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-rw-r--r--DESCRIPTION4
-rw-r--r--NEWS.md4
-rw-r--r--R/saemix.R134
3 files changed, 3 insertions, 139 deletions
diff --git a/DESCRIPTION b/DESCRIPTION
index d244ea1c..782fb543 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: mkin
Type: Package
Title: Kinetic Evaluation of Chemical Degradation Data
Version: 0.9.50.3
-Date: 2020-07-15
+Date: 2020-10-08
Authors@R: c(person("Johannes", "Ranke", role = c("aut", "cre", "cph"),
email = "jranke@uni-bremen.de",
comment = c(ORCID = "0000-0003-4371-6538")),
@@ -17,7 +17,7 @@ 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.
Imports: stats, graphics, methods, deSolve, R6, inline, parallel, numDeriv,
- lmtest, pkgbuild, nlme (>= 3.1-149), purrr, saemix (>= 3.1.9000)
+ lmtest, pkgbuild, nlme (>= 3.1-149), purrr
Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr,
benchmarkme, tibble, stats4
License: GPL
diff --git a/NEWS.md b/NEWS.md
index 822ac219..688440cf 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,9 +1,7 @@
-# mkin 0.9.50.3 (unreleased)
+# mkin 0.9.50.3
- 'parms': Add a method for mmkin objects
-- 'saemix_model', 'saemix_data': Helper functions to fit nonlinear mixed-effects models for mmkin row objects using the saemix package
-
- 'mmkin' and 'confint(method = 'profile'): Use all cores detected by parallel::detectCores() per default
- 'confint(method = 'profile'): Choose accuracy based on 'rel_tol' argument, relative to the bounds obtained by the quadratic approximation
diff --git a/R/saemix.R b/R/saemix.R
deleted file mode 100644
index 24c0f0d0..00000000
--- a/R/saemix.R
+++ /dev/null
@@ -1,134 +0,0 @@
-#' Create saemix models from mmkin row objects
-#'
-#' This function sets up a nonlinear mixed effects model for an mmkin row
-#' object for use with the saemix package. 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.
-#'
-#' Starting values for the fixed effects (population mean parameters, argument psi0 of
-#' [saemix::saemixModel()] are the mean values of the parameters found using
-#' mmkin. Starting variances of the random effects (argument omega.init) are the
-#' variances of the deviations of the parameters from these mean values.
-#'
-#' @param object An mmkin row object containing several fits of the same model to different datasets
-#' @param cores The number of cores to be used for multicore processing.
-#' On Windows machines, cores > 1 is currently not supported.
-#' @rdname saemix
-#' @importFrom saemix saemixData saemixModel
-#' @importFrom stats var
-#' @examples
-#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
-#' function(x) subset(x$data[c("name", "time", "value")]))
-#' names(ds) <- paste("Dataset", 6:10)
-#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
-#' A1 = mkinsub("SFO"))
-#' \dontrun{
-#' f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE)
-#' library(saemix)
-#' m_saemix <- saemix_model(f_mmkin)
-#' d_saemix <- saemix_data(f_mmkin)
-#' saemix_options <- list(seed = 123456,
-#' save = FALSE, save.graphs = FALSE, displayProgress = FALSE,
-#' nbiter.saemix = c(200, 80))
-#' f_saemix <- saemix(m_saemix, d_saemix, saemix_options)
-#' plot(f_saemix, plot.type = "convergence")
-#' }
-#' @return An [saemix::SaemixModel] object.
-#' @export
-saemix_model <- function(object, cores = parallel::detectCores()) {
- if (nrow(object) > 1) stop("Only row objects allowed")
-
- mkin_model <- object[[1]]$mkinmod
- analytical <- is.function(mkin_model$deg_func)
-
- degparms_optim <- mean_degparms(object)
- psi0 <- matrix(degparms_optim, nrow = 1)
- colnames(psi0) <- names(degparms_optim)
-
- degparms_fixed <- object[[1]]$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$', '', odeini_fixed_parm_names)
-
- model_function <- function(psi, id, xidep) {
-
- uid <- unique(id)
-
- res_list <- parallel::mclapply(uid, function(i) {
- transparms_optim <- psi[i, ]
- names(transparms_optim) <- names(degparms_optim)
-
- odeini_optim <- transparms_optim[odeini_optim_parm_names]
- names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names)
-
- odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
-
- ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names)
- odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model,
- transform_rates = object[[1]]$transform_rates,
- transform_fractions = object[[1]]$transform_fractions)
- odeparms <- c(odeparms_optim, odeparms_fixed)
-
- xidep_i <- subset(xidep, id == i)
-
- if (analytical) {
- out_values <- mkin_model$deg_func(xidep_i, odeini, odeparms)
- } else {
-
- i_time <- xidep_i$time
- i_name <- xidep_i$name
-
- out_wide <- mkinpredict(mkin_model,
- odeparms = odeparms, odeini = odeini,
- solution_type = object[[1]]$solution_type,
- outtimes = sort(unique(i_time)))
-
- out_index <- cbind(as.character(i_time), as.character(i_name))
- out_values <- out_wide[out_index]
- }
- return(out_values)
- }, mc.cores = cores)
- res <- unlist(res_list)
- return(res)
- }
-
- raneff_0 <- mean_degparms(object, random = TRUE)$random$ds
- var_raneff_0 <- apply(raneff_0, 2, var)
-
- res <- saemixModel(model_function, psi0,
- "Mixed model generated from mmkin object",
- transform.par = rep(0, length(degparms_optim)),
- omega.init = diag(var_raneff_0)
- )
- return(res)
-}
-
-#' @rdname saemix
-#' @param \dots Further parameters passed to [saemix::saemixData]
-#' @return An [saemix::SaemixData] object.
-#' @export
-saemix_data <- function(object, ...) {
- if (nrow(object) > 1) stop("Only row objects allowed")
- ds_names <- colnames(object)
-
- ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
- names(ds_list) <- ds_names
- ds_saemix_all <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
- ds_saemix <- data.frame(ds = ds_saemix_all$ds,
- name = as.character(ds_saemix_all$variable),
- time = ds_saemix_all$time,
- value = ds_saemix_all$observed,
- stringsAsFactors = FALSE)
-
- res <- saemixData(ds_saemix,
- name.group = "ds",
- name.predictors = c("time", "name"),
- name.response = "value", ...)
- return(res)
-}

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