order: Orders objects using hierarchical clustering
In gclus: Clustering Graphics

Description Usage Arguments Details Value Author(s) References See Also Examples

Reorders objects so that similar (or high-merit) object pairs are adjacent. A permutation vector is returned.

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order.single(merit,clusters=NULL)
order.endlink(merit,clusters=NULL)
order.hclust(merit, reorder=TRUE,...)

`merit`	is either a symmetric matrix of merit or similarity score, or a `dist`.
`clusters`	if non-null, specifies a partial ordering. It should be a list whose ith element contains the indices the objects in the ith ordered cluster.
`reorder`	if TRUE, reorders the default ordering from `hclust`.
`...`	arguments are passed to `hclust`.

order.single performs a variation on single-link cluster analysis, devised by Gruvaeus and Wainer (1972). When two ordered clusters are merged, the new cluster is formed by placing the most similar endpoints of the joining clusters adjacent to each other. When applied to variables, the resulting order is useful for scatterplot matrices.

order.endlink is another variation on single-link cluster analysis, where the similarity between two ordered clusters is defined as the minimum distance between their endpoints. When two ordered clusters are merged, the new cluster is formed by placing the most similar endpoints of the joining clusters adjacent to each other. When applied to variables, the resulting order is useful for parallel coordinate displays.

order.hclust returns the order of objects from hclust if reorder is FALSE. Otherwise, it reorders the objects using hclust.reorder so that when two ordered clusters are merged, the new cluster is formed by placing the most similar endpoints of the joining clusters adjacent to each other. order.hclust(m,method="single") is equivalent to order.single when clusters is NULL. The default method of hclust is "complete", see hclust for other possibilities.

A permutation of the objects represented by merit is returned.

Catherine B. Hurley

Hurley, Catherine B. “Clustering Visualisations of Multidimensional Data”, Journal of Computational and Graphical Statistics, vol. 13, (4), pp 788-806, 2004.

Gruvaeus, G. and Wainer, H. (1972), “Two Additions to Hierarchical Cluster Analysis”, British Journal of Mathematical and Statistical Psychology, 25, 200-206.

cpairs, cparcoord,plotcolors, reorder.hclust,order.clusters, hclust.

data(state)
state.cor <- cor(state.x77)
order.single(state.cor)
order.endlink(state.cor)
order.hclust(state.cor,method="average")

# Use for plotting...

cpairs(state.x77, panel.colors=dmat.color(state.cor), order.single(state.cor),pch=".",gap=.4)
cparcoord(state.x77, order.endlink(state.cor),panel.colors=dmat.color(state.cor))


# Order the states instead of the variables...

state.d <- dist(state.x77)
state.o <- order.single(-state.d)

op <- par(mar=c(1,6,1,1))
cmat <- dmat.color(as.matrix(state.d), rev(cm.colors(5)))
plotcolors(cmat[state.o,state.o], rlabels=state.name[state.o])
par(op)