Reintroduce saemix helper functions

1 files changed, 134 insertions, 0 deletions

diff --git a/R/saemix.R b/R/saemix.R
new file mode 100644
index 00000000..24c0f0d0
--- /dev/null
+++ b/R/saemix.R
@@ -0,0 +1,134 @@
+#' 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)
+}