check_fitted_values: Check pre-computed fitted values for call to vim, cv_vim, or...

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

Check pre-computed fitted values for call to vim, cv_vim, or sp_vim

Description

Check pre-computed fitted values for call to vim, cv_vim, or sp_vim

Usage

check_fitted_values(
  Y = NULL,
  f1 = NULL,
  f2 = NULL,
  cross_fitted_f1 = NULL,
  cross_fitted_f2 = NULL,
  sample_splitting_folds = NULL,
  cross_fitting_folds = NULL,
  cross_fitted_se = TRUE,
  V = NULL,
  ss_V = NULL,
  cv = FALSE
)

Arguments

Y	the outcome
f1	estimator of the population-optimal prediction function using all covariates
f2	estimator of the population-optimal prediction function using the reduced set of covariates
cross_fitted_f1	cross-fitted estimator of the population-optimal prediction function using all covariates
cross_fitted_f2	cross-fitted estimator of the population-optimal prediction function using the reduced set of covariates
sample_splitting_folds	the folds for sample-splitting (used for hypothesis testing)
cross_fitting_folds	the folds for cross-fitting (used for point estimates of variable importance in cv_vim and sp_vim)
cross_fitted_se	logical; should cross-fitting be used to estimate standard errors?
V	the number of cross-fitting folds
ss_V	the number of folds for CV (if sample_splitting is TRUE)
cv	a logical flag indicating whether or not to use cross-fitting

Details

Ensure that inputs to vim, cv_vim, and sp_vim follow the correct formats.

Value

None. Called for the side effect of stopping the algorithm if any inputs are in an unexpected format.

bdwilliamson/nova documentation built on Feb. 1, 2024, 10:04 p.m.