Helper function for PSPA Poisson regression
Arguments
- X_l
(matrix): n x p matrix of covariates in the labeled data.
- Y_l
(vector): n-vector of count labeled outcomes.
- f_l
(vector): n-vector of binary predictions in the labeled data.
- X_u
(matrix): N x p matrix of covariates in the unlabeled data.
- f_u
(vector): N-vector of binary predictions in the unlabeled data.
- weights
(array): p-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
Examples
dat <- simdat(model = "poisson")
form <- Y - f ~ X1
X_l <- model.matrix(form, data = dat[dat$set == "labeled",])
Y_l <- dat[dat$set == "labeled", all.vars(form)[1]] |> matrix(ncol = 1)
f_l <- dat[dat$set == "labeled", all.vars(form)[2]] |> matrix(ncol = 1)
X_u <- model.matrix(form, data = dat[dat$set == "unlabeled",])
f_u <- dat[dat$set == "unlabeled", all.vars(form)[2]] |> matrix(ncol = 1)
pspa_poisson(X_l, Y_l, f_l, X_u, f_u)
#> $est
#> [1] 2.9242392 0.7469597
#>
#> $se
#> [1] 0.3877309 0.1713424
#>