measure_anova: Estimate ANOVA decomposition-based variable importance. In vimp: Perform Inference on Algorithm-Agnostic Variable Importance

Description

Estimate ANOVA decomposition-based variable importance.

Usage

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 measure_anova( full, reduced, y, full_y = NULL, C = rep(1, length(y)), Z = NULL, ipc_weights = rep(1, length(y)), ipc_fit_type = "external", ipc_eif_preds = rep(1, length(y)), ipc_est_type = "aipw", scale = "identity", na.rm = FALSE, ... )

Arguments

 full fitted values from a regression function of the observed outcome on the full set of covariates. reduced fitted values from a regression on the reduced set of observed covariates. y the observed outcome. full_y the observed outcome (not used, defaults to NULL). C the indicator of coarsening (1 denotes observed, 0 denotes unobserved). Z either NULL (if no coarsening) or a matrix-like object containing the fully observed data. ipc_weights weights for inverse probability of coarsening (e.g., inverse weights from a two-phase sample) weighted estimation. Assumed to be already inverted (i.e., ipc_weights = 1 / [estimated probability weights]). ipc_fit_type if "external", then use ipc_eif_preds; if "SL", fit a SuperLearner to determine the correction to the efficient influence function. ipc_eif_preds if ipc_fit_type = "external", the fitted values from a regression of the full-data EIF on the fully observed covariates /outcome; otherwise, not used. ipc_est_type IPC correction, either "ipw" (for classical inverse probability weighting) or "aipw" (for augmented inverse probability weighting; the default). scale if doing an IPC correction, then the scale that the correction should be computed on (e.g., "identity"; or "logit" to logit-transform, apply the correction, and back-transform) na.rm logical; should NAs be removed in computation? (defaults to FALSE) ... other arguments to SuperLearner, if ipc_fit_type = "SL".

Value

A named list of: (1) the estimated ANOVA (based on a one-step correction) of the fitted regression functions; (2) the estimated influence function; (3) the naive ANOVA estimate; and (4) the IPC EIF predictions.

vimp documentation built on Aug. 16, 2021, 5:08 p.m.