Partition analysis evaluates the within-cluster to among-cluster
similarity of classifications as a measure of cluster validity
Usage
partana(c,dist)
## S3 method for class 'partana'
summary(object, ...)
## S3 method for class 'partana'
plot(x,panel='all',zlim=range(x$ptc),col=heat.colors(12),...)
Arguments
c
an integer or factor vector, or an object of class ‘clustering’,
‘partana’, ‘partition’, or ‘stride’
dist
an object of class ‘dist’ from functions dist,
dsvdis or vegdist
.
object
an object of class ‘partana’
x
an object of class ‘partana’
panel
an integer switch to indicate which panel to draw
zlim
the min and max values for the color map
col
a color map name (heat.colors(12) is the default)
...
ancillary arguments to pass to summary or plot
Details
Calculates mean object-to-cluster similarity, mean cluster-to-cluster
similarity, and mean within-cluster to among-cluster similarity. partana operates
on partitions or clusterings produced by a wide range of algorithms, including specific
methods for the products of functions optpart, slice,
pam and diana.
summary produces a matrix of the mean cluster-to-cluster similarities,
and the overall within-cluster/among-cluster similarity ratio.
plot plots two panels in sequence in the current device. The first shows
the mean similarity of every object to each cluster, sorted by mean similarity
to the other members of its own cluster, with objects as columns and clusters
as rows. The second panel shows the mean similarity of every cluster to every
other cluster and mean within-cluster similarity, ignoring cluster size. These
plots are known as ‘Mondriaan’ plots, where the similarities are given by lines
colored from min to max. If the ‘partana’ object was produced by optpart, a
third panel is plotted showing the trace of the optimization.