| cluscata_kmeans_jar {ClustBlock} | R Documentation |
Perform a cluster analysis of subjects in a JAR experiment.
Description
Partitionning of subject from a JAR experiment. Each cluster is associated with a compromise computed by the CATATIS method. Moreover, a noise cluster can be set up.
Usage
cluscata_kmeans_jar(Data, nprod, nsub, levelsJAR=3, beta=0.1, clust, nstart=100, rho=0,
Itermax=30, Graph_groups=TRUE, print_attempt=FALSE, Warnings=FALSE)
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. |
clust |
numerical vector or integer. Initial partition or number of starting partitions if integer. If numerical vector, the numbers must be 1,2,3,...,number of clusters |
nstart |
numerical. Number of starting partitions. Default: 100 |
rho |
numerical between 0 and 1. Threshold for the noise cluster. If 0, there is no noise cluster. Default: 0 |
Itermax |
numerical. Maximum of iterations by partitionning algorithm. Default: 30 |
Graph_groups |
logical. Should each cluster compromise graphical representation be plotted? Default: TRUE |
print_attempt |
logical. Print the number of remaining attempts in multi-start case? Default: FALSE |
Warnings |
logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? Default: FALSE |
Value
a list with:
group: the clustering partition. If rho>0, some subjects could be in the noise cluster ("K+1")
rho: the threshold for the noise cluster
homogeneity: percentage of homogeneity of the subjects in each cluster and the overall homogeneity
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: 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
param: parameters called
criterion: the CLUSCATA criterion error
type: parameter passed to other functions
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
plot.cluscata, summary.cluscata , catatis_jar, preprocess_JAR, cluscata_jar
Examples
data(cheese)
res=cluscata_kmeans_jar(Data=cheese, nprod=8, nsub=72, levelsJAR=5, clust=4)
#plot(res)
summary(res)