pmlCluster {phangorn}R Documentation

Stochastic Partitioning

Description

Stochastic Partitioning of genes into p cluster.

Usage

pmlCluster(formula, fit, weight, p = 4, part = NULL, ...)

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.
... 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

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=4)
sp
SH.test(sp)
## End(Not run)

[Package phangorn version 0.99-6 Index]