dplot {hopach} | R Documentation |
The hopach
clustering function orders the elements being clustered. This ordering can be used to rearrange the rows and columns in the corresponding distance matrix. A pseudo-color image of the ordered distance matrix will reveal the underlying patterns in the clustered data.
The functions 'heat.colors', 'terrain.colors' and 'topo.colors' create heat-spectrum (red to white) and topographical color schemes suitable for displaying ordered data, with 'n' giving the number of colors desired.
dplot(dist, hopachobj, ord = "final", col = heat.colors(12), main = NULL, xlab = NULL, ylab = NULL, labels = NULL, showclusters = TRUE, ...)
dist |
matrix of all pair wise distances between a set of 'p' elements,
as produced, for example, by the |
hopachobj |
output of the |
ord |
character string indicating which of the two orderings produced by |
col |
a list of colors such as that generated by 'rainbow', 'heat.colors', 'topo.colors', 'terrain.colors' or similar functions. |
main |
character string to be used as the main title |
xlab |
character string to be used as the horizontal axis label. If NULL, the label will be "" (no label). |
ylab |
character string to be used as the vertical axis label. If NULL, the label will be "" (no label). |
labels |
a vector of labels for the elements being clustered to be used on the axes. If labels=NULL, no axes are plotted - this is useful when there are a large number of elements being plotted. |
showclusters |
indicator of whether or not to show the cluster boundaries on the plot. If show.clusters=TRUE, dotted lines are drawn at the edges of the clusters. |
... |
additional arguments to the |
Thank you to Sandrine Dudoit <sandrine@stat.berkeley.edu> for her input.
Katherine S. Pollard <kpollard@gladstone.ucsf.edu> and Mark J. van der Laan <laan@stat.berkeley.edu>
van der Laan, M.J. and Pollard, K.S. A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap. Journal of Statistical Planning and Inference, 2003, 117, pp. 275-303.
http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/hopach.pdf
mydata<-matrix(rnorm(50),nrow=10) mydist<-distancematrix(mydata,d="euclid") clustresult<-hopach(mydata,dmat=mydist) dplot(mydist,clustresult,showclusters=FALSE) dplot(mydist,clustresult,col=topo.colors(15))