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#' 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. Using
#' more than 1 core is experimental and may lead to uncontrolled forking,
#' apparently depending on the BLAS version used. 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$data[c("name", "time", "value")]))
#' names(ds) <- paste("Dataset", 6:10)
#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
#' A1 = mkinsub("SFO"))
#' \dontrun{
#' f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE)
#' library(saemix)
#' m_saemix <- saemix_model(f_mmkin, cores = 1)
#' d_saemix <- saemix_data(f_mmkin)
#' saemix_options <- list(seed = 123456,
#' save = FALSE, save.graphs = FALSE, displayProgress = FALSE,
#' nbiter.saemix = c(200, 80))
#' f_saemix <- saemix(m_saemix, d_saemix, saemix_options)
#' plot(f_saemix, plot.type = "convergence")
#' }
#' # Synthetic data with two-component error
#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
#' dt50_sfo_in <- c(80, 90, 100, 111.111, 125)
#' k_in <- log(2) / dt50_sfo_in
#'
#' SFO <- mkinmod(parent = mkinsub("SFO"))
#'
#' pred_sfo <- function(k) {
#' mkinpredict(SFO, c(k_parent = k),
#' c(parent = 100), sampling_times)
#' }
#'
#' ds_sfo_mean <- lapply(k_in, pred_sfo)
#' set.seed(123456L)
#' ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {
#' add_err(ds, sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
#' n = 1)[[1]]
#' })
#' \dontrun{
#' f_mmkin_syn <- mmkin("SFO", ds_sfo_syn, error_model = "tc", quiet = TRUE)
#' # plot(f_mmkin_syn)
#' m_saemix_tc <- saemix_model(f_mmkin_syn, cores = 1)
#' d_saemix_tc <- saemix_data(f_mmkin_syn)
#' f_saemix_tc <- saemix(m_saemix_tc, d_saemix_tc, saemix_options)
#' plot(f_saemix_tc, plot.type = "convergence")
#' }
#' @return An [saemix::SaemixModel] object.
#' @export
saemix_model <- function(object, cores = 1) {
if (nrow(object) > 1) stop("Only row objects allowed")
mkin_model <- object[[1]]$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$ds
var_raneff_0 <- apply(raneff_0, 2, var)
error.model <- switch(object[[1]]$err_mod,
const = "constant",
tc = "combined",
obs = "constant")
if (object[[1]]$err_mod == "obs") {
warning("The error model 'obs' (variance by variable) was not transferred to the saemix model")
}
error.init <- switch(object[[1]]$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))
res <- saemixModel(model_function, psi0,
"Mixed model generated from mmkin object",
transform.par = rep(0, length(degparms_optim)),
error.model = error.model, error.init = error.init,
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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