Multiple-Instance Logistic Regression with LASSO Penalty

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 subject-level 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 "non-defective". 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 Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection.


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0.3.0 by Ping-Yang Chen, 3 months ago

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Authors: Ping-Yang Chen [aut, cre], ChingChuan Chen [aut], Chun-Hao Yang [aut], Sheng-Mao Chang [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports utils, pipeR, numDeriv, glmnet, Rcpp, RcppParallel

Suggests testthat, knitr, knitcitations, rmarkdown, data.table, ggplot2, plyr

Linking to Rcpp, RcppArmadillo, RcppParallel

System requirements: GNU make

See at CRAN