ClustVarLV (version 1.3.2)

LCLV: L-CLV for L-shaped data

Description

Define clusters of X-variables aroud latent components. In each cluster, two latent components are extracted, the first one is a linear combination of the external information collected for the rows of X and the second one is a linear combination of the external information associated with the columns of X.

Usage

LCLV(X, Xr, Xu, ccX = FALSE, sX = TRUE, sXr = FALSE, sXu = FALSE,
  nmax = 20, graph = TRUE)

Arguments

X

The matrix of variables to be clustered

Xr

The external variables associated with the rows of X

Xu

The external variables associated with the columns of X

ccX

TRUE/FALSE : double centering of X (FALSE, by default) If FALSE this implies that cX = TRUE : column-centering of X

sX

TRUE/FALSE : standardization or not of the columns X (TRUE by default)

sXr

TRUE/FALSE : standardization or not of the columns Xr (FALSE by default) (predefined -> cXr = TRUE : column-centering of Xr)

sXu

TRUE/FALSE : standardization or not of the columns Xu (FALSE by default) (predefined -> cXu= FALSE : no centering, Xu considered as a weight matrix)

nmax

maximum number of partitions for which the consolidation will be done (by default nmax=20)

graph

TRUE : dendrogram and evolution of the aggregation criterion before and after consolidation (default) FALSE : no graphs

Value

• tabres
  Results of the 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 (HAC)} Column 5{ : The value of the clustering criterion for the HAC} Column 6{ : The percentage of the explained initial criterion value} Column 7{ : The value of the clustering criterion after consolidation} Column 8{ : The percentage of the explained initial criterion value 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 }

item

• partition K
• compt
• compc
• loading_v
• loading_u

itemize

• clusters

References

Vigneau, E., Endrizzi, I.,& Qannari, E.M. (2011). Finding and explaining clusters of consumers using CLV approach. Food Quality and Preference, 22, 705-713. Vigneau, E., Charles, M.,& Chen, M. (2014). External preference segmentation with additional information on consumers: A case study on apples. Food Quality and Preference, 32, 83-92.