postpi_analytic_ols: PostPI OLS (Analytic Correction)

In ipd: Inference on Predicted Data

PostPI OLS (Analytic Correction)

Description

Helper function for PostPI OLS estimation (analytic correction)

Usage

postpi_analytic_ols(X_l, Y_l, f_l, X_u, f_u, original = FALSE)

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.
original	(boolean): Logical argument to use original method from Wang et al. (2020). Defaults to FALSE; TRUE retained for posterity.

Details

Methods for correcting inference based on outcomes predicted by machine learning (Wang et al., 2020) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.2001238117")}

Value

A list of outputs: estimate of the inference model parameters and corresponding standard error estimate.

Examples

dat <- simdat(model = "ols")

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_analytic_ols(X_l, Y_l, f_l, X_u, f_u)

ipd documentation built on March 11, 2026, 5:07 p.m.