ipd (version 0.4.0)

ppi_a_ols: PPI "All" OLS

Description

Helper function for PPI "All" for OLS estimation

Usage

ppi_a_ols(X_l, Y_l, f_l, X_u, f_u, w_l = NULL, w_u = NULL)

Value

(list): A list containing the following:

est

(vector): vector of PPI OLS regression coefficient estimates.

se

(vector): vector of standard errors of the coefficients.

rectifier_est

(vector): vector of the rectifier OLS regression coefficient estimates.

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.

w_l

(ndarray, optional): Sample weights for the labeled data set. Defaults to a vector of ones.

w_u

(ndarray, optional): Sample weights for the unlabeled data set. Defaults to a vector of ones.

Details

Another look at statistical inference with machine learning-imputed data (Gronsbell et al., 2026) tools:::Rd_expr_doi("10.48550/arXiv.2411.19908")

Examples

dat <- simdat()

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)

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