# R/measure_mse.R In vimp: Perform Inference on Algorithm-Agnostic Variable Importance

#### Documented in measure_mse

```#' Estimate mean squared error
#'
#' Compute nonparametric estimate of mean squared error.
#'
#' @param fitted_values fitted values from a regression function using the
#'   observed data (may be within a specified fold, for cross-fitted estimates).
#' @param y the observed outcome (may be within a specified fold, for
#'   cross-fitted estimates).
#' @param full_y the observed outcome (not used; defaults to \code{NULL}).
#' @param C the indicator of coarsening (1 denotes observed, 0 denotes
#'   unobserved).
#' @param Z either \code{NULL} (if no coarsening) or a matrix-like object
#'   containing the fully observed data.
#' @param 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]).
#' @param ipc_fit_type if "external", then use \code{ipc_eif_preds}; if "SL",
#'   fit a SuperLearner to determine the correction to the efficient
#'   influence function.
#' @param ipc_eif_preds if \code{ipc_fit_type = "external"}, the fitted values
#'   from a regression of the full-data EIF on the fully observed
#'   covariates/outcome; otherwise, not used.
#' @param ipc_est_type IPC correction, either \code{"ipw"} (for classical
#'   inverse probability weighting) or \code{"aipw"} (for augmented inverse
#'   probability weighting; the default).
#' @param 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).
#' @param na.rm logical; should \code{NA}s be removed in computation?
#'   (defaults to \code{FALSE})
#' @param ... other arguments to SuperLearner, if \code{ipc_fit_type = "SL"}.
#'
#' @return A named list of: (1) the estimated mean squared error of the fitted
#'   regression function; (2) the estimated influence function; and
#'   (3) the IPC EIF predictions.
#' @importFrom SuperLearner predict.SuperLearner SuperLearner
#' @export
measure_mse <- function(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, ...) {
# compute the EIF: if there is coarsening, do a correction
if (!all(ipc_weights == 1)) {
# observed mse
obs_mse <- mean(((y - fitted_values) ^ 2), na.rm = na.rm)
obs_grad <- ((y - fitted_values) ^ 2) - obs_mse
# if IPC EIF preds aren't entered, estimate the regression
if (ipc_fit_type != "external") {
ipc_eif_mod <- SuperLearner::SuperLearner(
Y = obs_grad, X = subset(Z, C == 1, drop = FALSE),
method = "method.CC_LS", ...
)
ipc_eif_preds <- SuperLearner::predict.SuperLearner(
ipc_eif_mod, newdata = Z, onlySL = TRUE
)\$pred
}
weighted_obs_grad <- rep(0, length(C))
weighted_obs_grad[C == 1] <- obs_grad * ipc_weights[C == 1]
grad <- weighted_obs_grad - (C * ipc_weights - 1) * ipc_eif_preds
obs_est <- mean((1 * ipc_weights[C == 1]) * (y - fitted_values) ^ 2,
na.rm = na.rm)
if (ipc_est_type == "ipw") {
est <- scale_est(obs_est, rep(0, length(grad)), scale = scale)
} else {
est <- scale_est(obs_est, grad, scale = scale)
}
} else {
est <- mean((y - fitted_values)^2, na.rm = na.rm)
# influence curves
grad <- (y - fitted_values)^2 - est
}
return(list(point_est = est, eif = grad, ipc_eif_preds = ipc_eif_preds))
}
```

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