boot_MI | R Documentation |
boot_MI
Bootstrapping followed by Multiple Imputation for internal validation.
Called by function psfmi_perform
.
boot_MI(
pobj,
data_orig,
nboot = 10,
nimp_mice,
p.crit,
direction,
miceImp,
...
)
pobj |
An object of class |
data_orig |
dataframe of original dataset that contains missing data. |
nboot |
The number of bootstrap resamples, default is 10. |
nimp_mice |
Numerical scalar. Number of multiple imputation runs. |
p.crit |
A numerical scalar. P-value selection criterium used for backward or forward selection during validation. When set at 1, validation is done without variable selection. |
direction |
The direction of predictor selection, "BW" is for backward selection and "FW" for forward selection. |
miceImp |
Wrapper function around the |
... |
Arguments as predictorMatrix, seed, maxit, etc that can be adjusted for
the |
This function bootstraps from the incomplete dataset and applies MI in each
bootstrap sample. The model that is selected by the psfmi_lr
function is
validated. When p.crit != 1, internal validation is conducted with variable selection.
The performance measures in the multiply imputed bootstrap samples are tested in the
original multiply imputed datasets (pooled) to determine the optimism.
Martijn Heymans, 2020
psfmi_perform
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