summary.cluscata_liking

This function shows the cluscata_liking results

CATA

liking

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. Different thresholds per cluster can be sets. 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 (Llobell, Bonnet & Giacalone (2024) <doi:10.1111/joss.12941>) . Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of observations (products in sensory analysis) in multi-block context (notably with ClusMB strategy) is also included (Llobell & Giacalone (2025) <doi:10.1111/joss.70024>).Performing clustering based on CATA and liking at the same time is possible thanks to cluscata_liking function (Vigneau, Cariou, Giacalone, Berget & Llobell (2022) <doi:10.1016/j.foodqual.2021.104358>). Clustering of variables (quantitative, qualitative or mixed) can be done thanks to the MixCluStatis() function. Clustering on JAR + Liking can be achieved thanks to preprocess_JAR_liking function.

Fabien Llobell

ClustBlock

Clustering of Datasets

Evelyne Vigneau

Veronique Cariou

El Mostafa Qannari

summary.cluscata_liking function

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

Arguments

Show the CLUSCATA-liking results — summary.cluscata_liking

<dl>

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


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


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

</dl>

summary.cluscata_liking: Show the CLUSCATA-liking results

Description

Usage

Value

Arguments

See Also