misimdata

A data set with 10 predictors and 1 outcome that can be used to try out the functions of the setweaver package

datasets

Create sets of variables based on a mutual
information approach. In this context, a set is a collection of
distinct elements (e.g., variables) that can also be treated as a
single entity.  Mutual information, a concept from probability theory,
quantifies the dependence between two variables by expressing how much
information about one variable can be gained from observing the other.
Furthermore, you can analyze, and visualize
these sets in order to better understand the relationships among
variables.

Nicolas Leenaerts

setweaver

Building Sets of Variables in a Probabilistic Framework

Aaron Fisher

misimdata function

A data frame with 2500 rows and 11 variables:<dl>
 <dt>y</dt>
<dd>Outcome</dd> <dt>x1</dt>
<dd>First binary predictor</dd> <dt>x2</dt>
<dd>Second binary predictor</dd> <dt>x3</dt>
<dd>Third binary predictor</dd> <dt>x4</dt>
<dd>Fourth binary predictor</dd> <dt>x5</dt>
<dd>Fifth binary predictor</dd> <dt>x6</dt>
<dd>Sixth binary predictor</dd> <dt>x7</dt>
<dd>Seventh binary predictor</dd> <dt>x8</dt>
<dd>Eighth binary predictor</dd> <dt>x9</dt>
<dd>Ninth binary predictor</dd> <dt>x10</dt>
<dd>Tenth binary predictor</dd>
</dl>

Format

misimdata — misimdata

A data frame with 2500 rows and 11 variables:<dl>
 <dt>y</dt>
<dd>Outcome</dd>

 <dt>x1</dt>
<dd>First binary predictor</dd>

 <dt>x2</dt>
<dd>Second binary predictor</dd>

 <dt>x3</dt>
<dd>Third binary predictor</dd>

 <dt>x4</dt>
<dd>Fourth binary predictor</dd>

 <dt>x5</dt>
<dd>Fifth binary predictor</dd>

 <dt>x6</dt>
<dd>Sixth binary predictor</dd>

 <dt>x7</dt>
<dd>Seventh binary predictor</dd>

 <dt>x8</dt>
<dd>Eighth binary predictor</dd>

 <dt>x9</dt>
<dd>Ninth binary predictor</dd>

 <dt>x10</dt>
<dd>Tenth binary predictor</dd>


</dl>

misimdata: misimdata

Description

Usage

Arguments

Format