HCPC {FactoMineR}R Documentation

Hierarchical Classification on Principle Components (HCPC)

Description

Performs an unsupervised hierarchical classification on results from a factor analysis. It is possible to cut the tree by clicking at the suggested (or an other) level. Results inlude paragons, description of the clusters, graphics.

Usage

HCPC(res, nb.clust=0, consol=TRUE, iter.max=10, min=3, 
  max=NULL, metric="euclidean", method="ward", order=TRUE,
  graph.scale="inertia", nb.par=5, graph=TRUE, proba=0.05, ...)

Arguments

res Either the result of a factor analysis, a dataframe, or a vector.
nb.clust an integer. If 0, the tree is cut at the level the user clicks on. If -1, the tree is automatically cut at the suggested level (see details). If a (positive) integer, the tree is cut with nb.cluters clusters.
consol a boolean. If TRUE, a k-means consolidation is performed.
iter.max An integer. The maximum number of iterations for the consolidation.
min an integer. The least possible number of clusters suggested.
max an integer. The higher possible number of clusters suggested.
metric The metric used to built the tree. See agnes for details.
method The method used to built the tree. See agnes for details.
order A boolean. If TRUE, clusters are ordered following their center coordinate on the first axis.
graph.scale A character string. By default "inertia" and the height of the tree corresponds to the inertia gain, else "sqrt-inertia" the square root of the inertia gain.
nb.par An integer. The number of edited paragons.
graph If TRUE, graphics are displayed. If FALSE, no graph are displayed.
proba The probability used to select axes and variables in catdes (see catdes for details.
... Other arguments from other methods.

Details

The function first built the tree with agnes. Then the sum of the intra-cluster inertia are calculated for each partition. The suggested partition is the one with the higher relative loss of inertia (i(clusters n+1)/i(cluster n)).

The absolut loss of inertia (i(cluster n)-i(cluster n+1)) is ploted with the tree.

Value

Returns a list including:

data.clust The original data with a supplementary row called class containing the partition.
desc.fact The description of the classes by factors (axes) or variables (var). See catdes for details.
call A list or parameters and internal objects.
ind.desc The paragons (para) and the more typical individuals of each cluster. See details.


Returns the tree and a barplot of the inertia gains, the individual factor map with the tree (3D), the factor map with individuals colored by cluster (2D).

Author(s)

Guillaume Le Ray, Quentin Molto, Francois Husson husson@agrocampus-ouest.fr

See Also

plot.HCPC, catdes

Examples

## Not run: 
data(iris)
# Principal Component Analysis:
res.pca <- PCA(iris[,1:4], ncp=10, graph=FALSE)
# Clustering, auto nb of clusters:
res.hcpc=HCPC(res.pca, nb.clust=-1)

## To choose an other partition on the tree:
res.HCPC=HCPC(res.hcpc)
## End(Not run)

[Package FactoMineR version 1.12 Index]