indicesClusters {ClustBlock}

Compute the indices to evaluate the quality of the cluster partition in multi-block context

Description

Compute the Il index to evaluate the agreement between each block and the global partition (in sensory: agreement between each subject and the global partition)

Compute the Jl index to evaluate if each block has a partition (in sensory: if each subject made a partition of products)

Usage

indicesClusters(Data, Blocks, cut, NameBlocks=NULL, center=TRUE, scale=FALSE)

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.

cut

numerical vector. The partition of the cluster analysis.

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

center

logical. Should the data variables be centered? Default: TRUE. Please set to FALSE for a CATA experiment

scale

logical. Should the data variables be scaled? Default: FALSE

Value

• Il: the Il indices

• jl: the jl indicess

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

clustRowsOnStatisAxes, , ClusMB

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)