milr: Multiple-Instance Logistic Regression with Lasso Penalty

The purpose of the milr package 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.

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

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For attribution, please cite this work as

Chen, et al., "The R Journal: milr: Multiple-Instance Logistic Regression with Lasso Penalty", The R Journal, 2017

BibTeX citation

  author = {Chen, Ping-Yang and Chen, Ching-Chuan and Yang, Chun-Hao and Chang, Sheng-Mao and Lee, Kuo-Jung},
  title = {The R Journal: milr: Multiple-Instance Logistic Regression with Lasso Penalty},
  journal = {The R Journal},
  year = {2017},
  note = {},
  doi = {10.32614/RJ-2017-013},
  volume = {9},
  issue = {1},
  issn = {2073-4859},
  pages = {446-457}