e+00  -2.3447e+00  -4.0630e+00  -1.0070e+00  -1.5871e-02   4.0198e-02   6.3507e-07   6.1981e-02   2.7259e-02   1.8786e-01   9.9168e-02   9.4781e-01   8.3355e-02   1.0046e+00   8.2752e-02
-#> 221:    1.0191e+02  -4.1203e+00  -2.3459e+00  -4.0638e+00  -1.0069e+00  -1.4298e-02   4.0161e-02   6.2865e-07   6.2113e-02   2.7201e-02   1.8810e-01   1.0163e-01   9.4863e-01   8.3154e-02   1.0042e+00   8.2670e-02
-#> 222:    1.0191e+02  -4.1203e+00  -2.3472e+00  -4.0646e+00  -1.0064e+00  -1.0921e-02   4.0310e-02   6.2450e-07   6.2436e-02   2.6979e-02   1.8736e-01   1.0306e-01   9.4914e-01   8.3081e-02   1.0050e+00   8.2757e-02
-#> 223:    1.0191e+02  -4.1203e+00  -2.3478e+00  -4.0650e+00  -1.0063e+00  -1.1053e-02   3.9741e-02   6.1973e-07   6.2918e-02   2.6636e-02   1.8806e-01   1.0506e-01   9.4996e-01   8.2927e-02   1.0054e+00   8.2579e-02
-#> 224:    1.0191e+02  -4.1203e+00  -2.3478e+00  -4.0653e+00  -1.0061e+00  -1.0324e-02   3.9480e-02   6.1421e-07   6.4180e-02   2.6403e-02   1.8833e-01   1.0733e-01   9.4750e-01   8.2697e-02   1.0033e+00   8.2517e-02
-#> 225:    1.0191e+02  -4.1203e+00  -2.3479e+00  -4.0654e+00  -1.0060e+00  -1.0650e-02   3.9188e-02   6.0959e-07   6.3862e-02   2.6122e-02   1.8815e-01   1.0786e-01   9.4504e-01   8.2833e-02   1.0002e+00   8.2398e-02
-#> 226:    1.0192e+02  -4.1204e+00  -2.3469e+00  -4.0657e+00  -1.0052e+00  -1.0205e-02   3.9129e-02   6.0577e-07   6.4045e-02   2.5875e-02   1.8762e-01   1.0921e-01   9.4663e-01   8.2599e-02   9.9857e-01   8.2472e-02
-#> 227:    1.0192e+02  -4.1203e+00  -2.3467e+00  -4.0658e+00  -1.0053e+00  -1.0189e-02   3.8797e-02   6.0837e-07   6.5125e-02   2.5679e-02   1.8721e-01   1.1060e-01   9.4729e-01   8.2470e-02   9.9802e-01   8.2753e-02
-#> 228:    1.0192e+02  -4.1204e+00  -2.3469e+00  -4.0657e+00  -1.0054e+00  -1.0575e-02   3.8741e-02   6.0738e-07   6.5467e-02   2.5448e-02   1.8548e-01   1.1134e-01   9.4840e-01   8.2580e-02   9.9829e-01   8.2888e-02
-#> 229:    1.0192e+02  -4.1204e+00  -2.3479e+00  -4.0650e+00  -1.0056e+00  -1.1215e-02   3.9360e-02   6.0182e-07   6.4817e-02   2.5237e-02   1.8448e-01   1.1090e-01   9.5039e-01   8.2625e-02   9.9900e-01   8.2896e-02
-#> 230:    1.0192e+02  -4.1204e+00  -2.3482e+00  -4.0652e+00  -1.0060e+00  -9.9775e-03   3.9501e-02   5.9385e-07   6.4132e-02   2.5093e-02   1.8510e-01   1.1122e-01   9.4938e-01   8.2763e-02   9.9961e-01   8.2886e-02
-#> 231:    1.0192e+02  -4.1204e+00  -2.3479e+00  -4.0654e+00  -1.0070e+00  -8.9509e-03   3.9907e-02   5.9290e-07   6.3744e-02   2.4829e-02   1.8560e-01   1.1062e-01   9.4790e-01   8.2872e-02   1.0022e+00   8.2955e-02
-#> 232:    1.0192e+02  -4.1204e+00  -2.3484e+00  -4.0657e+00  -1.0081e+00  -6.9066e-03   4.0738e-02   5.7862e-07   6.3242e-02   2.4729e-02   1.8626e-01   1.0975e-01   9.4866e-01   8.2846e-02   1.0036e+00   8.3065e-02
-#> 233:    1.0191e+02  -4.1204e+00  -2.3487e+00  -4.0660e+00  -1.0080e+00  -5.1163e-03   4.0708e-02   5.7326e-07   6.2392e-02   2.4475e-02   1.8701e-01   1.0932e-01   9.4816e-01   8.2933e-02   1.0059e+00   8.3155e-02
-#> 234:    1.0191e+02  -4.1204e+00  -2.3500e+00  -4.0660e+00  -1.0077e+00  -4.0637e-03   4.1065e-02   5.6885e-07   6.1938e-02   2.4418e-02   1.8673e-01   1.0923e-01   9.5001e-01   8.3005e-02   1.0080e+00   8.3207e-02
-#> 235:    1.0191e+02  -4.1204e+00  -2.3526e+00  -4.0653e+00  -1.0074e+00  -3.6541e-03   4.1151e-02   5.6498e-07   6.2228e-02   2.4447e-02   1.8667e-01   1.0995e-01   9.5059e-01   8.3101e-02   1.0055e+00   8.3101e-02
-#> 236:    1.0191e+02  -4.1204e+00  -2.3540e+00  -4.0648e+00  -1.0078e+00  -4.0127e-03   4.0966e-02   5.7047e-07   6.1779e-02   2.4457e-02   1.8777e-01   1.0971e-01   9.4919e-01   8.3203e-02   1.0044e+00   8.3078e-02
-#> 237:    1.0191e+02  -4.1204e+00  -2.3528e+00  -4.0645e+00  -1.0078e+00  -4.4251e-03   4.0491e-02   5.6811e-07   6.1507e-02   2.4421e-02   1.8827e-01   1.1047e-01   9.4870e-01   8.3149e-02   1.0031e+00   8.3008e-02
-#> 238:    1.0190e+02  -4.1204e+00  -2.3517e+00  -4.0647e+00  -1.0076e+00  -5.2540e-03   3.9988e-02   5.6832e-07   6.1612e-02   2.4262e-02   1.8801e-01   1.1019e-01   9.4737e-01   8.3172e-02   1.0037e+00   8.2959e-02
-#> 239:    1.0190e+02  -4.1204e+00  -2.3509e+00  -4.0650e+00  -1.0089e+00  -5.1598e-03   3.9373e-02   5.6396e-07   6.1635e-02   2.4040e-02   1.8812e-01   1.1055e-01   9.4885e-01   8.3140e-02   1.0053e+00   8.2956e-02
-#> 240:    1.0190e+02  -4.1204e+00  -2.3515e+00  -4.0643e+00  -1.0095e+00  -4.5817e-03   3.9031e-02   5.5929e-07   6.2233e-02   2.3840e-02   1.8766e-01   1.1014e-01   9.5018e-01   8.3172e-02   1.0066e+00   8.2937e-02
-#> 241:    1.0190e+02  -4.1204e+00  -2.3524e+00  -4.0642e+00  -1.0097e+00  -3.9061e-03   3.8686e-02   5.5496e-07   6.3349e-02   2.3663e-02   1.8759e-01   1.1046e-01   9.4922e-01   8.3162e-02   1.0064e+00   8.2940e-02
-#> 242:    1.0190e+02  -4.1204e+00  -2.3535e+00  -4.0642e+00  -1.0092e+00  -2.9411e-03   3.8674e-02   5.5359e-07   6.3930e-02   2.3604e-02   1.8742e-01   1.1027e-01   9.4748e-01   8.3177e-02   1.0052e+00   8.2955e-02
-#> 243:    1.0190e+02  -4.1204e+00  -2.3551e+00  -4.0642e+00  -1.0089e+00  -1.6071e-03   3.8635e-02   5.4669e-07   6.4141e-02   2.3570e-02   1.8666e-01   1.1022e-01   9.4770e-01   8.3208e-02   1.0048e+00   8.2962e-02
-#> 244:    1.0190e+02  -4.1204e+00  -2.3566e+00  -4.0645e+00  -1.0093e+00  -7.0474e-04   3.8502e-02   5.4194e-07   6.4399e-02   2.3591e-02   1.8627e-01   1.0938e-01   9.4615e-01   8.3402e-02   1.0043e+00   8.2891e-02
-#> 245:    1.0189e+02  -4.1204e+00  -2.3575e+00  -4.0649e+00  -1.0093e+00   1.3351e-03   3.8372e-02   5.4266e-07   6.4935e-02   2.3511e-02   1.8609e-01   1.0840e-01   9.4586e-01   8.3393e-02   1.0041e+00   8.2835e-02
-#> 246:    1.0189e+02  -4.1204e+00  -2.3595e+00  -4.0655e+00  -1.0085e+00   4.2316e-03   3.8487e-02   5.4393e-07   6.5284e-02   2.3457e-02   1.8581e-01   1.0811e-01   9.4656e-01   8.3372e-02   1.0036e+00   8.2746e-02
-#> 247:    1.0189e+02  -4.1204e+00  -2.3608e+00  -4.0659e+00  -1.0081e+00   6.1314e-03   3.8249e-02   5.4752e-07   6.5440e-02   2.3455e-02   1.8584e-01   1.0706e-01   9.4795e-01   8.3330e-02   1.0025e+00   8.2710e-02
-#> 248:    1.0189e+02  -4.1204e+00  -2.3617e+00  -4.0662e+00  -1.0084e+00   8.1978e-03   3.8017e-02   5.4713e-07   6.5853e-02   2.3439e-02   1.8637e-01   1.0634e-01   9.4748e-01   8.3377e-02   1.0016e+00   8.2677e-02
-#> 249:    1.0189e+02  -4.1204e+00  -2.3633e+00  -4.0667e+00  -1.0085e+00   9.8011e-03   3.7934e-02   5.5069e-07   6.6442e-02   2.3533e-02   1.8652e-01   1.0606e-01   9.4761e-01   8.3449e-02   1.0009e+00   8.2712e-02
-#> 250:    1.0189e+02  -4.1204e+00  -2.3644e+00  -4.0668e+00  -1.0087e+00   1.0992e-02   3.8199e-02   5.5486e-07   6.6746e-02   2.3638e-02   1.8739e-01   1.0611e-01   9.4838e-01   8.3442e-02   9.9958e-01   8.2692e-02
-#> 251:    1.0189e+02  -4.1204e+00  -2.3644e+00  -4.0671e+00  -1.0097e+00   1.2215e-02   3.8648e-02   5.5448e-07   6.6916e-02   2.3592e-02   1.8753e-01   1.0607e-01   9.4773e-01   8.3511e-02   9.9919e-01   8.2701e-02
-#> 252:    1.0189e+02  -4.1204e+00  -2.3645e+00  -4.0671e+00  -1.0100e+00   1.2881e-02   3.8792e-02   5.5615e-07   6.7323e-02   2.3559e-02   1.8811e-01   1.0641e-01   9.4665e-01   8.3575e-02   9.9809e-01   8.2743e-02
-#> 253:    1.0189e+02  -4.1204e+00  -2.3646e+00  -4.0675e+00  -1.0100e+00   1.3605e-02   3.9013e-02   5.5568e-07   6.7625e-02   2.3432e-02   1.8825e-01   1.0688e-01   9.4424e-01   8.3598e-02   9.9825e-01   8.2702e-02
-#> 254:    1.0189e+02  -4.1204e+00  -2.3642e+00  -4.0677e+00  -1.0101e+00   1.3119e-02   3.8838e-02   5.5231e-07   6.7802e-02   2.3429e-02   1.8849e-01   1.0680e-01   9.4281e-01   8.3706e-02   9.9829e-01   8.2631e-02
-#> 255:    1.0189e+02  -4.1204e+00  -2.3627e+00  -4.0679e+00  -1.0104e+00   1.2490e-02   3.8574e-02   5.4955e-07   6.8395e-02   2.3368e-02   1.8890e-01   1.0661e-01   9.4101e-01   8.3756e-02   9.9798e-01   8.2674e-02
-#> 256:    1.0189e+02  -4.1204e+00  -2.3615e+00  -4.0677e+00  -1.0102e+00   1.1525e-02   3.8502e-02   5.4764e-07   6.8824e-02   2.3405e-02   1.8912e-01   1.0649e-01   9.4109e-01   8.3709e-02   9.9811e-01   8.2698e-02
-#> 257:    1.0189e+02  -4.1204e+00  -2.3604e+00  -4.0673e+00  -1.0104e+00   1.0381e-02   3.8286e-02   5.4694e-07   6.9020e-02   2.3338e-02   1.8925e-01   1.0614e-01   9.4075e-01   8.3695e-02   9.9738e-01   8.2689e-02
-#> 258:    1.0189e+02  -4.1204e+00  -2.3591e+00  -4.0670e+00  -1.0103e+00   8.9559e-03   3.7972e-02   5.4665e-07   6.9077e-02   2.3267e-02   1.8919e-01   1.0590e-01   9.4089e-01   8.3618e-02   9.9742e-01   8.2681e-02
-#> 259:    1.0189e+02  -4.1204e+00  -2.3585e+00  -4.0669e+00  -1.0099e+00   8.6011e-03   3.7874e-02   5.4788e-07   6.9455e-02   2.3264e-02   1.8885e-01   1.0519e-01   9.3952e-01   8.3583e-02   9.9610e-01   8.2650e-02
-#> 260:    1.0189e+02  -4.1204e+00  -2.3584e+00  -4.0666e+00  -1.0098e+00   8.0471e-03   3.7771e-02   5.5294e-07   7.0269e-02   2.3292e-02   1.8877e-01   1.0442e-01   9.3898e-01   8.3519e-02   9.9504e-01   8.2641e-02
-#> 261:    1.0189e+02  -4.1204e+00  -2.3583e+00  -4.0664e+00  -1.0100e+00   7.9344e-03   3.7597e-02   5.5650e-07   7.1087e-02   2.3370e-02   1.8867e-01   1.0399e-01   9.3810e-01   8.3488e-02   9.9419e-01   8.2673e-02
-#> 262:    1.0189e+02  -4.1204e+00  -2.3575e+00  -4.0662e+00  -1.0106e+00   7.2123e-03   3.7203e-02   5.5375e-07   7.1794e-02   2.3393e-02   1.8855e-01   1.0356e-01   9.3773e-01   8.3458e-02   9.9406e-01   8.2739e-02
-#> 263:    1.0189e+02  -4.1204e+00  -2.3564e+00  -4.0659e+00  -1.0112e+00   6.6044e-03   3.6977e-02   5.5306e-07   7.2290e-02   2.3475e-02   1.8847e-01   1.0316e-01   9.3744e-01   8.3383e-02   9.9341e-01   8.2818e-02
-#> 264:    1.0189e+02  -4.1204e+00  -2.3549e+00  -4.0657e+00  -1.0118e+00   6.0119e-03   3.6749e-02   5.5152e-07   7.2896e-02   2.3530e-02   1.8849e-01   1.0277e-01   9.3658e-01   8.3443e-02   9.9248e-01   8.2877e-02
-#> 265:    1.0189e+02  -4.1204e+00  -2.3545e+00  -4.0655e+00  -1.0121e+00   5.6547e-03   3.6562e-02   5.4816e-07   7.3238e-02   2.3560e-02   1.8863e-01   1.0269e-01   9.3597e-01   8.3434e-02   9.9139e-01   8.2879e-02
-#> 266:    1.0189e+02  -4.1204e+00  -2.3545e+00  -4.0651e+00  -1.0121e+00   5.0995e-03   3.6357e-02   5.4458e-07   7.3522e-02   2.3561e-02   1.8883e-01   1.0270e-01   9.3607e-01   8.3407e-02   9.9133e-01   8.2857e-02
-#> 267:    1.0189e+02  -4.1204e+00  -2.3541e+00  -4.0648e+00  -1.0122e+00   4.0105e-03   3.6306e-02   5.4160e-07   7.3833e-02   2.3499e-02   1.8889e-01   1.0317e-01   9.3624e-01   8.3359e-02   9.9151e-01   8.2865e-02
-#> 268:    1.0189e+02  -4.1204e+00  -2.3530e+00  -4.0646e+00  -1.0122e+00   3.0925e-03   3.6248e-02   5.3845e-07   7.4663e-02   2.3413e-02   1.8895e-01   1.0371e-01   9.3624e-01   8.3277e-02   9.9210e-01   8.2909e-02
-#> 269:    1.0189e+02  -4.1204e+00  -2.3518e+00  -4.0643e+00  -1.0123e+00   2.0507e-03   3.6181e-02   5.3602e-07   7.5442e-02   2.3291e-02   1.8886e-01   1.0397e-01   9.3581e-01   8.3260e-02   9.9238e-01   8.2898e-02
-#> 270:    1.0189e+02  -4.1204e+00  -2.3513e+00  -4.0640e+00  -1.0127e+00   1.3309e-03   3.5900e-02   5.3234e-07   7.6677e-02   2.3220e-02   1.8860e-01   1.0367e-01   9.3573e-01   8.3250e-02   9.9169e-01   8.2904e-02
-#> 271:    1.0189e+02  -4.1204e+00  -2.3514e+00  -4.0637e+00  -1.0129e+00   1.1237e-03   3.5608e-02   5.3092e-07   7.7065e-02   2.3102e-02   1.8826e-01   1.0384e-01   9.3645e-01   8.3228e-02   9.9173e-01   8.2896e-02
-#> 272:    1.0189e+02  -4.1204e+00  -2.3510e+00  -4.0639e+00  -1.0134e+00   9.7855e-04   3.5328e-02   5.3100e-07   7.7173e-02   2.3014e-02   1.8817e-01   1.0367e-01   9.3538e-01   8.3266e-02   9.9139e-01   8.2943e-02
-#> 273:    1.0189e+02  -4.1204e+00  -2.3501e+00  -4.0643e+00  -1.0133e+00   1.1275e-03   3.5187e-02   5.3298e-07   7.7467e-02   2.2923e-02   1.8793e-01   1.0344e-01   9.3474e-01   8.3194e-02   9.9249e-01   8.2973e-02
-#> 274:    1.0189e+02  -4.1204e+00  -2.3498e+00  -4.0643e+00  -1.0134e+00   1.4524e-03   3.4996e-02   5.3407e-07   7.7929e-02   2.2819e-02   1.8837e-01   1.0316e-01   9.3399e-01   8.3168e-02   9.9307e-01   8.2981e-02
-#> 275:    1.0189e+02  -4.1204e+00  -2.3500e+00  -4.0641e+00  -1.0136e+00   1.3605e-03   3.4786e-02   5.3269e-07   7.8177e-02   2.2747e-02   1.8855e-01   1.0305e-01   9.3319e-01   8.3205e-02   9.9277e-01   8.2938e-02
-#> 276:    1.0189e+02  -4.1204e+00  -2.3504e+00  -4.0641e+00  -1.0136e+00   1.5273e-03   3.4581e-02   5.3172e-07   7.8495e-02   2.2764e-02   1.8824e-01   1.0297e-01   9.3267e-01   8.3223e-02   9.9204e-01   8.2884e-02
-#> 277:    1.0189e+02  -4.1204e+00  -2.3506e+00  -4.0643e+00  -1.0133e+00   1.2961e-03   3.4373e-02   5.2917e-07   7.8721e-02   2.2791e-02   1.8801e-01   1.0288e-01   9.3253e-01   8.3185e-02   9.9192e-01   8.2854e-02
-#> 278:    1.0189e+02  -4.1204e+00  -2.3508e+00  -4.0643e+00  -1.0129e+00   1.1750e-03   3.4396e-02   5.2693e-07   7.8999e-02   2.2787e-02   1.8793e-01   1.0278e-01   9.3279e-01   8.3113e-02   9.9144e-01   8.2856e-02
-#> 279:    1.0189e+02  -4.1204e+00  -2.3507e+00  -4.0642e+00  -1.0126e+00   1.2755e-03   3.4381e-02   5.2405e-07   7.9351e-02   2.2804e-02   1.8779e-01   1.0255e-01   9.3319e-01   8.3049e-02   9.9099e-01   8.2875e-02
-#> 280:    1.0189e+02  -4.1204e+00  -2.3507e+00  -4.0641e+00  -1.0127e+00   6.3408e-04   3.4519e-02   5.2180e-07   7.9825e-02   2.2801e-02   1.8775e-01   1.0292e-01   9.3349e-01   8.2970e-02   9.9076e-01   8.2918e-02
-#> 281:    1.0189e+02  -4.1204e+00  -2.3508e+00  -4.0639e+00  -1.0124e+00   6.2438e-04   3.4782e-02   5.1859e-07   8.0328e-02   2.2816e-02   1.8757e-01   1.0299e-01   9.3299e-01   8.3025e-02   9.9050e-01   8.2897e-02
-#> 282:    1.0189e+02  -4.1205e+00  -2.3511e+00  -4.0641e+00  -1.0122e+00   1.1770e-03   3.4754e-02   5.1798e-07   8.0649e-02   2.2836e-02   1.8766e-01   1.0297e-01   9.3351e-01   8.2989e-02   9.9171e-01   8.2893e-02
-#> 283:    1.0189e+02  -4.1205e+00  -2.3519e+00  -4.0644e+00  -1.0120e+00   2.1716e-03   3.4711e-02   5.1567e-07   8.0910e-02   2.2836e-02   1.8774e-01   1.0270e-01   9.3288e-01   8.3029e-02   9.9246e-01   8.2853e-02
-#> 284:    1.0189e+02  -4.1205e+00  -2.3524e+00  -4.0647e+00  -1.0115e+00   2.6623e-03   3.4646e-02   5.1350e-07   8.1153e-02   2.2950e-02   1.8775e-01   1.0277e-01   9.3238e-01   8.2990e-02   9.9212e-01   8.2836e-02
-#> 285:    1.0189e+02  -4.1205e+00  -2.3531e+00  -4.0649e+00  -1.0116e+00   3.7830e-03   3.4626e-02   5.1216e-07   8.1058e-02   2.3007e-02   1.8782e-01   1.0270e-01   9.3232e-01   8.3017e-02   9.9094e-01   8.2829e-02
-#> 286:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0651e+00  -1.0111e+00   5.1752e-03   3.4599e-02   5.0989e-07   8.0970e-02   2.3004e-02   1.8757e-01   1.0254e-01   9.3280e-01   8.3006e-02   9.9130e-01   8.2818e-02
-#> 287:    1.0189e+02  -4.1205e+00  -2.3541e+00  -4.0654e+00  -1.0112e+00   6.3747e-03   3.4592e-02   5.0930e-07   8.1117e-02   2.2959e-02   1.8756e-01   1.0222e-01   9.3212e-01   8.3146e-02   9.9183e-01   8.2863e-02
-#> 288:    1.0189e+02  -4.1205e+00  -2.3540e+00  -4.0656e+00  -1.0115e+00   6.5668e-03   3.4598e-02   5.0976e-07   8.1125e-02   2.2895e-02   1.8782e-01   1.0183e-01   9.3310e-01   8.3169e-02   9.9404e-01   8.2836e-02
-#> 289:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0658e+00  -1.0119e+00   7.3521e-03   3.4525e-02   5.1097e-07   8.1097e-02   2.2869e-02   1.8753e-01   1.0126e-01   9.3336e-01   8.3244e-02   9.9435e-01   8.2833e-02
-#> 290:    1.0189e+02  -4.1205e+00  -2.3539e+00  -4.0659e+00  -1.0122e+00   7.5226e-03   3.4377e-02   5.0846e-07   8.1212e-02   2.2831e-02   1.8724e-01   1.0073e-01   9.3292e-01   8.3261e-02   9.9415e-01   8.2837e-02
-#> 291:    1.0189e+02  -4.1205e+00  -2.3536e+00  -4.0659e+00  -1.0122e+00   7.2889e-03   3.4263e-02   5.0823e-07   8.1182e-02   2.2801e-02   1.8711e-01   1.0056e-01   9.3309e-01   8.3300e-02   9.9427e-01   8.2805e-02
-#> 292:    1.0189e+02  -4.1205e+00  -2.3531e+00  -4.0659e+00  -1.0123e+00   7.1827e-03   3.4146e-02   5.0825e-07   8.1696e-02   2.2760e-02   1.8703e-01   1.0039e-01   9.3324e-01   8.3306e-02   9.9379e-01   8.2784e-02
-#> 293:    1.0189e+02  -4.1205e+00  -2.3528e+00  -4.0660e+00  -1.0125e+00   7.7142e-03   3.4126e-02   5.0971e-07   8.2026e-02   2.2705e-02   1.8721e-01   1.0036e-01   9.3316e-01   8.3316e-02   9.9357e-01   8.2756e-02
-#> 294:    1.0188e+02  -4.1204e+00  -2.3529e+00  -4.0663e+00  -1.0126e+00   8.5146e-03   3.4314e-02   5.0823e-07   8.2197e-02   2.2608e-02   1.8743e-01   1.0009e-01   9.3356e-01   8.3308e-02   9.9367e-01   8.2719e-02
-#> 295:    1.0188e+02  -4.1204e+00  -2.3532e+00  -4.0666e+00  -1.0123e+00   9.2199e-03   3.4472e-02   5.0839e-07   8.2550e-02   2.2529e-02   1.8745e-01   9.9731e-02   9.3393e-01   8.3255e-02   9.9373e-01   8.2686e-02
-#> 296:    1.0188e+02  -4.1204e+00  -2.3537e+00  -4.0667e+00  -1.0121e+00   9.7869e-03   3.4678e-02   5.0983e-07   8.3059e-02   2.2497e-02   1.8729e-01   9.9260e-02   9.3395e-01   8.3198e-02   9.9300e-01   8.2681e-02
-#> 297:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0670e+00  -1.0118e+00   1.0166e-02   3.4957e-02   5.1049e-07   8.3080e-02   2.2448e-02   1.8710e-01   9.8969e-02   9.3321e-01   8.3178e-02   9.9255e-01   8.2663e-02
-#> 298:    1.0188e+02  -4.1204e+00  -2.3544e+00  -4.0673e+00  -1.0117e+00   1.0649e-02   3.5259e-02   5.1103e-07   8.3179e-02   2.2383e-02   1.8704e-01   9.8442e-02   9.3227e-01   8.3199e-02   9.9266e-01   8.2646e-02
-#> 299:    1.0188e+02  -4.1204e+00  -2.3542e+00  -4.0676e+00  -1.0117e+00   1.0927e-02   3.5438e-02   5.1128e-07   8.3068e-02   2.2378e-02   1.8699e-01   9.8203e-02   9.3263e-01   8.3138e-02   9.9353e-01   8.2671e-02
-#> 300:    1.0188e+02  -4.1204e+00  -2.3544e+00  -4.0678e+00  -1.0116e+00   1.1083e-02   3.5694e-02   5.1107e-07   8.2896e-02   2.2344e-02   1.8733e-01   9.7775e-02   9.3179e-01   8.3124e-02   9.9379e-01   8.2657e-02
-#> 301:    1.0188e+02  -4.1204e+00  -2.3542e+00  -4.0680e+00  -1.0115e+00   1.0992e-02   3.5896e-02   5.1262e-07   8.2816e-02   2.2349e-02   1.8753e-01   9.7431e-02   9.3209e-01   8.3086e-02   9.9388e-01   8.2674e-02
-#> 302:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0681e+00  -1.0113e+00   1.0410e-02   3.6050e-02   5.1256e-07   8.2817e-02   2.2308e-02   1.8734e-01   9.7153e-02   9.3221e-01   8.3073e-02   9.9402e-01   8.2670e-02
-#> 303:    1.0188e+02  -4.1204e+00  -2.3540e+00  -4.0681e+00  -1.0112e+00   1.0301e-02   3.6150e-02   5.1127e-07   8.2826e-02   2.2325e-02   1.8730e-01   9.6656e-02   9.3192e-01   8.3040e-02   9.9393e-01   8.2665e-02
-#> 304:    1.0188e+02  -4.1204e+00  -2.3536e+00  -4.0681e+00  -1.0113e+00   1.0235e-02   3.6393e-02   5.1176e-07   8.2606e-02   2.2353e-02   1.8724e-01   9.6171e-02   9.3161e-01   8.3068e-02   9.9361e-01   8.2698e-02
-#> 305:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0683e+00  -1.0112e+00   9.9655e-03   3.6369e-02   5.1442e-07   8.2520e-02   2.2378e-02   1.8707e-01   9.5656e-02   9.3113e-01   8.3109e-02   9.9338e-01   8.2731e-02
-#> 306:    1.0188e+02  -4.1204e+00  -2.3531e+00  -4.0684e+00  -1.0110e+00   9.9701e-03   3.6346e-02   5.1546e-07   8.2789e-02   2.2360e-02   1.8702e-01   9.5116e-02   9.3102e-01   8.3065e-02   9.9405e-01   8.2761e-02
-#> 307:    1.0188e+02  -4.1204e+00  -2.3530e+00  -4.0684e+00  -1.0112e+00   1.0194e-02   3.6300e-02   5.1196e-07   8.3035e-02   2.2381e-02   1.8704e-01   9.4760e-02   9.3082e-01   8.3003e-02   9.9410e-01   8.2779e-02
-#> 308:    1.0189e+02  -4.1204e+00  -2.3530e+00  -4.0685e+00  -1.0109e+00   9.9531e-03   3.6400e-02   5.1140e-07   8.3511e-02   2.2334e-02   1.8726e-01   9.4494e-02   9.3151e-01   8.2910e-02   9.9484e-01   8.2760e-02
-#> 309:    1.0188e+02  -4.1204e+00  -2.3530e+00  -4.0685e+00  -1.0107e+00   1.0089e-02   3.6382e-02   5.1081e-07   8.3917e-02   2.2276e-02   1.8728e-01   9.4285e-02   9.3133e-01   8.2875e-02   9.9545e-01   8.2757e-02
-#> 310:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0685e+00  -1.0105e+00   1.0805e-02   3.6375e-02   5.1041e-07   8.4245e-02   2.2246e-02   1.8753e-01   9.3894e-02   9.3052e-01   8.2899e-02   9.9500e-01   8.2743e-02
-#> 311:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0685e+00  -1.0103e+00   1.1449e-02   3.6311e-02   5.0884e-07   8.4434e-02   2.2231e-02   1.8783e-01   9.3542e-02   9.3039e-01   8.2864e-02   9.9458e-01   8.2733e-02
-#> 312:    1.0188e+02  -4.1204e+00  -2.3535e+00  -4.0685e+00  -1.0102e+00   1.2173e-02   3.6373e-02   5.0821e-07   8.4730e-02   2.2176e-02   1.8769e-01   9.3317e-02   9.2982e-01   8.2916e-02   9.9438e-01   8.2740e-02
-#> 313:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0688e+00  -1.0103e+00   1.2812e-02   3.6558e-02   5.0751e-07   8.5211e-02   2.2131e-02   1.8754e-01   9.3387e-02   9.2962e-01   8.2892e-02   9.9458e-01   8.2741e-02
-#> 314:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0690e+00  -1.0103e+00   1.3241e-02   3.6680e-02   5.0887e-07   8.5667e-02   2.2079e-02   1.8772e-01   9.3442e-02   9.2941e-01   8.2947e-02   9.9511e-01   8.2743e-02
-#> 315:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0691e+00  -1.0104e+00   1.3699e-02   3.6924e-02   5.0965e-07   8.5865e-02   2.2028e-02   1.8766e-01   9.3264e-02   9.2904e-01   8.2986e-02   9.9543e-01   8.2763e-02
-#> 316:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0693e+00  -1.0103e+00   1.4121e-02   3.7218e-02   5.1041e-07   8.6216e-02   2.2076e-02   1.8773e-01   9.3035e-02   9.2917e-01   8.3013e-02   9.9486e-01   8.2782e-02
-#> 317:    1.0188e+02  -4.1204e+00  -2.3533e+00  -4.0694e+00  -1.0102e+00   1.4588e-02   3.7304e-02   5.0994e-07   8.6513e-02   2.2128e-02   1.8773e-01   9.2766e-02   9.2943e-01   8.3025e-02   9.9441e-01   8.2779e-02
-#> 318:    1.0188e+02  -4.1204e+00  -2.3534e+00  -4.0693e+00  -1.0101e+00   1.4714e-02   3.7538e-02   5.0773e-07   8.6801e-02   2.2128e-02   1.8767e-01   9.2698e-02   9.2907e-01   8.3052e-02   9.9378e-01   8.2780e-02
-#> 319:    1.0187e+02  -4.1204e+00  -2.3533e+00  -4.0692e+00  -1.0099e+00   1.4582e-02   3.7563e-02   5.0550e-07   8.6669e-02   2.2135e-02   1.8775e-01   9.2604e-02   9.2925e-01   8.3042e-02   9.9356e-01   8.2773e-02
-#> 320:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0690e+00  -1.0102e+00   1.4511e-02   3.7580e-02   5.0281e-07   8.6617e-02   2.2121e-02   1.8780e-01   9.2508e-02   9.3001e-01   8.3032e-02   9.9322e-01   8.2780e-02
-#> 321:    1.0187e+02  -4.1204e+00  -2.3534e+00  -4.0688e+00  -1.0100e+00   1.4288e-02   3.7624e-02   5.0172e-07   8.6311e-02   2.2115e-02   1.8783e-01   9.2445e-02   9.3011e-01   8.3054e-02   9.9288e-01   8.2772e-02
-#> 322:    1.0187e+02  -4.1204e+00  -2.3532e+00  -4.0687e+00  -1.0098e+00   1.3834e-02   3.7497e-02   5.0086e-07   8.6187e-02   2.2111e-02   1.8791e-01   9.2699e-02   9.3037e-01   8.3069e-02   9.9284e-01   8.2773e-02
-#> 323:    1.0187e+02  -4.1204e+00  -2.3524e+00  -4.0683e+00  -1.0097e+00   1.2977e-02   3.7420e-02   4.9925e-07   8.6082e-02   2.2084e-02   1.8818e-01   9.3123e-02   9.3036e-01   8.3012e-02   9.9265e-01   8.2813e-02
-#> 324:    1.0187e+02  -4.1204e+00  -2.3523e+00  -4.0682e+00  -1.0096e+00   1.2679e-02   3.7420e-02   4.9836e-07   8.5721e-02   2.2071e-02   1.8829e-01   9.3535e-02   9.3062e-01   8.3011e-02   9.9241e-01   8.2827e-02
-#> 325:    1.0187e+02  -4.1204e+00  -2.3520e+00  -4.0680e+00  -1.0094e+00   1.2196e-02   3.7298e-02   4.9735e-07   8.5411e-02   2.2028e-02   1.8848e-01   9.3706e-02   9.3043e-01   8.3020e-02   9.9256e-01   8.2826e-02
-#> 326:    1.0187e+02  -4.1204e+00  -2.3517e+00  -4.0678e+00  -1.0091e+00   1.1924e-02   3.7185e-02   4.9661e-07   8.5453e-02   2.1983e-02   1.8830e-01   9.3688e-02   9.3050e-01   8.2996e-02   9.9284e-01   8.2806e-02
-#> 327:    1.0187e+02  -4.1204e+00  -2.3516e+00  -4.0677e+00  -1.0090e+00   1.1449e-02   3.7155e-02   4.9755e-07   8.5761e-02   2.1967e-02   1.8819e-01   9.3936e-02   9.3052e-01   8.2912e-02   9.9245e-01   8.2806e-02
-#> 328:    1.0187e+02  -4.1204e+00  -2.3514e+00  -4.0675e+00  -1.0089e+00   1.0758e-02   3.7146e-02   4.9892e-07   8.6019e-02   2.1971e-02   1.8806e-01   9.4361e-02   9.3070e-01   8.2833e-02   9.9182e-01   8.2840e-02
-#> 329:    1.0187e+02  -4.1204e+00  -2.3515e+00  -4.0672e+00  -1.0087e+00   1.0256e-02   3.7342e-02   5.0019e-07   8.5965e-02   2.1989e-02   1.8796e-01   9.4614e-02   9.3067e-01   8.2818e-02   9.9159e-01   8.2858e-02
-#> 330:    1.0187e+02  -4.1204e+00  -2.3520e+00  -4.0670e+00  -1.0086e+00   1.0021e-02   3.7376e-02   4.9911e-07   8.6124e-02   2.1978e-02   1.8796e-01   9.4836e-02   9.3036e-01   8.2819e-02   9.9148e-01   8.2866e-02
-#> 331:    1.0187e+02  -4.1204e+00  -2.3521e+00  -4.0668e+00  -1.0086e+00   9.5790e-03   3.7296e-02   4.9753e-07   8.6122e-02   2.1951e-02   1.8782e-01   9.5042e-02   9.3064e-01   8.2783e-02   9.9196e-01   8.2863e-02
-#> 332:    1.0187e+02  -4.1204e+00  -2.3523e+00  -4.0667e+00  -1.0085e+00   9.2971e-03   3.7221e-02   4.9729e-07   8.6215e-02   2.1952e-02   1.8787e-01   9.5082e-02   9.3103e-01   8.2782e-02   9.9224e-01   8.2861e-02
-#> 333:    1.0187e+02  -4.1204e+00  -2.3524e+00  -4.0667e+00  -1.0084e+00   9.2591e-03   3.7097e-02   4.9556e-07   8.6302e-02   2.1922e-02   1.8792e-01   9.5155e-02   9.3058e-01   8.2798e-02   9.9202e-01   8.2831e-02
-#> 334:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0667e+00  -1.0082e+00   9.5799e-03   3.6997e-02   4.9398e-07   8.6409e-02   2.1911e-02   1.8792e-01   9.5231e-02   9.3035e-01   8.2810e-02   9.9157e-01   8.2803e-02
-#> 335:    1.0187e+02  -4.1204e+00  -2.3529e+00  -4.0667e+00  -1.0080e+00   9.5724e-03   3.6912e-02   4.9206e-07   8.6310e-02   2.1923e-02   1.8791e-01   9.5379e-02   9.3054e-01   8.2759e-02   9.9143e-01   8.2776e-02
-#> 336:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0667e+00  -1.0082e+00   9.5794e-03   3.6983e-02   4.9255e-07   8.6282e-02   2.1882e-02   1.8789e-01   9.5422e-02   9.3064e-01   8.2705e-02   9.9138e-01   8.2778e-02
-#> 337:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0669e+00  -1.0083e+00   1.0008e-02   3.6943e-02   4.9278e-07   8.6483e-02   2.1844e-02   1.8794e-01   9.5240e-02   9.2981e-01   8.2753e-02   9.9100e-01   8.2774e-02
-#> 338:    1.0187e+02  -4.1204e+00  -2.3525e+00  -4.0669e+00  -1.0084e+00   1.0297e-02   3.6869e-02   4.9309e-07   8.6547e-02   2.1803e-02   1.8808e-01   9.5094e-02   9.2978e-01   8.2764e-02   9.9113e-01   8.2759e-02
-#> 339:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0669e+00  -1.0083e+00   1.0465e-02   3.6822e-02   4.9257e-07   8.6779e-02   2.1769e-02   1.8813e-01   9.5062e-02   9.3020e-01   8.2702e-02   9.9135e-01   8.2750e-02
-#> 340:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0668e+00  -1.0083e+00   1.0321e-02   3.6733e-02   4.9228e-07   8.7033e-02   2.1721e-02   1.8827e-01   9.4862e-02   9.3062e-01   8.2698e-02   9.9195e-01   8.2729e-02
-#> 341:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0670e+00  -1.0085e+00   1.0501e-02   3.6671e-02   4.9236e-07   8.7297e-02   2.1733e-02   1.8820e-01   9.4558e-02   9.3121e-01   8.2677e-02   9.9238e-01   8.2713e-02
-#> 342:    1.0187e+02  -4.1204e+00  -2.3534e+00  -4.0670e+00  -1.0085e+00   1.0818e-02   3.6715e-02   4.9084e-07   8.7450e-02   2.1726e-02   1.8801e-01   9.4252e-02   9.3160e-01   8.2657e-02   9.9232e-01   8.2708e-02
-#> 343:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0670e+00  -1.0087e+00   1.1046e-02   3.6784e-02   4.8872e-07   8.7718e-02   2.1718e-02   1.8799e-01   9.3887e-02   9.3187e-01   8.2645e-02   9.9225e-01   8.2722e-02
-#> 344:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0670e+00  -1.0087e+00   1.0652e-02   3.6736e-02   4.8852e-07   8.7725e-02   2.1730e-02   1.8792e-01   9.3731e-02   9.3202e-01   8.2618e-02   9.9218e-01   8.2712e-02
-#> 345:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0669e+00  -1.0090e+00   1.0445e-02   3.6714e-02   4.8690e-07   8.7782e-02   2.1751e-02   1.8798e-01   9.3450e-02   9.3223e-01   8.2582e-02   9.9239e-01   8.2706e-02
-#> 346:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0668e+00  -1.0089e+00   1.0173e-02   3.6743e-02   4.8656e-07   8.7733e-02   2.1810e-02   1.8814e-01   9.3151e-02   9.3257e-01   8.2575e-02   9.9181e-01   8.2708e-02
-#> 347:    1.0187e+02  -4.1205e+00  -2.3532e+00  -4.0668e+00  -1.0089e+00   9.9457e-03   3.6808e-02   4.8756e-07   8.7948e-02   2.1819e-02   1.8813e-01   9.3040e-02   9.3255e-01   8.2599e-02   9.9124e-01   8.2727e-02
-#> 348:    1.0187e+02  -4.1205e+00  -2.3534e+00  -4.0667e+00  -1.0090e+00   9.8498e-03   3.6967e-02   4.8883e-07   8.7998e-02   2.1841e-02   1.8820e-01   9.3180e-02   9.3259e-01   8.2612e-02   9.9148e-01   8.2750e-02
-#> 349:    1.0187e+02  -4.1205e+00  -2.3535e+00  -4.0668e+00  -1.0091e+00   9.6211e-03   3.6891e-02   4.8867e-07   8.8006e-02   2.1930e-02   1.8819e-01   9.3390e-02   9.3232e-01   8.2612e-02   9.9073e-01   8.2738e-02
-#> 350:    1.0187e+02  -4.1205e+00  -2.3534e+00  -4.0669e+00  -1.0090e+00   9.7176e-03   3.6813e-02   4.8925e-07   8.7923e-02   2.1964e-02   1.8820e-01   9.3434e-02   9.3224e-01   8.2600e-02   9.9031e-01   8.2734e-02
-#> 351:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0669e+00  -1.0090e+00   9.6652e-03   3.6769e-02   4.8873e-07   8.7985e-02   2.2046e-02   1.8814e-01   9.3529e-02   9.3220e-01   8.2558e-02   9.8978e-01   8.2728e-02
-#> 352:    1.0187e+02  -4.1204e+00  -2.3536e+00  -4.0669e+00  -1.0089e+00   9.8745e-03   3.6732e-02   4.8969e-07   8.8016e-02   2.2094e-02   1.8799e-01   9.3644e-02   9.3168e-01   8.2577e-02   9.8913e-01   8.2722e-02
-#> 353:    1.0187e+02  -4.1204e+00  -2.3537e+00  -4.0669e+00  -1.0088e+00   9.7530e-03   3.6700e-02   4.9008e-07   8.7949e-02   2.2116e-02   1.8798e-01   9.3769e-02   9.3165e-01   8.2559e-02   9.8871e-01   8.2711e-02
-#> 354:    1.0187e+02  -4.1204e+00  -2.3538e+00  -4.0667e+00  -1.0089e+00   9.4103e-03   3.6653e-02   4.9045e-07   8.7894e-02   2.2118e-02   1.8793e-01   9.3872e-02   9.3188e-01   8.2551e-02   9.8887e-01   8.2692e-02
-#> 355:    1.0187e+02  -4.1204e+00  -2.3540e+00  -4.0666e+00  -1.0088e+00   9.1684e-03   3.6536e-02   4.9125e-07   8.7920e-02   2.2107e-02   1.8812e-01   9.4123e-02   9.3223e-01   8.2517e-02   9.8893e-01   8.2687e-02
-#> 356:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0664e+00  -1.0086e+00   8.9025e-03   3.6431e-02   4.9325e-07   8.7949e-02   2.2110e-02   1.8827e-01   9.4135e-02   9.3252e-01   8.2503e-02   9.8907e-01   8.2649e-02
-#> 357:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0663e+00  -1.0085e+00   8.6757e-03   3.6417e-02   4.9505e-07   8.8052e-02   2.2096e-02   1.8848e-01   9.4192e-02   9.3281e-01   8.2490e-02   9.8957e-01   8.2624e-02
-#> 358:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0661e+00  -1.0084e+00   8.1812e-03   3.6349e-02   4.9610e-07   8.8344e-02   2.2104e-02   1.8844e-01   9.4129e-02   9.3294e-01   8.2493e-02   9.8977e-01   8.2614e-02
-#> 359:    1.0187e+02  -4.1204e+00  -2.3541e+00  -4.0659e+00  -1.0083e+00   8.0905e-03   3.6475e-02   4.9675e-07   8.8647e-02   2.2116e-02   1.8831e-01   9.4146e-02   9.3322e-01   8.2473e-02   9.8978e-01   8.2622e-02
-#> 360:    1.0187e+02  -4.1204e+00  -2.3542e+00  -4.0657e+00  -1.0082e+00   7.8390e-03   3.6468e-02   4.9649e-07   8.8981e-02   2.2120e-02   1.8815e-01   9.4249e-02   9.3361e-01   8.2430e-02   9.8997e-01   8.2616e-02
-#> 361:    1.0187e+02  -4.1204e+00  -2.3545e+00  -4.0656e+00  -1.0083e+00   7.9104e-03   3.6434e-02   4.9737e-07   8.9447e-02   2.2133e-02   1.8808e-01   9.4085e-02   9.3387e-01   8.2426e-02   9.9025e-01   8.2616e-02
-#> 362:    1.0187e+02  -4.1204e+00  -2.3547e+00  -4.0655e+00  -1.0087e+00   7.6341e-03   3.6428e-02   4.9748e-07   8.9872e-02   2.2148e-02   1.8805e-01   9.4025e-02   9.3407e-01   8.2456e-02   9.9070e-01   8.2609e-02
-#> 363:    1.0187e+02  -4.1204e+00  -2.3546e+00  -4.0653e+00  -1.0087e+00   7.2351e-03   3.6392e-02   4.9842e-07   9.0125e-02   2.2179e-02   1.8818e-01   9.4157e-02   9.3437e-01   8.2439e-02   9.9051e-01   8.2626e-02
-#> 364:    1.0187e+02  -4.1204e+00  -2.3543e+00  -4.0651e+00  -1.0089e+00   6.7851e-03   3.6303e-02   4.9890e-07   9.0448e-02   2.2189e-02   1.8831e-01   9.4432e-02   9.3513e-01   8.2433e-02   9.9051e-01   8.2655e-02
-#> 365:    1.0187e+02  -4.1204e+00  -2.3538e+00  -4.0650e+00  -1.0089e+00   6.2935e-03   3.6267e-02   4.9829e-07   9.0718e-02   2.2204e-02   1.8818e-01   9.4507e-02   9.3580e-01   8.2387e-02   9.9049e-01   8.2678e-02
-#> 366:    1.0187e+02  -4.1204e+00  -2.3535e+00  -4.0649e+00  -1.0088e+00   5.8910e-03   3.6339e-02   4.9911e-07   9.0727e-02   2.2231e-02   1.8801e-01   9.4683e-02   9.3567e-01   8.2359e-02   9.8997e-01   8.2681e-02
-#> 367:    1.0187e+02  -4.1204e+00  -2.3533e+00  -4.0649e+00  -1.0088e+00   5.8610e-03   3.6366e-02   5.0123e-07   9.0732e-02   2.2245e-02   1.8793e-01   9.4666e-02   9.3556e-01   8.2339e-02   9.8945e-01   8.2691e-02
-#> 368:    1.0187e+02  -4.1204e+00  -2.3531e+00  -4.0650e+00  -1.0088e+00   6.1043e-03   3.6424e-02   5.0107e-07   9.0729e-02   2.2248e-02   1.8780e-01   9.4599e-02   9.3554e-01   8.2315e-02   9.8903e-01   8.2705e-02
-#> 369:    1.0187e+02  -4.1204e+00  -2.3530e+00  -4.0650e+00  -1.0088e+00   6.1767e-03   3.6436e-02   5.0046e-07   9.0617e-02   2.2226e-02   1.8787e-01   9.4410e-02   9.3504e-01   8.2361e-02   9.8843e-01   8.2694e-02
-#> 370:    1.0187e+02  -4.1204e+00  -2.3528e+00  -4.0651e+00  -1.0088e+00   6.2532e-03   3.6467e-02   5.0024e-07   9.0741e-02   2.2223e-02   1.8794e-01   9.4288e-02   9.3472e-01   8.2374e-02   9.8781e-01   8.2703e-02
-#> 371:    1.0186e+02  -4.1204e+00  -2.3525e+00  -4.0652e+00  -1.0088e+00   6.2117e-03   3.6465e-02   4.9964e-07   9.0904e-02   2.2220e-02   1.8788e-01   9.4310e-02   9.3470e-01   8.2367e-02   9.8730e-01   8.2731e-02
-#> 372:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0651e+00  -1.0089e+00   6.1363e-03   3.6367e-02   5.0037e-07   9.1177e-02   2.2230e-02   1.8783e-01   9.4288e-02   9.3496e-01   8.2365e-02   9.8699e-01   8.2729e-02
-#> 373:    1.0186e+02  -4.1204e+00  -2.3523e+00  -4.0650e+00  -1.0089e+00   6.0384e-03   3.6353e-02   5.0195e-07   9.1402e-02   2.2219e-02   1.8764e-01   9.4343e-02   9.3478e-01   8.2430e-02   9.8641e-01   8.2747e-02
-#> 374:    1.0186e+02  -4.1204e+00  -2.3523e+00  -4.0650e+00  -1.0091e+00   5.9821e-03   3.6377e-02   5.0424e-07   9.1532e-02   2.2243e-02   1.8761e-01   9.4219e-02   9.3466e-01   8.2418e-02   9.8614e-01   8.2735e-02
-#> 375:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0649e+00  -1.0091e+00   5.8843e-03   3.6358e-02   5.0568e-07   9.1556e-02   2.2250e-02   1.8768e-01   9.4173e-02   9.3432e-01   8.2413e-02   9.8592e-01   8.2728e-02
-#> 376:    1.0186e+02  -4.1204e+00  -2.3526e+00  -4.0649e+00  -1.0090e+00   5.7256e-03   3.6406e-02   5.0673e-07   9.1590e-02   2.2260e-02   1.8765e-01   9.4159e-02   9.3417e-01   8.2400e-02   9.8565e-01   8.2701e-02
-#> 377:    1.0186e+02  -4.1204e+00  -2.3527e+00  -4.0647e+00  -1.0091e+00   5.2782e-03   3.6397e-02   5.0740e-07   9.1564e-02   2.2263e-02   1.8765e-01   9.4084e-02   9.3434e-01   8.2395e-02   9.8563e-01   8.2680e-02
-#> 378:    1.0186e+02  -4.1204e+00  -2.3524e+00  -4.0646e+00  -1.0091e+00   4.8184e-03   3.6478e-02   5.0759e-07   9.1590e-02   2.2213e-02   1.8766e-01   9.4162e-02   9.3432e-01   8.2353e-02   9.8595e-01   8.2681e-02
-#> 379:    1.0186e+02  -4.1204e+00  -2.3521e+00  -4.0646e+00  -1.0089e+00   4.4861e-03   3.6557e-02   5.0710e-07   9.1595e-02   2.2159e-02   1.8767e-01   9.3894e-02   9.3395e-01   8.2341e-02   9.8636e-01   8.2671e-02
-#> 380:    1.0186e+02  -4.1204e+00  -2.3517e+00  -4.0644e+00  -1.0089e+00   3.9799e-03   3.6543e-02   5.0682e-07   9.1532e-02   2.2143e-02   1.8768e-01   9.3854e-02   9.3372e-01   8.2331e-02   9.8640e-01   8.2678e-02
-#> 381:    1.0186e+02  -4.1204e+00  -2.3515e+00  -4.0643e+00  -1.0089e+00   3.6269e-03   3.6531e-02   5.0770e-07   9.1364e-02   2.2157e-02   1.8768e-01   9.3897e-02   9.3383e-01   8.2326e-02   9.8630e-01   8.2675e-02
-#> 382:    1.0186e+02  -4.1204e+00  -2.3513e+00  -4.0643e+00  -1.0090e+00   3.1691e-03   3.6469e-02   5.0860e-07   9.1318e-02   2.2188e-02   1.8767e-01   9.3787e-02   9.3433e-01   8.2306e-02   9.8643e-01   8.2670e-02
-#> 383:    1.0186e+02  -4.1204e+00  -2.3508e+00  -4.0642e+00  -1.0090e+00   2.6209e-03   3.6416e-02   5.0893e-07   9.1374e-02   2.2165e-02   1.8759e-01   9.3654e-02   9.3443e-01   8.2289e-02   9.8663e-01   8.2672e-02
-#> 384:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0640e+00  -1.0090e+00   2.1556e-03   3.6403e-02   5.0834e-07   9.1550e-02   2.2148e-02   1.8750e-01   9.3422e-02   9.3444e-01   8.2277e-02   9.8639e-01   8.2670e-02
-#> 385:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0638e+00  -1.0089e+00   1.7048e-03   3.6391e-02   5.0788e-07   9.1717e-02   2.2160e-02   1.8746e-01   9.3178e-02   9.3457e-01   8.2261e-02   9.8616e-01   8.2636e-02
-#> 386:    1.0186e+02  -4.1204e+00  -2.3504e+00  -4.0637e+00  -1.0089e+00   1.4309e-03   3.6372e-02   5.0847e-07   9.1895e-02   2.2157e-02   1.8754e-01   9.2918e-02   9.3439e-01   8.2246e-02   9.8601e-01   8.2617e-02
-#> 387:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0636e+00  -1.0089e+00   1.3524e-03   3.6446e-02   5.0896e-07   9.2022e-02   2.2182e-02   1.8768e-01   9.2684e-02   9.3470e-01   8.2216e-02   9.8593e-01   8.2620e-02
-#> 388:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0635e+00  -1.0089e+00   1.2887e-03   3.6478e-02   5.0904e-07   9.2117e-02   2.2174e-02   1.8761e-01   9.2506e-02   9.3463e-01   8.2221e-02   9.8563e-01   8.2609e-02
-#> 389:    1.0186e+02  -4.1204e+00  -2.3505e+00  -4.0635e+00  -1.0089e+00   1.2044e-03   3.6479e-02   5.0969e-07   9.2068e-02   2.2180e-02   1.8751e-01   9.2308e-02   9.3438e-01   8.2241e-02   9.8516e-01   8.2592e-02
-#> 390:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0635e+00  -1.0087e+00   1.1442e-03   3.6497e-02   5.0878e-07   9.1995e-02   2.2156e-02   1.8744e-01   9.2169e-02   9.3410e-01   8.2257e-02   9.8511e-01   8.2581e-02
-#> 391:    1.0186e+02  -4.1204e+00  -2.3508e+00  -4.0635e+00  -1.0089e+00   1.0925e-03   3.6454e-02   5.0876e-07   9.1945e-02   2.2177e-02   1.8739e-01   9.1989e-02   9.3439e-01   8.2254e-02   9.8472e-01   8.2579e-02
-#> 392:    1.0186e+02  -4.1204e+00  -2.3506e+00  -4.0633e+00  -1.0091e+00   7.9940e-04   3.6417e-02   5.0874e-07   9.1956e-02   2.2185e-02   1.8730e-01   9.1977e-02   9.3422e-01   8.2244e-02   9.8463e-01   8.2589e-02
-#> 393:    1.0186e+02  -4.1204e+00  -2.3504e+00  -4.0632e+00  -1.0093e+00   4.2112e-04   3.6433e-02   5.0843e-07   9.1868e-02   2.2211e-02   1.8739e-01   9.2106e-02   9.3464e-01   8.2217e-02   9.8458e-01   8.2594e-02
-#> 394:    1.0186e+02  -4.1204e+00  -2.3502e+00  -4.0631e+00  -1.0093e+00   1.4926e-04   3.6534e-02   5.0862e-07   9.1713e-02   2.2244e-02   1.8735e-01   9.2105e-02   9.3454e-01   8.2239e-02   9.8410e-01   8.2601e-02
-#> 395:    1.0186e+02  -4.1204e+00  -2.3499e+00  -4.0630e+00  -1.0095e+00   5.2506e-05   3.6667e-02   5.0955e-07   9.1548e-02   2.2269e-02   1.8733e-01   9.2151e-02   9.3450e-01   8.2256e-02   9.8389e-01   8.2612e-02
-#> 396:    1.0186e+02  -4.1204e+00  -2.3497e+00  -4.0630e+00  -1.0097e+00   1.6581e-05   3.6789e-02   5.1002e-07   9.1431e-02   2.2299e-02   1.8742e-01   9.2120e-02   9.3450e-01   8.2252e-02   9.8367e-01   8.2620e-02
-#> 397:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0629e+00  -1.0098e+00  -5.0310e-05   3.6860e-02   5.0949e-07   9.1311e-02   2.2323e-02   1.8738e-01   9.2130e-02   9.3467e-01   8.2250e-02   9.8388e-01   8.2628e-02
-#> 398:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0629e+00  -1.0097e+00  -1.4918e-04   3.6902e-02   5.0935e-07   9.1211e-02   2.2330e-02   1.8747e-01   9.2144e-02   9.3478e-01   8.2260e-02   9.8420e-01   8.2632e-02
-#> 399:    1.0186e+02  -4.1205e+00  -2.3497e+00  -4.0628e+00  -1.0097e+00  -2.2152e-04   3.6932e-02   5.0927e-07   9.1209e-02   2.2377e-02   1.8750e-01   9.2136e-02   9.3481e-01   8.2286e-02   9.8431e-01   8.2622e-02
-#> 400:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0629e+00  -1.0097e+00   3.2878e-05   3.6943e-02   5.0892e-07   9.1092e-02   2.2388e-02   1.8752e-01   9.2072e-02   9.3534e-01   8.2276e-02   9.8472e-01   8.2615e-02
-#> 401:    1.0186e+02  -4.1205e+00  -2.3501e+00  -4.0630e+00  -1.0097e+00   2.6776e-04   3.6950e-02   5.0860e-07   9.1038e-02   2.2395e-02   1.8740e-01   9.1911e-02   9.3515e-01   8.2331e-02   9.8459e-01   8.2615e-02
-#> 402:    1.0186e+02  -4.1205e+00  -2.3502e+00  -4.0632e+00  -1.0097e+00   3.9988e-04   3.6912e-02   5.0849e-07   9.0944e-02   2.2401e-02   1.8737e-01   9.1701e-02   9.3494e-01   8.2353e-02   9.8479e-01   8.2609e-02
-#> 403:    1.0186e+02  -4.1205e+00  -2.3503e+00  -4.0633e+00  -1.0098e+00   4.9714e-04   3.6935e-02   5.0805e-07   9.0895e-02   2.2404e-02   1.8741e-01   9.1609e-02   9.3444e-01   8.2372e-02   9.8505e-01   8.2638e-02
-#> 404:    1.0186e+02  -4.1205e+00  -2.3504e+00  -4.0633e+00  -1.0100e+00   5.8465e-04   3.6978e-02   5.0889e-07   9.0862e-02   2.2453e-02   1.8746e-01   9.1650e-02   9.3491e-01   8.2364e-02   9.8484e-01   8.2653e-02
-#> 405:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0634e+00  -1.0099e+00   5.5970e-04   3.6999e-02   5.0964e-07   9.0930e-02   2.2480e-02   1.8742e-01   9.1823e-02   9.3458e-01   8.2371e-02   9.8465e-01   8.2670e-02
-#> 406:    1.0186e+02  -4.1205e+00  -2.3507e+00  -4.0634e+00  -1.0098e+00   5.4464e-04   3.7123e-02   5.1046e-07   9.1008e-02   2.2478e-02   1.8749e-01   9.1930e-02   9.3449e-01   8.2361e-02   9.8440e-01   8.2666e-02
-#> 407:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0097e+00   3.5564e-04   3.7226e-02   5.0978e-07   9.0891e-02   2.2469e-02   1.8751e-01   9.2130e-02   9.3444e-01   8.2380e-02   9.8462e-01   8.2660e-02
-#> 408:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0635e+00  -1.0097e+00   3.8362e-04   3.7354e-02   5.0967e-07   9.0892e-02   2.2461e-02   1.8747e-01   9.2230e-02   9.3453e-01   8.2363e-02   9.8466e-01   8.2661e-02
-#> 409:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0635e+00  -1.0097e+00   2.6671e-04   3.7473e-02   5.0928e-07   9.0894e-02   2.2519e-02   1.8751e-01   9.2243e-02   9.3447e-01   8.2347e-02   9.8449e-01   8.2667e-02
-#> 410:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0098e+00   1.7963e-04   3.7438e-02   5.0981e-07   9.0898e-02   2.2600e-02   1.8764e-01   9.2237e-02   9.3502e-01   8.2320e-02   9.8430e-01   8.2663e-02
-#> 411:    1.0186e+02  -4.1205e+00  -2.3506e+00  -4.0634e+00  -1.0098e+00   1.0085e-04   3.7381e-02   5.0970e-07   9.0820e-02   2.2618e-02   1.8767e-01   9.2103e-02   9.3480e-01   8.2324e-02   9.8427e-01   8.2652e-02
-#> 412:    1.0186e+02  -4.1205e+00  -2.3508e+00  -4.0633e+00  -1.0097e+00   1.9452e-04   3.7315e-02   5.0984e-07   9.0784e-02   2.2605e-02   1.8772e-01   9.2118e-02   9.3504e-01   8.2314e-02   9.8431e-01   8.2636e-02
-#> 413:    1.0186e+02  -4.1205e+00  -2.3508e+00  -4.0632e+00  -1.0097e+00   1.8432e-04   3.7243e-02   5.0946e-07   9.0798e-02   2.2604e-02   1.8765e-01   9.2206e-02   9.3499e-01   8.2299e-02   9.8426e-01   8.2636e-02
-#> 414:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0632e+00  -1.0097e+00   2.1744e-04   3.7203e-02   5.0880e-07   9.0769e-02   2.2604e-02   1.8757e-01   9.2403e-02   9.3516e-01   8.2279e-02   9.8414e-01   8.2659e-02
-#> 415:    1.0186e+02  -4.1205e+00  -2.3505e+00  -4.0633e+00  -1.0097e+00   1.9330e-04   3.7197e-02   5.0896e-07   9.0657e-02   2.2618e-02   1.8764e-01   9.2565e-02   9.3514e-01   8.2264e-02   9.8435e-01   8.2655e-02
-#> 416:    1.0186e+02  -4.1205e+00  -2.3501e+00  -4.0634e+00  -1.0097e+00   2.1450e-04   3.7144e-02   5.0882e-07   9.0762e-02   2.2645e-02   1.8761e-01   9.2614e-02   9.3511e-01   8.2277e-02   9.8415e-01   8.2669e-02
-#> 417:    1.0186e+02  -4.1205e+00  -2.3498e+00  -4.0634e+00  -1.0099e+00   1.0737e-04   3.7092e-02   5.0932e-07   9.0804e-02   2.2631e-02   1.8754e-01   9.2581e-02   9.3509e-01   8.2284e-02   9.8430e-01   8.2667e-02
-#> 418:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0099e+00   2.4734e-05   3.7061e-02   5.0972e-07   9.0913e-02   2.2624e-02   1.8736e-01   9.2572e-02   9.3482e-01   8.2275e-02   9.8413e-01   8.2682e-02
-#> 419:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0099e+00  -3.9197e-05   3.7070e-02   5.1000e-07   9.1084e-02   2.2644e-02   1.8727e-01   9.2636e-02   9.3494e-01   8.2259e-02   9.8382e-01   8.2673e-02
-#> 420:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0633e+00  -1.0098e+00  -1.2434e-04   3.7103e-02   5.1037e-07   9.1152e-02   2.2631e-02   1.8733e-01   9.2862e-02   9.3515e-01   8.2244e-02   9.8388e-01   8.2656e-02
-#> 421:    1.0186e+02  -4.1205e+00  -2.3494e+00  -4.0632e+00  -1.0097e+00  -1.5440e-04   3.7123e-02   5.1205e-07   9.1233e-02   2.2626e-02   1.8744e-01   9.2935e-02   9.3523e-01   8.2241e-02   9.8360e-01   8.2652e-02
-#> 422:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0095e+00  -8.9184e-05   3.7182e-02   5.1296e-07   9.1123e-02   2.2617e-02   1.8749e-01   9.2915e-02   9.3509e-01   8.2276e-02   9.8367e-01   8.2637e-02
-#> 423:    1.0186e+02  -4.1205e+00  -2.3497e+00  -4.0634e+00  -1.0095e+00   6.7469e-05   3.7194e-02   5.1323e-07   9.1083e-02   2.2642e-02   1.8739e-01   9.3097e-02   9.3529e-01   8.2270e-02   9.8367e-01   8.2642e-02
-#> 424:    1.0186e+02  -4.1205e+00  -2.3498e+00  -4.0635e+00  -1.0094e+00   1.5970e-04   3.7258e-02   5.1292e-07   9.0998e-02   2.2667e-02   1.8730e-01   9.3311e-02   9.3525e-01   8.2262e-02   9.8362e-01   8.2648e-02
-#> 425:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0636e+00  -1.0095e+00   2.7004e-04   3.7298e-02   5.1307e-07   9.0839e-02   2.2665e-02   1.8744e-01   9.3429e-02   9.3497e-01   8.2282e-02   9.8395e-01   8.2657e-02
-#> 426:    1.0186e+02  -4.1205e+00  -2.3499e+00  -4.0636e+00  -1.0094e+00   3.9201e-04   3.7303e-02   5.1305e-07   9.0647e-02   2.2675e-02   1.8743e-01   9.3523e-02   9.3477e-01   8.2314e-02   9.8371e-01   8.2655e-02
-#> 427:    1.0186e+02  -4.1205e+00  -2.3496e+00  -4.0636e+00  -1.0093e+00   2.9359e-04   3.7366e-02   5.1245e-07   9.0630e-02   2.2673e-02   1.8738e-01   9.3813e-02   9.3495e-01   8.2291e-02   9.8368e-01   8.2653e-02
-#> 428:    1.0186e+02  -4.1204e+00  -2.3496e+00  -4.0635e+00  -1.0094e+00   2.5099e-04   3.7411e-02   5.1144e-07   9.0647e-02   2.2674e-02   1.8732e-01   9.3993e-02   9.3493e-01   8.2273e-02   9.8373e-01   8.2652e-02
-#> 429:    1.0186e+02  -4.1204e+00  -2.3495e+00  -4.0635e+00  -1.0095e+00   2.4723e-04   3.7543e-02   5.1084e-07   9.0600e-02   2.2677e-02   1.8723e-01   9.4269e-02   9.3518e-01   8.2286e-02   9.8396e-01   8.2659e-02
-#> 430:    1.0186e+02  -4.1204e+00  -2.3494e+00  -4.0635e+00  -1.0096e+00   2.7711e-04   3.7579e-02   5.1022e-07   9.0496e-02   2.2679e-02   1.8708e-01   9.4484e-02   9.3525e-01   8.2309e-02   9.8433e-01   8.2672e-02
-#> 431:    1.0186e+02  -4.1204e+00  -2.3494e+00  -4.0634e+00  -1.0095e+00   1.3934e-05   3.7631e-02   5.0908e-07   9.0378e-02   2.2671e-02   1.8708e-01   9.4770e-02   9.3528e-01   8.2302e-02   9.8470e-01   8.2682e-02
-#> 432:    1.0186e+02  -4.1204e+00  -2.3495e+00  -4.0633e+00  -1.0096e+00  -8.9401e-05   3.7677e-02   5.0861e-07   9.0278e-02   2.2654e-02   1.8702e-01   9.4882e-02   9.3518e-01   8.2318e-02   9.8488e-01   8.2667e-02
-#> 433:    1.0186e+02  -4.1205e+00  -2.3495e+00  -4.0633e+00  -1.0096e+00  -3.6841e-04   3.7706e-02   5.0854e-07   9.0108e-02   2.2652e-02   1.8703e-01   9.5039e-02   9.3487e-01   8.2331e-02   9.8494e-01   8.2669e-02
-#> 434:    1.0186e+02  -4.1205e+00  -2.3493e+00  -4.0632e+00  -1.0096e+00  -4.3399e-04   3.7671e-02   5.0796e-07   9.0036e-02   2.2661e-02   1.8701e-01   9.5122e-02   9.3474e-01   8.2331e-02   9.8474e-01   8.2675e-02
-#> 435:    1.0186e+02  -4.1205e+00  -2.3491e+00  -4.0632e+00  -1.0096e+00  -6.1398e-04   3.7654e-02   5.0727e-07   8.9940e-02   2.2664e-02   1.8691e-01   9.5242e-02   9.3451e-01   8.2346e-02   9.8466e-01   8.2677e-02
-#> 436:    1.0186e+02  -4.1205e+00  -2.3487e+00  -4.0632e+00  -1.0094e+00  -7.2148e-04   3.7647e-02   5.0649e-07   8.9838e-02   2.2661e-02   1.8694e-01   9.5465e-02   9.3429e-01   8.2365e-02   9.8475e-01   8.2683e-02
-#> 437:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0632e+00  -1.0093e+00  -1.1480e-03   3.7613e-02   5.0662e-07   8.9719e-02   2.2674e-02   1.8698e-01   9.5631e-02   9.3419e-01   8.2380e-02   9.8490e-01   8.2684e-02
-#> 438:    1.0186e+02  -4.1204e+00  -2.3482e+00  -4.0631e+00  -1.0092e+00  -1.5547e-03   3.7583e-02   5.0753e-07   8.9612e-02   2.2680e-02   1.8703e-01   9.5913e-02   9.3413e-01   8.2394e-02   9.8523e-01   8.2678e-02
-#> 439:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0630e+00  -1.0093e+00  -1.9392e-03   3.7463e-02   5.0769e-07   8.9410e-02   2.2706e-02   1.8706e-01   9.6149e-02   9.3392e-01   8.2425e-02   9.8512e-01   8.2670e-02
-#> 440:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0629e+00  -1.0094e+00  -2.1940e-03   3.7360e-02   5.0743e-07   8.9245e-02   2.2742e-02   1.8710e-01   9.6213e-02   9.3400e-01   8.2445e-02   9.8490e-01   8.2676e-02
-#> 441:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0095e+00  -2.3414e-03   3.7297e-02   5.0838e-07   8.9137e-02   2.2806e-02   1.8721e-01   9.6155e-02   9.3405e-01   8.2450e-02   9.8470e-01   8.2684e-02
-#> 442:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0628e+00  -1.0095e+00  -2.6378e-03   3.7241e-02   5.0923e-07   8.9066e-02   2.2846e-02   1.8727e-01   9.6135e-02   9.3389e-01   8.2465e-02   9.8454e-01   8.2686e-02
-#> 443:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0627e+00  -1.0094e+00  -2.8716e-03   3.7214e-02   5.1026e-07   8.9128e-02   2.2901e-02   1.8740e-01   9.6077e-02   9.3386e-01   8.2444e-02   9.8421e-01   8.2692e-02
-#> 444:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0627e+00  -1.0092e+00  -2.9147e-03   3.7196e-02   5.1104e-07   8.9190e-02   2.2985e-02   1.8744e-01   9.5999e-02   9.3390e-01   8.2424e-02   9.8381e-01   8.2696e-02
-#> 445:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0626e+00  -1.0090e+00  -2.9638e-03   3.7251e-02   5.1283e-07   8.9335e-02   2.3004e-02   1.8756e-01   9.5788e-02   9.3382e-01   8.2416e-02   9.8347e-01   8.2683e-02
-#> 446:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0627e+00  -1.0090e+00  -2.8796e-03   3.7331e-02   5.1479e-07   8.9470e-02   2.3017e-02   1.8762e-01   9.5656e-02   9.3368e-01   8.2405e-02   9.8325e-01   8.2680e-02
-#> 447:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0628e+00  -1.0091e+00  -2.7695e-03   3.7473e-02   5.1656e-07   8.9568e-02   2.3030e-02   1.8757e-01   9.5575e-02   9.3379e-01   8.2386e-02   9.8306e-01   8.2690e-02
-#> 448:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0091e+00  -2.6293e-03   3.7498e-02   5.1814e-07   8.9776e-02   2.3052e-02   1.8762e-01   9.5422e-02   9.3373e-01   8.2372e-02   9.8274e-01   8.2685e-02
-#> 449:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0628e+00  -1.0092e+00  -2.5640e-03   3.7542e-02   5.1867e-07   8.9888e-02   2.3056e-02   1.8763e-01   9.5364e-02   9.3400e-01   8.2365e-02   9.8239e-01   8.2691e-02
-#> 450:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0093e+00  -2.5816e-03   3.7622e-02   5.1849e-07   9.0050e-02   2.3061e-02   1.8765e-01   9.5274e-02   9.3435e-01   8.2341e-02   9.8235e-01   8.2699e-02
-#> 451:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0627e+00  -1.0094e+00  -2.4837e-03   3.7631e-02   5.1931e-07   9.0177e-02   2.3053e-02   1.8766e-01   9.5103e-02   9.3459e-01   8.2322e-02   9.8226e-01   8.2715e-02
-#> 452:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0627e+00  -1.0094e+00  -2.4156e-03   3.7606e-02   5.1901e-07   9.0333e-02   2.3047e-02   1.8763e-01   9.4959e-02   9.3485e-01   8.2289e-02   9.8210e-01   8.2713e-02
-#> 453:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0093e+00  -2.4619e-03   3.7552e-02   5.1874e-07   9.0495e-02   2.3066e-02   1.8761e-01   9.4960e-02   9.3485e-01   8.2293e-02   9.8178e-01   8.2703e-02
-#> 454:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0627e+00  -1.0092e+00  -2.4816e-03   3.7514e-02   5.1835e-07   9.0606e-02   2.3073e-02   1.8754e-01   9.4896e-02   9.3491e-01   8.2277e-02   9.8154e-01   8.2696e-02
-#> 455:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0092e+00  -2.3708e-03   3.7457e-02   5.1742e-07   9.0715e-02   2.3099e-02   1.8756e-01   9.4804e-02   9.3481e-01   8.2272e-02   9.8122e-01   8.2688e-02
-#> 456:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0627e+00  -1.0093e+00  -2.2313e-03   3.7409e-02   5.1680e-07   9.0906e-02   2.3131e-02   1.8743e-01   9.4814e-02   9.3476e-01   8.2261e-02   9.8108e-01   8.2694e-02
-#> 457:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0095e+00  -2.1182e-03   3.7342e-02   5.1630e-07   9.0986e-02   2.3158e-02   1.8733e-01   9.4843e-02   9.3488e-01   8.2244e-02   9.8094e-01   8.2700e-02
-#> 458:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0628e+00  -1.0095e+00  -1.9242e-03   3.7244e-02   5.1605e-07   9.1085e-02   2.3168e-02   1.8720e-01   9.4820e-02   9.3509e-01   8.2228e-02   9.8093e-01   8.2703e-02
-#> 459:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0629e+00  -1.0095e+00  -1.7643e-03   3.7203e-02   5.1566e-07   9.1179e-02   2.3175e-02   1.8715e-01   9.4809e-02   9.3516e-01   8.2216e-02   9.8087e-01   8.2690e-02
-#> 460:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.5479e-03   3.7151e-02   5.1547e-07   9.1211e-02   2.3155e-02   1.8712e-01   9.4703e-02   9.3539e-01   8.2201e-02   9.8100e-01   8.2683e-02
-#> 461:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0630e+00  -1.0095e+00  -1.4993e-03   3.7111e-02   5.1446e-07   9.1225e-02   2.3159e-02   1.8705e-01   9.4569e-02   9.3555e-01   8.2183e-02   9.8078e-01   8.2680e-02
-#> 462:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.4890e-03   3.7056e-02   5.1361e-07   9.1446e-02   2.3158e-02   1.8694e-01   9.4494e-02   9.3557e-01   8.2171e-02   9.8058e-01   8.2688e-02
-#> 463:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0629e+00  -1.0094e+00  -1.3999e-03   3.6996e-02   5.1319e-07   9.1659e-02   2.3176e-02   1.8695e-01   9.4436e-02   9.3570e-01   8.2153e-02   9.8053e-01   8.2686e-02
-#> 464:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0630e+00  -1.0095e+00  -1.1544e-03   3.6949e-02   5.1300e-07   9.1885e-02   2.3162e-02   1.8688e-01   9.4378e-02   9.3599e-01   8.2134e-02   9.8051e-01   8.2694e-02
-#> 465:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0630e+00  -1.0097e+00  -9.7372e-04   3.6943e-02   5.1235e-07   9.2014e-02   2.3136e-02   1.8692e-01   9.4288e-02   9.3605e-01   8.2141e-02   9.8053e-01   8.2693e-02
-#> 466:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0630e+00  -1.0097e+00  -9.2442e-04   3.6916e-02   5.1246e-07   9.2074e-02   2.3132e-02   1.8688e-01   9.4254e-02   9.3590e-01   8.2131e-02   9.8016e-01   8.2691e-02
-#> 467:    1.0186e+02  -4.1205e+00  -2.3485e+00  -4.0631e+00  -1.0098e+00  -8.2540e-04   3.6928e-02   5.1340e-07   9.2164e-02   2.3141e-02   1.8690e-01   9.4382e-02   9.3620e-01   8.2106e-02   9.7996e-01   8.2705e-02
-#> 468:    1.0186e+02  -4.1205e+00  -2.3484e+00  -4.0631e+00  -1.0097e+00  -7.3368e-04   3.6925e-02   5.1395e-07   9.2218e-02   2.3136e-02   1.8695e-01   9.4504e-02   9.3629e-01   8.2094e-02   9.7985e-01   8.2716e-02
-#> 469:    1.0186e+02  -4.1205e+00  -2.3483e+00  -4.0630e+00  -1.0096e+00  -7.4343e-04   3.6891e-02   5.1401e-07   9.2204e-02   2.3114e-02   1.8700e-01   9.4639e-02   9.3643e-01   8.2078e-02   9.7996e-01   8.2709e-02
-#> 470:    1.0186e+02  -4.1205e+00  -2.3482e+00  -4.0630e+00  -1.0096e+00  -7.8250e-04   3.6874e-02   5.1370e-07   9.2209e-02   2.3083e-02   1.8702e-01   9.4728e-02   9.3646e-01   8.2073e-02   9.7989e-01   8.2703e-02
-#> 471:    1.0186e+02  -4.1205e+00  -2.3480e+00  -4.0629e+00  -1.0095e+00  -1.0440e-03   3.6843e-02   5.1358e-07   9.2194e-02   2.3062e-02   1.8710e-01   9.4741e-02   9.3649e-01   8.2082e-02   9.8003e-01   8.2701e-02
-#> 472:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0096e+00  -9.5438e-04   3.6869e-02   5.1330e-07   9.2176e-02   2.3050e-02   1.8712e-01   9.4766e-02   9.3666e-01   8.2080e-02   9.7996e-01   8.2691e-02
-#> 473:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0096e+00  -8.2178e-04   3.6877e-02   5.1283e-07   9.2191e-02   2.3021e-02   1.8703e-01   9.4747e-02   9.3670e-01   8.2072e-02   9.8007e-01   8.2693e-02
-#> 474:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0096e+00  -7.0189e-04   3.6927e-02   5.1196e-07   9.2195e-02   2.2989e-02   1.8702e-01   9.4746e-02   9.3669e-01   8.2054e-02   9.8029e-01   8.2689e-02
-#> 475:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0630e+00  -1.0095e+00  -7.1989e-04   3.6993e-02   5.1125e-07   9.2159e-02   2.2963e-02   1.8700e-01   9.4813e-02   9.3681e-01   8.2051e-02   9.8027e-01   8.2680e-02
-#> 476:    1.0186e+02  -4.1205e+00  -2.3481e+00  -4.0629e+00  -1.0096e+00  -7.1806e-04   3.7018e-02   5.1105e-07   9.2091e-02   2.2933e-02   1.8696e-01   9.4837e-02   9.3713e-01   8.2067e-02   9.8033e-01   8.2674e-02
-#> 477:    1.0186e+02  -4.1205e+00  -2.3479e+00  -4.0630e+00  -1.0097e+00  -7.3438e-04   3.6986e-02   5.1121e-07   9.2046e-02   2.2909e-02   1.8698e-01   9.4809e-02   9.3743e-01   8.2059e-02   9.8045e-01   8.2693e-02
-#> 478:    1.0186e+02  -4.1205e+00  -2.3478e+00  -4.0630e+00  -1.0096e+00  -7.9338e-04   3.6912e-02   5.1224e-07   9.2056e-02   2.2881e-02   1.8698e-01   9.4791e-02   9.3757e-01   8.2042e-02   9.8072e-01   8.2682e-02
-#> 479:    1.0186e+02  -4.1205e+00  -2.3476e+00  -4.0629e+00  -1.0096e+00  -8.6158e-04   3.6882e-02   5.1284e-07   9.2159e-02   2.2867e-02   1.8694e-01   9.4774e-02   9.3749e-01   8.2051e-02   9.8088e-01   8.2679e-02
-#> 480:    1.0186e+02  -4.1205e+00  -2.3474e+00  -4.0629e+00  -1.0096e+00  -1.1334e-03   3.6851e-02   5.1423e-07   9.2253e-02   2.2869e-02   1.8696e-01   9.4820e-02   9.3751e-01   8.2063e-02   9.8097e-01   8.2693e-02
-#> 481:    1.0186e+02  -4.1205e+00  -2.3470e+00  -4.0629e+00  -1.0096e+00  -1.2444e-03   3.6785e-02   5.1490e-07   9.2397e-02   2.2853e-02   1.8694e-01   9.4838e-02   9.3770e-01   8.2031e-02   9.8124e-01   8.2707e-02
-#> 482:    1.0186e+02  -4.1205e+00  -2.3467e+00  -4.0629e+00  -1.0095e+00  -1.3612e-03   3.6750e-02   5.1658e-07   9.2440e-02   2.2842e-02   1.8683e-01   9.4800e-02   9.3786e-01   8.2041e-02   9.8107e-01   8.2719e-02
-#> 483:    1.0186e+02  -4.1205e+00  -2.3467e+00  -4.0628e+00  -1.0096e+00  -1.5168e-03   3.6783e-02   5.1708e-07   9.2590e-02   2.2804e-02   1.8674e-01   9.4804e-02   9.3790e-01   8.2042e-02   9.8116e-01   8.2719e-02
-#> 484:    1.0186e+02  -4.1205e+00  -2.3466e+00  -4.0628e+00  -1.0097e+00  -1.5218e-03   3.6848e-02   5.1669e-07   9.2717e-02   2.2775e-02   1.8670e-01   9.4940e-02   9.3798e-01   8.2028e-02   9.8106e-01   8.2719e-02
-#> 485:    1.0186e+02  -4.1205e+00  -2.3464e+00  -4.0628e+00  -1.0097e+00  -1.4177e-03   3.6867e-02   5.1615e-07   9.2806e-02   2.2765e-02   1.8669e-01   9.5018e-02   9.3816e-01   8.2020e-02   9.8090e-01   8.2721e-02
-#> 486:    1.0186e+02  -4.1205e+00  -2.3462e+00  -4.0628e+00  -1.0098e+00  -1.5257e-03   3.6968e-02   5.1513e-07   9.3019e-02   2.2762e-02   1.8663e-01   9.5111e-02   9.3816e-01   8.2013e-02   9.8071e-01   8.2732e-02
-#> 487:    1.0186e+02  -4.1205e+00  -2.3460e+00  -4.0628e+00  -1.0097e+00  -1.7055e-03   3.7021e-02   5.1446e-07   9.3161e-02   2.2732e-02   1.8652e-01   9.5373e-02   9.3832e-01   8.1997e-02   9.8078e-01   8.2737e-02
-#> 488:    1.0186e+02  -4.1205e+00  -2.3459e+00  -4.0628e+00  -1.0097e+00  -1.8502e-03   3.7069e-02   5.1391e-07   9.3282e-02   2.2741e-02   1.8641e-01   9.5414e-02   9.3818e-01   8.2001e-02   9.8064e-01   8.2738e-02
-#> 489:    1.0186e+02  -4.1205e+00  -2.3458e+00  -4.0628e+00  -1.0097e+00  -1.9091e-03   3.7017e-02   5.1291e-07   9.3286e-02   2.2738e-02   1.8639e-01   9.5453e-02   9.3808e-01   8.1991e-02   9.8047e-01   8.2728e-02
-#> 490:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0628e+00  -1.0097e+00  -1.8766e-03   3.6969e-02   5.1220e-07   9.3297e-02   2.2728e-02   1.8635e-01   9.5468e-02   9.3793e-01   8.1988e-02   9.8034e-01   8.2726e-02
-#> 491:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0627e+00  -1.0097e+00  -1.7736e-03   3.6915e-02   5.1153e-07   9.3298e-02   2.2716e-02   1.8634e-01   9.5548e-02   9.3772e-01   8.2005e-02   9.8025e-01   8.2722e-02
-#> 492:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0627e+00  -1.0097e+00  -1.7747e-03   3.6877e-02   5.1077e-07   9.3336e-02   2.2697e-02   1.8635e-01   9.5593e-02   9.3778e-01   8.2001e-02   9.8013e-01   8.2725e-02
-#> 493:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0628e+00  -1.0094e+00  -1.6324e-03   3.6857e-02   5.1020e-07   9.3348e-02   2.2668e-02   1.8636e-01   9.5735e-02   9.3764e-01   8.1984e-02   9.8019e-01   8.2723e-02
-#> 494:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0629e+00  -1.0094e+00  -1.5393e-03   3.6842e-02   5.1022e-07   9.3359e-02   2.2649e-02   1.8637e-01   9.5812e-02   9.3739e-01   8.1982e-02   9.8033e-01   8.2708e-02
-#> 495:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0629e+00  -1.0094e+00  -1.5166e-03   3.6841e-02   5.1004e-07   9.3321e-02   2.2642e-02   1.8640e-01   9.5849e-02   9.3716e-01   8.1979e-02   9.8016e-01   8.2700e-02
-#> 496:    1.0186e+02  -4.1205e+00  -2.3456e+00  -4.0630e+00  -1.0095e+00  -1.4947e-03   3.6841e-02   5.0969e-07   9.3236e-02   2.2646e-02   1.8640e-01   9.5916e-02   9.3719e-01   8.1963e-02   9.8028e-01   8.2702e-02
-#> 497:    1.0186e+02  -4.1205e+00  -2.3457e+00  -4.0629e+00  -1.0094e+00  -1.4507e-03   3.6827e-02   5.0937e-07   9.3185e-02   2.2663e-02   1.8638e-01   9.5991e-02   9.3707e-01   8.1954e-02   9.8047e-01   8.2718e-02
-#> 498:    1.0186e+02  -4.1205e+00  -2.3459e+00  -4.0630e+00  -1.0094e+00  -1.2569e-03   3.6805e-02   5.0854e-07   9.3089e-02   2.2677e-02   1.8634e-01   9.5931e-02   9.3719e-01   8.1952e-02   9.8051e-01   8.2718e-02
-#> 499:    1.0186e+02  -4.1205e+00  -2.3460e+00  -4.0630e+00  -1.0093e+00  -1.0466e-03   3.6769e-02   5.0789e-07   9.3029e-02   2.2690e-02   1.8631e-01   9.5862e-02   9.3729e-01   8.1956e-02   9.8046e-01   8.2731e-02
-#> 500:    1.0186e+02  -4.1205e+00  -2.3464e+00  -4.0630e+00  -1.0093e+00  -7.3346e-04   3.6766e-02   5.0769e-07   9.3093e-02   2.2701e-02   1.8633e-01   9.5687e-02   9.3739e-01   8.1977e-02   9.8039e-01   8.2728e-02
                                                                #> Calculating covariance matrix
                                                                #> 
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                # The following takes a very long time but gives
+
                                                                #> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>  
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc,     ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG,     addProp = .addProp, tol = .tol, itmax = .itmax, type = .type,     powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1
                                                                #> Timing stopped at: 1.281 0.142 1.422
                                                                # The following takes a very long time but gives
 f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei")
-
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
-#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
-#> F: Forward difference gradient approximation
-#> C: Central difference gradient approximation
-#> M: Mixed forward and central difference gradient approximation
-#> Unscaled parameters for Omegas=chol(solve(omega));
-#> Diagonals are transformed, as specified by foceiControl(diagXform=)
-#> |-----+---------------+-----------+-----------+-----------+-----------|
-#> |    #| Objective Fun |  parent_0 |  log_k_m1 |f_parent_qlogis |    log_k1 |
-#> |.....................|    log_k2 |  g_qlogis | sigma_low |  rsd_high |
-#> |.....................|        o1 |        o2 |        o3 |        o4 |
-#> |.....................|        o5 |        o6 |...........|...........|
-#> |    1|     496.98032 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     496.98032 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     496.98032 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    G|    Gill Diff. |     57.10 |   -0.1453 |   -0.1275 |    0.2854 |
-#> |.....................|   -0.6156 |  0.007043 |    -23.49 |    -32.87 |
-#> |.....................|     3.669 |    -17.46 |    -13.05 |    -13.08 |
-#> |.....................|    -16.16 |    -9.766 |...........|...........|
-#> |    2|     3094.8373 |    0.2572 |   -0.9978 |   -0.9392 |   -0.9714 |
-#> |.....................|   -0.9920 |   -0.9233 |   -0.6037 |   -0.4942 |
-#> |.....................|   -0.9579 |   -0.6658 |   -0.7293 |   -0.7310 |
-#> |.....................|   -0.6848 |   -0.7742 |...........|...........|
-#> |    U|     3094.8373 |     26.15 |    -4.052 |   -0.9415 |    -2.363 |
-#> |.....................|    -4.062 |  -0.01133 |    0.8386 |   0.08074 |
-#> |.....................|    0.6445 |     1.946 |     1.477 |     1.348 |
-#> |.....................|     1.794 |     1.297 |...........|...........|
-#> |    X|     3094.8373 |     26.15 |   0.01739 |    0.2806 |   0.09412 |
-#> |.....................|   0.01721 |    0.4972 |    0.8386 |   0.08074 |
-#> |.....................|    0.6445 |     1.946 |     1.477 |     1.348 |
-#> |.....................|     1.794 |     1.297 |...........|...........|
-#> |    3|     557.60681 |    0.9257 |   -0.9995 |   -0.9407 |   -0.9680 |
-#> |.....................|   -0.9992 |   -0.9232 |   -0.8787 |   -0.8790 |
-#> |.....................|   -0.9150 |   -0.8703 |   -0.8821 |   -0.8842 |
-#> |.....................|   -0.8739 |   -0.8885 |...........|...........|
-#> |    U|     557.60681 |     94.11 |    -4.053 |   -0.9430 |    -2.360 |
-#> |.....................|    -4.069 |  -0.01133 |    0.7386 |   0.06794 |
-#> |.....................|    0.6735 |     1.622 |     1.284 |     1.172 |
-#> |.....................|     1.513 |     1.165 |...........|...........|
-#> |    X|     557.60681 |     94.11 |   0.01736 |    0.2803 |   0.09444 |
-#> |.....................|   0.01709 |    0.4972 |    0.7386 |   0.06794 |
-#> |.....................|    0.6735 |     1.622 |     1.284 |     1.172 |
-#> |.....................|     1.513 |     1.165 |...........|...........|
-#> |    4|     543.47785 |    0.9926 |   -0.9997 |   -0.9408 |   -0.9677 |
-#> |.....................|   -0.9999 |   -0.9232 |   -0.9062 |   -0.9175 |
-#> |.....................|   -0.9107 |   -0.8907 |   -0.8974 |   -0.8995 |
-#> |.....................|   -0.8929 |   -0.9000 |...........|...........|
-#> |    U|     543.47785 |     100.9 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7286 |   0.06666 |
-#> |.....................|    0.6764 |     1.589 |     1.264 |     1.154 |
-#> |.....................|     1.485 |     1.152 |...........|...........|
-#> |    X|     543.47785 |     100.9 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7286 |   0.06666 |
-#> |.....................|    0.6764 |     1.589 |     1.264 |     1.154 |
-#> |.....................|     1.485 |     1.152 |...........|...........|
-#> |    5|     544.09017 |    0.9993 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9089 |   -0.9213 |
-#> |.....................|   -0.9103 |   -0.8928 |   -0.8990 |   -0.9010 |
-#> |.....................|   -0.8948 |   -0.9011 |...........|...........|
-#> |    U|     544.09017 |     101.6 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7276 |   0.06654 |
-#> |.....................|    0.6767 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.09017 |     101.6 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7276 |   0.06654 |
-#> |.....................|    0.6767 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    6|     544.17109 |    0.9999 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8949 |   -0.9012 |...........|...........|
-#> |    U|     544.17109 |     101.6 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.17109 |     101.6 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    7|     544.17937 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.17937 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.17937 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    8|     544.18025 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18025 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18025 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    9|     544.18033 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18033 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18033 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   10|     544.18034 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18034 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18034 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   11|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   12|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   13|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   14|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   15|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   16|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |   17|     544.18036 |     1.000 |   -0.9997 |   -0.9408 |   -0.9676 |
-#> |.....................|    -1.000 |   -0.9232 |   -0.9092 |   -0.9217 |
-#> |.....................|   -0.9102 |   -0.8930 |   -0.8991 |   -0.9012 |
-#> |.....................|   -0.8950 |   -0.9012 |...........|...........|
-#> |    U|     544.18036 |     101.7 |    -4.054 |   -0.9431 |    -2.359 |
-#> |.....................|    -4.070 |  -0.01132 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> |    X|     544.18036 |     101.7 |   0.01736 |    0.2803 |   0.09447 |
-#> |.....................|   0.01708 |    0.4972 |    0.7275 |   0.06652 |
-#> |.....................|    0.6768 |     1.586 |     1.262 |     1.152 |
-#> |.....................|     1.482 |     1.151 |...........|...........|
-#> calculating covariance matrix
-#> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm)
-
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #> 
                                                                #>                            df      AIC
-#> f_nlmixr_dfop_sfo_saem$nm  16      Inf
-#> f_nlmixr_dfop_sfo_focei$nm 14 886.4573
                                                                summary(f_nlmixr_dfop_sfo_sfo, data = TRUE)
+
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> 
                                                                #> → calculate sensitivities
                                                                #> 
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> 
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> 
                                                                #> → finding duplicate expressions in inner model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> 
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #> 
                                                                #> → compiling inner model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #> 
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #> 
                                                                #> → compiling EBE model...
                                                                #>  
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>  
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent);
+#> cmt(m1);
+#> rx_expr_6~ETA[1]+THETA[1];
+#> parent(0)=rx_expr_6;
+#> rx_expr_7~ETA[4]+THETA[4];
+#> rx_expr_8~ETA[6]+THETA[6];
+#> rx_expr_9~ETA[5]+THETA[5];
+#> rx_expr_12~exp(rx_expr_7);
+#> rx_expr_13~exp(rx_expr_9);
+#> rx_expr_15~t*rx_expr_12;
+#> rx_expr_16~t*rx_expr_13;
+#> rx_expr_19~exp(-(rx_expr_8));
+#> rx_expr_21~1+rx_expr_19;
+#> rx_expr_26~1/(rx_expr_21);
+#> rx_expr_28~(rx_expr_26);
+#> rx_expr_29~1-rx_expr_28;
+#> d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));
+#> rx_expr_10~ETA[2]+THETA[2];
+#> rx_expr_14~exp(rx_expr_10);
+#> d/dt(m1)=-rx_expr_14*m1+parent*f_parent_to_m1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));
+#> rx_expr_0~CMT==2;
+#> rx_expr_1~CMT==1;
+#> rx_expr_2~1-(rx_expr_0);
+#> rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);
+#> rx_expr_3~(rx_expr_0);
+#> rx_expr_5~(rx_expr_2);
+#> rx_expr_20~rx_expr_5*(rx_expr_1);
+#> rx_lambda_~rx_expr_20+rx_expr_3;
+#> rx_hi_~rx_expr_20+rx_expr_3;
+#> rx_low_~0;
+#> rx_expr_4~m1*(rx_expr_0);
+#> rx_expr_11~parent*(rx_expr_2);
+#> rx_expr_24~rx_expr_11*(rx_expr_1);
+#> rx_pred_=(rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1);
+#> rx_expr_17~Rx_pow_di(THETA[8],2);
+#> rx_expr_18~Rx_pow_di(THETA[7],2);
+#> rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_0)+(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_2)*(rx_expr_1);
+#> parent_0=THETA[1];
+#> log_k_m1=THETA[2];
+#> f_parent_qlogis=THETA[3];
+#> log_k1=THETA[4];
+#> log_k2=THETA[5];
+#> g_qlogis=THETA[6];
+#> sigma_low=THETA[7];
+#> rsd_high=THETA[8];
+#> eta.parent_0=ETA[1];
+#> eta.log_k_m1=ETA[2];
+#> eta.f_parent_qlogis=ETA[3];
+#> eta.log_k1=ETA[4];
+#> eta.log_k2=ETA[5];
+#> eta.g_qlogis=ETA[6];
+#> parent_0_model=rx_expr_6;
+#> k_m1=rx_expr_14;
+#> k1=rx_expr_12;
+#> k2=rx_expr_13;
+#> f_parent=1/(1+exp(-(ETA[3]+THETA[3])));
+#> g=1/(rx_expr_21);
+#> tad=tad();
+#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_m1" "CMT"           
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL,     lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL,     control = foceiControl(), thetaNames = NULL, etaNames = NULL,     etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) {    set.seed(control$seed)    .pt <- proc.time()    RxODE::.setWarnIdSort(FALSE)    on.exit(RxODE::.setWarnIdSort(TRUE))    loadNamespace("n1qn1")    if (!RxODE::rxIs(control, "foceiControl")) {        control <- do.call(foceiControl, control)    }    if (is.null(env)) {        .ret <- new.env(parent = emptyenv())    }    else {        .ret <- env    }    .ret$origData <- data    .ret$etaNames <- etaNames    .ret$thetaFixed <- fixed    .ret$control <- control    .ret$control$focei.mu.ref <- integer(0)    if (is(model, "RxODE") || is(model, "character")) {        .ret$ODEmodel <- TRUE        if (class(pred) != "function") {            stop("pred must be a function specifying the prediction variables in this model.")        }    }    else {        .ret$ODEmodel <- TRUE        model <- RxODE::rxGetLin(PKpars)        pred <- eval(parse(text = "function(){return(Central);}"))    }    .square <- function(x) x * x    .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform]    if (is.null(err)) {        err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]],             collapse = ""), "}")))    }    .covNames <- .parNames <- c()    .ret$adjLik <- control$adjLik    .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0    if (!exists("noLik", envir = .ret)) {        .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))        .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))        .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state))        .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state))        .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars,             err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE,             sum.prod = control$sumProd, theta.derivs = FALSE,             optExpression = control$optExpression, interaction = (control$interaction ==                 1L), only.numeric = !.mixed, run.internal = TRUE,             addProp = control$addProp)        if (!is.null(.ret$model$inner)) {            .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.atol)))            .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.rtol)))            .ret$control$rxControl$atol <- .atol            .ret$control$rxControl$rtol <- .rtol            .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssAtol)))            .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                 length(.ssRtol)))            .ret$control$rxControl$ssAtol <- .ssAtol            .ret$control$rxControl$ssRtol <- .ssRtol        }        .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)        .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",             "ETA"), "[", numbers, "]", end), .covNames) == -1]        colnames(data) <- sapply(names(data), function(x) {            if (any(x == .covNames)) {                return(x)            }            else {                return(toupper(x))            }        })        .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),             RxODE::rxLhs(.ret$model$pred.only))        if (length(.lhs) > 0) {            .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs),                 end), .covNames) == -1]        }        if (length(.covNames) > 0) {            if (!all(.covNames %in% names(data))) {                message("Model:")                RxODE::rxCat(.ret$model$pred.only)                message("Needed Covariates:")                nlmixrPrint(.covNames)                stop("Not all the covariates are in the dataset.")            }            message("Needed Covariates:")            print(.covNames)        }        .extraPars <- .ret$model$extra.pars    }    else {        if (.ret$noLik) {            .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state))            .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state))            .ret$model <- RxODE::rxSymPySetupPred(model, pred,                 PKpars, err, grad = FALSE, pred.minus.dv = TRUE,                 sum.prod = control$sumProd, theta.derivs = FALSE,                 optExpression = control$optExpression, run.internal = TRUE,                 only.numeric = TRUE, addProp = control$addProp)            if (!is.null(.ret$model$inner)) {                .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.atol)))                .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) -                   length(.rtol)))                .ret$control$rxControl$atol <- .atol                .ret$control$rxControl$rtol <- .rtol            }            .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only)            .covNames <- .covNames[regexpr(rex::rex(start, or("THETA",                 "ETA"), "[", numbers, "]", end), .covNames) ==                 -1]            colnames(data) <- sapply(names(data), function(x) {                if (any(x == .covNames)) {                  return(x)                }                else {                  return(toupper(x))                }            })            .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)),                 RxODE::rxLhs(.ret$model$pred.only))            if (length(.lhs) > 0) {                .covNames <- .covNames[regexpr(rex::rex(start,                   or(.lhs), end), .covNames) == -1]            }            if (length(.covNames) > 0) {                if (!all(.covNames %in% names(data))) {                  message("Model:")                  RxODE::rxCat(.ret$model$pred.only)                  message("Needed Covariates:")                  nlmixrPrint(.covNames)                  stop("Not all the covariates are in the dataset.")                }                message("Needed Covariates:")                print(.covNames)            }            .extraPars <- .ret$model$extra.pars        }        else {            .extraPars <- NULL        }    }    .ret$skipCov <- skipCov    if (is.null(skipCov)) {        if (is.null(fixed)) {            .tmp <- rep(FALSE, length(inits$THTA))        }        else {            if (length(fixed) < length(inits$THTA)) {                .tmp <- c(fixed, rep(FALSE, length(inits$THTA) -                   length(fixed)))            }            else {                .tmp <- fixed[1:length(inits$THTA)]            }        }        if (exists("uif", envir = .ret)) {            .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)]            .uifErr <- sapply(.uifErr, function(x) {                if (is.na(x)) {                  return(FALSE)                }                return(!any(x == c("pow2", "tbs", "tbsYj")))            })            .tmp <- (.tmp | .uifErr)        }        .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars)))        .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref    }    if (is.null(.extraPars)) {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)))    }    else {        .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)),             sprintf("ERR[%s]", seq_along(.extraPars)))    }    if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) ==         length(thetaNames)) {        .nms <- thetaNames    }    .ret$thetaNames <- .nms    .thetaReset$thetaNames <- .nms    if (length(lower) == 1) {        lower <- rep(lower, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        print(inits$THTA)        print(lower)        stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (length(upper) == 1) {        upper <- rep(upper, length(inits$THTA))    }    else if (length(lower) != length(inits$THTA)) {        stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.")    }    if (!is.null(.extraPars)) {        .ret$model$extra.pars <- eval(call(control$diagXform,             .ret$model$extra.pars))        if (length(.ret$model$extra.pars) > 0) {            inits$THTA <- c(inits$THTA, .ret$model$extra.pars)            .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars))            .upperErr <- rep(Inf, length(.ret$model$extra.pars))            lower <- c(lower, .lowerErr)            upper <- c(upper, .upperErr)        }    }    if (is.null(data$ID))         stop("\"ID\" not found in data")    if (is.null(data$DV))         stop("\"DV\" not found in data")    if (is.null(data$EVID))         data$EVID <- 0    if (is.null(data$AMT))         data$AMT <- 0    for (.v in c("TIME", "AMT", "DV", .covNames)) {        data[[.v]] <- as.double(data[[.v]])    }    .ret$dataSav <- data    .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME",         "AMT", "EVID", .covNames)]    .w <- which(tolower(names(data)) == "limit")    .limitName <- NULL    if (length(.w) == 1L) {        .limitName <- names(data)[.w]    }    .censName <- NULL    .w <- which(tolower(names(data)) == "cens")    if (length(.w) == 1L) {        .censName <- names(data[.w])    }    data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME",         "DV", "EVID", .covNames, .limitName, .censName)]    .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep)))    names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w]))    if (.mixed) {        .lh <- .parseOM(inits$OMGA)        .nlh <- sapply(.lh, length)        .osplt <- rep(1:length(.lh), .nlh)        .lini <- list(inits$THTA, unlist(.lh))        .nlini <- sapply(.lini, length)        .nsplt <- rep(1:length(.lini), .nlini)        .om0 <- .genOM(.lh)        if (length(etaNames) == dim(.om0)[1]) {            .ret$etaNames <- .ret$etaNames        }        else {            .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1]))        }        .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform)        .ret$xType <- .ret$rxInv$xType        .om0a <- .om0        .om0a <- .om0a/control$diagOmegaBoundLower        .om0b <- .om0        .om0b <- .om0b * control$diagOmegaBoundUpper        .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform)        .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform)        .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta,             b = .om0b$theta, diag = .om0a$theta.diag)        .omdf$lower <- with(.omdf, ifelse(a > b, b, a))        .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower))        .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower))        .omdf$upper <- with(.omdf, ifelse(a < b, b, a))        .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper))        .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper))        .ret$control$nomega <- length(.omdf$lower)        .ret$control$neta <- sum(.omdf$diag)        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)        lower <- c(lower, .omdf$lower)        upper <- c(upper, .omdf$upper)    }    else {        .ret$control$nomega <- 0        .ret$control$neta <- 0        .ret$xType <- -1        .ret$control$ntheta <- length(lower)        .ret$control$nfixed <- sum(fixed)    }    .ret$lower <- lower    .ret$upper <- upper    .ret$thetaIni <- inits$THTA    .scaleC <- double(length(lower))    if (is.null(control$scaleC)) {        .scaleC <- rep(NA_real_, length(lower))    }    else {        .scaleC <- as.double(control$scaleC)        if (length(lower) > length(.scaleC)) {            .scaleC <- c(.scaleC, rep(NA_real_, length(lower) -                 length(.scaleC)))        }        else if (length(lower) < length(.scaleC)) {            .scaleC <- .scaleC[seq(1, length(lower))]            warning("scaleC control option has more options than estimated population parameters, please check.")        }    }    .ret$scaleC <- .scaleC    if (exists("uif", envir = .ret)) {        .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err),             c("est", "err", "ntheta")]        for (.i in seq_along(.ini$err)) {            if (is.na(.ret$scaleC[.ini$ntheta[.i]])) {                if (any(.ini$err[.i] == c("boxCox", "yeoJohnson",                   "pow2", "tbs", "tbsYj"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 1                }                else if (any(.ini$err[.i] == c("prop", "add",                   "norm", "dnorm", "logn", "dlogn", "lnorm",                   "dlnorm"))) {                  .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i])                }            }        }        for (.i in .ini$model$extraProps$powTheta) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- 1        }        .ini <- as.data.frame(.ret$uif$ini)        for (.i in .ini$model$extraProps$factorial) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] +                   1))        }        for (.i in .ini$model$extraProps$gamma) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i]))        }        for (.i in .ini$model$extraProps$log) {            if (is.na(.ret$scaleC[.i]))                 .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i])        }        for (.i in .ret$logitThetas) {            .b <- .ret$logitThetasLow[.i]            .c <- .ret$logitThetasHi[.i]            .a <- .ini$est[.i]            if (is.na(.ret$scaleC[.i])) {                .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 +                   exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a))))            }        }    }    names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni))    if (is.null(etaMat) & !is.null(control$etaMat)) {        .ret$etaMat <- control$etaMat    }    else {        .ret$etaMat <- etaMat    }    .ret$setupTime <- (proc.time() - .pt)["elapsed"]    if (exists("uif", envir = .ret)) {        .tmp <- .ret$uif$logThetasList        .ret$logThetas <- .tmp[[1]]        .ret$logThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasList        .ret$logitThetas <- .tmp[[1]]        .ret$logitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListLow        .ret$logitThetasLow <- .tmp[[1]]        .ret$logitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$logitThetasListHi        .ret$logitThetasHi <- .tmp[[1]]        .ret$logitThetasHiF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasList        .ret$probitThetas <- .tmp[[1]]        .ret$probitThetasF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListLow        .ret$probitThetasLow <- .tmp[[1]]        .ret$probitThetasLowF <- .tmp[[2]]        .tmp <- .ret$uif$probitThetasListHi        .ret$probitThetasHi <- .tmp[[1]]        .ret$probitThetasHiF <- .tmp[[2]]    }    else {        .ret$logThetasF <- integer(0)        .ret$logitThetasF <- integer(0)        .ret$logitThetasHiF <- numeric(0)        .ret$logitThetasLowF <- numeric(0)        .ret$logitThetas <- integer(0)        .ret$logitThetasHi <- numeric(0)        .ret$logitThetasLow <- numeric(0)        .ret$probitThetasF <- integer(0)        .ret$probitThetasHiF <- numeric(0)        .ret$probitThetasLowF <- numeric(0)        .ret$probitThetas <- integer(0)        .ret$probitThetasHi <- numeric(0)        .ret$probitThetasLow <- numeric(0)    }    if (exists("noLik", envir = .ret)) {        if (!.ret$noLik) {            .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)),                 sprintf("ETA[%d]", seq(1, dim(.om0)[1])))            .ret$.thetan <- length(.ret$thetaIni)            .ret$nobs <- sum(data$EVID == 0)        }    }    .ret$control$printTop <- TRUE    .ret$control$nF <- 0    .est0 <- .ret$thetaIni    if (!is.null(.ret$model$pred.nolhs)) {        .ret$control$predNeq <- length(.ret$model$pred.nolhs$state)    }    else {        .ret$control$predNeq <- 0L    }    .fitFun <- function(.ret) {        this.env <- environment()        assign("err", "theta reset", this.env)        while (this.env$err == "theta reset") {            assign("err", "", this.env)            .ret0 <- tryCatch({                foceiFitCpp_(.ret)            }, error = function(e) {                if (regexpr("theta reset", e$message) != -1) {                  assign("zeroOuter", FALSE, this.env)                  assign("zeroGrad", FALSE, this.env)                  if (regexpr("theta reset0", e$message) != -1) {                    assign("zeroGrad", TRUE, this.env)                  }                  else if (regexpr("theta resetZ", e$message) !=                     -1) {                    assign("zeroOuter", TRUE, this.env)                  }                  assign("err", "theta reset", this.env)                }                else {                  assign("err", e$message, this.env)                }            })            if (this.env$err == "theta reset") {                .nm <- names(.ret$thetaIni)                .ret$thetaIni <- setNames(.thetaReset$thetaIni +                   0, .nm)                .ret$rxInv$theta <- .thetaReset$omegaTheta                .ret$control$printTop <- FALSE                .ret$etaMat <- .thetaReset$etaMat                .ret$control$etaMat <- .thetaReset$etaMat                .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations                .ret$control$nF <- .thetaReset$nF                .ret$control$gillRetC <- .thetaReset$gillRetC                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillRet <- .thetaReset$gillRet                .ret$control$gillDf <- .thetaReset$gillDf                .ret$control$gillDf2 <- .thetaReset$gillDf2                .ret$control$gillErr <- .thetaReset$gillErr                .ret$control$rEps <- .thetaReset$rEps                .ret$control$aEps <- .thetaReset$aEps                .ret$control$rEpsC <- .thetaReset$rEpsC                .ret$control$aEpsC <- .thetaReset$aEpsC                .ret$control$c1 <- .thetaReset$c1                .ret$control$c2 <- .thetaReset$c2                if (this.env$zeroOuter) {                  message("Posthoc reset")                  .ret$control$maxOuterIterations <- 0L                }                else if (this.env$zeroGrad) {                  message("Theta reset (zero gradient values); Switch to bobyqa")                  RxODE::rxReq("minqa")                  .ret$control$outerOptFun <- .bobyqa                  .ret$control$outerOpt <- -1L                }                else {                  message("Theta reset (ETA drift)")                }            }        }        if (this.env$err != "") {            stop(this.env$err)        }        else {            return(.ret0)        }    }    .ret0 <- try(.fitFun(.ret))    .n <- 1    while (inherits(.ret0, "try-error") && control$maxOuterIterations !=         0 && .n <= control$nRetries) {        message(sprintf("Restart %s", .n))        .ret$control$nF <- 0        .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) -             0.1 * .n        .estNew <- sapply(seq_along(.est0), function(.i) {            if (.ret$thetaFixed[.i]) {                return(.est0[.i])            }            else if (.estNew[.i] < lower[.i]) {                return(lower + (.Machine$double.eps)^(1/7))            }            else if (.estNew[.i] > upper[.i]) {                return(upper - (.Machine$double.eps)^(1/7))            }            else {                return(.estNew[.i])            }        })        .ret$thetaIni <- .estNew        .ret0 <- try(.fitFun(.ret))        .n <- .n + 1    }    if (inherits(.ret0, "try-error"))         stop("Could not fit data.")    .ret <- .ret0    if (exists("parHistData", .ret)) {        .tmp <- .ret$parHistData        .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) !=             "type"]        .iter <- .tmp$iter        .tmp <- .tmp[, names(.tmp) != "iter"]        .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter)        names(.ret$parHistStacked) <- c("val", "par", "iter")        .ret$parHist <- data.frame(iter = .iter, .tmp)    }    if (.mixed) {        .etas <- .ret$ranef        .thetas <- .ret$fixef        .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas)        .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega,             .pars$eta.lst, length(.etas$ID))        .updateParFixed(.ret)    }    else {        .updateParFixed(.ret)    }    if (!exists("table", .ret)) {        .ret$table <- tableControl()    }    if (control$calcTables) {        .ret <- addTable(.ret, updateObject = "no", keep = keep,             drop = drop, table = .ret$table)    }    .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod,     pred = function() {        return(nlmixr_pred)    }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper,     fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names,     control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 19.01 0.403 19.42
                                                                AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm)
+
                                                                #> Error in AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm): object 'f_nlmixr_dfop_sfo_saem' not found
                                                                summary(f_nlmixr_dfop_sfo_sfo, data = TRUE)
 
                                                                #> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found
                                                                # }
                                                                diff --git a/docs/dev/reference/tffm0.html b/docs/dev/reference/tffm0.html index d993e8ff..67f26b85 100644 --- a/docs/dev/reference/tffm0.html +++ b/docs/dev/reference/tffm0.html @@ -81,7 +81,7 @@ from RxODE." /> mkin - 1.0.5 + 1.1.0 diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index 150840e1..b5e83f34 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -246,4 +246,7 @@ https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html + + https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html + diff --git a/vignettes/web_only/dimethenamid_2018.html b/vignettes/web_only/dimethenamid_2018.html index e84a435c..df8200eb 100644 --- a/vignettes/web_only/dimethenamid_2018.html +++ b/vignettes/web_only/dimethenamid_2018.html @@ -1594,7 +1594,7 @@ div.tocify {

                                                                Example evaluations of the dimethenamid data from 2018

                                                                Johannes Ranke

                                                                -

                                                                Last change 23 June 2021, built on 25 Jun 2021

                                                                +

                                                                Last change 27 July 2021, built on 27 Jul 2021

                                                                @@ -1655,18 +1655,20 @@ f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds,

                                                                nlme

                                                                The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We use would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. However, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.

                                                                -
                                                                f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ])
-#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) # error
+
                                                                library(nlme)
+f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ])
+#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ])
+# maxIter = 50 reached
 f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ])
 f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ])
                                                                
 
                                                                Note that overparameterisation is also indicated by warnings obtained when fitting SFO or DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in some iterations). In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.
                                                                
 
                                                                f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ],
   random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1)))
 anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better
-f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc,
-  random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)))
+#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc,
+#  random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)))
 # using log Cholesky parameterisation for random effects (nlme default) does
-# not converge and gives lots of warnings about the LME step not converging
                                                                
+# not converge here and gives lots of warnings about the LME step not converging

                                                                The model comparison function of the nlme package can directly be applied to these fits showing a similar goodness-of-fit of the SFO model, but a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant. as the p-value is below 0.0001.

                                                                anova(
   f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
@@ -1685,24 +1687,24 @@ f_parent_nlme_dfop_tc       3 10 687.84 718.59 -333.92 2 vs 3 140.771  <.0001
 
                                                                The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit.
                                                                
 
                                                                The convergence plot for the SFO model using constant variance is shown below.
                                                                
 
                                                                library(saemix)
-f_parent_saemix_sfo_const <- saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
+f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
   transformations = "saemix")
 plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")
                                                                
 
                                                                
 
                                                                Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.
                                                                
-
                                                                f_parent_saemix_sfo_tc <- saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
+
                                                                f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
   transformations = "saemix")
 plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")
                                                                
 
                                                                
-
                                                                When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous. Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.
                                                                
-
                                                                f_parent_saemix_dfop_const <- saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
+
                                                                When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous (see the failure of nlme with the default number of iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.
                                                                
+
                                                                f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
   control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE,
     save = FALSE, save.graphs = FALSE, displayProgress = FALSE),
   transformations = "saemix")
 plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")
                                                                
 
                                                                
-
                                                                The same applies to the case where the DFOP model is fitted with the two-component error model.
                                                                
-
                                                                f_parent_saemix_dfop_tc_moreiter <- saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
+
                                                                The same applies to the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced by using the two-component error, it remains more or less stable already after 200 iterations of the first phase.
                                                                
+
                                                                f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
   control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE,
     save = FALSE, save.graphs = FALSE, displayProgress = FALSE),
   transformations = "saemix")
@@ -1710,20 +1712,31 @@ plot(f_parent_saemix_dfop_tc_moreiter$so, plot.type = "convergence")
                                                                
 
                                                                The four combinations can be compared using the model comparison function from the saemix package:
                                                                
 
                                                                compare.saemix(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
-  f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so)
                                                                
+  f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so)
                                                                
 
                                                                Likelihoods calculated by importance sampling
                                                                
 
                                                                     AIC    BIC
 1 818.37 817.33
 2 820.38 819.14
 3 725.91 724.04
-4 688.09 686.01
                                                                
+4 683.64 681.55
                                                                
 
                                                                As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. The numeric values are reasonably close to the ones obtained using nlme, considering that the algorithms for fitting the model and for the likelihood calculation are quite different.
                                                                
+
                                                                In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.
                                                                
+
                                                                f_parent_saemix_dfop_tc_moreiter$so <-
+  llgq.saemix(f_parent_saemix_dfop_tc_moreiter$so)
+AIC(f_parent_saemix_dfop_tc_moreiter$so)
                                                                
+
                                                                [1] 683.64
                                                                
+
                                                                AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "gq")
                                                                
+
                                                                [1] 683.7
                                                                
+
                                                                AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "lin")
                                                                
+
                                                                [1] 683.17
                                                                
+
                                                                The AIC values based on importance sampling and Gaussian quadrature are quite similar. Using linearisation is less accurate, but still gives a similar value.

                                                                nlmixr

                                                                In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.

                                                                First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared.

                                                                -
                                                                f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei")
+
                                                                library(nlmixr)
+f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei")
 f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei")
 f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei")
 f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei")
                                                                
@@ -1734,7 +1747,14 @@ f_parent_nlmixr_focei_sfo_const$nm   5 818.63
 f_parent_nlmixr_focei_sfo_tc$nm      6 820.61
 f_parent_nlmixr_focei_dfop_const$nm  9 728.11
 f_parent_nlmixr_focei_dfop_tc$nm    10 687.82
                                                                -

                                                                The AIC values are very close to the ones obtained with nlme.

                                                                +

                                                                The AIC values are very close to the ones obtained with nlme which are repeated below for convenience.

                                                                +
                                                                AIC(
+  f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
+)
                                                                +
                                                                                        df    AIC
+f_parent_nlme_sfo_const  5 818.63
+f_parent_nlme_sfo_tc     6 820.61
+f_parent_nlme_dfop_tc   10 687.84

                                                                Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance

                                                                f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem",
   control = nlmixr::saemControl(logLik = TRUE))
@@ -1743,17 +1763,17 @@ traceplot(f_parent_nlmixr_saem_sfo_const$nm)

                                                                for SFO with two-component error

                                                                f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem",
   control = nlmixr::saemControl(logLik = TRUE))
-nlmixr::traceplot(f_parent_nlmixr_saem_sfo_tc$nm)
                                                                +traceplot(f_parent_nlmixr_saem_sfo_tc$nm)

                                                                For DFOP with constant variance, the convergence plots show considerable instability of the fit, which can be alleviated by increasing the number of iterations and the number of parallel chains for the first phase of algorithm.

                                                                f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem",
   control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15)
-nlmixr::traceplot(f_parent_nlmixr_saem_dfop_const$nm)
                                                                +traceplot(f_parent_nlmixr_saem_dfop_const$nm)

                                                                For DFOP with two-component error, the same increase in iterations and parallel chains was used, but using the two-component error appears to lead to a less erratic convergence, so this may not be necessary to this degree.

                                                                f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem",
   control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15))
-nlmixr::traceplot(f_parent_nlmixr_saem_dfop_tc$nm)
                                                                +traceplot(f_parent_nlmixr_saem_dfop_tc$nm)

                                                                The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using the two-component error model is given as Infinity.

                                                                AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
@@ -1761,8 +1781,55 @@ nlmixr::traceplot(f_parent_nlmixr_saem_dfop_tc$nm)
                                                                                                   df    AIC
 f_parent_nlmixr_saem_sfo_const$nm   5 820.54
 f_parent_nlmixr_saem_sfo_tc$nm      6 835.26
-f_parent_nlmixr_saem_dfop_const$nm  9 850.72
-f_parent_nlmixr_saem_dfop_tc$nm    10    Inf
                                                                +f_parent_nlmixr_saem_dfop_const$nm 9 842.84 +f_parent_nlmixr_saem_dfop_tc$nm 10 684.51 +

                                                                The following table gives the AIC values obtained with the three packages.

                                                                +
                                                                AIC_all <- data.frame(
+  nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),
+  nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm,
+  f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC),
+  saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
+    f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so), AIC),
+  nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
+  f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC)
+)
+kable(AIC_all)
                                                                + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                nlmenlmixr_foceisaemixnlmixr_saem
                                                                818.63818.63818.37820.54
                                                                820.61820.61820.38835.26
                                                                NA728.11725.91842.84
                                                                687.84687.82683.64684.51
                                                                diff --git a/vignettes/web_only/dimethenamid_2018.rmd b/vignettes/web_only/dimethenamid_2018.rmd index d3541a34..30325044 100644 --- a/vignettes/web_only/dimethenamid_2018.rmd +++ b/vignettes/web_only/dimethenamid_2018.rmd @@ -1,7 +1,7 @@ --- title: Example evaluations of the dimethenamid data from 2018 author: Johannes Ranke -date: Last change 23 June 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` +date: Last change 27 July 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` output: html_document: toc: true @@ -163,8 +163,10 @@ tendency of the algorithm to try parameter combinations unsuitable for fitting these data. ```{r f_parent_nlme, warning = FALSE} +library(nlme) f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) -#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) # error +#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) +# maxIter = 50 reached f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ]) f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ]) ``` @@ -180,10 +182,10 @@ used for these attempts can be made visible below. f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better -f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc, - random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) +#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc, +# random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) # using log Cholesky parameterisation for random effects (nlme default) does -# not converge and gives lots of warnings about the LME step not converging +# not converge here and gives lots of warnings about the LME step not converging ``` The model comparison function of the nlme package can directly be applied @@ -221,7 +223,7 @@ The convergence plot for the SFO model using constant variance is shown below. ```{r f_parent_saemix_sfo_const, results = 'hide'} library(saemix) -f_parent_saemix_sfo_const <- saem(f_parent_mkin_const["SFO", ], quiet = TRUE, +f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, transformations = "saemix") plot(f_parent_saemix_sfo_const$so, plot.type = "convergence") ``` @@ -230,18 +232,19 @@ Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model. ```{r f_parent_saemix_sfo_tc, results = 'hide'} -f_parent_saemix_sfo_tc <- saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, +f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, transformations = "saemix") plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence") ``` When fitting the DFOP model with constant variance, parameter convergence -is not as unambiguous. Therefore, the number of iterations in the first +is not as unambiguous (see the failure of nlme with the default number of +iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence. ```{r f_parent_saemix_dfop_const, results = 'hide'} -f_parent_saemix_dfop_const <- saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, +f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE), transformations = "saemix") @@ -250,11 +253,11 @@ plot(f_parent_saemix_dfop_const$so, plot.type = "convergence") The same applies to the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced -by using the two-component error, it remains pretty stable already after 200 +by using the two-component error, it remains more or less stable already after 200 iterations of the first phase. ```{r f_parent_saemix_dfop_tc_moreiter, results = 'hide'} -f_parent_saemix_dfop_tc_moreiter <- saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, +f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE), transformations = "saemix") @@ -306,6 +309,7 @@ First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared. ```{r f_parent_nlmixr_focei, results = "hide", message = FALSE, warning = FALSE} +library(nlmixr) f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei") f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei") f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei") @@ -317,7 +321,14 @@ AIC(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm) ``` -The AIC values are very close to the ones obtained with nlme. +The AIC values are very close to the ones obtained with nlme which are repeated below +for convenience. + +```{r AIC_parent_nlme_rep} +AIC( + f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc +) +``` Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance @@ -333,7 +344,7 @@ for SFO with two-component error ```{r f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE} f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", control = nlmixr::saemControl(logLik = TRUE)) -nlmixr::traceplot(f_parent_nlmixr_saem_sfo_tc$nm) +traceplot(f_parent_nlmixr_saem_sfo_tc$nm) ``` For DFOP with constant variance, the convergence plots show considerable instability @@ -343,7 +354,7 @@ the number of parallel chains for the first phase of algorithm. ```{r f_parent_nlmixr_saem_dfop_const, results = "hide", warning = FALSE, message = FALSE} f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15) -nlmixr::traceplot(f_parent_nlmixr_saem_dfop_const$nm) +traceplot(f_parent_nlmixr_saem_dfop_const$nm) ``` For DFOP with two-component error, the same increase in iterations and parallel @@ -354,7 +365,7 @@ erratic convergence, so this may not be necessary to this degree. ```{r f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE} f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15)) -nlmixr::traceplot(f_parent_nlmixr_saem_dfop_tc$nm) +traceplot(f_parent_nlmixr_saem_dfop_tc$nm) ``` The AIC values are internally calculated using Gaussian quadrature. For an @@ -366,8 +377,20 @@ AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm) ``` - - +The following table gives the AIC values obtained with the three packages. + +```{r AIC_all} +AIC_all <- data.frame( + nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)), + nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, + f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC), + saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so), AIC), + nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, + f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC) +) +kable(AIC_all) +``` # References -- cgit v1.2.1 From 137612045c23198f10d6e8612c32e266c2a6c00e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 29 Jul 2021 12:17:56 +0200 Subject: Go back to 1.0.x version, update docs --- DESCRIPTION | 4 +-- NEWS.md | 2 ++ docs/dev/404.html | 2 +- docs/dev/articles/index.html | 2 +- docs/dev/articles/web_only/dimethenamid_2018.html | 14 ++++---- docs/dev/authors.html | 2 +- docs/dev/index.html | 7 +++- docs/dev/news/index.html | 3 +- docs/dev/pkgdown.yml | 2 +- docs/dev/reference/dimethenamid_2018.html | 32 +++++++++--------- docs/dev/reference/endpoints.html | 2 +- docs/dev/reference/index.html | 2 +- docs/dev/reference/mean_degparms.html | 2 +- docs/dev/reference/mkinmod.html | 6 ++-- docs/dev/reference/nlme-1.png | Bin 69667 -> 68943 bytes docs/dev/reference/nlme-2.png | Bin 93394 -> 94409 bytes docs/dev/reference/nlme.html | 18 +++++----- docs/dev/reference/nlme.mmkin.html | 2 +- docs/dev/reference/nlmixr.mmkin.html | 28 ++++++++-------- docs/dev/reference/plot.mixed.mmkin.html | 6 ++-- docs/dev/reference/reexports.html | 2 +- docs/dev/reference/saem.html | 38 +++++++++++----------- docs/dev/reference/summary.nlmixr.mmkin.html | 10 +++--- docs/dev/reference/summary.saem.mmkin.html | 10 +++--- docs/dev/reference/tffm0.html | 2 +- 25 files changed, 103 insertions(+), 95 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 4689cb2a..2364ee75 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data -Version: 1.1.0 -Date: 2021-06-23 +Version: 1.0.5 +Date: 2021-07-29 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "jranke@uni-bremen.de", diff --git a/NEWS.md b/NEWS.md index e668f1e5..7ecd7f96 100644 --- a/NEWS.md +++ b/NEWS.md @@ -10,6 +10,8 @@ - 'plot.mixed.mmkin': Gains arguments 'test_log_parms' and 'conf.level' +- 'vignettes/web_only/dimethenamid_2018.rmd': Example evaluations of the dimethenamid data. + # mkin 1.0.4 (2021-04-20) - All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters diff --git a/docs/dev/404.html b/docs/dev/404.html index 38898979..98c0b1e0 100644 --- a/docs/dev/404.html +++ b/docs/dev/404.html @@ -71,7 +71,7 @@ mkin - 1.1.0 + 1.0.5 diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index c0338df8..99ce950b 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -71,7 +71,7 @@ mkin - 1.1.0 + 1.0.5 diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html index 7648f75a..34d882a4 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018.html +++ b/docs/dev/articles/web_only/dimethenamid_2018.html @@ -32,7 +32,7 @@ mkin - 1.1.0 + 1.0.5 @@ -101,7 +101,7 @@

                                                                Example evaluations of the dimethenamid data from 2018

                                                                Johannes Ranke

                                                                -

                                                                Last change 27 July 2021, built on 27 Jul 2021

                                                                +

                                                                Last change 27 July 2021, built on 29 Jul 2021

                                                                Source: vignettes/web_only/dimethenamid_2018.rmd @@ -154,20 +154,20 @@ error_model = "tc", quiet = TRUE)

                                                                The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):

                                                                -plot(mixed(f_parent_mkin_const["SFO", ]))
                                                                +plot(mixed(f_parent_mkin_const["SFO", ]))

                                                                Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:

                                                                -plot(mixed(f_parent_mkin_const["DFOP", ]))
                                                                +plot(mixed(f_parent_mkin_const["DFOP", ]))

                                                                The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:

                                                                -plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)
                                                                +plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)

                                                                While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).

                                                                The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:

                                                                -plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)
                                                                +plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)

                                                                @@ -205,7 +205,7 @@ f_parent_nlme_sfo_tc 2 6 820.61 839.06 -404.31 1 vs 2 0.014 0.9049 f_parent_nlme_dfop_tc 3 10 687.84 718.59 -333.92 2 vs 3 140.771 <.0001

                                                                The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.

                                                                -plot(f_parent_nlme_dfop_tc)
                                                                +plot(f_parent_nlme_dfop_tc)

                                                                diff --git a/docs/dev/authors.html b/docs/dev/authors.html index 943cba1b..4208dc24 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -71,7 +71,7 @@ mkin - 1.1.0 + 1.0.5
                                                                diff --git a/docs/dev/index.html b/docs/dev/index.html index 8049b3a1..ddfce971 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -38,7 +38,7 @@ mkin - 1.1.0 + 1.0.5 @@ -206,6 +206,11 @@ + + + diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index cfe577cf..5b153f37 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -71,7 +71,7 @@ mkin - 1.1.0 + 1.0.5 @@ -152,6 +152,7 @@

                                                              • Reintroduce the interface to current development versions (not on CRAN) of saemix, in particular the generic function ‘saem’ with a generator ‘saem.mmkin’, currently using ‘saemix_model’ and ‘saemix_data’, summary and plot methods

                                                              • ‘mean_degparms’: New argument ‘test_log_parms’ that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends

                                                              • ‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ and ‘conf.level’

                                                                • +

                                                              • ‘vignettes/web_only/dimethenamid_2018.rmd’: Example evaluations of the dimethenamid data.

                                                                • diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 16f7f0d6..ab5aae58 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -11,7 +11,7 @@ articles: web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html web_only/dimethenamid_2018: dimethenamid_2018.html -last_built: 2021-07-27T15:54Z +last_built: 2021-07-29T09:41Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index 160dcaa3..c893da63 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> mkin - 1.1.0 + 1.0.5 @@ -295,7 +295,7 @@ specific pieces of information in the comments.

                                                                #> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) #> }) #> } -#> <environment: 0x555559c00ce8>
                                                                # The focei fit takes about four minutes on my system +#> <environment: 0x555559bfc940>
                                                                # The focei fit takes about four minutes on my system system.time( f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", control = nlmixr::foceiControl(print = 500)) @@ -303,7 +303,7 @@ specific pieces of information in the comments.

                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:02 #>
                                                                #> → calculate sensitivities
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:04 #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:01 -#>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:08 +#>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:09 #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:07 #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:07 #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 @@ -324,12 +324,12 @@ specific pieces of information in the comments.

                                                                #> |.....................| o9 | o10 |...........|...........| #> calculating covariance matrix #> done
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
                                                                #> Warning: last objective function was not at minimum, possible problems in optimization
                                                                #> Warning: S matrix non-positive definite
                                                                #> Warning: using R matrix to calculate covariance
                                                                #> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
                                                                #> user system elapsed -#> 227.879 9.742 237.728
                                                                summary(f_dmta_nlmixr_focei) +#> 227.223 8.444 235.624
                                                                summary(f_dmta_nlmixr_focei)
                                                                #> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.1.0 +#> mkin version used for pre-fitting: 1.0.5 #> R version used for fitting: 4.1.0 -#> Date of fit: Tue Jul 27 16:02:33 2021 -#> Date of summary: Tue Jul 27 16:02:34 2021 +#> Date of fit: Thu Jul 29 11:45:46 2021 +#> Date of summary: Thu Jul 29 11:45:46 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -350,7 +350,7 @@ specific pieces of information in the comments.

                                                                #> #> Degradation model predictions using RxODE #> -#> Fitted in 237.547 s +#> Fitted in 235.457 s #> #> Variance model: Two-component variance function #> @@ -489,11 +489,11 @@ specific pieces of information in the comments.

                                                                f_dmta_saemix <- saem(f_dmta_mkin_tc, test_log_parms = TRUE) )
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:02:34 2021" +#> [1] "Thu Jul 29 11:45:47 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:21:39 2021"
                                                                #> user system elapsed -#> 1213.394 0.087 1213.578
                                                                +#> [1] "Thu Jul 29 12:04:25 2021"
                                                                #> user system elapsed +#> 1185.594 0.028 1185.687
                                                                # nlmixr with est = "saem" is pretty fast with default iteration numbers, most # of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end # The likelihood calculated for the nlmixr fit is much lower than that found by saemix @@ -506,13 +506,13 @@ specific pieces of information in the comments.

                                                                )
                                                                #> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> 1: 98.3427 -3.5148 -3.3187 -3.7728 -2.1163 -2.8457 0.9482 -2.8064 -2.7412 -2.8745 2.7912 0.6805 0.8213 0.8055 0.8578 1.4980 2.9309 0.2850 0.2854 0.2850 4.0990 0.3821 3.5349 0.6537 5.4143 0.0002 4.5093 0.1905 #> 500: 97.8277 -4.3506 -4.0318 -4.1520 -3.0553 -3.5843 1.1326 -2.0873 -2.0421 -2.0751 0.2960 1.2515 0.2531 0.3807 0.7928 0.8863 6.5211 0.1433 0.1082 0.3353 0.8960 0.0470 0.7501 0.0475 0.9527 0.0281 0.7321 0.0594
                                                                #> Calculating covariance matrix
                                                                #>
                                                                #> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
                                                                #>
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> Needed Covariates:
                                                                #> [1] "CMT"
                                                                #> Calculating residuals/tables
                                                                #> done
                                                                #> user system elapsed -#> 818.782 3.808 154.926
                                                                traceplot(f_dmta_nlmixr_saem$nm) +#> 809.956 4.286 156.438
                                                                traceplot(f_dmta_nlmixr_saem$nm)
                                                                #> Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"
                                                                summary(f_dmta_nlmixr_saem)
                                                                #> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.1.0 +#> mkin version used for pre-fitting: 1.0.5 #> R version used for fitting: 4.1.0 -#> Date of fit: Tue Jul 27 16:25:23 2021 -#> Date of summary: Tue Jul 27 16:25:23 2021 +#> Date of fit: Thu Jul 29 12:08:09 2021 +#> Date of summary: Thu Jul 29 12:08:09 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -533,7 +533,7 @@ specific pieces of information in the comments.

                                                                #> #> Degradation model predictions using RxODE #> -#> Fitted in 154.632 s +#> Fitted in 156.17 s #> #> Variance model: Two-component variance function #> diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index aa5bd773..dc1d1f17 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> mkin - 1.1.0 + 1.0.5
                                                                diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index d5ec387a..bb030605 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ mkin - 1.1.0 + 1.0.5 diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html index 5981c625..f63dbc31 100644 --- a/docs/dev/reference/mean_degparms.html +++ b/docs/dev/reference/mean_degparms.html @@ -72,7 +72,7 @@ mkin - 1.1.0 + 1.0.5 diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html index e57e7062..5db8e719 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -76,7 +76,7 @@ components." /> mkin - 1.1.0 + 1.0.5 @@ -344,7 +344,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media

                                                                parent =mkinsub("SFO", "m1", full_name ="Test compound"), m1 =mkinsub("SFO", full_name ="Metabolite M1"), name ="SFO_SFO", dll_dir =DLL_dir, unload =TRUE, overwrite =TRUE) -
                                                                #> Copied DLL from /tmp/RtmpPWWdbW/fileccff46a6d9773.so to /home/jranke/.local/share/mkin/SFO_SFO.so
                                                                # Now we can save the model and restore it in a new session +
                                                                #> Copied DLL from /tmp/Rtmpjz6gts/filefa69f342e9d9d.so to /home/jranke/.local/share/mkin/SFO_SFO.so
                                                                # Now we can save the model and restore it in a new session saveRDS(SFO_SFO.2, file = "~/SFO_SFO.rds") # Terminate the R session here if you would like to check, and then do library(mkin) @@ -393,7 +393,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media

                                                                #> }) #> return(predicted) #> } -#> <environment: 0x5555645abab8>
                                                                +#> <environment: 0x55556283eae8>
                                                                # If we have several parallel metabolites # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R) m_synth_DFOP_par <- mkinmod( diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png index f739089a..365aaef0 100644 Binary files a/docs/dev/reference/nlme-1.png and b/docs/dev/reference/nlme-1.png differ diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png index d3b29bb0..40841404 100644 Binary files a/docs/dev/reference/nlme-2.png and b/docs/dev/reference/nlme-2.png differ diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index 184585df..55a94443 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> mkin - 1.1.0 + 1.0.5
                                                                @@ -216,28 +216,28 @@ datasets. They are used internally by the nlme.m #> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) #> Data: grouped_data #> AIC BIC logLik -#> 278.1355 287.7946 -134.0677 +#> 300.6824 310.2426 -145.3412 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual -#> StdDev: 3.406042 0.6437579 2.620833 +#> StdDev: 1.697361 0.6801209 3.666073 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 #> Value Std.Error DF t-value p-value -#> parent_0 101.50173 2.123709 47 47.79457 0 -#> log_k_parent_sink -3.07597 0.379775 47 -8.09945 0 +#> parent_0 100.99378 1.3890416 46 72.70753 0 +#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.01 +#> log_k_parent_sink 0.027 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -2.06889303 -0.50100169 -0.06268253 0.62557544 2.19922001 +#> Min Q1 Med Q3 Max +#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 #> -#> Number of Observations: 51 +#> Number of Observations: 50 #> Number of Groups: 3
                                                                plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
                                                                # augPred does not work on fits with more than one state # variable diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index 866091ca..db863392 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> mkin - 1.1.0 + 1.0.5
                                                                diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html index 328bad43..b7dfb9ca 100644 --- a/docs/dev/reference/nlmixr.mmkin.html +++ b/docs/dev/reference/nlmixr.mmkin.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.1.0 + 1.0.5 @@ -4501,7 +4501,7 @@ obtained by fitting the same model to a list of datasets using k_A1=rx_expr_11; #> f_parent=1/(1+exp(-(ETA[4]+THETA[4]))); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 5.548 0.415 5.961
                                                                f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 5.801 0.369 6.185
                                                                f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei")
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); #> cmt(A1); #> rx_expr_6~ETA[1]+THETA[1]; @@ -4550,7 +4550,7 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8); #> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.895 0.416 7.309
                                                                f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 7.136 0.414 7.548
                                                                f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei")
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); #> cmt(A1); #> rx_expr_6~ETA[1]+THETA[1]; @@ -4607,10 +4607,10 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> g=1/(rx_expr_20); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 15.03 0.478 15.51
                                                                +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 15.76 0.429 16.19
                                                                # Variance by variable is supported by 'saem' and 'focei' f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.294 0.134 1.427
                                                                f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.311 0.125 1.436
                                                                f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei")
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); #> cmt(A1); #> rx_expr_6~ETA[1]+THETA[1]; @@ -4659,8 +4659,8 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8); #> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.584 0.393 6.976
                                                                f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.302 0.142 1.443
                                                                f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 6.969 0.428 7.395
                                                                f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 1.38 0.122 1.504
                                                                f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei")
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); #> cmt(A1); #> rx_expr_6~ETA[1]+THETA[1]; @@ -4717,7 +4717,7 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> g=1/(rx_expr_19); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 14.58 0.482 15.06
                                                                +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 15.48 0.348 15.82
                                                                # Identical two-component error for all variables is only possible with # est = 'focei' in nlmixr f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei") @@ -4771,7 +4771,7 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8); #> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.484 0.401 8.883
                                                                f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.84 0.482 9.319
                                                                f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei")
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); #> cmt(A1); #> rx_expr_6~ETA[1]+THETA[1]; @@ -4830,12 +4830,12 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> g=1/(rx_expr_21); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 18.44 0.438 18.87
                                                                +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 19.56 0.433 19.99
                                                                # Two-component error by variable is possible with both estimation methods # Variance by variable is supported by 'saem' and 'focei' f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem", error_model = "obs_tc") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.784 0.028 0.812
                                                                f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.817 0.016 0.834
                                                                f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", error_model = "obs_tc")
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); #> cmt(A1); @@ -4887,9 +4887,9 @@ obtained by fitting the same model to a list of datasets using beta=exp(rx_expr_8); #> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.157 0.51 8.664
                                                                f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 8.572 0.437 9.008
                                                                f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", error_model = "obs_tc") -
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.81 0.045 0.854
                                                                f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", +
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1
                                                                #> Timing stopped at: 0.866 0.032 0.897
                                                                f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", error_model = "obs_tc")
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); #> cmt(A1); @@ -4949,7 +4949,7 @@ obtained by fitting the same model to a list of datasets using f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> g=1/(rx_expr_19); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 17.34 0.397 17.73
                                                                +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_A1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 19.34 0.349 19.69
                                                                AIC( f_nlmixr_sfo_sfo_focei_const$nm, f_nlmixr_fomc_sfo_focei_const$nm, diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index 7f3faa90..8962ce1c 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ mkin - 1.1.0 + 1.0.5
                                                                @@ -296,10 +296,10 @@ corresponding model prediction lines for the different datasets.

                                                                f_saem <- saem(f, transformations = "saemix")
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:30:50 2021" +#> [1] "Thu Jul 29 12:13:54 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:30:58 2021"
                                                                plot(f_saem) +#> [1] "Thu Jul 29 12:14:02 2021"
                                                                plot(f_saem)
                                                                f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs") f_nlmix <- nlmix(f_obs) diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index ac4fa4d9..f5ace044 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -81,7 +81,7 @@ below to see their documentation. mkin - 1.1.0 + 1.0.5
                                                                diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 15271c8a..0334e0e1 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> mkin - 1.1.0 + 1.0.5 @@ -288,27 +288,27 @@ using mmkin.

                                                                state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:02 2021" +#> [1] "Thu Jul 29 12:14:07 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:04 2021"
                                                                +#> [1] "Thu Jul 29 12:14:08 2021"
                                                                f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:06 2021" +#> [1] "Thu Jul 29 12:14:11 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:07 2021"
                                                                f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#> [1] "Thu Jul 29 12:14:12 2021"
                                                                f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:07 2021" +#> [1] "Thu Jul 29 12:14:12 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:09 2021"
                                                                f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#> [1] "Thu Jul 29 12:14:14 2021"
                                                                f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:10 2021" +#> [1] "Thu Jul 29 12:14:15 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:12 2021"
                                                                +#> [1] "Thu Jul 29 12:14:18 2021"
                                                                # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -357,10 +357,10 @@ using mmkin.

                                                                f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:16 2021" +#> [1] "Thu Jul 29 12:14:21 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:20 2021"
                                                                compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +#> [1] "Thu Jul 29 12:14:27 2021"
                                                                compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
                                                                #> Likelihoods calculated by importance sampling
                                                                #> AIC BIC #> 1 467.7096 464.9757 #> 2 469.6831 466.5586
                                                                @@ -381,15 +381,15 @@ using mmkin.

                                                                # four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:24 2021" +#> [1] "Thu Jul 29 12:14:31 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:29 2021"
                                                                f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#> [1] "Thu Jul 29 12:14:36 2021"
                                                                f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:30 2021" +#> [1] "Thu Jul 29 12:14:36 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:38 2021"
                                                                # We can use print, plot and summary methods to check the results +#> [1] "Thu Jul 29 12:14:46 2021"
                                                                # We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo)
                                                                #> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -430,10 +430,10 @@ using mmkin.

                                                                #> SD.g_qlogis 0.44816 -1.25437 2.1507
                                                                plot(f_saem_dfop_sfo)
                                                                summary(f_saem_dfop_sfo, data = TRUE)
                                                                #> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.1.0 +#> mkin version used for pre-fitting: 1.0.5 #> R version used for fitting: 4.1.0 -#> Date of fit: Tue Jul 27 16:31:39 2021 -#> Date of summary: Tue Jul 27 16:31:39 2021 +#> Date of fit: Thu Jul 29 12:14:46 2021 +#> Date of summary: Thu Jul 29 12:14:46 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -448,7 +448,7 @@ using mmkin.

                                                                #> #> Model predictions using solution type analytical #> -#> Fitted in 9.479 s using 300, 100 iterations +#> Fitted in 9.987 s using 300, 100 iterations #> #> Variance model: Constant variance #> diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html index 373ce75f..f0131701 100644 --- a/docs/dev/reference/summary.nlmixr.mmkin.html +++ b/docs/dev/reference/summary.nlmixr.mmkin.html @@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally mkin - 1.1.0 + 1.0.5
                                                                @@ -258,12 +258,12 @@ nlmixr authors for the parts inherited from nlmixr.

                                                                quiet = TRUE, error_model = "tc", cores = 5) f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo)
                                                                #> Running main SAEM algorithm -#> [1] "Tue Jul 27 16:31:43 2021" +#> [1] "Thu Jul 29 12:14:50 2021" #> .... #> Minimisation finished -#> [1] "Tue Jul 27 16:31:55 2021"
                                                                f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo) +#> [1] "Thu Jul 29 12:15:03 2021"
                                                                f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo)
                                                                #> Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
                                                                #> Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
                                                                f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem") -
                                                                #> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1
                                                                #> Timing stopped at: 1.281 0.142 1.422
                                                                # The following takes a very long time but gives +
                                                                #> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #>
                                                                #> → generate SAEM model
                                                                #> ✔ done
                                                                #> Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1
                                                                #> Timing stopped at: 1.464 0.114 1.576
                                                                # The following takes a very long time but gives f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei")
                                                                #> ℹ parameter labels from comments are typically ignored in non-interactive mode
                                                                #> ℹ Need to run with the source intact to parse comments
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → creating full model...
                                                                #> → pruning branches (`if`/`else`)...
                                                                #> ✔ done
                                                                #> → loading into symengine environment...
                                                                #> ✔ done
                                                                #> → calculate jacobian
                                                                #>
                                                                #> → calculate sensitivities
                                                                #>
                                                                #> → calculate ∂(f)/∂(η)
                                                                #>
                                                                #> → calculate ∂(R²)/∂(η)
                                                                #>
                                                                #> → finding duplicate expressions in inner model...
                                                                #>
                                                                #> → optimizing duplicate expressions in inner model...
                                                                #>
                                                                #> → finding duplicate expressions in EBE model...
                                                                #>
                                                                #> → optimizing duplicate expressions in EBE model...
                                                                #>
                                                                #> → compiling inner model...
                                                                #>
                                                                #> ✔ done
                                                                #> → finding duplicate expressions in FD model...
                                                                #>
                                                                #> → optimizing duplicate expressions in FD model...
                                                                #>
                                                                #> → compiling EBE model...
                                                                #>
                                                                #> ✔ done
                                                                #> → compiling events FD model...
                                                                #>
                                                                #> ✔ done
                                                                #> Model:
                                                                #> cmt(parent); #> cmt(m1); @@ -323,7 +323,7 @@ nlmixr authors for the parts inherited from nlmixr.

                                                                #> f_parent=1/(1+exp(-(ETA[3]+THETA[3]))); #> g=1/(rx_expr_21); #> tad=tad(); -#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_m1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 19.01 0.403 19.42
                                                                AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm) +#> dosenum=dosenum();
                                                                #> Needed Covariates:
                                                                #> [1] "f_parent_to_m1" "CMT"
                                                                #> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
                                                                #> Timing stopped at: 19.62 0.431 20.06
                                                                AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm)
                                                                #> Error in AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm): object 'f_nlmixr_dfop_sfo_saem' not found
                                                                summary(f_nlmixr_dfop_sfo_sfo, data = TRUE)
                                                                #> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found
                                                                # } diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html index fdfdaf4b..aeb08d12 100644 --- a/docs/dev/reference/summary.saem.mmkin.html +++ b/docs/dev/reference/summary.saem.mmkin.html @@ -260,15 +260,15 @@ saemix authors for the parts inherited from saemix.

                                                                quiet = TRUE, error_model = "tc", cores = 5) f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
                                                                #> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:58:28 2021" +#> [1] "Thu Jul 29 12:15:32 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:58:40 2021"
                                                                summary(f_saem_dfop_sfo, data = TRUE) +#> [1] "Thu Jul 29 12:15:44 2021"
                                                                summary(f_saem_dfop_sfo, data = TRUE)
                                                                #> saemix version used for fitting: 3.1.9000 #> mkin version used for pre-fitting: 1.0.5 #> R version used for fitting: 4.1.0 -#> Date of fit: Fri Jun 11 10:58:41 2021 -#> Date of summary: Fri Jun 11 10:58:41 2021 +#> Date of fit: Thu Jul 29 12:15:45 2021 +#> Date of summary: Thu Jul 29 12:15:45 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -283,7 +283,7 @@ saemix authors for the parts inherited from saemix.

                                                                #> #> Model predictions using solution type analytical #> -#> Fitted in 12.75 s using 300, 100 iterations +#> Fitted in 13.372 s using 300, 100 iterations #> #> Variance model: Two-component variance function #> diff --git a/docs/dev/reference/tffm0.html b/docs/dev/reference/tffm0.html index 67f26b85..d993e8ff 100644 --- a/docs/dev/reference/tffm0.html +++ b/docs/dev/reference/tffm0.html @@ -81,7 +81,7 @@ from RxODE." /> mkin - 1.1.0 + 1.0.5
                                                                -- cgit v1.2.1 From 1bef803fc9d83989352f1b029d83e66918a0e58a Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 29 Jul 2021 12:37:27 +0200 Subject: Add citations to the DESCRIPTION --- DESCRIPTION | 11 ++++++++--- docs/dev/index.html | 11 ++++++++--- docs/dev/pkgdown.yml | 2 +- docs/dev/reference/Rplot001.png | Bin 1011 -> 70653 bytes 4 files changed, 17 insertions(+), 7 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2364ee75..8f243796 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -14,9 +14,14 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential - equation models are solved using automatically generated C functions. Please - note that no warranty is implied for correctness of results or fitness for a - particular purpose. + equation models are solved using automatically generated C functions. + Heteroscedasticity can be taken into account using variance by variable or + two-component error models as described by Ranke and Meinecke (2018) + . Interfaces to several nonlinear + mixed-effects model packages are available, some of which are described by + Ranke et al. (2021) . Please note that no + warranty is implied for correctness of results or fitness for a particular + purpose. Depends: R (>= 2.15.1), parallel Imports: stats, graphics, methods, deSolve, R6, inline (>= 0.3.19), numDeriv, dplyr, lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix, nlmixr diff --git a/docs/dev/index.html b/docs/dev/index.html index ddfce971..8b0b2386 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -16,9 +16,14 @@ 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: Rtools) is installed, differential - equation models are solved using automatically generated C functions. Please - note that no warranty is implied for correctness of results or fitness for a - particular purpose."> + equation models are solved using automatically generated C functions. + Heteroscedasticity can be taken into account using variance by variable or + two-component error models as described by Ranke and Meinecke (2018) + <doi:10.3390/environments6120124>. Interfaces to several nonlinear + mixed-effects model packages are available, some of which are described by + Ranke et al. (2021) <doi:10.3390/environments8080071>. Please note that no + warranty is implied for correctness of results or fitness for a particular + purpose."> 

                                                                summary(m.L2.FOMC, data = FALSE)
                                                                -
                                                                ## mkin version used for fitting:    1.0.4 
-## R version used for fitting:       4.1.0 
-## Date of fit:     Fri Jul 23 18:03:19 2021 
-## Date of summary: Fri Jul 23 18:03:19 2021 
+
                                                                ## mkin version used for fitting:    1.1.0 
+## R version used for fitting:       4.1.1 
+## Date of fit:     Thu Sep 16 13:57:35 2021 
+## Date of summary: Thu Sep 16 13:57:35 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 239 model solutions performed in 0.049 s
+## Fitted using 239 model solutions performed in 0.048 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1819,10 +1816,10 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
      main = "FOCUS L2 - DFOP")
                                                                
 
                                                                
 
                                                                summary(m.L2.DFOP, data = FALSE)
                                                                
-
                                                                ## mkin version used for fitting:    1.0.4 
-## R version used for fitting:       4.1.0 
-## Date of fit:     Fri Jul 23 18:03:19 2021 
-## Date of summary: Fri Jul 23 18:03:19 2021 
+
                                                                ## mkin version used for fitting:    1.1.0 
+## R version used for fitting:       4.1.1 
+## Date of fit:     Thu Sep 16 13:57:36 2021 
+## Date of summary: Thu Sep 16 13:57:36 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1919,10 +1916,10 @@ plot(mm.L3)
                                                                
 
                                                                The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.
                                                                
 
                                                                We can extract the summary and plot for e.g. the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.
                                                                
 
                                                                summary(mm.L3[["DFOP", 1]])
                                                                
-
                                                                ## mkin version used for fitting:    1.0.4 
-## R version used for fitting:       4.1.0 
-## Date of fit:     Fri Jul 23 18:03:19 2021 
-## Date of summary: Fri Jul 23 18:03:20 2021 
+
                                                                ## mkin version used for fitting:    1.1.0 
+## R version used for fitting:       4.1.1 
+## Date of fit:     Thu Sep 16 13:57:36 2021 
+## Date of summary: Thu Sep 16 13:57:36 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1931,7 +1928,7 @@ plot(mm.L3)
                                                                
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 376 model solutions performed in 0.081 s
+## Fitted using 376 model solutions performed in 0.079 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -2027,10 +2024,10 @@ plot(mm.L4)
                                                                
 
                                                                
 
                                                                The χ2 error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the χ2 test passes is slightly lower for the FOMC model. However, the difference appears negligible.
                                                                
 
                                                                summary(mm.L4[["SFO", 1]], data = FALSE)
                                                                
-
                                                                ## mkin version used for fitting:    1.0.4 
-## R version used for fitting:       4.1.0 
-## Date of fit:     Fri Jul 23 18:03:20 2021 
-## Date of summary: Fri Jul 23 18:03:20 2021 
+
                                                                ## mkin version used for fitting:    1.1.0 
+## R version used for fitting:       4.1.1 
+## Date of fit:     Thu Sep 16 13:57:36 2021 
+## Date of summary: Thu Sep 16 13:57:36 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -2091,10 +2088,10 @@ plot(mm.L4)
                                                                
 ##        DT50 DT90
 ## parent  106  352
                                                                
 
                                                                summary(mm.L4[["FOMC", 1]], data = FALSE)
                                                                
-
                                                                ## mkin version used for fitting:    1.0.4 
-## R version used for fitting:       4.1.0 
-## Date of fit:     Fri Jul 23 18:03:20 2021 
-## Date of summary: Fri Jul 23 18:03:20 2021 
+
                                                                ## mkin version used for fitting:    1.1.0 
+## R version used for fitting:       4.1.1 
+## Date of fit:     Thu Sep 16 13:57:36 2021 
+## Date of summary: Thu Sep 16 13:57:36 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index 80bc9a0d..58d49112 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -1591,7 +1591,7 @@ div.tocify {
 
 
                                                                Introduction to mkin
                                                                
 
                                                                Johannes Ranke
                                                                
-
                                                                Last change 15 February 2021 (rebuilt 2021-09-15)
                                                                
+
                                                                Last change 15 February 2021 (rebuilt 2021-09-16)
                                                                
 
 
 
diff --git a/vignettes/references.bib b/vignettes/references.bib
index f7eb4692..55bd483c 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -133,6 +133,19 @@
 }
 
 
+@Article{ranke2021,
+  AUTHOR = {Ranke, Johannes and Wöltjen, Janina and Schmidt, Jana and Comets, Emmanuelle},
+  TITLE = {Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models},
+  JOURNAL = {Environments},
+  VOLUME = {8},
+  YEAR = {2021},
+  NUMBER = {8},
+  ARTICLE-NUMBER = {71},
+  URL = {https://www.mdpi.com/2076-3298/8/8/71},
+  ISSN = {2076-3298},
+  ABSTRACT = {When data on the degradation of a chemical substance have been collected in a number of environmental media (e.g., in different soils), two strategies can be followed for data evaluation. Currently, each individual dataset is evaluated separately, and representative degradation parameters are obtained by calculating averages of the kinetic parameters. However, such averages often take on unrealistic values if certain degradation parameters are ill-defined in some of the datasets. Moreover, the most appropriate degradation model is selected for each individual dataset, which is time consuming and then requires workarounds for averaging parameters from different models. Therefore, a simultaneous evaluation of all available data is desirable. If the environmental media are viewed as random samples from a population, an advanced strategy based on assumptions about the statistical distribution of the kinetic parameters across the population can be used. Here, we show the advantages of such simultaneous evaluations based on nonlinear mixed-effects models that incorporate such assumptions in the evaluation process. The advantages of this approach are demonstrated using synthetically generated data with known statistical properties and using publicly available experimental degradation data on two pesticidal active substances.},
+  DOI = {10.3390/environments8080071}
+}
 
 @Article{gao11,
   Title                    = {Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares},
diff --git a/vignettes/twa.html b/vignettes/twa.html
index fe9586e0..25a4c396 100644
--- a/vignettes/twa.html
+++ b/vignettes/twa.html
@@ -253,7 +253,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
 
 
                                                                Calculation of time weighted average concentrations with mkin
                                                                
 
                                                                Johannes Ranke
                                                                
-
                                                                Last change 18 September 2019 (rebuilt 2021-09-15)
                                                                
+
                                                                Last change 18 September 2019 (rebuilt 2021-09-16)
                                                                
 
 
 
diff --git a/vignettes/web_only/.build.timestamp b/vignettes/web_only/.build.timestamp
deleted file mode 100644
index e69de29b..00000000
diff --git a/vignettes/web_only/dimethenamid_2018.R b/vignettes/web_only/dimethenamid_2018.R
new file mode 100644
index 00000000..2c01bc14
--- /dev/null
+++ b/vignettes/web_only/dimethenamid_2018.R
@@ -0,0 +1,116 @@
+## ---- include = FALSE---------------------------------------------------------
+require(knitr)
+options(digits = 5)
+opts_chunk$set(
+  comment = "",
+  tidy = FALSE,
+  cache = TRUE
+)
+
+## ----saemix_control-----------------------------------------------------------
+library(saemix)
+saemix_control <- saemixControl(nbiter.saemix = c(800, 200), nb.chains = 15,
+    print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)
+
+## ----f_parent_saemix_sfo_const, results = 'hide', dependson = "saemix_control"----
+f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
+  control = saemix_control, transformations = "saemix")
+plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")
+
+## ----f_parent_saemix_sfo_tc, results = 'hide', dependson = "saemix_control"----
+f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
+  control = saemix_control, transformations = "saemix")
+plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")
+
+## ----f_parent_saemix_dfop_const, results = 'hide', dependson = "saemix_control"----
+f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
+  control = saemix_control, transformations = "saemix")
+plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")
+
+## ----f_parent_saemix_dfop_tc, results = 'hide', dependson = "saemix_control"----
+f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
+  control = saemix_control, transformations = "saemix")
+plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence")
+
+## ----AIC_parent_saemix--------------------------------------------------------
+compare.saemix(
+  f_parent_saemix_sfo_const$so,
+  f_parent_saemix_sfo_tc$so,
+  f_parent_saemix_dfop_const$so,
+  f_parent_saemix_dfop_tc$so)
+
+## ----AIC_parent_saemix_methods------------------------------------------------
+f_parent_saemix_dfop_tc$so <-
+  llgq.saemix(f_parent_saemix_dfop_tc$so)
+AIC(f_parent_saemix_dfop_tc$so)
+AIC(f_parent_saemix_dfop_tc$so, method = "gq")
+AIC(f_parent_saemix_dfop_tc$so, method = "lin")
+
+## ----f_parent_nlmixr_focei, results = "hide", message = FALSE, warning = FALSE----
+library(nlmixr)
+f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei")
+f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei")
+f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei")
+f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei")
+
+## ----AIC_parent_nlmixr_focei--------------------------------------------------
+aic_nlmixr_focei <- sapply(
+  list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm,
+    f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm),
+  AIC)
+
+## ----AIC_parent_nlme_rep------------------------------------------------------
+aic_nlme <- sapply(
+  list(f_parent_nlme_sfo_const, NA, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc),
+  function(x) if (is.na(x[1])) NA else AIC(x))
+aic_nlme_nlmixr_focei <- data.frame(
+  "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"),
+  "Error model" = rep(c("constant variance", "two-component"), 2),
+  "AIC (nlme)" = aic_nlme,
+  "AIC (nlmixr with FOCEI)" = aic_nlmixr_focei,
+  check.names = FALSE
+)
+
+## ----nlmixr_saem_control------------------------------------------------------
+nlmixr_saem_control <- saemControl(logLik = TRUE,
+  nBurn = 800, nEm = 200, nmc = 15)
+
+## ----f_parent_nlmixr_saem_sfo_const, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"----
+f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem",
+  control = nlmixr_saem_control)
+traceplot(f_parent_nlmixr_saem_sfo_const$nm)
+
+## ----f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"----
+f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem",
+  control = nlmixr_saem_control)
+traceplot(f_parent_nlmixr_saem_sfo_tc$nm)
+
+## ----f_parent_nlmixr_saem_dfop_const, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"----
+f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem",
+  control = nlmixr_saem_control)
+traceplot(f_parent_nlmixr_saem_dfop_const$nm)
+
+## ----f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"----
+f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem",
+  control = nlmixr_saem_control)
+traceplot(f_parent_nlmixr_saem_dfop_tc$nm)
+
+## ----AIC_parent_nlmixr_saem---------------------------------------------------
+AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
+  f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm)
+
+## ----AIC_all------------------------------------------------------------------
+AIC_all <- data.frame(
+  check.names = FALSE,
+  "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"),
+  "Error model" = c("const", "tc", "const", "tc"),
+  nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),
+  nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm,
+  f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC),
+  saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
+    f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC),
+  nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm,
+  f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC)
+)
+kable(AIC_all)
+
diff --git a/vignettes/web_only/dimethenamid_2018.html b/vignettes/web_only/dimethenamid_2018.html
index df8200eb..aa1905da 100644
--- a/vignettes/web_only/dimethenamid_2018.html
+++ b/vignettes/web_only/dimethenamid_2018.html
@@ -27,11 +27,8 @@ document.addEventListener('DOMContentLoaded', function(e) {
   }
 });
 
-
                                                                +Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. Environments 8 (8) 71 doi:10.3390/environments8080071 +
                                                                Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data Environments 6 (12) 124 doi:10.3390/environments6120124