ipd (version 0.1.4)

postpi_analytic_ols: 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, scale_se = TRUE, n_t = Inf)

Value

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

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.

scale_se

(boolean): Logical argument to scale relationship model error variance. Defaults to TRUE; FALSE option is retained for posterity.

n_t

(integer, optional) Size of the dataset used to train the prediction function (necessary if n_t < nrow(X_l). Defaults to Inf.

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 = "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)