gclus (version 1.3.2)

order.clusters: Orders clustered objects using hierarchical clustering

Description

Reorders objects so that similar (or high-merit) object pairs are adjacent. The clusters argument specifies (possibly ordered) groups, and objects within a group are kept together.

Usage

order.clusters(merit,clusters,within.order = order.single, 
    between.order= order.single,...)

Value

A permutation of the objects represented by merit is returned.

Arguments

merit

is either a symmetric matrix of merit or similarity score, or a dist.

clusters

specifies a partial grouping. It should either be a list whose ith element contains the indices of the objects in the ith cluster, or a vector of integers whose ith element gives the cluster membership of the ith object. Either representation may be used to specify grouping, the first is preferrable to specify adjacencies.

within.order

is a function used to order the objects within each cluster.

between.order

is a function used to order the clusters.

...

arguments are passed to within.order.

Author

Catherine B. Hurley

Details

within.order may be NULL, in which case objects within a cluster are assumed to be in order. Otherwise, within.order should be one of the ordering functions order.single,order.endlink or order.hclust.

between.order may be NULL, in which case cluster order is preserved. Otherwise, betweem.order should be one of the ordering functions that uses a partial ordering, order.single or order.endlink.

See Also

order.single,order.endlink,order.hclust.

Examples

data(state)
state.d <- dist(state.x77)


# Order the states, keeping states in a division together.
state.o <- order.clusters(-state.d, as.numeric(state.division))
cmat <- dmat.color(as.matrix(state.d), rev(cm.colors(5)))


op <- par(mar=c(1,6,1,1))
rlabels <- state.name[state.o]
plotcolors(cmat[state.o,state.o], rlabels=rlabels)
par(op)


# Alternatively, use kmeans to place the  states into 6 clusters
state.km <- kmeans(state.d,6)$cluster

# An ordering obtained from the kmeans clustering...
state.o <- unlist(memship2clus(state.km))


layout(matrix(1:2,nrow=1,ncol=2),widths=c(0.1,1))
op <- par(mar=c(1,1,1,.2))
state.colors <- cbind(state.km,state.km)
plotcolors(state.colors[state.o,])

par(mar=c(1,6,1,1))
rlabels <- state.name[state.o]
plotcolors(cmat[state.o,state.o], rlabels=rlabels)

par(op)
layout(matrix(1,1))



# In the ordering above, the ordering of clusters and the
# ordering of objects within the clusters is arbitrary.
# order.clusters gives an improved order but preserves the kmeans clusters.

state.o <- order.clusters(-state.d, state.km)

# and replot
layout(matrix(1:2,nrow=1,ncol=2),widths=c(0.1,1))
op <- par(mar=c(1,1,1,.2))
state.colors <- cbind(state.km,state.km)
plotcolors(state.colors[state.o,])

par(mar=c(1,6,1,1))
rlabels <- state.name[state.o]
plotcolors(cmat[state.o,state.o], rlabels=rlabels)

par(op)
layout(matrix(1,1))