APResult-class: Class "APResult"

In apcluster: Affinity Propagation Clustering

Class "APResult"

Description

S4 class for storing results of affinity propagation clustering. It extends the class ExClust.

Objects

Objects of this class can be created by calling apcluster or apclusterL for a given similarity matrix or calling one of these procedures with a data set and a similarity measure.

Slots

The following slots are defined for APResult objects. Most names are taken from Frey's and Dueck's original Matlab package:

sweeps:

number of times leveraged clustering ran with different subsets of samples

it:

number of iterations the algorithm ran

p:

input preference (either set by user or computed by apcluster or apclusterL)

netsim:

final total net similarity, defined as the sum of expref and dpsim (see below)

dpsim:

final sum of similarities of data points to exemplars

expref:

final sum of preferences of the identified exemplars

netsimLev:

total net similarity of the individual sweeps for leveraged clustering; only available for leveraged clustering

netsimAll:

vector containing the total net similarity for each iteration; only available if apcluster was called with details=TRUE

exprefAll:

vector containing the sum of preferences of the identified exemplars for each iteration; only available if apcluster was called with details=TRUE

dpsimAll:

vector containing the sum of similarities of data points to exemplars for each iteration; only available if apcluster was called with details=TRUE

idxAll:

matrix with sample-to-exemplar indices for each iteration; only available if apcluster was called with details=TRUE

Extends

Class "ExClust", directly.

Methods

plot

signature(x="APResult"): see plot-methods

plot

signature(x="ExClust", y="matrix"): see plot-methods

heatmap

signature(x="ExClust"): see heatmap-methods

heatmap

signature(x="ExClust", y="matrix"): see heatmap-methods

show

signature(object="APResult"): see show-methods

labels

signature(object="APResult"): see labels-methods

cutree

signature(object="APResult"): see cutree-methods

length

signature(x="APResult"): gives the number of clusters.

sort

signature(x="ExClust"): see sort-methods

as.hclust

signature(x="ExClust"): see coerce-methods

as.dendrogram

signature(object="ExClust"): see coerce-methods

Accessors

In the following code snippets, x is an APResult object.

[[

signature(x="APResult", i="index", j="missing"): x[[i]] returns the i-th cluster as a list of indices of samples belonging to the i-th cluster.

[

signature(x="APResult", i="index", j="missing", drop="missing"): x[i] returns a list of integer vectors with the indices of samples belonging to this cluster. The list has as many components as the argument i has elements. A list is returned even if i is a single integer.

similarity

signature(x="APResult"): gives the similarity matrix.

Author(s)

Ulrich Bodenhofer, Andreas Kothmeier, Johannes Palme

References

https://github.com/UBod/apcluster

APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463-2464. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btr406")}.

Frey, B. J. and Dueck, D. (2007) Clustering by passing messages between data points. Science 315, 972-976. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1126/science.1136800")}.

See Also

apcluster, apclusterL, show-methods, plot-methods, labels-methods, 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, details=TRUE)

## show details of clustering results
show(apres)

## plot information about clustering run
plot(apres)

## plot clustering result
plot(apres, x)

## plot heatmap
heatmap(apres, sim)

apcluster documentation built on May 29, 2024, 2:25 a.m.