ClustVarLV (version 1.3.2)

CLV: Hierarchical clustering of variables with consolidation

Description

Hierarchical Cluster Analysis of a set of variables with consolidation. Directional or local groups may be defined. Each group of variables is associated with a latent component. Moreover, the latent component may be constrained using external information collected on the observations or on the variables.

Usage

CLV(X, Xu = NULL, Xr = NULL, method, sX = TRUE, sXr = FALSE,
  sXu = FALSE, nmax = 20, maxiter = 20, graph = TRUE)

Arguments

X

: The matrix of variables to be clustered

Xu

: The external variables associated with the columns of X

Xr

: The external variables associated with the rows of X

method

: The criterion to be use in the cluster analysis. 1 : the squared covariance is used as a measure of proximity (directional groups). 2 : the covariance is used as a measure of proximity (local groups)

sX

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

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)

maxiter

: maximum number of iterations allowed for the consolidation/partitioning algorithm (by default maxiter=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 (method 1 => % var. expl. by the latent comp.)} Column 7{ : The value of the clustering criterion after consolidation} Column 8{ : The percentage of the explained initial criterion value after consolidation} Column 9{ : The number of iterations in the partitioning algorithm. Remark : A zero in columns 7 to 9 indicates that no consolidation was done}

item

• partition K
• comp
• loading

itemize

• clusters

Details

If external variables are used, define either Xr or Xu, but not both. Use the LCLV function when Xr and Xu are simultaneously provided.

See Also

CLV_kmeans, LCLV

Examples

data(apples_sh)
#directional groups
resclvX <- CLV(X = apples_sh$senso, method = 1, sX = TRUE, graph = TRUE)
#local groups with external variables Xr
resclvYX <- CLV(X = apples_sh$pref, Xr = apples_sh$senso,
                method = 2, sX = FALSE, sXr = TRUE, graph = TRUE)