ppi_plusplus_logistic: PPI++ Logistic Regression

In ipd: Inference on Predicted Data

PPI++ Logistic Regression

Description

Helper function for PPI++ logistic regression

Usage

ppi_plusplus_logistic(
  X_l,
  Y_l,
  f_l,
  X_u,
  f_u,
  lhat = NULL,
  coord = NULL,
  opts = NULL,
  w_l = NULL,
  w_u = NULL
)

Arguments

X_l	(matrix): n x p matrix of covariates in the labeled data.
Y_l	(vector): n-vector of labeled outcomes.
f_l	(vector): n-vector of predictions in the labeled data.
X_u	(matrix): N x p matrix of covariates in the unlabeled data.
f_u	(vector): N-vector of predictions in the unlabeled data.
lhat	(float, optional): Power-tuning parameter (see https://arxiv.org/abs/2311.01453). The default value, NULL, will estimate the optimal value from the data. Setting lhat = 1 recovers PPI with no power tuning, and setting lhat = 0 recovers the classical point estimate.
coord	(int, optional): Coordinate for which to optimize lhat = 1. If NULL, it optimizes the total variance over all coordinates. Must be in (1, ..., d) where d is the dimension of the estimand.
opts	(list, optional): Options to pass to the optimizer. See ?optim for details.
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

(list): A list containing the following:

est

(vector): vector of PPI++ logistic regression coefficient estimates.

se

(vector): vector of standard errors of the coefficients.

lambda

(float): estimated power-tuning parameter.

rectifier_est

(vector): vector of the rectifier logistic regression coefficient estimates.

var_u

(matrix): covariance matrix for the gradients in the unlabeled data.

var_l

(matrix): covariance matrix for the gradients in the labeled data.

grads

(matrix): matrix of gradients for the labeled data.

grads_hat_unlabeled

(matrix): matrix of predicted gradients for the unlabeled data.

grads_hat

(matrix): matrix of predicted gradients for the labeled data.

inv_hessian

(matrix): inverse Hessian matrix.

Examples

dat <- simdat(model = "logistic")

form <- Y - f ~ X1

X_l <- model.matrix(form, data = dat[dat$set_label == "labeled",])

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)

X_u <- model.matrix(form, data = dat[dat$set_label == "unlabeled",])

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

ppi_plusplus_logistic(X_l, Y_l, f_l, X_u, f_u)

ipd documentation built on April 4, 2025, 4:41 a.m.