CATATIS method on binary blocks. Additional outputs are also computed
catatis(Data,nblo,NameBlocks=NULL, NameVar=NULL, Graph=TRUE, Graph_weights=TRUE)
data frame or matrix where the blocks of binary variables are merged horizontally. If you have a different format, see change_cata_format
numerical. Number of blocks (subjects).
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
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
logical. Show the graphical representation? Default: TRUE
logical. Should the barplot of the weights be plotted? Default: TRUE
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
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: the number of 1 in each block, i.e. the number of checked attributes by subject.
param: parameters called
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.
# NOT RUN {
data(straw)
res.cat=catatis(straw, nblo=114)
summary(res.cat)
# }
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