AggExResult-class {apcluster} | R Documentation |
S4 class for storing results of exemplar-based agglomerative clustering
Objects of this class can be created by calling aggExCluster
for a given similarity matrix.
The following slots are defined for AggExResult objects:
l
:number of samples in the data set
sel
:subset of samples used for leveraged clustering (empty for normal clustering)
maxNoClusters
:maximum number of clusters in the
cluster hierarchy, i.e. it
contains clusterings with 1 - maxNoClusters
clusters.
exemplars
:list of length maxNoClusters
;
the i
-th component of the list is a vector of i
exemplars (corresponding to the level with i
clusters).
clusters
:list of length maxNoClusters
;
the i
-th component of clusters
is a list of i
clusters, each of which is a vector of sample indices.
merge
:a maxNoClusters-1
by 2 matrix that
contains the merging hierarchy; fully analogous to the
slot merge
in the class hclust
.
height
:a vector of length maxNoClusters-1
that
contains the merging objective of each merge; largely analogous to
the slot height
in the class hclust
except
that the slot height
in AggExResult
objects is
supposed to be non-increasing, since aggExCluster
is based on similarities, whereas hclust
uses
dissimilarities.
order
:a vector containing a permutation of indices
that can be used for plotting proper dendrograms without crossing
branches; fully analogous to the
slot order
in the class hclust
.
labels
:a character vector containing labels of clustered objects used for plotting dendrograms.
sim
:similarity matrix; only available if
aggExCluster
was called with similarity
function and includeSim=TRUE
.
call
:method call used to produce this clustering result
signature(x="AggExResult")
: see
plot-methods
signature(x="AggExResult", y="matrix")
: see
plot-methods
signature(x="AggExResult")
: see
heatmap-methods
signature(x="AggExResult", y="matrix")
: see
heatmap-methods
signature(object="AggExResult")
: see
show-methods
signature(object="AggExResult", k="ANY",
h="ANY")
: see cutree-methods
signature(x="AggExResult")
: gives the number of
clustering levels in the clustering result.
signature(x="AggExResult")
: see
coerce-methods
signature(object="AggExResult")
: see
coerce-methods
In the following code snippets, x
is an AggExResult
object.
x[[i]]
: Returns an object of class
ExClust
corresponding to the clustering level
with i
clusters; synonymous to cutree(x, i)
.
x[i]
: Returns a list of ExClust
objects with all clustering levels specified in vector i
.
So, the list has as many components as the argument i
has
elements. A list is returned even if i
is a single level.
signature(x="AggExResult")
: gives the similarity
matrix.
Ulrich Bodenhofer, Andreas Kothmeier & Johannes Palme apcluster@bioinf.jku.at
http://www.bioinf.jku.at/software/apcluster
Bodenhofer, U., Kothmeier, A., and Hochreiter, S. (2011) APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463-2464. DOI: 10.1093/bioinformatics/btr406.
aggExCluster
, show-methods
,
plot-methods
, cutree-methods
## create two Gaussian clouds cl1 <- cbind(rnorm(50, 0.2, 0.05), rnorm(50, 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) ## compute agglomerative clustering from scratch aggres1 <- aggExCluster(sim) ## show results show(aggres1) ## plot dendrogram plot(aggres1) ## plot heatmap along with dendrogram heatmap(aggres1, sim) ## plot level with two clusters plot(aggres1, x, k=2) ## run affinity propagation apres <- apcluster(sim, q=0.7) ## create hierarchy of clusters determined by affinity propagation aggres2 <- aggExCluster(sim, apres) ## show results show(aggres2) ## plot dendrogram plot(aggres2) ## plot heatmap heatmap(aggres2, sim) ## plot level with two clusters plot(aggres2, x, k=2)