ppi_mean: PPI Mean Estimation

In ipd: Inference on Predicted Data

PPI Mean Estimation

Description

Helper function for PPI mean estimation

Usage

ppi_mean(Y_l, f_l, f_u, alpha = 0.05, alternative = "two-sided")

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.
alpha	(scalar): type I error rate for hypothesis testing - values in (0, 1); defaults to 0.05.
alternative	(string): Alternative hypothesis. Must be one of "two-sided", "less", or "greater".

Details

Prediction Powered Inference (Angelopoulos et al., 2023) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1126/science.adi6000")}

Value

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

Examples

dat <- simdat(model = "mean")

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)

ppi_mean(Y_l, f_l, f_u)

ipd documentation built on March 11, 2026, 5:07 p.m.