Description Usage Arguments Note Author(s) References See Also Examples
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.
1 2 |
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
1 2 3 4 5 | 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))
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