#' Create saemix models
#'
#' The saemix function defined in this package is an S3 generic function
#' using [saemix::saemix()] as its method for [saemix::SaemixModel] objects.
#'
#' The method for mmkin row objects sets up a nonlinear mixed effects model 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].
#'
#' @param model For the default method, this is an [saemix::saemixModel] object.
#' If this is an [mmkin] row object, the [saemix::saemixModel] is created
#' internally from the [mmkin] object.
#' @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?
#' @param \dots Further parameters passed to [saemix::saemixData]
#' and [saemix::saemixModel].
#' @return An [saemix::SaemixObject].
#' @examples
#' \dontrun{
#' # We do not load the saemix package, as this would override our saemix
#' # generic
#' 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, cores = 1,
#' state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
#' f_saemix_p0_fixed <- saemix(f_mmkin_parent_p0_fixed)
#'
#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
#' f_saemix_sfo <- saemix(f_mmkin_parent["SFO", ])
#' f_saemix_fomc <- saemix(f_mmkin_parent["FOMC", ])
#' f_saemix_dfop <- saemix(f_mmkin_parent["DFOP", ])
#'
#' # We can use functions from the saemix package by prepending saemix::
#' saemix::compare.saemix(list(f_saemix_sfo, f_saemix_fomc, f_saemix_dfop))
#'
#' f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
#' f_saemix_fomc_tc <- saemix(f_mmkin_parent_tc["FOMC", ])
#' saemix::compare.saemix(list(f_saemix_fomc, f_saemix_fomc_tc))
#'
#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
#' A1 = mkinsub("SFO"))
#' f_mmkin <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)
#' f_saemix <- saemix(f_mmkin)
#'
#' }
#' @export
saemix <- function(model, data, control, ...) UseMethod("saemix")
#' @rdname saemix
#' @export
saemix.mmkin <- function(model, data,
control = list(displayProgress = FALSE, print = FALSE,
save = FALSE, save.graphs = FALSE),
verbose = FALSE, suppressPlot = TRUE, ...)
{
m_saemix <- saemix_model(model, verbose = verbose)
d_saemix <- saemix_data(model, verbose = verbose)
if (suppressPlot) {
# We suppress the log-likelihood curve that saemix currently
# produces at the end of the fit by plotting to a file
# that we discard afterwards
tmp <- tempfile()
png(tmp)
}
result <- saemix::saemix(m_saemix, d_saemix, control)
if (suppressPlot) {
dev.off()
unlink(tmp)
}
class(result) <- c("saemix.mmkin", "saemix")
return(result)
}
#' @rdname saemix
#' @param cores The number of cores to be used for multicore processing using
#' [parallel::mclapply()]. Using more than 1 core is experimental and may
#' lead to uncontrolled forking, apparently depending on the BLAS version
#' used.
#' @return An [saemix::SaemixModel] object.
#' @export
saemix_model <- function(object, cores = 1, verbose = FALSE, ...) {
if (nrow(object) > 1) stop("Only row objects allowed")
mkin_model <- object[[1]]$mkinmod
solution_type <- object[[1]]$solution_type
degparms_optim <- mean_degparms(object)
degparms_fixed <- object[[1]]$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$', '', odeini_fixed_parm_names)
model_function <- FALSE
if (length(mkin_model$spec) == 1 & mkin_model$use_of_ff == "max") {
parent_type <- mkin_model$spec[[1]]$type
if (length(odeini_fixed) == 1) {
if (parent_type == "SFO") {
stop("saemix needs at least two parameters to work on.")
}
if (parent_type == "FOMC") {
model_function <- function(psi, id, xidep) {
odeini_fixed / (xidep[, "time"]/exp(psi[id, 2]) + 1)^exp(psi[id, 1])
}
}
if (parent_type == "DFOP") {
model_function <- function(psi, id, xidep) {
g <- plogis(psi[id, 3])
t = xidep[, "time"]
odeini_fixed * (g * exp(- exp(psi[id, 1]) * t) +
(1 - g) * exp(- exp(psi[id, 2]) * t))
}
}
if (parent_type == "HS") {
model_function <- function(psi, id, xidep) {
tb <- exp(psi[id, 3])
t = xidep[, "time"]
k1 = exp(psi[id, 1])
odeini_fixed * ifelse(t <= tb,
exp(- k1 * t),
exp(- k1 * t) * exp(- exp(psi[id, 2]) * (t - tb)))
}
}
} else {
if (length(odeparms_fixed) == 0) {
if (parent_type == "SFO") {
model_function <- function(psi, id, xidep) {
psi[id, 1] * exp( - exp(psi[id, 2]) * xidep[, "time"])
}
}
if (parent_type == "FOMC") {
model_function <- function(psi, id, xidep) {
psi[id, 1] / (xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 2])
}
}
if (parent_type == "DFOP") {
model_function <- function(psi, id, xidep) {
g <- plogis(psi[id, 4])
t = xidep[, "time"]
psi[id, 1] * (g * exp(- exp(psi[id, 2]) * t) +
(1 - g) * exp(- exp(psi[id, 3]) * t))
}
}
if (parent_type == "HS") {
model_function <- function(psi, id, xidep) {
tb <- exp(psi[id, 4])
t = xidep[, "time"]
k1 = exp(psi[id, 2])
psi[id, 1] * ifelse(t <= tb,
exp(- k1 * t),
exp(- k1 * t) * exp(- exp(psi[id, 3]) * (t - tb)))
}
}
}
}
}
if (!is.function(model_function)) {
model_function <- function(psi, id, xidep) {
uid <- unique(id)
res_list <- parallel::mclapply(uid, function(i) {
transparms_optim <- psi[i, ]
names(transparms_optim) <- names(degparms_optim)
odeini_optim <- transparms_optim[odeini_optim_parm_names]
names(odeini_optim) <- gsub('_0$', '', 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]]$transform_fractions)
odeparms <- c(odeparms_optim, odeparms_fixed)
xidep_i <- subset(xidep, id == i)
if (solution_type == "analytical") {
out_values <- mkin_model$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)))
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)
}
}
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$errparms)), b = 1))
psi0_matrix <- matrix(degparms_optim, nrow = 1)
colnames(psi0_matrix) <- names(degparms_optim)
res <- saemix::saemixModel(model_function,
psi0 = psi0_matrix,
"Mixed model generated from mmkin object",
transform.par = transform.par,
error.model = error.model,
error.init = error.init,
verbose = verbose
)
return(res)
}
#' @rdname saemix
#' @return An [saemix::SaemixData] object.
#' @export
saemix_data <- function(object, verbose = FALSE, ...) {
if (nrow(object) > 1) stop("Only row objects allowed")
ds_names <- colnames(object)
ds_list <- lapply(object, function(x) x$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)
}
