ClustOfVar (version 1.1)

cutreevar: Cut a tree into groups of variables

Description

Cuts a hierarchical tree of variables resulting from hclustvar into several clusters by specifying the desired number of clusters.

Usage

cutreevar(obj, k = NULL, matsim = FALSE)

Arguments

obj

an object of class 'hclustvar'.

k

an integer scalar with the desired number of clusters.

matsim

boolean, if TRUE, the matrices of similarities between variables in same cluster are calculated.

Value

var

a list of matrices of squared loadings i.e. for each cluster of variables, the squared loadings on first principal component of PCAmix. For quantitative variables (resp. qualitative), squared loadings are the squared correlations (resp. the correlation ratios) with the first PC (the cluster center).

sim

a list of matrices of similarities i.e. for each cluster, similarities between their variables. The similarity between two variables is defined as a square cosine: the square of the Pearson correlation when the two variables are quantitative; the correlation ratio when one variable is quantitative and the other one is qualitative; the square of the canonical correlation between two sets of dummy variables, when the two variables are qualitative. sim is 'NULL' if 'matsim' is 'FALSE'.

cluster

a vector of integers indicating the cluster to which each variable is allocated.

wss

the within-cluster sum of squares for each cluster: the sum of the correlation ratio (for qualitative variables) and the squared correlation (for quantitative variables) between the variables and the center of the cluster.

E

the pourcentage of homogeneity which is accounted by the partition in k clusters.

size

the number of variables in each cluster.

scores

a n by k numerical matrix which contains the k cluster centers. The center of a cluster is a synthetic variable: the first principal component calculated by PCAmix. The k columns of scores contain the scores of the n observations units on the first PCs of the k clusters.

coef

a list of the coefficients of the linear combinations defining the synthetic variable of each cluster.

See Also

hclustvar

Examples

# NOT RUN {
data(decathlon)
tree <- hclustvar(decathlon[,1:10])
plot(tree)
#choice of the number of clusters
stability(tree,B=40)
part <- cutreevar(tree,4)
print(part)
summary(part)

# }