R: Display Clustering Result Objects

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 `APResult`, `ExClust`, or `AggExResult`

### 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 & 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: 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))

[Package apcluster version 1.4.7 Index]