ClusMB {ClustBlock} | R Documentation |
Perform a cluster analysis of rows in a Multi-block context with the ClusMB method
Description
Clustering of rows (products in sensory analysis) in a Multi-block context. The hierarchical clustering is followed by a partitioning algorithm (consolidation).
Usage
ClusMB(Data, Blocks, NameBlocks=NULL, scale=FALSE, center=TRUE,
nclust=NULL, gpmax=6)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
Blocks |
numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data. |
NameBlocks |
string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL |
scale |
logical. Should the data variables be scaled? Default: FALSE |
center |
logical. Should the data variables be centered? Default: TRUE. Please set to FALSE for a CATA experiment |
nclust |
numerical. Number of clusters to consider. If NULL, the Hartigan index advice is taken. |
gpmax |
logical. What is maximum number of clusters to consider? Default: min(6, number of blocks -2) |
Value
group: the clustering partition after consolidation.
nbgH: Advised number of clusters per Hartigan index
nbgCH: Advised number of clusters per Calinski-Harabasz index
cutree_k: the partition obtained by cutting the dendrogram in K clusters (before consolidation).
dend: The ClusMB dendrogram
param: parameters called
type: parameter passed to other functions
References
Llobell, F., Qannari, E.M. (June 10, 2022). Cluster analysis in a multi-bloc setting. SMTDA, Athens, Greece.
Llobell, F., Giacalone, D., Qannari, E. M. (Pangborn 2021). Cluster Analysis of products in CATA experiments.
Paper submitted
See Also
indicesClusters
, summary.clusRows
, clustRowsOnStatisAxes
Examples
#####projective mapping####
library(ClustBlock)
data(smoo)
res1=ClusMB(smoo, rep(2,24))
summary(res1)
indicesClusters(smoo, rep(2,24), res1$group)
####CATA####
data(fish)
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res2=ClusMB(Data= chang2$Datafinal, Blocks= rep(27, 11), center=FALSE)
indicesClusters(Data= chang2$Datafinal, Blocks= rep(27, 11),cut = res2$group, center=FALSE)