ppi_quantile: PPI Quantile Estimation

In ipd: Inference on Predicted Data

PPI Quantile Estimation

Description

Helper function for PPI quantile estimation

Usage

ppi_quantile(Y_l, f_l, f_u, q, alpha = 0.05, exact_grid = FALSE)

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).
alpha	(scalar): type I error rate for hypothesis testing - values in (0, 1); defaults to 0.05.
exact_grid	(bool, optional): Whether to compute the exact solution (TRUE) or an approximate solution based on a linearly spaced grid of 5000 values (FALSE).

Details

Prediction Powered Inference (Angelopoulos et al., 2023) https://www.science.org/doi/10.1126/science.adi6000

Value

tuple: Lower and upper bounds of the prediction-powered confidence interval for the quantile.

Examples

dat <- simdat(model = "quantile")

form <- Y - f ~ X1

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)

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

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