phangorn (version 0.0-1)

pml: Likelihood of a tree.

Description

optim.pml computes the likelihood of a phylogenetic tree given a sequence alignment and a model. optim.pml optimizes the different model parameters.

Usage

pml(tree, data, bf=rep(1/length(levels),length(levels)), shape=1, 
    k=1, Q=rep(1,length(levels)*(length(levels)-1)/2), levels=attr(data,"levels"), 
	inv=0, g=NULL, w=NULL, eig=NULL, INV=NULL, ll.0=NULL, ...)     
optim.pml(object, optNni=FALSE, optBf=FALSE, optQ=FALSE,
    optInv=FALSE, optGamma=FALSE, optEdge=TRUE, control = list(maxit=10, eps=0.001))

Arguments

tree

A phylogenetic tree, object of class phylo.

data

The (DNA) alignment.

bf

Base frequencies.

shape

Shape parameter of the gamma distribution.

k

Number of intervalls of the discrete gamma distribution.

Q

A vector containing the lower triangular part of the rate matrix.

levels

Factor levels of the data.

inv

Proportion of invariable sites.

g

Rate.

w

Weight of the mixture.

eig

The eigenvalues and eigenvectors of the transition matrix. .

INV

For internal use.

object

An object of class pml.

optNni

Logical value indicating whether toplogy gets optimized (NNI).

optBf

Logical value indicating whether base frequencies gets optimized.

optQ

Logical value indicating whether rate matrix gets optimized. For dna this means: FALSE=Jukes-Cantor, TRUE=GTR

optInv

Logical value indicating whether proportion of variable size gets optimized.

optGamma

Logical value indicating whether gamma rate parameter gets optimized.

optEdge

Logical value indicating the edge lengths gets optimized.

control

A list of parameters for controlling the fitting process.

ll.0

For internal use

...

Further arguments passed to or from other methods.

Value

• Returns a list of class ll.phylo
• logLik
  Log likelihood of the tree.
• siteLik
  Site log likelihoods.
• root
  likelihood in the root node.
• weight
  Weight of the site patterns.

Details

The input variables w, g, eig, INV are used to speed up computation, especially during the optimising used in optim.pml, but are not intended to be used by the enduser. The toppology search uses a nearest neighbour interchange (NNI) and is similar to phyML.

References

Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maxumum likelihood approach. Journal of Molecular Evolution, 17, 368--376. Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates, Sunderland. Yang, Z. (2006). Computational Molecular evolution. Oxford University Press, Oxford.

See Also

For a different implementation see mlphylo.

Examples

example(NJ)
# Jukes-Cantor + Gamma + I   
  fitJC <- pml(tree, Laurasiatherian, k=4, inv=.25) 
  summary(fitJC)
# just optimise edge length parameter     
  fitJC <- optim.pml(fitJC) 
  summary(fitJC)
  plot(fitJC$tree)
  
# optimise parameter     
  fitJC <- optim.pml(fitJC, optNni=TRUE, optGamma=TRUE, optInv=TRUE) 
  summary(fitJC)
# F81 + Gamma + I - model
  fitF81 <- optim.pml(fitJC, optNni=TRUE, optGamma=TRUE, optInv=TRUE, optBf=TRUE)
  summary(fitF81) 
# GTR + Gamma + I - model
  fitGTR <- optim.pml(fitF81, optNni=TRUE, optGamma=TRUE, optInv=TRUE, optBf=TRUE, optQ=TRUE) 
  summary(fitGTR)

# 2-state data (RY-coded)  
    
  dat <- as.character(Laurasiatherian)
  # RY-coding
  dat[dat=="a"] <- "r"
  dat[dat=="g"] <- "r"
  dat[dat=="c"] <- "y"
  dat[dat=="t"] <- "y"
  dat <- phyDat(dat, levels=c("r","y"))
  fit2ST <- pml(tree, dat, k=4, inv=.25) 
  fit2ST <- optim.pml(fit2ST,optNni=TRUE, optGamma=TRUE, optInv=TRUE) 
  
  methods(class="pml")