ctLOO: K fold cross validation for ctStanFit objects

In ctsem: Continuous Time Structural Equation Modelling

K fold cross validation for ctStanFit objects

Description

K fold cross validation for ctStanFit objects

Usage

ctLOO(
  fit,
  folds = 10,
  cores = 2,
  parallelFolds = FALSE,
  subjectwise = ifelse(length(unique(fit$standata$subject)) > folds, TRUE, FALSE),
  keepfirstobs = FALSE,
  leaveOutN = NA,
  refit = TRUE
)

Arguments

fit	ctStanfit object
folds	Number of cross validation splits to use – 10 folds implies that the model is re-fit 10 times, each time to a data set with 1/10 of the observations randomly removed.
cores	Number of processor cores to use.
parallelFolds	compute folds in parallel or use cores to finish single folds faster. parallelFolds will use folds times as much memory.
subjectwise	drop random subjects instead of data rows?
keepfirstobs	do not drop first observation (more stable estimates)
leaveOutN	if a positive integer is given, the folds argument is ignored and instead the folds are calculated by leaving out every Nth row from the data when fitting. Leaving 2 out would result in 3 folds (starting at rows 1,2,3), each containing one third of the data.
refit	if FALSE, do not optimise parameters for the new data set, just compute the likelihoods etc from the original parameters

Value

list

Examples

 
ctLOO(ctstantestfit)

ctsem documentation built on Nov. 2, 2023, 6:03 p.m.