# ward.cluster: Hierarchic Classification by Ward's Method In FactoClass: Combination of Factorial Methods and Cluster Analysis

## Description

Performs the classification by Ward's method from the matrix of Euclidean distances.

## Usage

 `1` ```ward.cluster(dista, peso = NULL , plots = TRUE, h.clust = 2, n.indi = 25 ) ```

## Arguments

 `dista ` matrix of Euclidean distances ( class(dista)=="dist" ). `peso ` (Optional) weight of the individuals, by default equal weights `plots ` it makes dendrogram and histogram of the Ward's method `h.clust ` if it is '0' returns a object of class `hclust` and a table of level indices, if it is '1' returns a object of class `hclust`, if it is '2' returns a table of level indices. `n.indi ` number of indices to draw in the histogram (default 25).

## Details

It is an entrance to the function `h.clus` to obtain the results of the procedure presented in Lebart et al. (1995). Initially the matrix of distances of Ward of the elements to classify is calculated:

The Ward's distance between two elements to classify \$i\$ and \$l\$ is given by:

W(i,l) = (m_i * m_l)/(m_i + m_i) * dist(i,l)^2

where \$m_i\$ y \$m_l\$ are the weights and \$dist(i,l)\$ is the Euclidean distance between them.

## Value

It returns an object of class hclust and a table of level indices (depending of h.clust). If plots = TRUE it draws the indices of level and the dendrogram.

## Author(s)

Pedro Cesar del Campo [email protected], Campo Elias Pardo [email protected] http://www.docentes.unal.edu.co/cepardot

## References

Lebart, L. and Morineau, A. and Piron, M. (1995) Statisitique exploratoire multidimensionnelle, Paris.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```data(ardeche) ca <- dudi.coa(ardeche\$tab,scannf=FALSE,nf=4) ward.cluster( dista= dist(ca\$li), peso=ca\$lw ) dev.new() HW <- ward.cluster( dista= dist(ca\$li), peso=ca\$lw ,h.clust = 1) plot(HW) rect.hclust(HW, k=4, border="red") ```

FactoClass documentation built on March 18, 2018, 1:37 p.m.