phangorn (version 0.0-2)

pml: Likelihood of a tree.

Description

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=NULL, Q=NULL, inv=0, k=1, shape=1, rate=1, ...)     
optim.pml(object, optNni=FALSE, optBf=FALSE, optQ=FALSE,
    optInv=FALSE, optGamma=FALSE, optEdge=TRUE, optRate=FALSE, 
    control = pml.control(maxit=10, eps=0.001, trace=TRUE))

Arguments

tree

A phylogenetic tree, object of class phylo.

data

The (DNA) alignment.

bf

Base frequencies.

Q

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

inv

Proportion of invariable sites.

k

Number of intervalls of the discrete gamma distribution.

shape

Shape parameter of the gamma distribution.

rate

Rate.

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.

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.

optRate

Logical value indicating the overall rate gets optimized.

control

A list of parameters for controlling the fitting process.

...

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 w0, pl0, rate are mainly for the use in mixture or partition models and are only used internally. The toppology search uses a nearest neighbour interchange (NNI) and the implenatation is similar to phyML. Here is a overview how to estimate different phylogenetic models with pml: lll{ model optBf optQ Jukes-Cantor FALSE FALSE F81 TRUE FALSE GTR TRUE TRUE }

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 (starting tree from NJ)  
  fitJC <- pml(tree, Laurasiatherian) 
# optimise edge length parameter     
  fitJC <- optim.pml(fitJC)
  summary(fitJC)

# search for a better tree using NNI rearrangements     
  fitJC <- optim.pml(fitJC, optNni=TRUE)   
  summary(fitJC)
  plot(fitJC$tree)

# JC + Gamma + I - model
  fitJC_GI <- update(fitJC, k=4, inv=.2)
# optimise shape parameter + proportion of invariant sites     
  fitJC_GI <- optim.pml(fitJC_GI, optGamma=TRUE, optInv=TRUE)
  summary(fitJC_GI) 
# GTR + Gamma + I - model
  fitGTR <- optim.pml(fitJC_GI, 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) 
  
  # show some of the methods available for class pml
  methods(class="pml")