MLE_logit_bootstrap: Bootstrap estimator for logistic regression with binary...

In philohistoria/CCER: Correct Machine Learning Classification Error in Next-Step Regression

Description

This functions takes binary classification as outcome variables, apply MLE estimator, and returned corrected coefficient estimates.

Usage

MLE_logit_bootstrap(
  formula,
  data,
  tl,
  pl,
  nboot = 10000,
  return_boot_samples = FALSE
)

Arguments

formula	R formula, such as y ~ x
data	a data.frame object
tl	vector of true values
pl	vector of predicted values; needs to be of the same dimension of the vevtor of true values.
nboot	number of bootstrap samples

Value

_boot_samples whether to return raw bootstrap estimates

if return_boot_samples is false, return mean/standard error/quantile statistics from bootstrap sample only; otherwise return a list, the first element of which is mean/standard error/quantile statistics from bootstrap sample , and the second ele,ent of which is nboot samples from bootstrap draws

philohistoria/CCER documentation built on Dec. 22, 2021, 7:50 a.m.