MI_cv_naive: Naive method for Cross-validation in Multiply Imputed...

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

Naive method for Cross-validation in Multiply Imputed datasets

Description

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

Usage

MI_cv_naive(pobj, folds = 3, p.crit = 1, BW = FALSE, cv_naive_appt = TRUE)

Arguments

pobj	An object of class pmods (pooled models), produced by a previous call to psfmi_lr.
folds	The number of folds, default is 3.
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.
cv_naive_appt	Default is TRUE for showing the cross-validation apparent (train) and test results. Set to FALSE to only give test results.

Author(s)

Martijn Heymans, 2020

See Also

psfmi_perform

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