From 7035cde3a53781721fe15a8893fdf328c789bdd2 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 7 Mar 2022 12:03:40 +0100 Subject: Remove nlmixr interface for release of mkin 1.1.0 I am postponing my attempts to get the nlmixr interface to CRAN, given some problems with nlmixr using R-devel under Windows, see https://github.com/nlmixrdevelopment/nlmixr/issues/596 and https://github.com/r-hub/rhub/issues/512, which is fixed by the removal of nlmixr from the testsuite. For the tests to be more platform independent, the biphasic mixed effects models test dataset was defined in a way that fitting should be more robust (less ill-defined). --- DESCRIPTION | 4 +- NAMESPACE | 15 - NEWS.md | 4 +- R/endpoints.R | 4 +- R/intervals.R | 84 - R/nlmixr.R | 584 --- R/saem.R | 3 +- R/summary.nlmixr.mmkin.R | 250 -- R/tffm0.R | 51 - R/transform_odeparms.R | 6 +- README.md | 13 +- _pkgdown.yml | 4 - cran-comments.md | 0 docs/404.html | 2 +- docs/articles/FOCUS_D.html | 10 +- docs/articles/FOCUS_L.html | 38 +- docs/articles/index.html | 2 +- docs/articles/mkin.html | 8 +- docs/articles/twa.html | 4 +- docs/articles/web_only/FOCUS_Z.html | 4 +- docs/articles/web_only/dimethenamid_2018.html | 219 +- docs/authors.html | 2 +- docs/index.html | 4 +- docs/news/index.html | 7 +- docs/pkgdown.yml | 2 +- docs/reference/AIC.mmkin.html | 2 +- docs/reference/CAKE_export.html | 2 +- docs/reference/D24_2014.html | 2 +- docs/reference/DFOP.solution.html | 2 +- docs/reference/Extract.mmkin.html | 2 +- docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html | 2 +- docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html | 2 +- docs/reference/FOCUS_2006_HS_ref_A_to_F.html | 2 +- docs/reference/FOCUS_2006_SFO_ref_A_to_F.html | 2 +- docs/reference/FOCUS_2006_datasets.html | 2 +- docs/reference/FOMC.solution.html | 2 +- docs/reference/HS.solution.html | 2 +- docs/reference/IORE.solution.html | 2 +- docs/reference/NAFTA_SOP_2015.html | 2 +- docs/reference/NAFTA_SOP_Attachment.html | 2 +- docs/reference/Rplot001.png | Bin 1011 -> 14083 bytes docs/reference/Rplot002.png | Bin 60607 -> 13699 bytes docs/reference/Rplot003.png | Bin 15369 -> 48687 bytes docs/reference/Rplot004.png | Bin 10665 -> 59260 bytes docs/reference/SFO.solution.html | 2 +- docs/reference/SFORB.solution.html | 2 +- docs/reference/add_err.html | 2 +- docs/reference/aw.html | 2 +- docs/reference/confint.mkinfit.html | 6 +- docs/reference/create_deg_func.html | 10 +- docs/reference/dimethenamid_2018.html | 518 +-- docs/reference/endpoints.html | 6 +- docs/reference/experimental_data_for_UBA.html | 2 +- docs/reference/f_time_norm_focus.html | 2 +- docs/reference/focus_soil_moisture.html | 2 +- docs/reference/get_deg_func.html | 2 +- docs/reference/ilr.html | 2 +- docs/reference/index.html | 18 +- docs/reference/intervals.saem.mmkin.html | 2 +- docs/reference/loftest.html | 2 +- docs/reference/logLik.mkinfit.html | 2 +- docs/reference/logistic.solution.html | 2 +- docs/reference/lrtest.mkinfit.html | 2 +- docs/reference/max_twa_parent.html | 2 +- docs/reference/mccall81_245T.html | 2 +- docs/reference/mean_degparms.html | 2 +- docs/reference/mixed.html | 2 +- docs/reference/mkin_long_to_wide.html | 2 +- docs/reference/mkin_wide_to_long.html | 2 +- docs/reference/mkinds.html | 2 +- docs/reference/mkindsg.html | 2 +- docs/reference/mkinerrmin.html | 2 +- docs/reference/mkinerrplot.html | 2 +- docs/reference/mkinfit.html | 20 +- docs/reference/mkinmod.html | 6 +- docs/reference/mkinparplot.html | 2 +- docs/reference/mkinplot.html | 2 +- docs/reference/mkinpredict.html | 10 +- docs/reference/mkinresplot.html | 2 +- docs/reference/mmkin.html | 6 +- docs/reference/nafta.html | 2 +- docs/reference/nlme.html | 2 +- docs/reference/nlme.mmkin.html | 2 +- docs/reference/nobs.mkinfit.html | 2 +- docs/reference/parms.html | 2 +- docs/reference/plot.mixed.mmkin-3.png | Bin 0 -> 173804 bytes docs/reference/plot.mixed.mmkin-4.png | Bin 0 -> 176780 bytes docs/reference/plot.mixed.mmkin.html | 7 +- docs/reference/plot.mkinfit.html | 2 +- docs/reference/plot.mmkin.html | 2 +- docs/reference/plot.nafta.html | 2 +- docs/reference/reexports.html | 12 +- docs/reference/residuals.mkinfit.html | 2 +- docs/reference/saem-1.png | Bin 0 -> 46419 bytes docs/reference/saem-2.png | Bin 0 -> 49282 bytes docs/reference/saem-3.png | Bin 0 -> 128609 bytes docs/reference/saem-4.png | Bin 0 -> 174097 bytes docs/reference/saem.html | 340 +- docs/reference/schaefer07_complex_case.html | 2 +- docs/reference/sigma_twocomp.html | 2 +- docs/reference/summary.mkinfit.html | 8 +- docs/reference/summary.nlme.mmkin.html | 8 +- docs/reference/summary.saem.mmkin.html | 267 +- docs/reference/synthetic_data_for_UBA_2014.html | 8 +- docs/reference/test_data_from_UBA_2014.html | 2 +- docs/reference/transform_odeparms.html | 2 +- docs/reference/update.mkinfit.html | 2 +- man/endpoints.Rd | 4 +- man/intervals.nlmixr.mmkin.Rd | 25 - man/nlmixr.mmkin.Rd | 245 -- man/reexports.Rd | 6 +- man/summary.nlmixr.mmkin.Rd | 103 - man/tffm0.Rd | 46 - test.log | 45 +- ...t-for-saem-object-with-mkin-transformations.svg | 4392 ++++++++++---------- tests/testthat/print_sfo_saem_1.txt | 16 +- tests/testthat/setup_script.R | 14 +- tests/testthat/summary_nlmixr_saem_biphasic.txt | 97 - tests/testthat/summary_saem_biphasic_s.txt | 40 +- tests/testthat/test_dmta.R | 66 - tests/testthat/test_mixed.R | 17 +- tests/testthat/test_tffm0.R | 10 - vignettes/web_only/dimethenamid_2018.html | 189 +- vignettes/web_only/dimethenamid_2018.rmd | 212 +- 124 files changed, 3230 insertions(+), 4983 deletions(-) delete mode 100644 R/nlmixr.R delete mode 100644 R/summary.nlmixr.mmkin.R delete mode 100644 R/tffm0.R delete mode 100644 cran-comments.md create mode 100644 docs/reference/plot.mixed.mmkin-3.png create mode 100644 docs/reference/plot.mixed.mmkin-4.png create mode 100644 docs/reference/saem-1.png create mode 100644 docs/reference/saem-2.png create mode 100644 docs/reference/saem-3.png create mode 100644 docs/reference/saem-4.png delete mode 100644 man/intervals.nlmixr.mmkin.Rd delete mode 100644 man/nlmixr.mmkin.Rd delete mode 100644 man/summary.nlmixr.mmkin.Rd delete mode 100644 man/tffm0.Rd delete mode 100644 tests/testthat/summary_nlmixr_saem_biphasic.txt delete mode 100644 tests/testthat/test_tffm0.R diff --git a/DESCRIPTION b/DESCRIPTION index c2aefe50..2492a7fc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data Version: 1.1.0 -Date: 2022-03-03 +Date: 2022-03-07 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "jranke@uni-bremen.de", @@ -24,7 +24,7 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006, 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 (>= 3.0), nlmixr + lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix (>= 3.0) Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr, benchmarkme, tibble, stats4 License: GPL diff --git a/NAMESPACE b/NAMESPACE index aa3899ac..c5a4a389 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -8,7 +8,6 @@ S3method(aw,mmkin) S3method(confint,mkinfit) S3method(f_time_norm_focus,mkindsg) S3method(f_time_norm_focus,numeric) -S3method(intervals,nlmixr.mmkin) S3method(intervals,saem.mmkin) S3method(loftest,mkinfit) S3method(logLik,mkinfit) @@ -18,7 +17,6 @@ S3method(mixed,mmkin) S3method(mkinpredict,mkinfit) S3method(mkinpredict,mkinmod) S3method(nlme,mmkin) -S3method(nlmixr,mmkin) S3method(nobs,mkinfit) S3method(parms,mkinfit) S3method(parms,mmkin) @@ -33,17 +31,14 @@ 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) -S3method(print,summary.nlmixr.mmkin) S3method(print,summary.saem.mmkin) 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) @@ -65,7 +60,6 @@ export(get_deg_func) export(ilr) export(intervals) export(invilr) -export(invtffm0) export(loftest) export(logistic.solution) export(lrtest) @@ -94,9 +88,6 @@ export(nafta) export(nlme) export(nlme_data) export(nlme_function) -export(nlmixr) -export(nlmixr_data) -export(nlmixr_model) export(parms) export(plot_err) export(plot_res) @@ -105,19 +96,15 @@ export(saem) export(saemix_data) export(saemix_model) export(sigma_twocomp) -export(tffm0) export(transform_odeparms) import(deSolve) import(graphics) import(nlme) importFrom(R6,R6Class) -importFrom(dplyr,"%>%") importFrom(grDevices,dev.cur) importFrom(lmtest,lrtest) importFrom(methods,signature) importFrom(nlme,intervals) -importFrom(nlmixr,nlmixr) -importFrom(nlmixr,tableControl) importFrom(parallel,detectCores) importFrom(parallel,mclapply) importFrom(parallel,parLapply) @@ -128,7 +115,6 @@ 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) @@ -148,7 +134,6 @@ 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 61d81f20..944e4df1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,8 +2,6 @@ ## Mixed-effects models -- 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 saemix (now on CRAN), 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 @@ -12,7 +10,7 @@ - 'vignettes/web_only/dimethenamid_2018.rmd': Example evaluations of the dimethenamid data. -- 'intervals': Provide methods of this nlme function for 'nlmixr.mmkin' and 'saem.mmkin' objects. +- 'intervals': Provide a method of this nlme function for 'saem.mmkin' objects. # mkin 1.0.5 (2021-09-15) diff --git a/R/endpoints.R b/R/endpoints.R index 6bf52f07..e81e7a0a 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], [saem.mmkin] or -#' [nlmixr.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. diff --git a/R/intervals.R b/R/intervals.R index 8ab2b7ec..258eb4ad 100644 --- a/R/intervals.R +++ b/R/intervals.R @@ -95,87 +95,3 @@ intervals.saem.mmkin <- function(object, level = 0.95, backtransform = TRUE, ... attr(res, "level") <- level return(res) } - -#' Confidence intervals for parameters in nlmixr.mmkin objects -#' -#' @param object The fitted saem.mmkin object -#' @param level The confidence level. -#' @param backtransform Should we backtransform the parameters where a one to -#' one correlation between transformed and backtransformed parameters exists? -#' @param \dots For compatibility with the generic method -#' @importFrom nlme intervals -#' @return An object with 'intervals.saem.mmkin' and 'intervals.lme' in the -#' class attribute -#' @export -intervals.nlmixr.mmkin <- function(object, level = 0.95, backtransform = TRUE, ...) -{ - - # Fixed effects - mod_vars <- names(object$mkinmod$diffs) - - conf.int <- confint(object$nm) - dpnames <- setdiff(rownames(conf.int), names(object$mean_ep_start)) - ndp <- length(dpnames) - - confint_trans <- as.matrix(conf.int[dpnames, c(3, 1, 4)]) - colnames(confint_trans) <- c("lower", "est.", "upper") - - if (backtransform) { - bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, - object$transform_rates, object$transform_fractions) - bpnames <- names(bp) - - # Transform boundaries of CI for one parameter at a time, - # with the exception of sets of formation fractions (single fractions are OK). - f_names_skip <- character(0) - for (box in mod_vars) { # Figure out sets of fractions to skip - f_names <- grep(paste("^f", box, sep = "_"), dpnames, value = TRUE) - n_paths <- length(f_names) - if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) - } - - confint_back <- matrix(NA, nrow = length(bp), ncol = 3, - dimnames = list(bpnames, colnames(confint_trans))) - confint_back[, "est."] <- bp - - for (pname in dpnames) { - if (!pname %in% f_names_skip) { - par.lower <- confint_trans[pname, "lower"] - par.upper <- confint_trans[pname, "upper"] - names(par.lower) <- names(par.upper) <- pname - bpl <- backtransform_odeparms(par.lower, object$mkinmod, - object$transform_rates, - object$transform_fractions) - bpu <- backtransform_odeparms(par.upper, object$mkinmod, - object$transform_rates, - object$transform_fractions) - confint_back[names(bpl), "lower"] <- bpl - confint_back[names(bpu), "upper"] <- bpu - } - } - confint_ret <- confint_back - } else { - confint_ret <- confint_trans - } - attr(confint_ret, "label") <- "Fixed effects:" - - # Random effects - ranef_ret <- as.matrix(data.frame(lower = NA, - "est." = sqrt(diag(object$nm$omega)), upper = NA)) - rownames(ranef_ret) <- paste0(gsub("eta\\.", "sd(", rownames(ranef_ret)), ")") - attr(ranef_ret, "label") <- "Random effects:" - - # Error model - enames <- names(object$nm$sigma) - err_ret <- as.matrix(conf.int[enames, c(3, 1, 4)]) - colnames(err_ret) <- c("lower", "est.", "upper") - - res <- list( - fixed = confint_ret, - random = ranef_ret, - errmod = err_ret - ) - class(res) <- c("intervals.nlmixr.mmkin", "intervals.lme") - attr(res, "level") <- level - return(res) -} diff --git a/R/nlmixr.R b/R/nlmixr.R deleted file mode 100644 index 5d05f814..00000000 --- a/R/nlmixr.R +++ /dev/null @@ -1,584 +0,0 @@ -utils::globalVariables(c("predicted", "std", "ID", "TIME", "CMT", "DV", "IPRED", "IRES", "IWRES")) - -#' @export -nlmixr::nlmixr - -#' 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) or First Order Conditional -#' Estimation with Interaction (FOCEI). -#' -#' 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 -#' @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. -#' @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 -#' 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 data Not used, as the data are extracted from the mmkin row object -#' @param table Passed to [nlmixr::nlmixr] -#' @param error_model Optional argument 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) -#' -#' library(nlmixr) -#' f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' 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", ])) -#' -#' # The FOCEI fit of FOMC with constant variance or the tc error model is best -#' plot(f_nlmixr_fomc_saem_tc) -#' -#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), -#' A1 = mkinsub("SFO"), quiet = TRUE) -#' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), -#' A1 = mkinsub("SFO"), quiet = TRUE) -#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), -#' A1 = mkinsub("SFO"), quiet = TRUE) -#' -#' 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") -#' -#' nlmixr_model(f_mmkin_const["SFO-SFO", ]) -#' -#' # 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$nm -#' ) -#' # Currently, FOMC-SFO with two-component error by variable fitted by focei gives the -#' # lowest AIC -#' plot(f_nlmixr_fomc_sfo_focei_obs_tc) -#' summary(f_nlmixr_fomc_sfo_focei_obs_tc) -#' -#' # Two parallel metabolites -#' dmta_ds <- lapply(1:7, function(i) { -#' ds_i <- dimethenamid_2018$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$title) -#' dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) -#' dmta_ds[["Elliot 1"]] <- NULL -#' dmta_ds[["Elliot 2"]] <- NULL -#' sfo_sfo2 <- mkinmod( -#' DMTA = mkinsub("SFO", c("M23", "M27")), -#' M23 = mkinsub("SFO"), -#' M27 = mkinsub("SFO"), -#' quiet = TRUE -#' ) -#' f_dmta_sfo_sfo2 <- mmkin( -#' list("SFO-SFO2" = sfo_sfo2), -#' dmta_ds, quiet = TRUE, error_model = "obs") -#' nlmixr_model(f_dmta_sfo_sfo2) -#' nlmixr_focei_dmta_sfo_sfo2 <- nlmixr(f_dmta_sfo_sfo2, est = "focei") -#' } -#' @export -nlmixr.mmkin <- function(object, data = NULL, - est = NULL, control = list(), - table = tableControl(), - error_model = object[[1]]$err_mod, - test_log_parms = TRUE, - conf.level = 0.6, - degparms_start = "auto", - eta_start = "auto", - ..., - 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, - degparms_start = degparms_start, eta_start = eta_start - ) - 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, control = control) - }) - - 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) %>% - dplyr::arrange(ds, name, time) - - 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 -#' @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, - est = c("saem", "focei"), - degparms_start = "auto", - eta_start = "auto", - test_log_parms = TRUE, conf.level = 0.6, - error_model = object[[1]]$err_mod, add_attributes = FALSE) -{ - if (nrow(object) > 1) stop("Only row objects allowed") - est = match.arg(est) - - mkin_model <- object[[1]]$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$fixed - - degparms_optim_ilr_names <- grep("^f_.*_ilr", names(degparms_optim), value = TRUE) - obs_vars_ilr <- unique(gsub("f_(.*)_ilr.*$", "\\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]]$transform_fractions) - - if (degparms_start[1] == "auto") { - degparms_start <- degparms_optim_noilr - for (obs_var_ilr in obs_vars_ilr) { - ff_names <- grep(paste0("^f_", obs_var_ilr, "_"), - names(degparms_optim_back), value = TRUE) - f_tffm0 <- tffm0(degparms_optim_back[ff_names]) - f_tffm0_qlogis <- qlogis(f_tffm0) - names(f_tffm0_qlogis) <- paste0("f_", obs_var_ilr, - "_tffm0_", 1:length(f_tffm0), "_qlogis") - degparms_start <- c(degparms_start, f_tffm0_qlogis) - } - } - - if (eta_start[1] == "auto") { - eta_start <- degparms_mmkin$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) - 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_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 - ini_block <- paste0( - ini_block, - parm_name, " = ", - as.character(signif(degparms_start[parm_name], 2)), - "\n", - "eta.", parm_name, " ~ ", - as.character(signif(eta_start[parm_name], 2)), - "\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$observed)), na.rm = TRUE) * - errparms_mkin[2] - - if (error_model == "const") { - if (error_model_mkin == "tc") { - errparms_ini <- sigma_tc_mkin - } else { - errparms_ini <- mean(errparms_mkin) - } - names(errparms_ini) <- "sigma" - } - - if (error_model == "obs") { - errparms_ini <- switch(error_model_mkin, - const = rep(errparms_mkin["sigma"], length(obs_vars)), - obs = errparms_mkin, - tc = sigma_tc_mkin) - names(errparms_ini) <- paste0("sigma_", obs_vars) - } - - if (error_model == "tc") { - if (error_model_mkin != "tc") { - stop("Not supported") - } else { - errparms_ini <- errparms_mkin - } - } - - if (error_model == "obs_tc") { - if (error_model_mkin != "tc") { - stop("Not supported") - } else { - errparms_ini <- rep(errparms_mkin, length(obs_vars)) - names(errparms_ini) <- paste0( - rep(names(errparms_mkin), length(obs_vars)), - "_", - rep(obs_vars, each = 2)) - } - } - - for (parm_name in names(errparms_ini)) { - ini_block <- paste0( - ini_block, - parm_name, " = ", - as.character(signif(errparms_ini[parm_name], 2)), - "\n" - ) - } - - ini_block <- paste0(ini_block, "})") - - body(f)[2] <- parse(text = ini_block) - - model_block <- "model({" - - # Population initial values for the ODE state variables - for (parm_name in odeini_optim_parm_names) { - model_block <- paste0( - model_block, - parm_name, "_model = ", - parm_name, " + eta.", parm_name, "\n", - gsub("(.*)_0", "\\1(0)", parm_name), " = ", parm_name, "_model\n") - } - - # Population initial values for log rate constants - for (parm_name in grep("^log_", names(degparms_start), value = TRUE)) { - model_block <- paste0( - model_block, - gsub("^log_", "", parm_name), " = ", - "exp(", parm_name, " + eta.", parm_name, ")\n") - } - - # Population initial values for logit transformed parameters - for (parm_name in grep("_qlogis$", names(degparms_start), value = TRUE)) { - if (grepl("_tffm0_", parm_name)) { - parm_name_new <- gsub("_qlogis$", "", parm_name) - } else { - parm_name_new <- names( - backtransform_odeparms(degparms_start[parm_name], - object[[1]]$mkinmod, - object[[1]]$transform_rates, - object[[1]]$transform_fractions)) - } - model_block <- paste0( - model_block, - parm_name_new, " = ", - "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_(.*)$") - target_vars <- gsub(pattern, "\\1", - grep(paste0("^f_", obs_var_ilr, "_to_"), names(degparms_optim_back), value = TRUE)) - for (i in 1:length(target_vars)) { - ff_name <- ff_names[i] - ff_line <- paste0(ff_name, " = f_", obs_var_ilr, "_tffm0_", i) - if (i > 1) { - for (j in (i - 1):1) { - ff_line <- paste0(ff_line, " * (1 - f_", obs_var_ilr, "_tffm0_", j , ")") - } - } - model_block <- paste0( - model_block, - ff_line, - "\n" - ) - } - } - - # Differential equations - model_block <- paste0( - model_block, - paste( - gsub("d_(.*) =", "d/dt(\\1) =", mkin_model$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, "eta_start") <- degparms_mmkin$eta - attr(f, "mean_ep_start") <- errparms_ini - } - - return(f) -} - -#' @rdname nlmixr.mmkin -#' @return An dataframe suitable for use with [nlmixr::nlmixr] -#' @export -nlmixr_data <- function(object, ...) { - if (nrow(object) > 1) stop("Only row objects allowed") - d <- lapply(object, function(x) x$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$observed, - stringsAsFactors = FALSE) - - return(ds_nlmixr_renamed) -} diff --git a/R/saem.R b/R/saem.R index 26ea1c8d..d3b23861 100644 --- a/R/saem.R +++ b/R/saem.R @@ -227,7 +227,8 @@ print.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("\nFitted parameters:\n") conf.int <- x$so@results@conf.int[c("estimate", "lower", "upper")] rownames(conf.int) <- x$so@results@conf.int[["name"]] - print(conf.int, digits = digits) + conf.int.var <- grepl("^Var\\.", rownames(conf.int)) + print(conf.int[!conf.int.var, ], digits = digits) invisible(x) } diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R deleted file mode 100644 index 94d4ed93..00000000 --- a/R/summary.nlmixr.mmkin.R +++ /dev/null @@ -1,250 +0,0 @@ -#' 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. -#' -#' @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? -#' @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_back}{Backtransformed parameters, with confidence intervals if available} -#' \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$nm) -#' summary(f_nlmixr_dfop_sfo_sfo, data = TRUE) -#' } -#' -#' @export -summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) { - - mod_vars <- names(object$mkinmod$diffs) - - conf.int <- confint(object$nm) - 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, - 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 = "_"), dpnames, 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 dpnames) { - 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(dpnames, dpnames)) - - object$confint_trans <- confint_trans - object$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? - - names(object$data)[4] <- "observed" # rename value to observed - - object$verbose <- verbose - - object$fixed <- object$mmkin_orig[[1]]$fixed - object$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$SFORB - class(object) <- c("summary.nlmixr.mmkin") - return(object) -} - -#' @rdname summary.nlmixr.mmkin -#' @export -print.summary.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$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$Rversion, "\n") - - cat("Date of fit: ", x$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("\nDegradation model predictions using RxODE\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", - 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$fixed, digits = digits) - - cat("\nResults:\n\n") - cat("Likelihood calculated by", nlmixr::getOfvType(x$nm), " \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:", rdig = digits, ...) - } - - cat("\nRandom effects (omega):\n") - print(x$nm$omega, digits = digits) - - cat("\nVariance model:\n") - print(x$nm$sigma, digits = digits) - - 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/R/tffm0.R b/R/tffm0.R deleted file mode 100644 index 56283a5d..00000000 --- a/R/tffm0.R +++ /dev/null @@ -1,51 +0,0 @@ -#' Transform formation fractions as in the first published mkin version -#' -#' This transformation was used originally in mkin, in order to implement a -#' constraint for the sum of formation fractions to be smaller than 1. 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, the original 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. -#' -#' If the transformed formation fractions are restricted to the interval -#' between 0 and 1, the sum of backtransformed values is restricted -#' to this interval. -#' -#' @param ff Vector of untransformed formation fractions. The sum -#' must be smaller or equal to one -#' @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 -#' 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 -#' @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) - 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/R/transform_odeparms.R b/R/transform_odeparms.R index 174e7c2d..bf988331 100644 --- a/R/transform_odeparms.R +++ b/R/transform_odeparms.R @@ -230,11 +230,7 @@ backtransform_odeparms <- function(transparms, mkinmod, if(transform_fractions) { if (any(grepl("qlogis", names(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 - } + parms[f_names] <- f_tmp } else { f_tmp <- invilr(trans_f) if (spec[[box]]$sink) { diff --git a/README.md b/README.md index 00223748..b55b0bc7 100644 --- a/README.md +++ b/README.md @@ -96,9 +96,16 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). interpretation of the model parameters. * 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](https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html) 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). + [nlme.mmkin](https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html) and + [saem.mmkin](https://pkgdown.jrwb.de/mkin/reference/saem.mmkin.html) and + methods. 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). The saem method uses the `saemix` package as a backend. Analytical + solutions suitable for use with this package have been implemented for parent + only models and the most important models including one metabolite (SFO-SFO + and DFOP-SFO). Fitting other models with `saem.mmkin`, while it makes use + of the compiled ODE models that mkin provides, has longer run times (at least + six minutes on my system). ### Performance diff --git a/_pkgdown.yml b/_pkgdown.yml index a214c209..77cb0323 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -43,10 +43,8 @@ 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 @@ -54,7 +52,6 @@ reference: - mixed - intervals - intervals.saem.mmkin - - intervals.nlmixr.mmkin - title: Datasets and known results contents: - focus_soil_moisture @@ -90,7 +87,6 @@ reference: - mkinpredict - transform_odeparms - ilr - - tffm0 - logLik.mkinfit - residuals.mkinfit - nobs.mkinfit diff --git a/cran-comments.md b/cran-comments.md deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/404.html b/docs/404.html index 3215181e..ea7f1350 100644 --- a/docs/404.html +++ b/docs/404.html @@ -42,7 +42,7 @@ Functions and data
  • - Functions and data
  • - vignettes/FOCUS_D.rmd @@ -192,8 +192,8 @@ summary(fit) 
    ## mkin version used for fitting:    1.1.0 
 ## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 13:44:18 2022 
-## Date of summary: Wed Mar  2 13:44:18 2022 
+## Date of fit:     Mon Mar  7 13:15:58 2022 
+## Date of summary: Mon Mar  7 13:15:59 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -201,7 +201,7 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 401 model solutions performed in 0.145 s
+## Fitted using 401 model solutions performed in 0.165 s
 ## 
 ## Error model: Constant variance 
 ## 
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index 9efea93a..d7412a56 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -43,7 +43,7 @@
   Functions and data
  • - vignettes/FOCUS_L.rmd @@ -133,8 +133,8 @@ summary(m.L1.SFO) 
    ## mkin version used for fitting:    1.1.0 
 ## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 13:44:22 2022 
-## Date of summary: Wed Mar  2 13:44:22 2022 
+## Date of fit:     Mon Mar  7 13:16:01 2022 
+## Date of summary: Mon Mar  7 13:16:01 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -239,15 +239,15 @@
 ## doubtful
    ## mkin version used for fitting:    1.1.0 
 ## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 13:44:22 2022 
-## Date of summary: Wed Mar  2 13:44:22 2022 
+## Date of fit:     Mon Mar  7 13:16:02 2022 
+## Date of summary: Mon Mar  7 13:16:02 2022 
 ## 
 ## 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 0.081 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -351,8 +351,8 @@
 summary(m.L2.FOMC, data = FALSE)
    ## mkin version used for fitting:    1.1.0 
 ## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 13:44:23 2022 
-## Date of summary: Wed Mar  2 13:44:23 2022 
+## Date of fit:     Mon Mar  7 13:16:03 2022 
+## Date of summary: Mon Mar  7 13:16:03 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -432,8 +432,8 @@
 summary(m.L2.DFOP, data = FALSE)
    ## mkin version used for fitting:    1.1.0 
 ## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 13:44:23 2022 
-## Date of summary: Wed Mar  2 13:44:23 2022 
+## Date of fit:     Mon Mar  7 13:16:03 2022 
+## Date of summary: Mon Mar  7 13:16:03 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -442,7 +442,7 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 581 model solutions performed in 0.131 s
+## Fitted using 581 model solutions performed in 0.132 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -538,8 +538,8 @@
 summary(mm.L3[["DFOP", 1]])
    ## mkin version used for fitting:    1.1.0 
 ## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 13:44:24 2022 
-## Date of summary: Wed Mar  2 13:44:24 2022 
+## Date of fit:     Mon Mar  7 13:16:04 2022 
+## Date of summary: Mon Mar  7 13:16:04 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -548,7 +548,7 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 376 model solutions performed in 0.079 s
+## Fitted using 376 model solutions performed in 0.08 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -651,8 +651,8 @@
 summary(mm.L4[["SFO", 1]], data = FALSE)
    ## mkin version used for fitting:    1.1.0 
 ## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 13:44:25 2022 
-## Date of summary: Wed Mar  2 13:44:25 2022 
+## Date of fit:     Mon Mar  7 13:16:04 2022 
+## Date of summary: Mon Mar  7 13:16:05 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -716,8 +716,8 @@
 summary(mm.L4[["FOMC", 1]], data = FALSE)
    ## mkin version used for fitting:    1.1.0 
 ## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 13:44:25 2022 
-## Date of summary: Wed Mar  2 13:44:25 2022 
+## Date of fit:     Mon Mar  7 13:16:04 2022 
+## Date of summary: Mon Mar  7 13:16:05 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 9868dc6d..f340896b 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -26,7 +26,7 @@
   Functions and data
  • - Functions and data
  • - vignettes/mkin.rmd @@ -226,10 +226,10 @@

    Ranke, J. 2021. ‘mkin‘: Kinetic Evaluation of Chemical Degradation Data. https://CRAN.R-project.org/package=mkin.

    -

    Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In SETAC World 20-24 May. Berlin.

    +

    Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In SETAC World 20-24 May. Berlin. https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf.

    -

    ———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In XV Symposium on Pesticide Chemistry 2-4 September 2015. Piacenza. http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf.

    +

    ———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In XV Symposium on Pesticide Chemistry 2-4 September 2015. Piacenza. https://jrwb.de/posters/piacenza_2015.pdf.

    Ranke, Johannes, and Stefan Meinecke. 2019. “Error Models for the Kinetic Evaluation of Chemical Degradation Data.” Environments 6 (12). https://doi.org/10.3390/environments6120124.

    diff --git a/docs/articles/twa.html b/docs/articles/twa.html index 5b1c333d..d45b0ff4 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -43,7 +43,7 @@ Functions and data
  • - vignettes/twa.rmd diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html index 326fc121..43508280 100644 --- a/docs/articles/web_only/FOCUS_Z.html +++ b/docs/articles/web_only/FOCUS_Z.html @@ -43,7 +43,7 @@ Functions and data
  • - vignettes/web_only/FOCUS_Z.rmd diff --git a/docs/articles/web_only/dimethenamid_2018.html b/docs/articles/web_only/dimethenamid_2018.html index 40e8a913..0ba9d5a8 100644 --- a/docs/articles/web_only/dimethenamid_2018.html +++ b/docs/articles/web_only/dimethenamid_2018.html @@ -43,7 +43,7 @@ Functions and data
  • - vignettes/web_only/dimethenamid_2018.rmd @@ -118,8 +118,8 @@

    Introduction

    -

    During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics (Ranke et al. 2021) 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, as many model variants do not converge when fitted with nlme, and not all relevant error models can be fitted with saemix.

    -

    This vignette is an attempt to satisfy this need.

    +

    A first analysis of the data analysed here was presented in a recent journal article on nonlinear mixed-effects models in degradation kinetics (Ranke et al. 2021). That analysis was based on the nlme package and a development version of the saemix package that was unpublished at the time. Meanwhile, version 3.0 of the saemix package is available from the CRAN repository. Also, it turned out that there was an error in the handling of the Borstel data in the mkin package at the time, leading to the duplication of a few data points from that soil. The dataset in the mkin package has been corrected, and the interface to saemix in the mkin package has been updated to use the released version.

    +

    This vignette is intended to present an up to date analysis of the data, using the corrected dataset and released versions of mkin and saemix.

    Data @@ -155,20 +155,20 @@ 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)

    However, note that in the case of using this error model, the fits to the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by the fact that they did not converge:

    @@ -222,14 +222,14 @@ f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  <.0001
 
    While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random effects does not improve the fits.
    
 
    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)

    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 conveniently 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. As we will compare the SAEM implementation of saemix to the results obtained using the nlmixr package later, we define control settings that work well for all the parent data fits shown in this vignette.

    +

    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. We define control settings that work well for all the parent data fits shown in this vignette.

     library(saemix)
 saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,
@@ -244,7 +244,7 @@ f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  <.0001
   control = saemix_control, 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.

    +

    Obviously the selected 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,
   control = saemix_control, transformations = "saemix")
@@ -256,18 +256,72 @@ f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  <.0001
   control = saemix_control, transformations = "saemix")
 plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")

    -

    This is improved when the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced, it remains more or less stable already after 200 iterations of the first phase.

    +print(f_parent_saemix_dfop_const)
    +
    Kinetic nonlinear mixed-effects model fit by SAEM
+Structural model:
+d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+           * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Likelihood computed by importance sampling
+  AIC BIC logLik
+  706 704   -344
+
+Fitted parameters:
+          estimate    lower   upper
+DMTA_0    97.99583 96.50079 99.4909
+k1         0.06377  0.03432  0.0932
+k2         0.00848  0.00444  0.0125
+g          0.95701  0.91313  1.0009
+a.1        1.82141  1.65974  1.9831
+SD.DMTA_0  1.64787  0.45779  2.8379
+SD.k1      0.57439  0.24731  0.9015
+SD.k2      0.03296 -2.50143  2.5673
+SD.g       1.10266  0.32371  1.8816
    +

    While the other parameters converge to credible values, the variance of k2 (omega2.k2) converges to a very small value. The printout of the saem.mmkin model shows that the estimated standard deviation of k2 across the population of soils (SD.k2) is ill-defined, indicating overparameterisation of this model.

    +

    When the DFOP model is fitted with the two-component error model, we also observe that the estimated variance of k2 becomes very small, while being ill-defined, as illustrated by the excessive confidence interval of SD.k2.

    +
     f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
   control = saemix_control, transformations = "saemix")
 f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
   control = saemix_control_moreiter, transformations = "saemix")
 plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence")

    -

    Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero. This has been submitted as a bug to the saemix package, as the algorithm does not converge in this case.

    +
    +print(f_parent_saemix_dfop_tc)
    +
    Kinetic nonlinear mixed-effects model fit by SAEM
+Structural model:
+d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+           * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Likelihood computed by importance sampling
+  AIC BIC logLik
+  666 664   -323
+
+Fitted parameters:
+          estimate    lower    upper
+DMTA_0    98.27617  96.3088 100.2436
+k1         0.06437   0.0337   0.0950
+k2         0.00880   0.0063   0.0113
+g          0.95249   0.9100   0.9949
+a.1        1.06161   0.8625   1.2607
+b.1        0.02967   0.0226   0.0367
+SD.DMTA_0  2.06075   0.4187   3.7028
+SD.k1      0.59357   0.2561   0.9310
+SD.k2      0.00292 -10.2960  10.3019
+SD.g       1.05725   0.3808   1.7337
    +

    Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero and has been submitted as a bug to the saemix package, as the algorithm does not converge in this case.

    An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin. When using this option, convergence is slower, but eventually the algorithm stops as well with the same error message.

    The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and the version with increased iterations can be compared using the model comparison function of the saemix package:

    -
    +
     AIC_parent_saemix <- saemix::compare.saemix(
   f_parent_saemix_sfo_const$so,
   f_parent_saemix_sfo_tc$so,
@@ -275,7 +329,7 @@ f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  <.0001
   f_parent_saemix_dfop_tc$so,
   f_parent_saemix_dfop_tc_moreiter$so)
    
 
    Likelihoods calculated by importance sampling
    
-
    +
     rownames(AIC_parent_saemix) <- c(
   "SFO const", "SFO tc", "DFOP const", "DFOP tc", "DFOP tc more iterations")
 print(AIC_parent_saemix)
    
@@ -286,7 +340,7 @@ DFOP const              705.75 703.88
 DFOP tc                 665.65 663.57
 DFOP tc more iterations 665.88 663.80
    
 
    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$so <-
   saemix::llgq.saemix(f_parent_saemix_dfop_tc$so)
 AIC_parent_saemix_methods <- c(
@@ -298,7 +352,7 @@ DFOP tc more iterations 665.88 663.80
    
 
        is     gq    lin 
 665.65 665.68 665.11 
    
 
    The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value. In order to illustrate that the comparison of the three method depends on the degree of convergence obtained in the fit, the same comparison is shown below for the fit using the defaults for the number of iterations and the number of MCMC chains.
    
-
    +
     f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc["DFOP", ])
 f_parent_saemix_dfop_tc_defaults$so <-
   saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so)
@@ -311,114 +365,21 @@ DFOP tc more iterations 665.88 663.80
    
 
        is     gq    lin 
 668.27 718.36 666.49 
    
 
    
-
    
-
    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 the 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.
    
-
    -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")
    
-
    For the SFO model with constant variance, the AIC values are the same, for the DFOP model, there are significant differences between the AIC values. These may be caused by different solutions that are found, but also by the fact that the AIC values for the nlmixr fits are calculated based on Gaussian quadrature, not on linearisation.
    
-
    -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_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
-)
-print(aic_nlme_nlmixr_focei)
    
-
      Degradation model       Error model AIC (nlme) AIC (nlmixr with FOCEI)
-1               SFO constant variance     796.60                  796.60
-2               SFO     two-component         NA                  798.64
-3              DFOP constant variance     798.60                  745.87
-4              DFOP     two-component     671.91                  740.42
    
-
    Secondly, we use the SAEM estimation routine and check the convergence plots. The control parameters, which were also used for the saemix fits, are defined beforehand.
    
-
    -nlmixr_saem_control_800 <- saemControl(logLik = TRUE,
-  nBurn = 800, nEm = 300, nmc = 15)
-nlmixr_saem_control_moreiter <- saemControl(logLik = TRUE,
-  nBurn = 1600, nEm = 300, nmc = 15)
-nlmixr_saem_control_10k <- saemControl(logLik = TRUE,
-  nBurn = 10000, nEm = 1000, nmc = 15)
    
-
    Then we fit SFO with constant variance
    
-
    -f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem",
-  control = nlmixr_saem_control_800)
-traceplot(f_parent_nlmixr_saem_sfo_const$nm)
    
-
    
-
    and SFO with two-component error.
    
-
    -f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem",
-  control = nlmixr_saem_control_800)
-traceplot(f_parent_nlmixr_saem_sfo_tc$nm)
    
-
    
-
    For DFOP with constant variance, the convergence plots show considerable instability of the fit, which indicates overparameterisation which was already observed above for this model combination. Also note that the variance of k2 approximates zero, which was already observed in the saemix fits of the DFOP model.
    
-
    -f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem",
-  control = nlmixr_saem_control_800)
-traceplot(f_parent_nlmixr_saem_dfop_const$nm)
    
-
    
-
    For DFOP with two-component error, a less erratic convergence is seen, but the variance of k2 again approximates zero.
    
-
    -f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem",
-  control = nlmixr_saem_control_800)
-traceplot(f_parent_nlmixr_saem_dfop_tc$nm)
    
-
    
-
    To check if an increase in the number of iterations improves the fit, we repeat the fit with 1000 iterations for the burn in phase and 300 iterations for the second phase.
    
-
    -f_parent_nlmixr_saem_dfop_tc_moreiter <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem",
-  control = nlmixr_saem_control_moreiter)
-traceplot(f_parent_nlmixr_saem_dfop_tc_moreiter$nm)
    
-
    
-
    Here the fit looks very similar, but we will see below that it shows a higher AIC than the fit with 800 iterations in the burn in phase. Next we choose 10 000 iterations for the burn in phase and 1000 iterations for the second phase for comparison with saemix.
    
-
    -f_parent_nlmixr_saem_dfop_tc_10k <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem",
-  control = nlmixr_saem_control_10k)
-traceplot(f_parent_nlmixr_saem_dfop_tc_10k$nm)
    
-
    
-
    The AIC values are internally calculated using Gaussian quadrature.
    
-
    -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,
-  f_parent_nlmixr_saem_dfop_tc_moreiter$nm,
-  f_parent_nlmixr_saem_dfop_tc_10k$nm)
    
-
                                             df     AIC
-f_parent_nlmixr_saem_sfo_const$nm         5  798.71
-f_parent_nlmixr_saem_sfo_tc$nm            6  808.64
-f_parent_nlmixr_saem_dfop_const$nm        9 1995.96
-f_parent_nlmixr_saem_dfop_tc$nm          10  664.96
-f_parent_nlmixr_saem_dfop_tc_moreiter$nm 10 4464.93
-f_parent_nlmixr_saem_dfop_tc_10k$nm      10     Inf
    
-
    We can see that again, the DFOP/tc model shows the best goodness of fit. However, increasing the number of burn-in iterations from 800 to 1600 results in a higher AIC. If we further increase the number of iterations to 10 000 (burn-in) and 1000 (second phase), the AIC cannot be calculated for the nlmixr/saem fit, confirming that this fit does not converge properly with the SAEM algorithm.
    
 
    
-
    
-
    Comparison
-
    
-
    The following table gives the AIC values obtained with the three packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).
    
-
    +
    
+
    Comparison
+
    
+
    The following table gives the AIC values obtained with both backend packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains). Note that
    
+
     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)
+  saemix_lin = 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, method = "lin"),
+  saemix_is = 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, method = "is")
 )
 kable(AIC_all)
    
 
@@ -426,9 +387,8 @@ f_parent_nlmixr_saem_dfop_tc_10k$nm      10     Inf
-
-
-
+
+
@@ -437,36 +397,37 @@ f_parent_nlmixr_saem_dfop_tc_10k$nm      10     Inf
-
-
+
-
-
+
-
-
+
-
    
 
    Degradation model
 Error model
 nlmenlmixr_foceisaemixnlmixr_saemsaemix_linsaemix_is
 
    
     
 
    
 796.60
 796.60
 796.38798.71
 
    
 
    
 SFO
 tc
 798.60798.64798.60
 798.38808.64
 
    
 
    
 DFOP
 const
 NA745.87671.98
 705.751995.96
 
    
 
    
 DFOP
 tc
 671.91740.42665.11
 665.65664.96
 
    
     
 
    
 
    
 
    
+
    
+
    Conclusion
+
    
+
    A more detailed analysis of the dimethenamid dataset confirmed that the DFOP model provides the most appropriate description of the decline of the parent compound in these data. On the other hand, closer inspection of the results revealed that the variability of the k2 parameter across the population of soils is ill-defined. This coincides with the observation that this parameter cannot robustly be quantified in some for some of the soils.
    
+
    Regarding the regulatory use of these data, it is claimed that an improved characterisation of the mean parameter values across the population is obtained using the nonlinear mixed-effects models presented here. However, attempts to quantify the variability of the slower rate constant of the biphasic decline of dimethenamid indicate that the data are not sufficient to characterise this variability to a satisfactory precision.
    
 
    
 
    
 
    References
diff --git a/docs/authors.html b/docs/authors.html
index 6a360df5..2f901309 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -26,7 +26,7 @@
   Functions and data
  • - Functions and data
  • -
  • transform_odeparms so their estimators can more reasonably be expected to follow a normal distribution.
  • When parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which cannot occur in a single experiment with a single defined radiolabel position.
  • When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model used by other tools for biphasic metabolite curves. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.
    • -
  • 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).
    • +
  • 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 and saem.mmkin and methods. 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). The saem method uses the saemix package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with saem.mmkin, while it makes use of the compiled ODE models that mkin provides, has longer run times (at least six minutes on my system).
    • diff --git a/docs/news/index.html b/docs/news/index.html index 0a73f051..075b4e22 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -26,7 +26,7 @@ Functions and data
  • -

    Mixed-effects models

    -

    • 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 saemix (now on CRAN), in particular the generic function ‘saem’ with a generator ‘saem.mmkin’, currently using ‘saemix_model’ and ‘saemix_data’, summary and plot methods

      • +

      • Reintroduce the interface to saemix (now on CRAN), 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.

        • -

      • ‘intervals’: Provide methods of this nlme function for ‘nlmixr.mmkin’ and ‘saem.mmkin’ objects.

        • +

      • ‘intervals’: Provide a method of this nlme function for ‘saem.mmkin’ objects.

    • diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 5bd267be..f1010950 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -11,7 +11,7 @@ articles: benchmarks: web_only/benchmarks.html compiled_models: web_only/compiled_models.html dimethenamid_2018: web_only/dimethenamid_2018.html -last_built: 2022-03-03T10:27Z +last_built: 2022-03-07T13:50Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index 35becede..a049850d 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -27,7 +27,7 @@ same dataset.">