pspa_logistic: PSPA Logistic Regression

In ipd: Inference on Predicted Data

PSPA Logistic Regression

Description

Helper function for PSPA logistic regression

Usage

pspa_logistic(X_l, Y_l, f_l, X_u, f_u, weights = NA, alpha = 0.05)

Arguments

X_l	(matrix): n x p matrix of covariates in the labeled data.
Y_l	(vector): n-vector of binary labeled outcomes.
f_l	(vector): n-vector of binary predictions in the labeled data.
X_u	(matrix): N x p matrix of covariates in the unlabeled data.
f_u	(vector): N-vector of binary predictions in the unlabeled data.
weights	(array): p-dimensional array of weights vector for variance reduction. PSPA will estimate the weights if not specified.
alpha	(scalar): type I error rate for hypothesis testing - values in (0, 1); defaults to 0.05

Details

Post-prediction adaptive inference (Miao et al. 2023) https://arxiv.org/abs/2311.14220

Value

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

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)

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

ipd documentation built on April 4, 2025, 4:41 a.m.