PSPA Logistic Regression

Helper function for PSPA logistic regression

Usage

pspa_logistic(X_l, Y_l, f_l, X_u, f_u, weights = NA, alpha = 0.05)

Arguments

X_l

(matrix): n x p matrix of covariates in the labeled data.

Y_l

(vector): n-vector of binary 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

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 = "logistic")

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_logistic(X_l, Y_l, f_l, X_u, f_u)
#> $est
#> [1] 0.5159124 0.7573798
#> 
#> $se
#> [1] 0.1260037 0.1348756
#>