ppi_plusplus_mean {ipd} | R Documentation |
PPI++ Mean Estimation
Description
Helper function for PPI++ mean estimation
Usage
ppi_plusplus_mean(
Y_l,
f_l,
f_u,
alpha = 0.05,
alternative = "two-sided",
lhat = NULL,
coord = NULL,
w_l = NULL,
w_u = NULL
)
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
|
lhat |
(float, optional): Power-tuning parameter (see
https://arxiv.org/abs/2311.01453). The default value, |
coord |
(int, optional): Coordinate for which to optimize
|
w_l |
(ndarray, optional): Sample weights for the labeled data set. Defaults to a vector of ones. |
w_u |
(ndarray, optional): Sample weights for the unlabeled data set. Defaults to a vector of ones. |
Details
PPI++: Efficient Prediction Powered Inference (Angelopoulos et al., 2023) https://arxiv.org/abs/2311.01453'
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_plusplus_mean(Y_l, f_l, f_u)