clustatis {ClustBlock}

Perform a cluster analysis of blocks of quantitative variables

Description

Hierarchical clustering of quantitative Blocks followed by a partitioning algorithm (consolidation). Each cluster of blocks is associated with a compromise computed by the STATIS method. Moreover, a noise cluster can be set up.

Usage

clustatis(Data,Blocks,NameBlocks=NULL,Noise_cluster=FALSE,scale=FALSE,
  Itermax=30, Graph_dend=TRUE, Graph_bar=TRUE,
  printlevel=FALSE, gpmax=min(6, length(Blocks)-2), Testonlyoneclust=FALSE,
  alpha=0.05, nperm=50)

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

Noise_cluster

logical. Should a noise cluster be computed? Default: FALSE

scale

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

Itermax

numerical. Maximum of iteration for the partitioning algorithm. Default: 30

Graph_dend

logical. Should the dendrogram be plotted? Default: TRUE

Graph_bar

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

printlevel

logical. Print the number of remaining levels during the hierarchical clustering algorithm? Default: FALSE

gpmax

logical. What is maximum number of clusters to consider? Default: min(6, length(Blocks)-2)

Testonlyoneclust

logical. Test if there is more than one cluster? Default: FALSE

alpha

numerical between 0 and 1. What is the threshold to test if there is more than one cluster? Default: 0.05

nperm

numerical. How many permutations are required to test if there is more than one cluster? Default: 50

Value

Each partitionK contains a list for each number of clusters of the partition, K=1 to gpmax with:

• group: the clustering partition of datasets after consolidation. If Noise_cluster=TRUE, some blocks could be in the noise cluster ("K+1")

• rho: the threshold for the noise cluster

• homogeneity: homogeneity index (

• rv_with_compromise: RV coefficient of each block with its cluster compromise

• weights: weight associated with each block in its cluster

• comp_RV: RV coefficient between the compromises associated with the various clusters

• compromise: the W compromise of each cluster

• coord: the coordinates of objects of each cluster

• inertia: percentage of total variance explained by each axis for each cluster

• rv_all_cluster: the RV coefficient between each block and each cluster compromise

• criterion: the CLUSTATIS criterion error

• param: parameters called in the consolidation

• type: parameter passed to other functions

There is also at the end of the list:

• dend: The CLUSTATIS dendrogram

• cutree_k: the partition obtained by cutting the dendrogram for 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

References

Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.
Llobell, F., Vigneau, E., Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104.

See Also

plot.clustatis, summary.clustatis , clustatis_kmeans, statis

Examples

 data(smoo)
 NameBlocks=paste0("S",1:24)
 cl=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks)
 #plot(cl, ngroups=3, Graph_dend=FALSE)
 summary(cl)
 #with noise cluster
 cl2=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks,
 Noise_cluster=TRUE, Graph_dend=FALSE, Graph_bar=FALSE)