optpart (version 3.0-3)

partana: Partition Analysis

Description

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 partana
summary(object, …)
# S3 method for 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

Value

an object of class ‘partana’ with components:

ptc

matrix of mean object-to-cluster similarity

ctc

matrix of mean cluster-to-cluster similarity

clustering

vector of numeric cluster assignments

ratio

within-cluster to among-cluster similarity ratio

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.

See Also

partition, optpart, plot.partana

Examples

# NOT RUN {
data(shoshveg)
data(shoshsite)
dis.bc <- dsvdis(shoshveg,'bray/curtis')
demo.part <- partana(cut(shoshsite$elev,5),dis.bc)
summary(demo.part)
# }