CLV {ClustVarLV} | R Documentation |
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 = NULL,
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. |
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) |
sXu |
,TRUE/FALSE : standardization or not of the columns Xu (FALSE by default) |
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/FALSE (by default TRUE) : dendrogram and variation of the optimization criterion. |
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.
Value
tabres |
Results of the clustering algorithm. In each line you find the results of one specific step of the hierarchical clustering.
|
partition K |
contains a list for each number of clusters of the partition, K=2 to nmax with
|
References
Vigneau E., Qannari E.M. (2003). Clustering of variables around latents components. Comm. Stat, 32(4), 1131-1150.
Vigneau E., Chen M., Qannari E.M. (2015). ClustVarLV: An R Package for the clustering of Variables around Latent Variables. The R Journal, 7(2), 134-148
See Also
CLV_kmeans, LCLV
Examples
data(apples_sh)
#directional groups
resclvX <- CLV(X = apples_sh$senso, method = "directional", sX = TRUE)
plot(resclvX,type="dendrogram")
plot(resclvX,type="delta")
#local groups with external variables Xr
resclvYX <- CLV(X = apples_sh$pref, Xr = apples_sh$senso, method = "local", sX = FALSE, sXr = TRUE)