This repository hosts the R package that implements the POP-Inf method described in the paper: Assumption-lean and data-adaptive post-prediction inference.
POP-Inf provides valid and powerful inference based on ML predictions for parameters defined through estimation equations.
# install.packages("devtools")
devtools::install_github("qlu-lab/POPInf")
Here are examples of POP-Inf for M-estimation tasks including: mean estimation, linear regression, logistic regression, and Poisson regrssion. The main function is pop_M(), where the argument method indicates which task to do.
# Load the package
library(POPInf)
# Load the simulated data
set.seed(999)
data <- sim_data_y()
X_lab = data$X_lab ## Covariates in the labeled data
X_unlab = data$X_unlab ## Covariates in the unlabeled data
Y_lab = data$Y_lab ## Observed outcome in the labeled data
Yhat_lab = data$Yhat_lab ## Predicted outcome in the labeled data
Yhat_unlab = data$Yhat_unlab ## Predicted outcome in the unlabeled data
# Run POP-Inf mean estimation
fit_mean <- pop_M(Y_lab = Y_lab, Yhat_lab = Yhat_lab, Yhat_unlab = Yhat_unlab,
alpha = 0.05, method = "mean")
print(fit_mean)
# Estimate Std.Error Lower.CI Upper.CI P.value Weight
# 1 3.505484 0.05720132 3.393371 3.617596 0 0.9044718
# Run POP-Inf linear regression
fit_ols <- pop_M(X_lab = X_lab, X_unlab = X_unlab,
Y_lab = Y_lab, Yhat_lab = Yhat_lab, Yhat_unlab = Yhat_unlab,
alpha = 0.05, method = "ols")
print(fit_ols)
# Estimate Std.Error Lower.CI Upper.CI P.value Weight
# 3.5089480 0.05591387 3.3993588 3.618537 0.000000e+00 0.8611889
# X1 0.8980173 0.08565766 0.7301313 1.065903 1.025461e-25 1.0000000
# Load the simulated data
set.seed(999)
data <- sim_data_y(binary = T)
X_lab = data$X_lab
X_unlab = data$X_unlab
Y_lab = data$Y_lab
Yhat_lab = data$Yhat_lab
Yhat_unlab = data$Yhat_unlab
# Run POP-Inf logistic regression
fit_logistic <- pop_M(X_lab = X_lab, X_unlab = X_unlab,
Y_lab = Y_lab, Yhat_lab = Yhat_lab, Yhat_unlab = Yhat_unlab,
alpha = 0.05, method = "logistic")
print(fit_logistic)
# Estimate Std.Error Lower.CI Upper.CI P.value Weight
# -0.1289001 0.08347881 -0.2925156 0.03471532 1.225626e-01 0.4290559
# X1 0.5749601 0.08653142 0.4053617 0.74455861 3.041970e-11 0.5337078
# Load the simulated data
set.seed(999)
data <- sim_data_y()
X_lab = data$X_lab
X_unlab = data$X_unlab
Y_lab = round(data$Y_lab - min(data$Y_lab))
Yhat_lab = round(data$Yhat_lab - min(data$Yhat_lab))
Yhat_unlab = round(data$Yhat_unlab - min(Yhat_unlab))
# Run POP-Inf Poisson regression
fit_poisson <- pop_M(X_lab = X_lab, X_unlab = X_unlab,
Y_lab = Y_lab, Yhat_lab = Yhat_lab, Yhat_unlab = Yhat_unlab,
alpha = 0.05, method = "poisson")
print(fit_poisson)
# Estimate Std.Error Lower.CI Upper.CI P.value Weight
# 0.9732937 0.02261537 0.9289684 1.0176191 0.000000e+00 0.8392517
# X1 0.3188511 0.03125507 0.2575923 0.3801099 1.950752e-24 0.8303991
We provide the script for analysis in the POP-Inf paper here.
Please submit an issue or contact Jiacheng ([email protected]) or Xinran ([email protected]) for questions.
Assumption-lean and Data-adaptive Post-Prediction Inference
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