catatis {ClustBlock}

Perform the CATATIS method on different blocks from a CATA experiment

Description

CATATIS method. Additional outputs are also computed. Non-binary data are accepted and weights can be tested.

Usage

catatis(Data,nblo,NameBlocks=NULL, NameVar=NULL, Graph=TRUE, Graph_weights=TRUE,
 Test_weights=FALSE, nperm=100)

Arguments

Data

data frame or matrix where the blocks of binary variables are merged horizontally. If you have a different format, see change_cata_format

nblo

integer. Number of blocks (subjects).

NameBlocks

string vector. Name of each block (subject). Length must be equal to the number of blocks. If NULL, the names are S1,...Sm. Default: NULL

NameVar

string vector. Name of each variable (attribute, the same names for each subject). Length must be equal to the number of attributes. If NULL, the colnames of the first block are taken. Default: NULL

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 correspondence analysis performed on the compromise dataset

• eigenvalues: the eigenvalues associated to the correspondence analysis

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

• scalefactors: the scaling factors of each subject

• nb_1: the number of 1 in each block, i.e. the number of checked attributes by subject.

• param: parameters called

References

Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data and the clustering of subjects in a CATA experiment. Food Quality and Preference, 72, 31-39.
Bonnet, L., Ferney, T., Riedel, T., Qannari, E.M., Llobell, F. (September 14, 2022) .Using CATA for sensory profiling: assessment of the panel performance. Eurosense, Turku, Finland.

See Also

plot.catatis, summary.catatis, cluscata, change_cata_format, change_cata_format2

Examples

data(straw)
res.cat=catatis(straw, nblo=114)
summary(res.cat)
plot(res.cat)

#Vertical format with sessions
data("fish")
chang=change_cata_format2(fish, nprod= 6, nattr= 27, nsub = 12, nsess= 3)
res.cat2=catatis(Data= chang$Datafinal, nblo = 12, NameBlocks =  chang$NameSub, Test_weights=TRUE)

#Vertical format without sessions
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res.cat3=catatis(Data= chang2$Datafinal, nblo = 11, NameBlocks =  chang2$NameSub)