milr: 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 multinomial, and, we can only observe the subject-level outcomes. For example, in manufactory 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.

Author
Ping-Yang Chen [aut, cre], ChingChuan Chen [aut], Chun-Hao Yang [aut], Sheng-Mao Chang [aut]
Date of publication
2016-07-14 20:15:18
Maintainer
Ping-Yang Chen <pychen.ping@gmail.com>
License
MIT + file LICENSE
Version
0.1.0
URLs

View on CRAN

Man pages

DGP
DGP: data generation
logit
logit link function
milr
Maximum likelihood estimation of multiple-instance logistic...
milr-package
The milr package: multiple-instance logistic regression with...
softmax
Multiple-instance logistic regression via softmax function

Files in this package

milr
milr/src
milr/src/Makevars
milr/src/milr.cpp
milr/src/Makevars.win
milr/src/RcppExports.cpp
milr/NAMESPACE
milr/R
milr/R/milr-package.R
milr/R/RcppExports.R
milr/R/milr.R
milr/R/DGP.R
milr/R/softmax.R
milr/MD5
milr/DESCRIPTION
milr/man
milr/man/softmax.Rd
milr/man/milr-package.Rd
milr/man/milr.Rd
milr/man/DGP.Rd
milr/man/logit.Rd
milr/LICENSE