diff options
| -rw-r--r-- | .travis.yml | 2 | ||||
| -rw-r--r-- | DESCRIPTION | 9 | ||||
| -rw-r--r-- | NAMESPACE | 8 | ||||
| -rw-r--r-- | NEWS.md | 14 | ||||
| -rw-r--r-- | R/mean_degparms.R | 61 | ||||
| -rw-r--r-- | R/nlme.R | 55 | ||||
| -rw-r--r-- | R/nlme.mmkin.R | 2 | ||||
| -rw-r--r-- | R/nlmixr.R | 467 | ||||
| -rw-r--r-- | R/plot.mixed.mmkin.R | 17 | ||||
| -rw-r--r-- | R/saem.R | 6 | ||||
| -rw-r--r-- | R/summary.nlmixr.mmkin.R | 250 | ||||
| -rw-r--r-- | build.log | 2 | ||||
| -rw-r--r-- | check.log | 68 | ||||
| -rw-r--r-- | man/mean_degparms.Rd | 27 | ||||
| -rw-r--r-- | man/nlme.Rd | 17 | ||||
| -rw-r--r-- | man/nlme.mmkin.Rd | 2 | ||||
| -rw-r--r-- | man/nlmixr.mmkin.Rd | 188 | ||||
| -rw-r--r-- | man/plot.mixed.mmkin.Rd | 5 | ||||
| -rw-r--r-- | man/summary.nlmixr.mmkin.Rd | 100 | ||||
| -rw-r--r-- | man/summary.saem.mmkin.Rd | 24 |
20 files changed, 1209 insertions, 115 deletions
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 @@ -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) @@ -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)$ds) + + # For nlmixr we can specify starting values for standard deviations eta, and + # we ignore uncertain parameters if test_log_parms is FALSE + eta <- apply(degparm_mat_trans_OK, 1, sd, na.rm = TRUE) + + return(list(fixed = fixed, random = list(ds = random), eta = eta)) + } else { + return(fixed) + } +} + @@ -125,61 +125,6 @@ nlme_function <- function(object) { } #' @rdname nlme -#' @return If random is FALSE (default), a named vector containing mean values -#' of the fitted degradation model parameters. If random is TRUE, a list with -#' fixed and random effects, in the format required by the start argument of -#' nlme for the case of a single grouping variable ds. -#' @param random Should a list with fixed and random effects be returned? -#' @param test_log_parms If TRUE, log parameters are only considered in -#' the mean calculations if their untransformed counterparts (most likely -#' rate constants) pass the t-test for significant difference from zero. -#' @param conf.level Possibility to adjust the required confidence level -#' for parameter that are tested if requested by 'test_log_parms'. -#' @export -mean_degparms <- function(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) -{ - if (nrow(object) > 1) stop("Only row objects allowed") - parm_mat_trans <- sapply(object, parms, transformed = TRUE) - - if (test_log_parms) { - parm_mat_dim <- dim(parm_mat_trans) - parm_mat_dimnames <- dimnames(parm_mat_trans) - - log_parm_trans_names <- grep("^log_", rownames(parm_mat_trans), value = TRUE) - log_parm_names <- gsub("^log_", "", log_parm_trans_names) - - t_test_back_OK <- matrix( - sapply(object, function(o) { - suppressWarnings(summary(o)$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 #' @export 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), @@ -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) +} @@ -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’ @@ -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 <jranke@uni-bremen.de>’ - -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} |