# measure_accuracy: Estimate the classification accuracy In bdwilliamson/nova: Perform Inference on Algorithm-Agnostic Variable Importance

 measure_accuracy R Documentation

## Estimate the classification accuracy

### Description

Compute nonparametric estimate of classification accuracy.

### Usage

``````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 = "logit",
na.rm = FALSE,
nuisance_estimators = NULL,
a = NULL,
...
)
``````

### 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 `NA`s 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`. `...` 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.

bdwilliamson/nova documentation built on Feb. 1, 2024, 10:04 p.m.