# measure_cross_entropy: Estimate the cross-entropy In vimp: Perform Inference on Algorithm-Agnostic Variable Importance

## Description

Compute nonparametric estimate of cross-entropy.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```measure_cross_entropy( fitted_values, 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

 `fitted_values` fitted values from a regression function using the observed data. `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 `NA`s be removed in computation? (defaults to `FALSE`) `...` other arguments to SuperLearner, if `ipc_fit_type = "SL"`.

## Value

A named list of: (1) the estimated cross-entropy of the fitted regression function; (2) the estimated influence function; and (3) the IPC EIF predictions.

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