boot_MI function

Bootstrap validation in Multiply Imputed datasets

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,
  ...
)

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.
• 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 mice function.
• ...: Arguments as predictorMatrix, seed, maxit, etc that can be adjusted for the mice function.

Details

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.

See Also

psfmi_perform

Author(s)

Martijn Heymans, 2020