add_vimp {tidyhte}

Adds variable importance information

Description

This adds a variable importance quantity of interest to the outputs.

Usage

add_vimp(hte_cfg, sample_splitting = TRUE, linear_only = FALSE)

Arguments

hte_cfg

HTE_cfg object to update.

sample_splitting

Logical indicating whether to use sample splitting or not. Choosing not to use sample splitting means that inference will only be valid for moderators with non-null importance.

linear_only

Logical indicating whether the variable importance should use only a single linear-only model. Variable importance measure will only be consistent for the population quantity if the true model of pseudo-outcomes is linear.

Value

Updated HTE_cfg object

References

• Williamson, B. D., Gilbert, P. B., Carone, M., & Simon, N. (2021). Nonparametric variable importance assessment using machine learning techniques. Biometrics, 77(1), 9-22.

• Williamson, B. D., Gilbert, P. B., Simon, N. R., & Carone, M. (2021). A general framework for inference on algorithm-agnostic variable importance. Journal of the American Statistical Association, 1-14.

Examples

library("dplyr")
basic_config() %>%
   add_vimp(sample_splitting = FALSE) -> hte_cfg