Multiple-Instance Logistic Regression with LASSO Penalty
DGP: data generation
Fitted Response of milr Fits
Fitted Response of softmax Fits
logit link function
The milr package: multiple-instance logistic regression with lasso pen...
Maximum likelihood estimation of multiple-instance logistic regression...
Predict Method for milr Fits
Predict Method for softmax Fits
Multiple-instance logistic regression via softmax function
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.