The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subjectlevel outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "nondefective". The 'milr' package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the ExpectationMaximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection.
Package details 


Author  PingYang Chen [aut, cre], ChingChuan Chen [aut], ChunHao Yang [aut], ShengMao Chang [aut] 
Date of publication  20170608 16:37:15 UTC 
Maintainer  PingYang Chen <pychen.ping@gmail.com> 
License  MIT + file LICENSE 
Version  0.3.0 
URL  https://github.com/PingYangChen/milr 
Package repository  View on CRAN 
Installation 
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