phangorn (version 2.8.1)

multiphyDat2pmlPart: Partition model.

Description

Model to estimate phylogenies for partitioned data.

Usage

multiphyDat2pmlPart(x, rooted = FALSE, ...)
pmlPart2multiPhylo(x)
pmlPart(formula, object, control = pml.control(epsilon = 1e-08, maxit = 10,
  trace = 1), model = NULL, rooted = FALSE, ...)

Arguments

x

an object of class pmlPart

rooted

Are the gene trees rooted (ultrametric) or unrooted.

…

Further arguments passed to or from other methods.

formula

a formula object (see details).

object

an object of class pml or a list of objects of class pml .

control

A list of parameters for controlling the fitting process.

model

A vector containing the models containing a model for each partition.

Value

kcluster returns a list with elements

logLik

log-likelihood of the fit

trees

a list of all trees during the optimization.

object

an object of class "pml" or "pmlPart"

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 partitions, on the right the parameter which are optimized specific to each partition. The parameters available are "nni", "bf", "Q", "inv", "shape", "edge", "rate". Each parameters can be used only once in the formula. "rate" is only available for the right side of the formula.

For partitions with different edge weights, but same topology, pmlPen can try to find more parsimonious models (see example).

pmlPart2multiPhylo is a convenience function to extract the trees out of a pmlPart object.

See Also

pml,pmlCluster,pmlMix,SH.test

Examples

# NOT RUN {
data(yeast)
dm <- dist.logDet(yeast)
tree <- NJ(dm)
fit <- pml(tree,yeast)
fits <- optim.pml(fit)

weight=xtabs(~ index+genes,attr(yeast, "index"))[,1:10]

sp <- pmlPart(edge ~ rate + inv, fits, weight=weight)
sp

# }
# NOT RUN {
sp2 <- pmlPart(~ edge + inv, fits, weight=weight)
sp2
AIC(sp2)

sp3 <- pmlPen(sp2, lambda = 2)
AIC(sp3)
# }
# NOT RUN {
# }