generate.catecls

Generate an (NxJ) clustered categorical data matrix given by prop.J.list and true cluster allocation.

Performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the
package refers to Mariko Takagishi and Michel van de Velden (2022)<doi:10.1080/10618600.2022.2035737>.

Mariko Takagishi

mccca

Visualizing Class Specific Heterogeneous Tendencies in
Categorical Data

generate.catecls function

<dl><dt>N</dt>
<dd>The number of observations.</dd>
<dt>J</dt>
<dd>The number of active variables.</dd>
<dt>q.vec</dt>
<dd>A vector of length J giving the number of categories for each active variable.</dd>
<dt>Ktrue</dt>
<dd>An integer indicating the number of content-based clusters used for CCRS estimation.</dd>
<dt>prop.J.list</dt>
<dd>a list of length J, where each list is a (Ktrue x qj) matrix giving the proportion for each qj category in each of the <code>Ktrue</code> cluster.</dd>
<dt>clstr.vec</dt>
<dd>A vector of length N giving true clusters for each observations.</dd></dl>

Arguments

Generate (NxJ) clustered categorical data matrix. — generate.catecls

<dl>

<dt>N</dt>
<dd>The number of observations.</dd>


<dt>J</dt>
<dd>The number of active variables.</dd>


<dt>q.vec</dt>
<dd>A vector of length J giving the number of categories for each active variable.</dd>


<dt>Ktrue</dt>
<dd>An integer indicating the number of content-based clusters used for CCRS estimation.</dd>


<dt>prop.J.list</dt>
<dd>a list of length J, where each list is a (Ktrue x qj) matrix giving the proportion for each qj category in each of the <code>Ktrue</code> cluster.</dd>


<dt>clstr.vec</dt>
<dd>A vector of length N giving true clusters for each observations.</dd>

</dl>

generate.catecls: Generate (NxJ) clustered categorical data matrix.

Description

Usage

Value

Arguments