ClustBlock (version 4.0.0)

clustRowsOnStatisAxes: Perform a cluster analysis of rows in a Multi-block context with clustering on STATIS axes

Description

Clustering of rows (products in sensory analysis) in a Multi-block context. The STATIS method is followed by a hierarchical algorithm.

Usage

clustRowsOnStatisAxes(Data, Blocks, NameBlocks=NULL, scale=FALSE,
nclust=NULL, gpmax=6, ncomp=5)

Value

• group: the clustering partition.

• 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

• dend: The dendrogram

• param: parameters called

• type: parameter passed to other functions

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

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? min(6, number of blocks -2)

ncomp

numerical. Number of axes to consider. Default:5

References

Paper submitted

See Also

indicesClusters, summary.clusRows , ClusMB

Examples

#####projective mapping####
library(ClustBlock)
data(smoo)
res1=clustRowsOnStatisAxes(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=clustRowsOnStatisAxes(Data= chang2$Datafinal, Blocks= rep(27, 11))
indicesClusters(Data= chang2$Datafinal, Blocks= rep(27, 11),cut = res2$group, center=FALSE)