pspa_quantile {ipd} | R Documentation |
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)
[Package ipd version 0.1.4 Index]