PostPI Logistic Regression (Bootstrap Correction)

Helper function for PostPI logistic regression (bootstrap correction)

Usage

postpi_boot_logistic(X_l, Y_l, f_l, X_u, f_u, nboot = 100, se_type = "par")

Arguments

X_l

(matrix): n x p matrix of covariates in the labeled data.

Y_l

(vector): n-vector of labeled outcomes.

f_l

(vector): n-vector of predictions in the labeled data.

X_u

(matrix): N x p matrix of covariates in the unlabeled data.

f_u

(vector): N-vector of predictions in the unlabeled data.

nboot

(integer): Number of bootstrap samples. Defaults to 100.

se_type

(string): Which method to calculate the standard errors. Options include "par" (parametric) or "npar" (nonparametric). Defaults to "par".

Value

A list of outputs: estimate of inference model parameters and corresponding standard error based on both parametric and non-parametric bootstrap methods.

Details

Methods for correcting inference based on outcomes predicted by machine learning (Wang et al., 2020) https://www.pnas.org/doi/abs/10.1073/pnas.2001238117

Examples

dat <- simdat(model = "logistic")
#> Loading required package: ggplot2
#> Loading required package: lattice

form <- Y - f ~ X1

X_l <- model.matrix(form, data = dat[dat$set_label == "labeled", ])

Y_l <- dat[dat$set_label == "labeled", all.vars(form)[1]] |>
  matrix(ncol = 1)

f_l <- dat[dat$set_label == "labeled", all.vars(form)[2]] |>
  matrix(ncol = 1)

X_u <- model.matrix(form, data = dat[dat$set_label == "unlabeled", ])

f_u <- dat[dat$set_label == "unlabeled", all.vars(form)[2]] |>
  matrix(ncol = 1)

postpi_boot_logistic(X_l, Y_l, f_l, X_u, f_u, nboot = 200)
#> $est
#> [1] 0.7048938 0.4037607
#> 
#> $se
#> [1] 0.1255146 0.1364894
#>