ipd (version 0.1.4)

postpi_boot_logistic: PostPI Logistic Regression (Bootstrap Correction)

Description

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",
  seed = NULL
)

Value

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

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".

seed

(optional) An integer seed for random number generation.

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")

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)