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authorJohannes Ranke <jranke@uni-bremen.de>2022-11-14 20:03:42 +0100
committerJohannes Ranke <jranke@uni-bremen.de>2022-11-14 20:03:42 +0100
commit21ad91256dc29423bd905de5c298fd23862b1f3b (patch)
tree225e2f69ed75b96e5528bcf7b8f25eb3864b75da /R
parent0db7df4bf632a013099b17d5e817a7dc7146c394 (diff)
Automatic starting parameters for saem.mmkin
For the case of mkin transformations. This gives faster convergence, and appears to avoid problems with numeric ODE solutions
Diffstat (limited to 'R')
-rw-r--r--R/saem.R29
1 files changed, 28 insertions, 1 deletions
diff --git a/R/saem.R b/R/saem.R
index 87718603..e980ed1f 100644
--- a/R/saem.R
+++ b/R/saem.R
@@ -37,14 +37,22 @@ utils::globalVariables(c("predicted", "std"))
#' @param no_random_effect Character vector of degradation parameters for
#' which there should be no variability over the groups. Only used
#' if the covariance model is not explicitly specified.
-#' @param covariance.model Will be passed to [saemix::SaemixModel()]. Per
+#' @param covariance.model Will be passed to [saemix::saemixModel()]. Per
#' default, uncorrelated random effects are specified for all degradation
#' parameters.
+#' @param omega.init Will be passed to [saemix::saemixModel()]. If using
+#' mkin transformations and the default covariance model with optionally
+#' excluded random effects, the variances of the degradation parameters
+#' are estimated using [mean_degparms], with testing of untransformed
+#' log parameters for significant difference from zero. If not using
+#' mkin transformations or a custom covariance model, the default
+#' initialisation of [saemix::saemixModel] is used for omega.init.
#' @param covariates A data frame with covariate data for use in
#' 'covariate_models', with dataset names as row names.
#' @param covariate_models A list containing linear model formulas with one explanatory
#' variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available
#' in the 'covariates' data frame.
+#' @param error.init Will be passed to [saemix::saemixModel()].
#' @param quiet Should we suppress the messages saemix prints at the beginning
#' and the end of the optimisation process?
#' @param nbiter.saemix Convenience option to increase the number of
@@ -136,9 +144,11 @@ saem.mmkin <- function(object,
conf.level = 0.6,
solution_type = "auto",
covariance.model = "auto",
+ omega.init = "auto",
covariates = NULL,
covariate_models = NULL,
no_random_effect = NULL,
+ error.init = c(3, 0.1),
nbiter.saemix = c(300, 100),
control = list(displayProgress = FALSE, print = FALSE,
nbiter.saemix = nbiter.saemix,
@@ -154,8 +164,10 @@ saem.mmkin <- function(object,
solution_type = solution_type,
transformations = transformations,
covariance.model = covariance.model,
+ omega.init = omega.init,
covariates = covariates,
covariate_models = covariate_models,
+ error.init = error.init,
no_random_effect = no_random_effect,
...)
d_saemix <- saemix_data(object, covariates = covariates, verbose = verbose)
@@ -283,7 +295,9 @@ saemix_model <- function(object, solution_type = "auto",
transformations = c("mkin", "saemix"), error_model = "auto",
degparms_start = numeric(),
covariance.model = "auto", no_random_effect = NULL,
+ omega.init = "auto",
covariates = NULL, covariate_models = NULL,
+ error.init = numeric(),
test_log_parms = FALSE, conf.level = 0.6, verbose = FALSE, ...)
{
if (nrow(object) > 1) stop("Only row objects allowed")
@@ -637,6 +651,18 @@ saemix_model <- function(object, solution_type = "auto",
covariance.model = diag(covariance_diagonal)
}
+ if (omega.init[1] == "auto") {
+ if (transformations == "mkin") {
+ degparms_eta_ini <- as.numeric( # remove names
+ mean_degparms(object,
+ random = TRUE, test_log_parms = TRUE)$eta)
+
+ omega.init <- 2 * diag(degparms_eta_ini^2)
+ } else {
+ omega.init <- matrix(nrow = 0, ncol = 0)
+ }
+ }
+
if (is.null(covariate_models)) {
covariate.model <- matrix(nrow = 0, ncol = 0) # default in saemixModel()
} else {
@@ -680,6 +706,7 @@ saemix_model <- function(object, solution_type = "auto",
verbose = verbose,
covariance.model = covariance.model,
covariate.model = covariate.model,
+ omega.init = omega.init,
...
)
attr(res, "mean_dp_start") <- degparms_optim

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