longitudinal_folds: Longitudinal folds

In LEGIT: Latent Environmental & Genetic InTeraction (LEGIT) Model

Description

Function to create folds adequately for longitudinal datasets by forcing every observation with the same id to be in the same fold. Can be used with LEGIT_cv to make sure that the cross-validation folds are appropriate when using longitudinal data.

Usage

longitudinal_folds(
  cv_iter = 1,
  cv_folds = 10,
  id,
  formula = NULL,
  data = NULL,
  data_needed = NULL,
  print = TRUE
)

Arguments

cv_iter	Number of cross-validation iterations (Default = 1).
cv_folds	Number of cross-validation folds (Default = 10).
id	Factor vector containing the id number of each observation.
formula	Optional Model formula. If data and formula are provided, only the non-missing observations will be used when creating the folds (Put "formula" here if you have missing data).
data	Optional data.frame used for the formula. If data and formula are provided, only the non-missing observations will be used when creating the folds (Put "data" here if you have missing data).
data_needed	Optional data.frame with variables that have to be included (Put "cbind(genes,env)"" or "latent_var" here if you have missing data).
print	If FALSE, nothing except warnings will be printed. (Default = TRUE).

Value

Returns a list of vectors containing the fold number for each observation

Examples

train = example_2way(500, 1, seed=777)
# Assuming it's longitudinal with 4 timepoints, even though it's not
id = factor(rep(1:125,each=4))
fit_cv = LEGIT_cv(train$data, train$G, train$E, y ~ G*E, folds=longitudinal_folds(1,10, id))

LEGIT documentation built on Jan. 12, 2022, 1:08 a.m.