predict.milr

A fitted obejct of class inheriting from <code>"milr"</code>.

object

Default is <code>NULL</code>. A matrix with variables to predict.

newdata

Default is <code>NULL</code>. A vector. The labels of instances to bags.
If <code>newdata</code> and <code>bag_newdata</code> both are <code>NULL</code>, return the fitted result.

bag_newdata

The type of prediction required. Default is <code>"bag"</code>, the predicted labels of bags.
The <code>"instance"</code> option returns the predicted labels of instances.

type

further arguments passed to or from other methods.

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.

predict.milr: Predict Method for milr Fits

Description

Usage

Arguments