| show-methods {apcluster} | R Documentation |
Display Clustering Result Objects
Description
Display methods for S4 classes APResult,
ExClust, and AggExResult
Usage
## S4 method for signature 'APResult'
show(object)
## S4 method for signature 'ExClust'
show(object)
## S4 method for signature 'AggExResult'
show(object)
Arguments
object |
an object of class
|
Details
show displays the most important information stored in
object.
For APResult objects,
the number of data samples, the number of clusters, the number of
iterations, the input preference, the final objective
function values, the vector of exemplars, the list of clusters and
for leveraged clustering the selected sample subset are printed.
For ExClust objects,
the number of data samples, the number of clusters,
the vector of exemplars, and list of clusters are printed.
For AggExResult objects,
only the number of data samples and the maximum
number of clusters are printed. For retrieving a particular
clustering level, use the function cutree.
For accessing more detailed information, it is necessary to
access the slots of object directly. Use
str to get a compact overview of all slots of an object.
Value
show returns an invisible NULL
Author(s)
Ulrich Bodenhofer, Andreas Kothmeier, and Johannes Palme
References
https://github.com/UBod/apcluster
Bodenhofer, U., Kothmeier, A., and Hochreiter, S. (2011) APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463-2464. DOI: doi:10.1093/bioinformatics/btr406.
See Also
APResult,
ExClust, AggExResult,
cutree-methods
Examples
## create two Gaussian clouds
cl1 <- cbind(rnorm(100, 0.2, 0.05), rnorm(100, 0.8, 0.06))
cl2 <- cbind(rnorm(50, 0.7, 0.08), rnorm(50, 0.3, 0.05))
x <- rbind(cl1, cl2)
## compute similarity matrix (negative squared Euclidean)
sim <- negDistMat(x, r=2)
## run affinity propagation
apres <- apcluster(sim)
## show details of clustering results
show(apres)
## apply agglomerative clustering to apres
aggres <- aggExCluster(sim, apres)
## display overview of result
show(aggres)
## show clustering level with two clusters
show(cutree(aggres, 2))