Hierarchical clustering of subjects from a JAR experiment. Each cluster of subjects is associated with a compromise computed by the CATATIS method. The hierarchical clustering is followed by a partitioning algorithm (consolidation).
cluscata_jar(Data, nprod, nsub, levelsJAR=3, beta=0.1, Noise_cluster=FALSE,
Itermax=30, Graph_dend=TRUE, Graph_bar=TRUE, printlevel=FALSE,
gpmax=min(6, nsub-2), rhoparam=NULL,
Testonlyoneclust=FALSE, alpha=0.05, nperm=50, Warnings=FALSE)Each partitionK contains a list for each number of clusters of the partition, K=1 to gpmax with:
group: the clustering partition after consolidation. If Noise_cluster=TRUE, some subjects could be in the noise cluster ("K+1")
rho: the threshold for the noise cluster
homogeneity: homogeneity index (
s_with_compromise: similarity coefficient of each subject with its cluster compromise
weights: weight associated with each subject in its cluster
compromise: the compromise of each cluster
CA: list. the correspondance analysis results on each cluster compromise (coordinates, contributions...)
inertia: percentage of total variance explained by each axis of the CA for each cluster
s_all_cluster: the similarity coefficient between each subject and each cluster compromise
criterion: the CLUSCATA criterion error
param: parameters called
type: parameter passed to other functions
There is also at the end of the list:
dend: The CLUSCATA dendrogram
cutree_k: the partition obtained by cutting the dendrogram in K clusters (before consolidation).
overall_homogeneity_ng: percentage of overall homogeneity by number of clusters before consolidation (and after if there is no noise cluster)
diff_crit_ng: variation of criterion when a merging is done before consolidation (and after if there is no noise cluster)
test_one_cluster: decision and pvalue to know if there is more than one cluster
param: parameters called
type: parameter passed to other functions
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)
integer. Number of products.
integer. Number of subjects.
integer. 3 or 5 levels. If 5, the data will be transformed in 3 levels.
numerical. Parameter for agreement between JAR and other answers. Between 0 and 0.5.
logical. Should a noise cluster be computed? Default: FALSE
numerical. Maximum of iteration for the partitioning algorithm. Default:30
logical. Should the dendrogram be plotted? Default: TRUE
logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging step of the hierarchical algorithm be plotted? Default: TRUE
logical. Print the number of remaining levels during the hierarchical clustering algorithm? Default: FALSE
logical. What is maximum number of clusters to consider? Default: min(6, nblo-2)
numerical. What is the threshold for the noise cluster? Between 0 and 1, high value can imply lot of blocks set aside. If NULL, automatic threshold is computed.
logical. Test if there is more than one cluster? Default: FALSE
numerical between 0 and 1. What is the threshold to test if there is more than one cluster? Default: 0.05
numerical. How many permutations are required to test if there is more than one cluster? Default: 50
logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? Default: FALSE
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.
plot.cluscata, summary.cluscata , catatis_jar, preprocess_JAR, cluscata_kmeans_jar
# \donttest{
data(cheese)
res=cluscata_jar(Data=cheese, nprod=8, nsub=72, levelsJAR=5)
#plot(res, ngroups=4, Graph_dend=FALSE)
summary(res, ngroups=4)
# }
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