# apclusterL

##### Leveraged Affinity Propagation

Runs leveraged affinity propagation clustering

- Keywords
- cluster

##### Usage

```
# S4 method for matrix,missing
apclusterL(s, x,
sel, p=NA, q=NA, maxits=1000, convits=100, lam=0.9,
includeSim=FALSE, nonoise=FALSE, seed=NA)
# S4 method for character,ANY
apclusterL(s, x,
frac, sweeps, p=NA, q=NA, maxits=1000, convits=100, lam=0.9,
includeSim=TRUE, nonoise=FALSE, seed=NA, ...)
# S4 method for function,ANY
apclusterL(s, x,
frac, sweeps, p=NA, q=NA, maxits=1000, convits=100, lam=0.9,
includeSim=TRUE, nonoise=FALSE, seed=NA, ...)
```

##### Arguments

- s
an \(l \times length(sel)\) similarity matrix or a similarity function either specified as the name of a package provided similarity function as character string or a user provided function object for similarity calculation. If

`s`

is supplied as a similarity matrix, the columns must correspond to the same sub-selection of samples as specified in the`sel`

argument and must be in the same increasing order. For a package- or user-defined similarity function, additional parameters can be specified as appropriate for the chosen method and are passed on to the similarity function via the`...`

argument (see below). See the package vignette for a non-trivial example or supplying a user-defined similarity measure.- x
input data to be clustered; if

`x`

is a matrix or data frame, rows are interpreted as samples and columns are interpreted as features; apart from matrices or data frames,`x`

may be any other structured data type that contains multiple data items - provided that an appropriate`length`

function is available that returns the number of items- frac
fraction of samples that should be used for leveraged clustering. The similarity matrix will be generated for all samples against a random fraction of the samples as specified by this parameter.

- sweeps
number of sweeps of leveraged clustering performed with changing randomly selected subset of samples.

- sel
selected sample indices; a vector containing the sample indices of the sample subset used for leveraged AP clustering in increasing order.

- p
input preference; can be a vector that specifies individual preferences for each data point. If scalar, the same value is used for all data points. If

`NA`

, exemplar preferences are initialized according to the distribution of non-Inf values in`s`

. How this is done is controlled by the parameter`q`

. See also`apcluster`

.- q
if

`p=NA`

, exemplar preferences are initialized according to the distribution of non-Inf values in`s`

. If`q=NA`

, exemplar preferences are set to the median of non-Inf values in`s`

. If`q`

is a value between 0 and 1, the sample quantile with threshold`q`

is used, whereas`q=0.5`

again results in the median. See also`apcluster`

.- maxits
maximal number of iterations that should be executed

- convits
the algorithm terminates if the examplars have not changed for

`convits`

iterations- lam
damping factor; should be a value in the range [0.5, 1); higher values correspond to heavy damping which may be needed if oscillations occur

- includeSim
if

`TRUE`

, the similarity matrix (either computed internally or passed via the`s`

argument) is stored to the slot`sim`

of the returned`object. The default is`

`FALSE`

if`apclusterL`

has been called for a similarity matrix, otherwise the default is`TRUE`

.- nonoise
`apcluster`

adds a small amount of noise to`s`

to prevent degenerate cases; if`TRUE`

, this is disabled- seed
for reproducibility, the seed of the random number generator can be set to a fixed value before adding noise (see above), if

`NA`

, the seed remains unchanged- ...
all other arguments are passed to the selected similarity function as they are; note that possible name conflicts between arguments of

`apcluster`

and arguments of the similarity function may occur; therefore, we recommend to write user-defined similarity functions without additional parameters or to use closures to fix parameters (such as, in the example below);

##### Details

Affinity Propagation clusters data using a set of real-valued pairwise similarities as input. Each cluster is represented by a representative cluster center (the so-called exemplar). The method is iterative and searches for clusters maximizing an objective function called net similarity.

Leveraged Affinity Propagation reduces dynamic and static load for large datasets. Only a subset of the samples are considered in the clustering process assuming that they provide already enough information about the cluster structure.

When called with input data and the name of a package provided or a user
provided similarity function the function selects a random sample subset
according to the `frac`

parameter, calculates a rectangular
similarity matrix of all samples against this subset and repeats
affinity propagation `sweep`

times. A new sample subset is used
for each repetition. The clustering result of the sweep with the highest
net similarity is returned. Any parameters specific to the chosen
method of similarity calculation can be passed to `apcluster`

in addition to the parameters described above. The similarity matrix
for the best trial is also returned in the result object when requested
by the user (argument `includeSim`

).

When called with a rectangular similarity matrix (which represents a
column subset of the full similarity matrix) the function performs
AP clustering on this similarity matrix. The information
about the selected samples is passed to clustering with the
parameter `sel`

. This function is only needed when the user needs full
control of distance calculation or sample subset selection.

Apart from minor adaptations and optimizations, the implementation
of the function `apclusterL`

is largely analogous to Frey's and Dueck's Matlab code
(see http://www.psi.toronto.edu/affinitypropagation/).

##### Value

Upon successful completion, both functions returns an
` object.`

##### 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: 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: 10.1093/bioinformatics/btr406.

##### See Also

`, `

`show-methods`

,
`plot-methods`

, `labels-methods`

,
`preferenceRange`

, `apcluster-methods`

,
`apclusterK`

##### Examples

```
## create two Gaussian clouds
cl1 <- cbind(rnorm(150,0.2,0.05),rnorm(150,0.8,0.06))
cl2 <- cbind(rnorm(100,0.7,0.08),rnorm(100,0.3,0.05))
x <- rbind(cl1,cl2)
## leveraged apcluster
apres <- apclusterL(negDistMat(r=2), x, frac=0.2, sweeps=3, p=-0.2)
## show details of leveraged clustering results
show(apres)
## plot leveraged clustering result
plot(apres, x)
## plot heatmap of clustering result
heatmap(apres)
## show net similarities of single sweeps
apres@netsimLev
## show samples on which best sweep was based
apres@sel
```

*Documentation reproduced from package apcluster, version 1.4.3, License: GPL (>= 2)*