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||2017-01-10 19:03:54|
|Maintainer||Ping-Yang Chen <email@example.com>|
|License||MIT + file LICENSE|
DGP: DGP: data generation
fitted.milr: Fitted Response of milr Fits
fitted.softmax: Fitted Response of softmax Fits
logit: logit link function
milr: Maximum likelihood estimation of multiple-instance logistic...
milr-package: The milr package: multiple-instance logistic regression with...
predict.milr: Predict Method for milr Fits
predict.softmax: Predict Method for softmax Fits
softmax: Multiple-instance logistic regression via softmax function