AggExResult-class {apcluster} | R Documentation |

## Class "AggExResult"

### Description

S4 class for storing results of exemplar-based agglomerative clustering

### Objects

Objects of this class can be created by calling `aggExCluster`

for a given similarity matrix.

### Slots

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

### Methods

- plot
`signature(x="AggExResult")`

: see`plot-methods`

- plot
`signature(x="AggExResult", y="matrix")`

: see`plot-methods`

- heatmap
`signature(x="AggExResult")`

: see`heatmap-methods`

- heatmap
`signature(x="AggExResult", y="matrix")`

: see`heatmap-methods`

- show
`signature(object="AggExResult")`

: see`show-methods`

- cutree
`signature(object="AggExResult", k="ANY", h="ANY")`

: see`cutree-methods`

- length
`signature(x="AggExResult")`

: gives the number of clustering levels in the clustering result.- as.hclust
`signature(x="AggExResult")`

: see`coerce-methods`

- as.dendrogram
`signature(object="AggExResult")`

: see`coerce-methods`

### Accessors

In the following code snippets, `x`

is an `AggExResult`

object.

- [[
`signature(x="AggExResult", i="index", j="missing")`

:`x[[i]]`

returns an object of class`ExClust`

corresponding to the clustering level with`i`

clusters; synonymous to`cutree(x, i)`

.- [
`signature(x="AggExResult", i="index", j="missing", drop="missing")`

:`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.- similarity
`signature(x="AggExResult")`

: gives the similarity matrix.

### Author(s)

Ulrich Bodenhofer, Andreas Kothmeier & Johannes Palme apcluster@bioinf.jku.at

### References

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: doi:10.1093/bioinformatics/btr406.

### See Also

`aggExCluster`

, `show-methods`

,
`plot-methods`

, `cutree-methods`

### Examples

```
## 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)
```

*apcluster*version 1.4.11 Index]