ipd (version 0.4.0)

chen_ols: Chen & Chen OLS

Description

Helper function for Chen & Chen OLS estimation

Usage

chen_ols(X_l, Y_l, f_l, X_u, f_u, intercept = TRUE)

Value

(list): A list containing the following:

est

(vector): vector of Chen & Chen OLS regression coefficient estimates.

se

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

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.

intercept

(Logical): Do the design matrices include intercept columns? Default is TRUE.

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(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)

chen_ols(X_l, Y_l, f_l, X_u, f_u, intercept = TRUE)