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authorJohannes Ranke <jranke@uni-bremen.de>2021-02-03 16:41:31 +0100
committerJohannes Ranke <jranke@uni-bremen.de>2021-02-03 18:18:19 +0100
commitf0ef23a7598e5d19648ae4edc2b74e0fba27a41c (patch)
tree03af20e730330e148acf3a7c008d82387dbe52eb /R
parent82814b17ec182467c25325d747fffa8ffbe4bb33 (diff)
Prepare for v1.0.0v1.0.0
- Improve authorship and copyright information - Prepare pkgdown config - Remove dependence on saemix as we need the development version which is not ready for CRAN - Temporarily remove saemix interface to check code coverage of the rest
Diffstat (limited to 'R')
-rw-r--r--R/endpoints.R8
-rw-r--r--R/plot.mixed.mmkin.R23
-rw-r--r--R/saem.R512
-rw-r--r--R/summary.saem.mmkin.R268
4 files changed, 5 insertions, 806 deletions
diff --git a/R/endpoints.R b/R/endpoints.R
index f1f47581..b5872e68 100644
--- a/R/endpoints.R
+++ b/R/endpoints.R
@@ -10,8 +10,8 @@
#' Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from
#' HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models
#'
-#' @param fit An object of class [mkinfit], [nlme.mmkin] or
-#' [saem.mmkin]. Or another object that has list components
+#' @param fit An object of class [mkinfit] or [nlme.mmkin]
+#' or another object that has list components
#' mkinmod containing an [mkinmod] degradation model, and two numeric vectors,
#' bparms.optim and bparms.fixed, that contain parameter values
#' for that model.
@@ -20,8 +20,8 @@
#' and, if applicable, a vector of formation fractions named ff
#' and, if the SFORB model was in use, a vector of eigenvalues
#' of these SFORB models, equivalent to DFOP rate constants
-#' @note The function is used internally by [summary.mkinfit],
-#' [summary.nlme.mmkin] and [summary.saem.mmkin].
+#' @note The function is used internally by [summary.mkinfit]
+#' and [summary.nlme.mmkin]
#' @author Johannes Ranke
#' @examples
#'
diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R
index 1674d855..5a0b7412 100644
--- a/R/plot.mixed.mmkin.R
+++ b/R/plot.mixed.mmkin.R
@@ -2,7 +2,7 @@ utils::globalVariables("ds")
#' Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object
#'
-#' @param x An object of class [mixed.mmkin], [saem.mmkin] or [nlme.mmkin]
+#' @param x An object of class [mixed.mmkin], [nlme.mmkin]
#' @param i A numeric index to select datasets for which to plot the individual predictions,
#' in case plots get too large
#' @inheritParams plot.mkinfit
@@ -39,15 +39,6 @@ utils::globalVariables("ds")
#' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))
#' plot(f_nlme)
#'
-#' f_saem <- saem(f, transformations = "saemix")
-#' plot(f_saem)
-#'
-#' # We can overlay the two variants if we generate predictions
-#' pred_nlme <- mkinpredict(dfop_sfo,
-#' f_nlme$bparms.optim[-1],
-#' c(parent = f_nlme$bparms.optim[[1]], A1 = 0),
-#' seq(0, 180, by = 0.2))
-#' plot(f_saem, pred_over = list(nlme = pred_nlme))
#' }
#' @export
plot.mixed.mmkin <- function(x,
@@ -91,18 +82,6 @@ plot.mixed.mmkin <- function(x,
type = ifelse(standardized, "pearson", "response"))
}
- if (inherits(x, "saem.mmkin")) {
- if (x$transformations == "saemix") backtransform = FALSE
- degparms_i <- saemix::psi(x$so)
- rownames(degparms_i) <- ds_names
- degparms_i_names <- setdiff(x$so@results@name.fixed, names(fit_1$errparms))
- colnames(degparms_i) <- degparms_i_names
- residual_type = ifelse(standardized, "standardized", "residual")
- residuals <- x$data[[residual_type]]
- degparms_pop <- x$so@results@fixed.effects
- names(degparms_pop) <- degparms_i_names
- }
-
degparms_fixed <- fit_1$fixed$value
names(degparms_fixed) <- rownames(fit_1$fixed)
degparms_all <- cbind(as.matrix(degparms_i),
diff --git a/R/saem.R b/R/saem.R
deleted file mode 100644
index fd2a77b4..00000000
--- a/R/saem.R
+++ /dev/null
@@ -1,512 +0,0 @@
-utils::globalVariables(c("predicted", "std"))
-
-#' Fit nonlinear mixed models with SAEM
-#'
-#' This function uses [saemix::saemix()] as a backend for fitting nonlinear mixed
-#' effects models created from [mmkin] row objects using the Stochastic Approximation
-#' Expectation Maximisation algorithm (SAEM).
-#'
-#' An mmkin row object is essentially a list of mkinfit objects that have been
-#' obtained by fitting the same model to a list of datasets using [mkinfit].
-#'
-#' Starting values for the fixed effects (population mean parameters, argument
-#' psi0 of [saemix::saemixModel()] are the mean values of the parameters found
-#' using [mmkin].
-#'
-#' @param object An [mmkin] row object containing several fits of the same
-#' [mkinmod] model to different datasets
-#' @param verbose Should we print information about created objects of
-#' type [saemix::SaemixModel] and [saemix::SaemixData]?
-#' @param transformations Per default, all parameter transformations are done
-#' in mkin. If this argument is set to 'saemix', parameter transformations
-#' are done in 'saemix' for the supported cases. Currently this is only
-#' supported in cases where the initial concentration of the parent is not fixed,
-#' SFO or DFOP is used for the parent and there is either no metabolite or one.
-#' @param degparms_start Parameter values given as a named numeric vector will
-#' be used to override the starting values obtained from the 'mmkin' object.
-#' @param solution_type Possibility to specify the solution type in case the
-#' automatic choice is not desired
-#' @param quiet Should we suppress the messages saemix prints at the beginning
-#' and the end of the optimisation process?
-#' @param control Passed to [saemix::saemix]
-#' @param \dots Further parameters passed to [saemix::saemixModel].
-#' @return An S3 object of class 'saem.mmkin', containing the fitted
-#' [saemix::SaemixObject] as a list component named 'so'. The
-#' object also inherits from 'mixed.mmkin'.
-#' @seealso [summary.saem.mmkin] [plot.mixed.mmkin]
-#' @examples
-#' \dontrun{
-#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
-#' function(x) subset(x$data[c("name", "time", "value")]))
-#' names(ds) <- paste("Dataset", 6:10)
-#' f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds,
-#' state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
-#' f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
-#'
-#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
-#' f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
-#' f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
-#' f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
-#'
-#' # The returned saem.mmkin object contains an SaemixObject, therefore we can use
-#' # functions from saemix
-#' library(saemix)
-#' compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so))
-#' plot(f_saem_fomc$so, plot.type = "convergence")
-#' plot(f_saem_fomc$so, plot.type = "individual.fit")
-#' plot(f_saem_fomc$so, plot.type = "npde")
-#' plot(f_saem_fomc$so, plot.type = "vpc")
-#'
-#' f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
-#' f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
-#' compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
-#'
-#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
-#' A1 = mkinsub("SFO"))
-#' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
-#' A1 = mkinsub("SFO"))
-#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-#' A1 = mkinsub("SFO"))
-#' # The following fit uses analytical solutions for SFO-SFO and DFOP-SFO,
-#' # and compiled ODEs for FOMC that are much slower
-#' f_mmkin <- mmkin(list(
-#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
-#' ds, quiet = TRUE)
-#' # saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds
-#' # each on this system, as we use analytical solutions written for saemix.
-#' # When using the analytical solutions written for mkin this took around
-#' # four minutes
-#' f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
-#' f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
-#' # We can use print, plot and summary methods to check the results
-#' print(f_saem_dfop_sfo)
-#' plot(f_saem_dfop_sfo)
-#' summary(f_saem_dfop_sfo, data = TRUE)
-#'
-#' # The following takes about 6 minutes
-#' #f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",
-#' # control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
-#'
-#' #saemix::compare.saemix(list(
-#' # f_saem_dfop_sfo$so,
-#' # f_saem_dfop_sfo_deSolve$so))
-#'
-#' # If the model supports it, we can also use eigenvalue based solutions, which
-#' # take a similar amount of time
-#' #f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",
-#' # control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
-#' }
-#' @export
-saem <- function(object, ...) UseMethod("saem")
-
-#' @rdname saem
-#' @export
-saem.mmkin <- function(object,
- transformations = c("mkin", "saemix"),
- degparms_start = numeric(),
- solution_type = "auto",
- control = list(displayProgress = FALSE, print = FALSE,
- save = FALSE, save.graphs = FALSE),
- verbose = FALSE, quiet = FALSE, ...)
-{
- transformations <- match.arg(transformations)
- m_saemix <- saemix_model(object, verbose = verbose,
- degparms_start = degparms_start, solution_type = solution_type,
- transformations = transformations, ...)
- d_saemix <- saemix_data(object, verbose = verbose)
-
- fit_time <- system.time({
- utils::capture.output(f_saemix <- saemix::saemix(m_saemix, d_saemix, control), split = !quiet)
- })
-
- transparms_optim <- f_saemix@results@fixed.effects
- names(transparms_optim) <- f_saemix@results@name.fixed
-
- if (transformations == "mkin") {
- bparms_optim <- backtransform_odeparms(transparms_optim,
- object[[1]]$mkinmod,
- object[[1]]$transform_rates,
- object[[1]]$transform_fractions)
- } else {
- bparms_optim <- transparms_optim
- }
-
- return_data <- nlme_data(object)
-
- return_data$predicted <- f_saemix@model@model(
- psi = saemix::psi(f_saemix),
- id = as.numeric(return_data$ds),
- xidep = return_data[c("time", "name")])
-
- return_data <- transform(return_data,
- residual = predicted - value,
- std = sigma_twocomp(predicted,
- f_saemix@results@respar[1], f_saemix@results@respar[2]))
- return_data <- transform(return_data,
- standardized = residual / std)
-
- result <- list(
- mkinmod = object[[1]]$mkinmod,
- mmkin = object,
- solution_type = object[[1]]$solution_type,
- transformations = transformations,
- transform_rates = object[[1]]$transform_rates,
- transform_fractions = object[[1]]$transform_fractions,
- so = f_saemix,
- time = fit_time,
- mean_dp_start = attr(m_saemix, "mean_dp_start"),
- bparms.optim = bparms_optim,
- bparms.fixed = object[[1]]$bparms.fixed,
- data = return_data,
- err_mod = object[[1]]$err_mod,
- date.fit = date(),
- saemixversion = as.character(utils::packageVersion("saemix")),
- mkinversion = as.character(utils::packageVersion("mkin")),
- Rversion = paste(R.version$major, R.version$minor, sep=".")
- )
-
- class(result) <- c("saem.mmkin", "mixed.mmkin")
- return(result)
-}
-
-#' @export
-#' @rdname saem
-#' @param x An saem.mmkin object to print
-#' @param digits Number of digits to use for printing
-print.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) {
- cat( "Kinetic nonlinear mixed-effects model fit by SAEM" )
- cat("\nStructural model:\n")
- diffs <- x$mmkin[[1]]$mkinmod$diffs
- nice_diffs <- gsub("^(d.*) =", "\\1/dt =", diffs)
- writeLines(strwrap(nice_diffs, exdent = 11))
- cat("\nData:\n")
- cat(nrow(x$data), "observations of",
- length(unique(x$data$name)), "variable(s) grouped in",
- length(unique(x$data$ds)), "datasets\n")
-
- cat("\nLikelihood computed by importance sampling\n")
- print(data.frame(
- AIC = AIC(x$so, type = "is"),
- BIC = BIC(x$so, type = "is"),
- logLik = logLik(x$so, type = "is"),
- row.names = " "), digits = digits)
-
- cat("\nFitted parameters:\n")
- conf.int <- x$so@results@conf.int[c("estimate", "lower", "upper")]
- rownames(conf.int) <- x$so@results@conf.int[["name"]]
- print(conf.int, digits = digits)
-
- invisible(x)
-}
-
-#' @rdname saem
-#' @return An [saemix::SaemixModel] object.
-#' @export
-saemix_model <- function(object, solution_type = "auto", transformations = c("mkin", "saemix"),
- degparms_start = numeric(), verbose = FALSE, ...)
-{
- if (nrow(object) > 1) stop("Only row objects allowed")
-
- mkin_model <- object[[1]]$mkinmod
-
- degparms_optim <- mean_degparms(object)
- if (transformations == "saemix") {
- degparms_optim <- backtransform_odeparms(degparms_optim,
- object[[1]]$mkinmod,
- object[[1]]$transform_rates,
- object[[1]]$transform_fractions)
- }
- degparms_fixed <- object[[1]]$bparms.fixed
-
- # Transformations are done in the degradation function
- transform.par = rep(0, length(degparms_optim))
-
- odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE)
- odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE)
-
- odeparms_fixed_names <- setdiff(names(degparms_fixed), odeini_fixed_parm_names)
- odeparms_fixed <- degparms_fixed[odeparms_fixed_names]
-
- odeini_fixed <- degparms_fixed[odeini_fixed_parm_names]
- names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names)
-
- model_function <- FALSE
-
- # Model functions with analytical solutions
- # Fixed parameters, use_of_ff = "min" and turning off sinks currently not supported here
- # In general, we need to consider exactly how the parameters in mkinfit were specified,
- # as the parameters are currently mapped by position in these solutions
- sinks <- sapply(mkin_model$spec, function(x) x$sink)
- if (length(odeparms_fixed) == 0 & mkin_model$use_of_ff == "max" & all(sinks)) {
- # Parent only
- if (length(mkin_model$spec) == 1) {
- parent_type <- mkin_model$spec[[1]]$type
- if (length(odeini_fixed) == 1) {
- if (parent_type == "SFO") {
- stop("saemix needs at least two parameters to work on.")
- }
- if (parent_type == "FOMC") {
- model_function <- function(psi, id, xidep) {
- odeini_fixed / (xidep[, "time"]/exp(psi[id, 2]) + 1)^exp(psi[id, 1])
- }
- }
- if (parent_type == "DFOP") {
- model_function <- function(psi, id, xidep) {
- g <- plogis(psi[id, 3])
- t <- xidep[, "time"]
- odeini_fixed * (g * exp(- exp(psi[id, 1]) * t) +
- (1 - g) * exp(- exp(psi[id, 2]) * t))
- }
- }
- if (parent_type == "HS") {
- model_function <- function(psi, id, xidep) {
- tb <- exp(psi[id, 3])
- t <- xidep[, "time"]
- k1 = exp(psi[id, 1])
- odeini_fixed * ifelse(t <= tb,
- exp(- k1 * t),
- exp(- k1 * tb) * exp(- exp(psi[id, 2]) * (t - tb)))
- }
- }
- } else {
- if (parent_type == "SFO") {
- if (transformations == "mkin") {
- model_function <- function(psi, id, xidep) {
- psi[id, 1] * exp( - exp(psi[id, 2]) * xidep[, "time"])
- }
- } else {
- model_function <- function(psi, id, xidep) {
- psi[id, 1] * exp( - psi[id, 2] * xidep[, "time"])
- }
- transform.par = c(0, 1)
- }
- }
- if (parent_type == "FOMC") {
- model_function <- function(psi, id, xidep) {
- psi[id, 1] / (xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 2])
- }
- }
- if (parent_type == "DFOP") {
- if (transformations == "mkin") {
- model_function <- function(psi, id, xidep) {
- g <- plogis(psi[id, 4])
- t <- xidep[, "time"]
- psi[id, 1] * (g * exp(- exp(psi[id, 2]) * t) +
- (1 - g) * exp(- exp(psi[id, 3]) * t))
- }
- } else {
- model_function <- function(psi, id, xidep) {
- g <- psi[id, 4]
- t <- xidep[, "time"]
- psi[id, 1] * (g * exp(- psi[id, 2] * t) +
- (1 - g) * exp(- psi[id, 3] * t))
- }
- transform.par = c(0, 1, 1, 3)
- }
- }
- if (parent_type == "HS") {
- model_function <- function(psi, id, xidep) {
- tb <- exp(psi[id, 4])
- t <- xidep[, "time"]
- k1 = exp(psi[id, 2])
- psi[id, 1] * ifelse(t <= tb,
- exp(- k1 * t),
- exp(- k1 * tb) * exp(- exp(psi[id, 3]) * (t - tb)))
- }
- }
- }
- }
-
- # Parent with one metabolite
- # Parameter names used in the model functions are as in
- # https://nbviewer.jupyter.org/urls/jrwb.de/nb/Symbolic%20ODE%20solutions%20for%20mkin.ipynb
- types <- unname(sapply(mkin_model$spec, function(x) x$type))
- if (length(mkin_model$spec) == 2 &! "SFORB" %in% types ) {
- # Initial value for the metabolite (n20) must be fixed
- if (names(odeini_fixed) == names(mkin_model$spec)[2]) {
- n20 <- odeini_fixed
- parent_name <- names(mkin_model$spec)[1]
- if (identical(types, c("SFO", "SFO"))) {
- if (transformations == "mkin") {
- model_function <- function(psi, id, xidep) {
- t <- xidep[, "time"]
- n10 <- psi[id, 1]
- k1 <- exp(psi[id, 2])
- k2 <- exp(psi[id, 3])
- f12 <- plogis(psi[id, 4])
- ifelse(xidep[, "name"] == parent_name,
- n10 * exp(- k1 * t),
- (((k2 - k1) * n20 - f12 * k1 * n10) * exp(- k2 * t)) / (k2 - k1) +
- (f12 * k1 * n10 * exp(- k1 * t)) / (k2 - k1)
- )
- }
- } else {
- model_function <- function(psi, id, xidep) {
- t <- xidep[, "time"]
- n10 <- psi[id, 1]
- k1 <- psi[id, 2]
- k2 <- psi[id, 3]
- f12 <- psi[id, 4]
- ifelse(xidep[, "name"] == parent_name,
- n10 * exp(- k1 * t),
- (((k2 - k1) * n20 - f12 * k1 * n10) * exp(- k2 * t)) / (k2 - k1) +
- (f12 * k1 * n10 * exp(- k1 * t)) / (k2 - k1)
- )
- }
- transform.par = c(0, 1, 1, 3)
- }
- }
- if (identical(types, c("DFOP", "SFO"))) {
- if (transformations == "mkin") {
- model_function <- function(psi, id, xidep) {
- t <- xidep[, "time"]
- n10 <- psi[id, 1]
- k2 <- exp(psi[id, 2])
- f12 <- plogis(psi[id, 3])
- l1 <- exp(psi[id, 4])
- l2 <- exp(psi[id, 5])
- g <- plogis(psi[id, 6])
- ifelse(xidep[, "name"] == parent_name,
- n10 * (g * exp(- l1 * t) + (1 - g) * exp(- l2 * t)),
- ((f12 * g - f12) * l2 * n10 * exp(- l2 * t)) / (l2 - k2) -
- (f12 * g * l1 * n10 * exp(- l1 * t)) / (l1 - k2) +
- ((((l1 - k2) * l2 - k2 * l1 + k2^2) * n20 +
- ((f12 * l1 + (f12 * g - f12) * k2) * l2 -
- f12 * g * k2 * l1) * n10) * exp( - k2 * t)) /
- ((l1 - k2) * l2 - k2 * l1 + k2^2)
- )
- }
- } else {
- model_function <- function(psi, id, xidep) {
- t <- xidep[, "time"]
- n10 <- psi[id, 1]
- k2 <- psi[id, 2]
- f12 <- psi[id, 3]
- l1 <- psi[id, 4]
- l2 <- psi[id, 5]
- g <- psi[id, 6]
- ifelse(xidep[, "name"] == parent_name,
- n10 * (g * exp(- l1 * t) + (1 - g) * exp(- l2 * t)),
- ((f12 * g - f12) * l2 * n10 * exp(- l2 * t)) / (l2 - k2) -
- (f12 * g * l1 * n10 * exp(- l1 * t)) / (l1 - k2) +
- ((((l1 - k2) * l2 - k2 * l1 + k2^2) * n20 +
- ((f12 * l1 + (f12 * g - f12) * k2) * l2 -
- f12 * g * k2 * l1) * n10) * exp( - k2 * t)) /
- ((l1 - k2) * l2 - k2 * l1 + k2^2)
- )
- }
- transform.par = c(0, 1, 3, 1, 1, 3)
- }
- }
- }
- }
- }
-
- if (is.function(model_function) & solution_type == "auto") {
- solution_type = "analytical saemix"
- } else {
-
- if (solution_type == "auto")
- solution_type <- object[[1]]$solution_type
-
- model_function <- function(psi, id, xidep) {
-
- uid <- unique(id)
-
- res_list <- lapply(uid, function(i) {
-
- transparms_optim <- as.numeric(psi[i, ]) # psi[i, ] is a dataframe when called in saemix.predict
- names(transparms_optim) <- names(degparms_optim)
-
- odeini_optim <- transparms_optim[odeini_optim_parm_names]
- names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names)
-
- odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
-
- ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names)
- odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model,
- transform_rates = object[[1]]$transform_rates,
- transform_fractions = object[[1]]$transform_fractions)
- odeparms <- c(odeparms_optim, odeparms_fixed)
-
- xidep_i <- subset(xidep, id == i)
-
- if (solution_type == "analytical") {
- out_values <- mkin_model$deg_func(xidep_i, odeini, odeparms)
- } else {
-
- i_time <- xidep_i$time
- i_name <- xidep_i$name
-
- out_wide <- mkinpredict(mkin_model,
- odeparms = odeparms, odeini = odeini,
- solution_type = solution_type,
- outtimes = sort(unique(i_time)),
- na_stop = FALSE
- )
-
- out_index <- cbind(as.character(i_time), as.character(i_name))
- out_values <- out_wide[out_index]
- }
- return(out_values)
- })
- res <- unlist(res_list)
- return(res)
- }
- }
-
- error.model <- switch(object[[1]]$err_mod,
- const = "constant",
- tc = "combined",
- obs = "constant")
-
- if (object[[1]]$err_mod == "obs") {
- warning("The error model 'obs' (variance by variable) can currently not be transferred to an saemix model")
- }
-
- error.init <- switch(object[[1]]$err_mod,
- const = c(a = mean(sapply(object, function(x) x$errparms)), b = 1),
- tc = c(a = mean(sapply(object, function(x) x$errparms[1])),
- b = mean(sapply(object, function(x) x$errparms[2]))),
- obs = c(a = mean(sapply(object, function(x) x$errparms)), b = 1))
-
- degparms_psi0 <- degparms_optim
- degparms_psi0[names(degparms_start)] <- degparms_start
- psi0_matrix <- matrix(degparms_psi0, nrow = 1)
- colnames(psi0_matrix) <- names(degparms_psi0)
-
- res <- saemix::saemixModel(model_function,
- psi0 = psi0_matrix,
- "Mixed model generated from mmkin object",
- transform.par = transform.par,
- error.model = error.model,
- verbose = verbose
- )
- attr(res, "mean_dp_start") <- degparms_optim
- return(res)
-}
-
-#' @rdname saem
-#' @return An [saemix::SaemixData] object.
-#' @export
-saemix_data <- function(object, verbose = FALSE, ...) {
- if (nrow(object) > 1) stop("Only row objects allowed")
- ds_names <- colnames(object)
-
- ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
- names(ds_list) <- ds_names
- ds_saemix_all <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
- ds_saemix <- data.frame(ds = ds_saemix_all$ds,
- name = as.character(ds_saemix_all$variable),
- time = ds_saemix_all$time,
- value = ds_saemix_all$observed,
- stringsAsFactors = FALSE)
-
- res <- saemix::saemixData(ds_saemix,
- name.group = "ds",
- name.predictors = c("time", "name"),
- name.response = "value",
- verbose = verbose,
- ...)
- return(res)
-}
diff --git a/R/summary.saem.mmkin.R b/R/summary.saem.mmkin.R
deleted file mode 100644
index e92c561c..00000000
--- a/R/summary.saem.mmkin.R
+++ /dev/null
@@ -1,268 +0,0 @@
-#' Summary method for class "saem.mmkin"
-#'
-#' Lists model equations, initial parameter values, optimised parameters
-#' for fixed effects (population), random effects (deviations from the
-#' population mean) and residual error model, as well as the resulting
-#' endpoints such as formation fractions and DT50 values. Optionally
-#' (default is FALSE), the data are listed in full.
-#'
-#' @param object an object of class [saem.mmkin]
-#' @param x an object of class [summary.saem.mmkin]
-#' @param data logical, indicating whether the full data should be included in
-#' the summary.
-#' @param verbose Should the summary be verbose?
-#' @param distimes logical, indicating whether DT50 and DT90 values should be
-#' included.
-#' @param digits Number of digits to use for printing
-#' @param \dots optional arguments passed to methods like \code{print}.
-#' @return The summary function returns a list based on the [saemix::SaemixObject]
-#' obtained in the fit, with at least the following additional components
-#' \item{saemixversion, mkinversion, Rversion}{The saemix, mkin and R versions used}
-#' \item{date.fit, date.summary}{The dates where the fit and the summary were
-#' produced}
-#' \item{diffs}{The differential equations used in the degradation model}
-#' \item{use_of_ff}{Was maximum or minimum use made of formation fractions}
-#' \item{data}{The data}
-#' \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals}
-#' \item{confint_back}{Backtransformed parameters, with confidence intervals if available}
-#' \item{confint_errmod}{Error model parameters with confidence intervals}
-#' \item{ff}{The estimated formation fractions derived from the fitted
-#' model.}
-#' \item{distimes}{The DT50 and DT90 values for each observed variable.}
-#' \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.}
-#' The print method is called for its side effect, i.e. printing the summary.
-#' @importFrom stats predict vcov
-#' @author Johannes Ranke for the mkin specific parts
-#' saemix authors for the parts inherited from saemix.
-#' @examples
-#' # Generate five datasets following DFOP-SFO kinetics
-#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"),
-#' m1 = mkinsub("SFO"), quiet = TRUE)
-#' set.seed(1234)
-#' k1_in <- rlnorm(5, log(0.1), 0.3)
-#' k2_in <- rlnorm(5, log(0.02), 0.3)
-#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3))
-#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3))
-#' k_m1_in <- rlnorm(5, log(0.02), 0.3)
-#'
-#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {
-#' mkinpredict(dfop_sfo,
-#' c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),
-#' c(parent = 100, m1 = 0),
-#' sampling_times)
-#' }
-#'
-#' ds_mean_dfop_sfo <- lapply(1:5, function(i) {
-#' mkinpredict(dfop_sfo,
-#' c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],
-#' f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),
-#' c(parent = 100, m1 = 0),
-#' sampling_times)
-#' })
-#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5)
-#'
-#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {
-#' add_err(ds,
-#' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
-#' n = 1)[[1]]
-#' })
-#'
-#' \dontrun{
-#' # Evaluate using mmkin and saem
-#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,
-#' quiet = TRUE, error_model = "tc", cores = 5)
-#' f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
-#' summary(f_saem_dfop_sfo, data = TRUE)
-#' }
-#'
-#' @export
-summary.saem.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) {
-
- mod_vars <- names(object$mkinmod$diffs)
-
- pnames <- names(object$mean_dp_start)
- np <- length(pnames)
-
- conf.int <- object$so@results@conf.int
- rownames(conf.int) <- conf.int$name
- confint_trans <- as.matrix(conf.int[pnames, c("estimate", "lower", "upper")])
- colnames(confint_trans)[1] <- "est."
-
- # In case objects were produced by earlier versions of saem
- if (is.null(object$transformations)) object$transformations <- "mkin"
-
- if (object$transformations == "mkin") {
- bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod,
- object$transform_rates, object$transform_fractions)
- bpnames <- names(bp)
-
- # Transform boundaries of CI for one parameter at a time,
- # with the exception of sets of formation fractions (single fractions are OK).
- f_names_skip <- character(0)
- for (box in mod_vars) { # Figure out sets of fractions to skip
- f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)
- n_paths <- length(f_names)
- if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names)
- }
-
- confint_back <- matrix(NA, nrow = length(bp), ncol = 3,
- dimnames = list(bpnames, colnames(confint_trans)))
- confint_back[, "est."] <- bp
-
- for (pname in pnames) {
- if (!pname %in% f_names_skip) {
- par.lower <- confint_trans[pname, "lower"]
- par.upper <- confint_trans[pname, "upper"]
- names(par.lower) <- names(par.upper) <- pname
- bpl <- backtransform_odeparms(par.lower, object$mkinmod,
- object$transform_rates,
- object$transform_fractions)
- bpu <- backtransform_odeparms(par.upper, object$mkinmod,
- object$transform_rates,
- object$transform_fractions)
- confint_back[names(bpl), "lower"] <- bpl
- confint_back[names(bpu), "upper"] <- bpu
- }
- }
- } else {
- confint_back <- confint_trans
- }
-
- # Correlation of fixed effects (inspired by summary.nlme)
- varFix <- vcov(object$so)[1:np, 1:np]
- stdFix <- sqrt(diag(varFix))
- object$corFixed <- array(
- t(varFix/stdFix)/stdFix,
- dim(varFix),
- list(pnames, pnames))
-
- # Random effects
- rnames <- paste0("SD.", pnames)
- confint_ranef <- as.matrix(conf.int[rnames, c("estimate", "lower", "upper")])
- colnames(confint_ranef)[1] <- "est."
-
- # Error model
- enames <- if (object$err_mod == "const") "a.1" else c("a.1", "b.1")
- confint_errmod <- as.matrix(conf.int[enames, c("estimate", "lower", "upper")])
- colnames(confint_errmod)[1] <- "est."
-
-
- object$confint_trans <- confint_trans
- object$confint_ranef <- confint_ranef
- object$confint_errmod <- confint_errmod
- object$confint_back <- confint_back
-
- object$date.summary = date()
- object$use_of_ff = object$mkinmod$use_of_ff
- object$error_model_algorithm = object$mmkin_orig[[1]]$error_model_algorithm
- err_mod = object$mmkin_orig[[1]]$err_mod
-
- object$diffs <- object$mkinmod$diffs
- object$print_data <- data # boolean: Should we print the data?
- so_pred <- object$so@results@predictions
-
- names(object$data)[4] <- "observed" # rename value to observed
-
- object$verbose <- verbose
-
- object$fixed <- object$mmkin_orig[[1]]$fixed
- object$AIC = AIC(object$so)
- object$BIC = BIC(object$so)
- object$logLik = logLik(object$so, method = "is")
-
- ep <- endpoints(object)
- if (length(ep$ff) != 0)
- object$ff <- ep$ff
- if (distimes) object$distimes <- ep$distimes
- if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB
- class(object) <- c("summary.saem.mmkin")
- return(object)
-}
-
-#' @rdname summary.saem.mmkin
-#' @export
-print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) {
- cat("saemix version used for fitting: ", x$saemixversion, "\n")
- cat("mkin version used for pre-fitting: ", x$mkinversion, "\n")
- cat("R version used for fitting: ", x$Rversion, "\n")
-
- cat("Date of fit: ", x$date.fit, "\n")
- cat("Date of summary:", x$date.summary, "\n")
-
- cat("\nEquations:\n")
- nice_diffs <- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]])
- writeLines(strwrap(nice_diffs, exdent = 11))
-
- cat("\nData:\n")
- cat(nrow(x$data), "observations of",
- length(unique(x$data$name)), "variable(s) grouped in",
- length(unique(x$data$ds)), "datasets\n")
-
- cat("\nModel predictions using solution type", x$solution_type, "\n")
-
- cat("\nFitted in", x$time[["elapsed"]], "s using", paste(x$so@options$nbiter.saemix, collapse = ", "), "iterations\n")
-
- cat("\nVariance model: ")
- cat(switch(x$err_mod,
- const = "Constant variance",
- obs = "Variance unique to each observed variable",
- tc = "Two-component variance function"), "\n")
-
- cat("\nMean of starting values for individual parameters:\n")
- print(x$mean_dp_start, digits = digits)
-
- cat("\nFixed degradation parameter values:\n")
- if(length(x$fixed$value) == 0) cat("None\n")
- else print(x$fixed, digits = digits)
-
- cat("\nResults:\n\n")
- cat("Likelihood computed by importance sampling\n")
- print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik,
- row.names = " "), digits = digits)
-
- cat("\nOptimised parameters:\n")
- print(x$confint_trans, digits = digits)
-
- if (nrow(x$confint_trans) > 1) {
- corr <- x$corFixed
- class(corr) <- "correlation"
- print(corr, title = "\nCorrelation:", ...)
- }
-
- cat("\nRandom effects:\n")
- print(x$confint_ranef, digits = digits)
-
- cat("\nVariance model:\n")
- print(x$confint_errmod, digits = digits)
-
- if (x$transformations == "mkin") {
- cat("\nBacktransformed parameters:\n")
- print(x$confint_back, digits = digits)
- }
-
- printSFORB <- !is.null(x$SFORB)
- if(printSFORB){
- cat("\nEstimated Eigenvalues of SFORB model(s):\n")
- print(x$SFORB, digits = digits,...)
- }
-
- printff <- !is.null(x$ff)
- if(printff){
- cat("\nResulting formation fractions:\n")
- print(data.frame(ff = x$ff), digits = digits,...)
- }
-
- printdistimes <- !is.null(x$distimes)
- if(printdistimes){
- cat("\nEstimated disappearance times:\n")
- print(x$distimes, digits = digits,...)
- }
-
- if (x$print_data){
- cat("\nData:\n")
- print(format(x$data, digits = digits, ...), row.names = FALSE)
- }
-
- invisible(x)
-}

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