R: Stochastic Partitioning

pmlCluster {phangorn} | R Documentation

## Stochastic Partitioning

### Description

Stochastic Partitioning of genes into p 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

`logLik` | log-likelihood of the fit
`trees` | a list of all trees during the optimization.
`fits` | fits for the final partitions

### Author(s)

Klaus Schliep klaus.schliep@gmail.com

### 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

`pml`,`pmlPart`,`pmlMix`,`SH.test`

### Examples

    ## 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)

    ## End(Not run)

[Package phangorn version 2.6.2 Index]