smoo

datasets

Hierarchical and partitioning algorithms of 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.

Fabien Llobell

ClustBlock

Clustering of Datasets

Evelyne Vigneau

Veronique Cariou

El Mostafa Qannari

smoo function

<p>Projective mapping (or Napping) data. A data frame with 8 rows (the number of smoothies) and 48 columns (the number of consumers * 2).
 For each consumer, we have the coordinates of the products on the sheet of paper.</p>

Format

smoothies data — smoo

smoothies data

smoo: smoothies data

Description

Usage

Arguments

Format

References

Examples