catatis_rata: Perform the CATATIS method on different blocks from a RATA...

In ClustBlock: Clustering of Datasets

Perform the CATATIS method on different blocks from a RATA experiment

Description

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

Usage

catatis_rata(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 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

• 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.
Bonnet, L., Llobell, F., Qannari, E.M. (Pangborn 2023). Assessment of the panel performance in a RATA experiment.

See Also

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

Examples

#RATA data with session
data(RATAchoc)
chang2=change_cata_format2(RATAchoc, nprod= 12, nattr= 13, nsub = 9, nsess= 3)
res.cat4=catatis_rata(Data= chang2$Datafinal, nblo = 9, NameBlocks =  chang2$NameSub)
summary(res.cat4)

#RATA data without session
Data=RATAchoc[1:108,2:16]
chang2=change_cata_format2(Data, nprod= 12, nattr= 13, nsub = 9, nsess = 1)
res.cat5=catatis_rata(Data= chang2$Datafinal, nblo = 9, NameBlocks =  chang2$NameSub)
summary(res.cat5)
graphics.off()

ClustBlock documentation built on Aug. 30, 2023, 5:08 p.m.