pspa_quantile: PSPA Quantile Estimation

In ipd: Inference on Predicted Data

PSPA Quantile Estimation

Description

Helper function for PSPA quantile estimation

Usage

pspa_quantile(Y_l, f_l, f_u, q, weights = NA, alpha = 0.05)

Arguments

Y_l	(vector): n-vector of labeled outcomes.
f_l	(vector): n-vector of predictions in the labeled data.
f_u	(vector): N-vector of predictions in the unlabeled data.
q	(float): Quantile to estimate. Must be in the range (0, 1).
weights	(array): 1-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 = "quantile")

form <- Y - f ~ 1

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)

f_u <- dat[dat$set_label == "unlabeled", all.vars(form)[2]] |> matrix(ncol = 1)

pspa_quantile(Y_l, f_l, f_u, q = 0.5)

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