PSPA Mean Estimation — pspa

Helper function for PSPA mean estimation

Usage

pspa_mean(Y_l, f_l, f_u, 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.
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.

Value

A list of outputs: estimate of inference model parameters and corresponding standard error.

Details

Post-prediction adaptive inference (Miao et al., 2023) https://arxiv.org/abs/2311.14220

Examples


dat <- simdat(model = "mean")

form <- Y - f ~ 1

Y_l <- dat[dat$set == "labeled",   all.vars(form)[1]] |> matrix(ncol = 1)

f_l <- dat[dat$set == "labeled",   all.vars(form)[2]] |> matrix(ncol = 1)

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

pspa_mean(Y_l, f_l, f_u)
#> $est
#> [1] 1.077107
#> 
#> $se
#> [1] 0.05781881
#>