measure_accuracy: Estimate the classification accuracy In vimp: Perform Inference on Algorithm-Agnostic Variable Importance

Description

Compute nonparametric estimate of classification accuracy.

Usage

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 measure_accuracy( 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 (may be within a specified fold, for cross-fitted estimates). y the observed outcome (may be within a specified fold, for cross-fitted estimates). 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 (IPC) (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 IPC 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 classification accuracy 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.