extract_sampled_split_predictions: Extract sampled-split predictions from a CV.SuperLearner...

In bdwilliamson/npvi: Perform Inference on Algorithm-Agnostic Variable Importance

Extract sampled-split predictions from a CV.SuperLearner object

Description

Use the cross-validated Super Learner and a set of specified sample-splitting folds to extract cross-fitted predictions on separate splits of the data. This is primarily for use in cases where you have already fit a CV.SuperLearner and want to use the fitted values to compute variable importance without having to re-fit. The number of folds used in the CV.SuperLearner must be even.

Usage

extract_sampled_split_predictions(
  cvsl_obj = NULL,
  sample_splitting = TRUE,
  sample_splitting_folds = NULL,
  full = TRUE,
  preds = NULL,
  cross_fitting_folds = NULL,
  vector = TRUE
)

Arguments

cvsl_obj	An object of class "CV.SuperLearner"; must be entered unless preds is specified.
sample_splitting	logical; should we use sample-splitting or not? Defaults to TRUE.
sample_splitting_folds	A vector of folds to use for sample splitting
full	logical; is this the fit to all covariates (TRUE) or not (FALSE)?
preds	a vector of predictions; must be entered unless cvsl_obj is specified.
cross_fitting_folds	a vector of folds that were used in cross-fitting.
vector	logical; should we return a vector (where each element is the prediction when the corresponding row is in the validation fold) or a list?

Value

The predictions on validation data in each split-sample fold.

See Also

CV.SuperLearner for usage of the CV.SuperLearner function.

bdwilliamson/npvi documentation built on Feb. 13, 2023, 9:58 a.m.