cv_MI_RR: Cross-validation in Multiply Imputed datasets

In psfmi: Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets

Cross-validation in Multiply Imputed datasets

Description

cv_MI_RR Cross-validation by applying multiply imputed pooled models in train and test folds. Called by function psfmi_perform.

Usage

cv_MI_RR(pobj, data_orig, folds, nimp_mice, p.crit, BW, 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_mice	Numerical scalar. Number of multiple imputation runs.
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.
BW	If TRUE backward selection is conducted within cross-validation. Default is FALSE.
miceImp	Wrapper function around the mice function.
...	Arguments as predictorMatrix, seed, maxit, etc that can be adjusted for the mice function.

Author(s)

Martijn Heymans, 2020

See Also

psfmi_perform

psfmi documentation built on July 9, 2023, 7:02 p.m.