Logistic Regression Gradient and Hessian

Computes the statistics needed for the logstic regression-based prediction-powered inference.

Usage

logistic_get_stats(
  est,
  X_l,
  Y_l,
  f_l,
  X_u,
  f_u,
  w_l = NULL,
  w_u = NULL,
  use_u = TRUE
)

Arguments

est

(vector): Point estimates of the coefficients.

X_l

(matrix): Covariates for the labeled data set.

Y_l

(vector): Labels for the labeled data set.

f_l

(vector): Predictions for the labeled data set.

X_u

(matrix): Covariates for the unlabeled data set.

f_u

(vector): Predictions for the unlabeled data set.

w_l

(vector, optional): Sample weights for the labeled data set.

w_u

(vector, optional): Sample weights for the unlabeled data set.

use_u

(bool, optional): Whether to use the unlabeled data set.

Value

(list): A list containing the following:

grads

(matrix): n x p matrix gradient of the loss function with respect to the coefficients.

grads_hat

(matrix): n x p matrix gradient of the loss function with respect to the coefficients, evaluated using the labeled predictions.

grads_hat_unlabeled

(matrix): N x p matrix gradient of the loss function with respect to the coefficients, evaluated using the unlabeled predictions.

inv_hessian

(matrix): p x p matrix inverse Hessian of the loss function with respect to the coefficients.

Examples

dat <- simdat(model = "logistic")
#> Loading required package: ggplot2
#> Loading required package: lattice

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)

est <- ppi_plusplus_logistic_est(X_l, Y_l, f_l, X_u, f_u)

stats <- logistic_get_stats(est, X_l, Y_l, f_l, X_u, f_u)