data_logistic_model

A toy dataset to demonstrate running this package on multimodal logistic models.

datasets

Provides methods for quantifying the information gain contributed by individual
modalities in multimodal regression models. Information gain is measured using Expected
Relative Entropy (ERE) or pseudo-R² metrics, with corresponding p-values and confidence
intervals. Currently supports linear and logistic regression models with plans for
extension to additional Generalized Linear Models and Cox proportional hazard model.

Wanting Jin

multiModTest

Information Assessment for Individual Modalities in Multimodal
Regression Models

Quefeng Li

data_logistic_model function

A data object that contains<dl>
 <dt><code>y</code></dt>
<dd>A vector 200 observations of outcomes. (0 or 1)</dd> <dt><code>X</code></dt>
<dd>A 200 \(\times\) 600 matrix containing all training data.</dd> <dt><code>mod.idx</code></dt>
<dd>A list of modality indices.</dd>
</dl>

Format

Example Dataset — data_logistic_model

A data object that contains<dl>
 <dt><code>y</code></dt>
<dd>A vector 200 observations of outcomes. (0 or 1)</dd>

 <dt><code>X</code></dt>
<dd>A 200 \(\times\) 600 matrix containing all training data.</dd>

 <dt><code>mod.idx</code></dt>
<dd>A list of modality indices.</dd>


</dl>

data_logistic_model: Example Dataset

Description

Usage

Arguments

Format