boot_scrnp: Compute the bootstrap-corrected estimator of SCRNP.

In benkeser/predtmle: Small sample estimators of cross-validated prediction metrics

Description

This estimator is computed by re-sampling with replacement (i.e., bootstrap sampling) from the data. The SCRNP is computed for the learner trained on the full data. The SCRNP is then computed for the learner trained on each bootstrap sample. The average difference between the full data-trained learner and the bootstrap-trained learner is computed to estimate the bias in the full-data-estimated SCRNP. The final estimate of SCRNP is given by the difference in the full-data SCRNP and the estimated bias.

Usage

boot_scrnp(Y, X, B = 200, learner = "glm_wrapper", sens = 0.95,
  correct632 = FALSE, ...)

Arguments

Y	A numeric vector of outcomes, assume to equal 0 or 1.
X	A data.frame of variables for prediction.
B	The number of bootstrap samples.
learner	A wrapper that implements the desired method for building a prediction algorithm. See TODO: ADD DOCUMENTATION FOR WRITING
sens	The sensitivity constraint to use.
correct632	A boolean indicating whether to use the .632 correction.
...	Other options, not currently used.

Value

A list with $scrnp the bootstrap-corrected estimate of SCRNP.

Examples

# simulate data
X <- data.frame(x1 = rnorm(50))
Y <- rbinom(50, 1, plogis(X$x1))
# compute bootstrap estimate of scrnp for logistic regression
# use small B for fast run
boot <- boot_scrnp(Y = Y, X = X, B = 25, learner = "glm_wrapper")

benkeser/predtmle documentation built on May 20, 2019, 5:41 p.m.