ClustVarLV: CLV3W – R documentation

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CLV3W

Hierarchical clustering of variables (associated with mode 2 three-way array) with consolidation

Description

Hierarchical Cluster Analysis of a set of variables (mode 2) given a three-way array with a further consolidation step. Each group of variables is associated with a one-rank PARAFAC model (comp x loading x weight). Moreover, a Non Negativity (NN) constraint may be added to the model, so that the loading coefficients have positive values. Return an object of class clv3w.

Usage

CLV3W(X,mode.scale=0,NN=FALSE,moddendoinertie=TRUE,gmax=20,graph=TRUE,cp.rand=10)

Arguments

`X`	: a three way array - variables of mode 2 will be clustered
`mode.scale`	: scaling parameter applied to X, by default centering of X (for mode 2 x mode 3) is done. By default no scaling (mode.scale=0) 0 : no scaling only centering - the default 1 : scaling with standard deviation of (mode 2 x mode 3) elements 2 : global scaling (each block i.e. each mode 2 slice will have the same inertia ) 3 : global scaling (each block i.e. each mode 3 slice will have the same inertia )
`NN`	: non Negativity constraint to be added on the loading coefficients. By default no constraint (NN=FALSE) TRUE : a non negativity constrained is applied on the loading coefficients to set them as positive values FALSE : loading coefficients may be either positive or negative
`moddendoinertie`	: dendrogram. By default it is based on the delta clustering criterion (moddendoinertie =TRUE) TRUE : dendrogram associated with the clustering criterion delta FALSE : dendrogram associated with the the height (cumulative delta)
`gmax`	: maximum number of partitions for which the consolidation will be done (default : gmax=11)
`graph`	: boolean, if TRUE, the graphs associated with the dendrogram and the evolution of the aggregation criterion are displayed (default : graph=TRUE)
`cp.rand`	: number of random starts associated with the one rank Candecomp/Parafac model (By default cp.rand=10)

Value

`tabres`	Results of the hierarchical clustering algorithm. In each line you find the results of one specific step of the hierarchical clustering. Columns 1 and 2 : the numbers of the two groups which are merged Column 3 : name of the new cluster Column 4 : the value of the aggregation criterion for the Hierarchical Ascendant Clustering (delta) : delta loss Column 5 : the loss value of the clustering criterion for the HAC Column 6 : the percentage of explained inertia of the data array X Column 7 : the loss value of the clustering criterion after consolidation Column 8 : the percentage of explained inertia of the data array X after consolidation Column 9 : number of iterations in the partitioning algorithm. Remark : A zero in columns 7 to 9 indicates that no consolidation was done
`hclust`	contains the results of the HCA
`partition K`	contains a list for each number of clusters of the partition, K=1 to gmax with clusters : in line 1, the groups membership before consolidation; in line 2 the groups membership after consolidation comp : the latent components of the clusters associated with the first mode (after consolidation) loading : the vector of loadings associated with the second mode by cluster (after consolidation) weigth : the vector of weights associated with the third mode by cluster (after consolidation) criterion : vector of loss giving for each cluster the residual amount between the sub-array and its reconstitution associated with the cluster one rank PARAFAC model (after consolidation)
`param`	contains the clustering parameters gmax : maximum number of partitions for which the consolidation has been done X : the scaled three-way array

call : call of the method

Author(s)

Veronique Cariou, veronique.cariou@oniris-nantes.fr

References

Wilderjans, T. F., & Cariou, V. (2016). CLV3W: A clustering around latent variables approach to detect panel disagreement in three-way conventional sensory profiling data. Food quality and preference, 47, 45-53.

Cariou, V., & Wilderjans, T. F. (2018). Consumer segmentation in multi-attribute product evaluation by means of non-negatively constrained CLV3W. Food Quality and Preference, 67, 18-26.

Examples

data(ciders)
## Cluster Analysis of cider sensory descriptors with block scaling
## to set the assessors to the same footing
res.cider<-CLV3W(ciders,mode.scale=3,NN=FALSE,moddendoinertie=FALSE,gmax=20,graph=FALSE,cp.rand=5)
plot(res.cider,type="delta")
plot(res.cider,type="dendrogram")
print(res.cider)
summary(res.cider,2)
get_comp(res.cider,2)
get_loading(res.cider,2)
get_weight(res.cider,2)

ClustVarLV

Clustering of Variables Around Latent Variables

v2.0.1

GPL-3

Authors

Evelyne Vigneau [aut, cre], Mingkun Chen [ctb], Veronique Cariou [aut]

Initial release