ctLOO: K fold cross validation for ctStanFit objects
In cdriveraus/ctsem: Continuous Time Structural Equation Modelling

ctLOO

R Documentation

K fold cross validation for ctStanFit objects

Description

K fold cross validation for ctStanFit objects

Usage

ctLOO(
  fit,
  folds = 10,
  cores = 2,
  parallelFolds = FALSE,
  tol = 1e-05,
  subjectwise = ifelse(length(unique(fit$standata$subject)) >= folds, TRUE, FALSE),
  keepfirstobs = FALSE,
  leaveOutN = NA,
  refit = TRUE,
  casewiseApproximation = FALSE
)

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.
`tol`	tolerance for optimisation of refitted samples, can generally be more relaxed than the tolerance used for fitting initially.
`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
`casewiseApproximation`	if TRUE, use a bootstrapped gradient contributions approach to approximate the cross validation parameters – much faster but less reliable.