The R Journal: accepted article

This article will be copy edited and may be changed before publication.

milr: Multiple-Instance Logistic Regression with Lasso Penalty
Ping-Yang Chen, Ching-Chuan Chen, Chun-Hao Yang, Sheng-Mao Chang and Kuo-Jung Lee

Abstract The purpose of the proposed package milr is to analyze multiple-instance data. Ordinary multiple-instance data consists of many independent bags, and each bag is composed of several instances. The statuses of bags and instances are binary. Moreover, the statuses of instances are not observed, whereas the statuses of bags are observed. The functions in this package are applicable for analyzing multiple-instance data, simulating data via logistic regression, and selecting important covariates in the regression model. To this end, maximum likelihood estimation with an expectation maximization algorithm is implemented for model estimation, and a lasso penalty added to the likelihood function is applied for variable selection. Additionally, an “milr” object is applicable to generic functions fitted, predict and summary. Simulated data and a real example are given to demonstrate the features of this package.

Received: 2016-11-16; online 2017-03-27
CRAN packages: milr

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Ping-Yang Chen and Ching-Chuan Chen and Chun-Hao Yang and
          Sheng-Mao Chang and Kuo-Jung Lee},
  title = {{milr: Multiple-Instance Logistic Regression with Lasso
  year = {2017},
  journal = {{The R Journal}},
  url = {}