Generating the multiple-instance data set.

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.

PingYang Chen

milr

Multiple-Instance Logistic Regression with LASSO Penalty

Ping-Yang Chen

ChingChuan Chen

Chun-Hao Yang

Sheng-Mao Chang

DGP function

<dl><dt>n</dt>
<dd>an integer. The number of bags.</dd>
<dt>m</dt>
<dd>an integer or vector of length <code>n</code>. If <code>m</code> is an integer, each bag has the identical number of instances, <code>m</code>. 
If <code>m</code> is a vector, the <code>i</code>th bag has <code>m[i]</code> instances.</dd>
<dt>beta</dt>
<dd>a vector. The true regression coefficients.</dd></dl>

Arguments

DGP: data generation — DGP

<dl>

<dt>n</dt>
<dd>an integer. The number of bags.</dd>


<dt>m</dt>
<dd>an integer or vector of length <code>n</code>. If <code>m</code> is an integer, each bag has the identical number of instances, <code>m</code>. 
If <code>m</code> is a vector, the <code>i</code>th bag has <code>m[i]</code> instances.</dd>


<dt>beta</dt>
<dd>a vector. The true regression coefficients.</dd>

</dl>

DGP: DGP: data generation

Description

Usage

Value

Arguments

Examples