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

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

`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 |

`n.indi ` |
number of indices to draw in the histogram (default 25). |

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.

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.

Pedro Cesar del Campo pcdelcampon@unal.edu.co, Campo Elias Pardo cepardot@unal.edu.co http://www.docentes.unal.edu.co/cepardot

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

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")
``` |

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