Compute nonparametric estimates of the chosen measure of predictiveness.
est_predictiveness(
fitted_values,
y,
a = NULL,
full_y = NULL,
type = "r_squared",
C = rep(1, length(y)),
Z = NULL,
ipc_weights = rep(1, length(C)),
ipc_fit_type = "external",
ipc_eif_preds = rep(1, length(C)),
ipc_est_type = "aipw",
scale = "identity",
na.rm = FALSE,
nuisance_estimators = NULL,
...
)A list, with: the estimated predictiveness; the estimated efficient influence function; and the predictions of the EIF based on inverse probability of censoring.
fitted values from a regression function using the observed data.
the observed outcome.
the observed treatment assignment (may be within a specified fold,
for cross-fitted estimates). Only used if type = "average_value".
the observed outcome (from the entire dataset, for cross-fitted estimates).
which parameter are you estimating (defaults to r_squared,
for R-squared-based variable importance)?
the indicator of coarsening (1 denotes observed, 0 denotes unobserved).
either NULL (if no coarsening) or a matrix-like object
containing the fully observed data.
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]).
if "external", then use ipc_eif_preds; if "SL",
fit a SuperLearner to determine the correction to the efficient influence
function.
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 correction, either "ipw" (for classical
inverse probability weighting) or "aipw" (for augmented inverse
probability weighting; the default).
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).
logical; should NA's be removed in computation?
(defaults to FALSE)
(only used if type = "average_value")
a list of nuisance function estimators on the
observed data (may be within a specified fold, for cross-fitted estimates).
Specifically: an estimator of the optimal treatment rule; an estimator of the
propensity score under the estimated optimal treatment rule; and an estimator
of the outcome regression when treatment is assigned according to the estimated optimal rule.
other arguments to SuperLearner, if ipc_fit_type = "SL".
See the paper by Williamson, Gilbert, Simon, and Carone for more details on the mathematics behind this function and the definition of the parameter of interest.