Compute nonparametric estimates of the chosen variable importance parameter, with a correction for using data-adaptive techniques to estimate the conditional means only if necessary.
vimp_point_est(
full,
reduced,
y,
folds,
weights = rep(1, length(y)),
type = "r_squared",
na.rm = FALSE
)fitted values from a regression of the outcome on the full set of covariates.
fitted values from a regression of the fitted values from the full regression on the reduced set of covariates.
the outcome.
the folds for hypothesis testing
weights for the computed influence curve (e.g., inverse probability weights for coarsened-at-random settings)
which parameter are you estimating (defaults to anova, for ANOVA-based variable importance)?
logical; should NA's be removed in computation? (defaults to FALSE)
The estimated variable importance for the given group of left-out covariates.
See the paper by Williamson, Gilbert, Simon, and Carone for more details on the mathematics behind this function and the definition of the parameter of interest.