Milr: Multiple-instance logistic regression with lasso penalty

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

研究成果: Article

4 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)446-457
頁數12
期刊R Journal
9
發行號1
出版狀態Published - 2017 六月 1

All Science Journal Classification (ASJC) codes

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

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  • 引用此

    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.