RATAchoc

datasets

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

RATAchoc function

<p>RATA data with sessions. A data frame with 3 sessions, 9 panelists, 12 products and 27 RATA attributes.</p>

Format

RATA data on chocolates — RATAchoc

RATA data on chocolates

RATAchoc: RATA data on chocolates

Description

Usage

Arguments

Format

References

Examples