preferenceRange {apcluster} | R Documentation |

## Determine Meaningful Ranges for Input Preferences

### Description

Determines meaningful ranges for affinity propagation input preference

### Usage

```
## S4 method for signature 'matrix'
preferenceRange(s, exact=FALSE)
## S4 method for signature 'Matrix'
preferenceRange(s, exact=FALSE)
## S4 method for signature 'dgTMatrix'
preferenceRange(s, exact=FALSE)
## S4 method for signature 'sparseMatrix'
preferenceRange(s, exact=FALSE)
```

### Arguments

`s` |
an |

`exact` |
flag indicating whether exact ranges should be computed,
which is relatively slow; if bounds are sufficient,
supply |

### Details

Affinity Propagation clustering relies on an appropriate choice of input preferences. This function helps in finding a good choice by determining meaningful lower and upper bounds.

If the similarity matrix `s`

is sparse or if it contains
`-Inf`

similarities, only the similarities are taken into account
that are specified in `s`

and larger than `-Inf`

. In such
cases, the lower bound returned by `preferenceRange`

need not
correspond to one or two clusters. Moreover, it may also happen in
degenerate cases that the lower bound exceeds the upper bound.
In such a case, no warning or error is issued, so it is the user's
responsibility to ensure a proper interpretation of the results.
The method `apclusterK`

makes use of this function
internally and checks the plausibility of the result
returned by `preferenceRange`

.

### Value

returns a vector with two entries, the first of which is the minimal input preference (which would lead to 1 or 2 clusters) and the second of which is the maximal input prefence (which would lead to as many clusters as data samples).

### Author(s)

Ulrich Bodenhofer & Andreas Kothmeier apcluster@bioinf.jku.at

### References

http://www.bioinf.jku.at/software/apcluster/

Frey, B. J. and Dueck, D. (2007) Clustering by passing messages
between data points. *Science* **315**, 972-976.
DOI: doi:10.1126/science.1136800.

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

### 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)
## create similarity matrix
sim <- negDistMat(x, r=2)
## determine bounds
preferenceRange(sim)
## determine exact range
preferenceRange(sim, exact=TRUE)
```

*apcluster*version 1.4.11 Index]