create.prop

Creates a list length J of category proportion for each cluster.

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

create.prop function

<dl><dt>J</dt>
<dd>The number of active variable.!!!</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>The number of clusters in J active variables.</dd>
<dt>strongprop</dt>
<dd>A numeric value giving the strongest proportion of categories (common for all J active variables).</dd>
<dt>which.noise</dt>
<dd>A vector of length (&lt;= J) giving the index of noise variables in J active variables. NULL indicating all variable is non-noise.</dd></dl>

Arguments

Creates a list length J of category proportion for each cluster. — create.prop

<dl>

<dt>J</dt>
<dd>The number of active variable.!!!</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>The number of clusters in J active variables.</dd>


<dt>strongprop</dt>
<dd>A numeric value giving the strongest proportion of categories (common for all J active variables).</dd>


<dt>which.noise</dt>
<dd>A vector of length (&lt;= J) giving the index of noise variables in J active variables. NULL indicating all variable is non-noise.</dd>

</dl>

create.prop: Creates a list length J of category proportion for each cluster.

Description

Usage

Value

Arguments