Milr: Multiple-instance logistic regression with lasso penalty

Ping Yang Chen, Ching Chuan Chen, Chun Hao Yang, Sheng-Mao Chang, Kuo-Jung Lee

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The purpose of the milr package is to analyze multiple-instance data. Ordinary multipleinstance 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.

Original languageEnglish
Pages (from-to)446-457
Number of pages12
JournalR Journal
Volume9
Issue number1
Publication statusPublished - 2017 Jun 1

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Cite this

Chen, P. Y., Chen, C. C., Yang, C. H., Chang, S-M., & Lee, K-J. (2017). Milr: Multiple-instance logistic regression with lasso penalty. R Journal, 9(1), 446-457.