measure_sensitivity: Estimate the sensitivity

In vimp: Perform Inference on Algorithm-Agnostic Variable Importance

Estimate the sensitivity

Description

Compute nonparametric estimate of sensitivity.

Usage

measure_sensitivity(
  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 = "logit",
  na.rm = FALSE,
  nuisance_estimators = NULL,
  a = NULL,
  cutoff = 0.5,
  ...
)

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)
nuisance_estimators	not used; for compatibility with measure_average_value.
a	not used; for compatibility with measure_average_value.
cutoff	The risk score cutoff at which the specificity is evaluated. Fitted values above cutoff are interpreted as positive tests.
...	other arguments to SuperLearner, if ipc_fit_type = "SL".

Value

A named list of: (1) the estimated sensitivity of the fitted regression function using specified cutoff; (2) the estimated influence function; and (3) the IPC EIF predictions.

vimp documentation built on Aug. 29, 2025, 5:17 p.m.