summary.cluscata

This function shows the cluscata results

CATA

Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of rows in multi-block context (notably with ClusMB strategy) is also included.

Fabien Llobell

ClustBlock

Clustering of Datasets

Evelyne Vigneau

Veronique Cariou

El Mostafa Qannari

summary.cluscata function

<dl><dt>object</dt>
<dd>object of class 'cluscata'.</dd>
<dt>ngroups</dt>
<dd>number of groups to consider. Ignored for cluscata_kmeans results. Default: recommended number of clusters</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods</dd></dl>

Arguments

Show the CLUSCATA results — summary.cluscata

<dl>

<dt>object</dt>
<dd>object of class 'cluscata'.</dd>


<dt>ngroups</dt>
<dd>number of groups to consider. Ignored for cluscata_kmeans results. Default: recommended number of clusters</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods</dd>

</dl>

summary.cluscata: Show the CLUSCATA results

Description

Usage

Value

Arguments

See Also