# pmlCluster

##### Stochastic Partitioning

Stochastic Partitioning of genes into p cluster.

- Keywords
- cluster

##### Usage

```
pmlCluster(formula, fit, weight, p = 1:5, part = NULL, nrep = 10,
control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), ...)
```

##### Arguments

- formula
a formula object (see details).

- fit
an object of class

`pml`

.- weight
`weight`

is matrix of frequency of site patterns for all genes.- p
number of clusters.

- part
starting partition, otherwise a random partition is generated.

- nrep
number of replicates for each p.

- control
A list of parameters for controlling the fitting process.

- …
Further arguments passed to or from other methods.

##### Details

The `formula`

object allows to specify which parameter get optimized.
The formula is generally of the form ```
edge + bf + Q ~ rate + shape +
…{}
```

, on the left side are the parameters which get optimized over all
cluster, on the right the parameter which are optimized specific to each
cluster. The parameters available are ```
"nni", "bf", "Q", "inv",
"shape", "edge", "rate"
```

. Each parameter can be used only once in the
formula. There are also some restriction on the combinations how parameters
can get used. `"rate"`

is only available for the right side. When
`"rate"`

is specified on the left hand side `"edge"`

has to be
specified (on either side), if `"rate"`

is specified on the right hand
side it follows directly that `edge`

is too.

##### Value

`pmlCluster`

returns a list with elements

log-likelihood of the fit

a list of all trees during the optimization.

fits for the final partitions

##### References

K. P. Schliep (2009). Some Applications of statistical phylogenetics (PhD Thesis)

Lanfear, R., Calcott, B., Ho, S.Y.W. and Guindon, S. (2012) PartitionFinder:
Combined Selection of Partitioning Schemes and Substitution Models for
Phylogenetic Analyses. *Molecular Biology and Evolution*, **29(6)**,
1695-1701

##### See Also

##### Examples

```
# NOT RUN {
# }
# NOT RUN {
data(yeast)
dm <- dist.logDet(yeast)
tree <- NJ(dm)
fit <- pml(tree,yeast)
fit <- optim.pml(fit)
weight <- xtabs(~ index+genes,attr(yeast, "index"))
set.seed(1)
sp <- pmlCluster(edge~rate, fit, weight, p=1:4)
sp
SH.test(sp)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package phangorn, version 2.5.5, License: GPL (>= 2)*