pspa_y: 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
)

Value

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

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".

Examples

Run this code


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")

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