catatis_jar {ClustBlock}

Perform the CATATIS method on Just About Right data.

Description

CATATIS method adapted to JAR data.

Usage

catatis_jar(Data, nprod, nsub, levelsJAR=3, beta=0.1, Graph=TRUE, Graph_weights=TRUE,
Test_weights=FALSE, nperm=100)

Arguments

Data

data frame where the first column is the Assessors, the second is the products and all other columns the JAR attributes with numbers (1 to 3 or 1 to 5, see levelsJAR)

nprod

integer. Number of products.

nsub

integer. Number of subjects.

levelsJAR

integer. 3 or 5 levels. If 5, the data will be transformed in 3 levels.

beta

numerical. Parameter for agreement between JAR and other answers. Between 0 and 0.5.

Graph

logical. Show the graphical representation? Default: TRUE

Graph_weights

logical. Should the barplot of the weights be plotted? Default: TRUE

Test_weights

logical. Should the the weights be tested? Default: FALSE

nperm

integer. Number of permutation for the weight tests. Default: 100

Value

a list with:

• S: the S matrix: a matrix with the similarity coefficient among the subjects

• compromise: a matrix which is the compromise of the subjects (akin to a weighted average)

• weights: the weights associated with the subjects to build the compromise

• weights_tests: the weights tests results

• lambda: the first eigenvalue of the S matrix

• overall error: the error for the CATATIS criterion

• error_by_sub: the error by subject (CATATIS criterion)

• error_by_prod: the error by product (CATATIS criterion)

• s_with_compromise: the similarity coefficient of each subject with the compromise

• homogeneity: homogeneity of the subjects (in percentage)

• CA: the results of correspondance analysis performed on the compromise dataset

• eigenvalues: the eigenvalues associated to the correspondance analysis

• inertia: the percentage of total variance explained by each axis of the CA

• scalefactors: the scaling factors of each subject

• nb_1: Can be ignored

• param: parameters called

References

Llobell, F., Vigneau, E. & Qannari, E. M. ((September 14, 2022). Multivariate data analysis and clustering of subjects in a Just about right task. Eurosense, Turku, Finland.

See Also

catatis, plot.catatis, summary.catatis, cluscata_jar, preprocess_JAR, cluscata_kmeans_jar

Examples

data(cheese)
res.cat=catatis_jar(Data=cheese, nprod=8, nsub=72, levelsJAR=5)
summary(res.cat)
#plot(res.cat)