cv_MI function

Cross-validation in Multiply Imputed datasets

cv_MI Cross-validation by applying multiple single imputation runs in train and test folds. Called by function psfmi_perform.

cv_MI(pobj, data_orig, folds, nimp_cv, BW, p.crit, miceImp, ...)

Arguments

• pobj: An object of class pmods (pooled models), produced by a previous call to psfmi_lr.
• data_orig: dataframe of original dataset that contains missing data.
• folds: The number of folds, default is 3.
• nimp_cv: Numerical scalar. Number of (multiple) imputation runs.
• BW: If TRUE backward selection is conducted within cross-validation. Default is FALSE.
• p.crit: A numerical scalar. P-value selection criterium used for backward during cross-validation. When set at 1, pooling and internal validation is done without backward selection.
• miceImp: Wrapper function around the mice function.
• ...: Arguments as predictorMatrix, seed, maxit, etc that can be adjusted for the mice function.

See Also

psfmi_perform

Author(s)

Martijn Heymans, 2020