cv_MI {psfmi} | R Documentation |
Cross-validation in Multiply Imputed datasets
Description
cv_MI
Cross-validation by applying multiple single imputation runs in train
and test folds. Called by function psfmi_perform
.
Usage
cv_MI(pobj, data_orig, folds, nimp_cv, BW, p.crit, miceImp, ...)
Arguments
pobj |
An object of class |
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 |
... |
Arguments as predictorMatrix, seed, maxit, etc that can be adjusted for
the |
Author(s)
Martijn Heymans, 2020