pspa_y: PSPA M-Estimation for ML-predicted labels

In ipd: Inference on Predicted Data

PSPA M-Estimation for ML-predicted labels

Description

pspa_y function conducts post-prediction M-Estimation.

Usage

pspa_y(
  X_l = NA,
  X_u = NA,
  Y_l,
  f_l,
  f_u,
  alpha = 0.05,
  weights = NA,
  quant = NA,
  intercept = FALSE,
  method
)

Arguments

X_l	Array or data.frame containing observed covariates in labeled data.
X_u	Array or data.frame containing observed or predicted covariates in unlabeled data.
Y_l	Array or data.frame of observed outcomes in labeled data.
f_l	Array or data.frame of predicted outcomes in labeled data.
f_u	Array or data.frame of predicted outcomes in unlabeled data.
alpha	Specifies the confidence level as 1 - alpha for confidence intervals.
weights	weights vector PSPA linear regression (d-dimensional, where d equals the number of covariates).
quant	quantile for quantile estimation
intercept	Boolean indicating if the input covariates' data contains the intercept (TRUE if the input data contains)
method	indicates the method to be used for M-estimation. Options include "mean", "quantile", "ols", "logistic", and "poisson".

Value

A summary table presenting point estimates, standard error, confidence intervals (1 - alpha), P-values, and weights.

ipd documentation built on March 11, 2026, 5:07 p.m.