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_lab = NA,
  X_unlab = NA,
  Y_lab,
  Yhat_lab,
  Yhat_unlab,
  alpha = 0.05,
  weights = NA,
  quant = NA,
  intercept = FALSE,
  method
)

Arguments

X_lab	Array or data.frame containing observed covariates in labeled data.
X_unlab	Array or data.frame containing observed or predicted covariates in unlabeled data.
Y_lab	Array or data.frame of observed outcomes in labeled data.
Yhat_lab	Array or data.frame of predicted outcomes in labeled data.
Yhat_unlab	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.

Examples

data <- sim_data_y()

X_lab <- data$X_lab

X_unlab <- data$X_unlab

Y_lab <- data$Y_lab

Yhat_lab <- data$Yhat_lab

Yhat_unlab <- data$Yhat_unlab

pspa_y(X_lab = X_lab, X_unlab = X_unlab,

 Y_lab = Y_lab, Yhat_lab = Yhat_lab, Yhat_unlab = Yhat_unlab,

 alpha = 0.05, method = "ols")

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