# Orders clustered objects using hierarchical clustering 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. ```r order.clusters(merit,clusters,within.order = order.single, between.order= order.single,...) ``` ## 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`. ## 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`. ## Returns A permutation of the objects represented by `merit` is returned. ## Author(s) Catherine B. Hurley ## See Also order.single ,order.endlink ,order.hclust . ## Examples ```r 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)) ```

order.clusters function