Cross-validation of conditional item infit

Creates k random folds using rsample::vfold_cv(); "V-fold cross-validation (also known as k-fold cross-validation) randomly splits the data into V groups of roughly equal size (called "folds"). A resample of the analysis data consists of V-1 of the folds", see https://rsample.tidymodels.org/reference/vfold_cv.html

Usage

RIinfitKfold(
  data,
  k = 5,
  output = "table",
  sim_iter = 100,
  sim_cpu = 4,
  cutoff = c(0.001, 0.999)
)

Arguments

data

Dataframe with item responses

k

Number of folds to use (default is 5)

output

Default table, options dataframe and raw

sim_iter

Number of iterations (depends on sample size)

sim_cpu

Number of CPU cores to use

cutoff

Truncation at percentile values (see ?RIitemfit)

Details

Each V-1 dataset is used both for calculating item fit and expected item fit critical values (using RIgetfit()). If output = "table" (default), results are summarized indicating upper and lower bounds for each item's calculated infit and simulated expected range. This is based on all V-1 fold combinations.