Learn R Programming

glmtrans (version 2.1.0)

Transfer Learning under Regularized Generalized Linear Models

Description

We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The details of methods can be found in "Tian, Y., & Feng, Y. (2023). Transfer learning under high-dimensional generalized linear models. Journal of the American Statistical Association, 118(544), 2684-2697.".

Copy Link

Version

Install

install.packages('glmtrans')

Monthly Downloads

260

Version

2.1.0

License

GPL-2

Maintainer

Ye Tian

Last Published

March 1st, 2025

Functions in glmtrans (2.1.0)

print.glmtrans

Print a fitted "glmtrans" object.
predict.glmtrans

Predict for new data from a "glmtrans" object.
plot.glmtrans

Visualize the losses of different sources and the threshold to determine transferability.
glmtrans_inf

Calculate asymptotic confidence intervals based on desparsified Lasso and two-step transfer learning method.
models

Generate data from Gaussian, logistic and Poisson models.
source_detection

Transferable source detection for GLM transfer learning algorithm.
glmtrans

Fit a transfer learning generalized linear model (GLM) with elasticnet regularization.