cv_scrnp: Compute CVTML estimates of cross-validated AUC

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

Description

TO DO: Add description

Usage

cv_scrnp(Y, X, K = 10, sens = 0.95, learner = "glm_wrapper",
  nested_cv = TRUE, nested_K = K - 1, parallel = FALSE,
  max_cvtmle_iter = 10, cvtmle_ictol = 1/length(Y),
  quantile_type = 8, prediction_list = NULL, ...)

Arguments

Y	A numeric vector of outcomes, assume to equal 0 or 1.
X	A data.frame or matrix of variables for prediction.
K	The number of cross-validation folds (default is 10).
sens	The sensitivity constraint imposed on the rate of negative prediction (see description).
learner	A wrapper that implements the desired method for building a prediction algorithm. See TODO: ADD DOCUMENTATION FOR WRITING
nested_cv	A boolean indicating whether nested cross validation should be used to estimate the distribution of the prediction function. Default (TRUE) is best choice for aggressive learner's, while FALSE is reasonable for smooth learner's (e.g., logistic regression).
nested_K	If nested cross validation is used, how many inner folds should there be? Default (K-1) affords quicker computation by reusing training fold learner fits.
parallel	A boolean indicating whether prediction algorithms should be trained in parallel. Default to FALSE.
max_cvtmle_iter	Maximum number of iterations for the bias correction step of the CV-TMLE estimator (default 10).
cvtmle_ictol	The CV-TMLE will iterate max_cvtmle_iter is reached or mean of cross-validated efficient influence function is less than cvtmle_cvtmle_ictol.
quantile_type	Type of quantile estimator to be used. See quantile for description.
prediction_list	For power users: a list of predictions made by learner that has a format compatible with cvauc.
...	Other arguments, not currently used

Value

A list TO DO: more documentation here.

Examples

n <- 200
p <- 10
X <- data.frame(matrix(rnorm(n*p), nrow = n, ncol = p))
Y <- rbinom(n, 1, plogis(X[,1] + X[,10]))
fit <- cv_scrnp(Y = Y, X = X, K = 5, nested_cv = FALSE, learner = "glm_wrapper")

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