phangorn (version 1.0-2)

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, model="", ...)     
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), model = NULL, subs = NULL, ...)

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 intervals of the discrete gamma distribution.

shape

Shape parameter of the gamma distribution.

rate

Rate.

model

Amino acid models: one of "WAG", "JTT", "Dayhoff" or "LG" or nucleotide model

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.

subs

a (integer) vector same length as Q to specify the optimization of Q

...

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 topology search uses a nearest neighbor interchange (NNI) and the implementation is similar to phyML. The option model in pml is only used for amino acid models. The option model defines the nucleotide model which is getting optmised, all models which are included in modeltest can be chosen. Setting this option (e.g. "K81" or "GTR") overrules options optBf and optQ. Here is a overview how to estimate different phylogenetic models with pml: lll{ model optBf optQ Jukes-Cantor FALSE FALSE F81 TRUE FALSE symmetric FALSE TRUE GTR TRUE TRUE } Via model in optim.pml the following nucleotide models can be specified: JC, F81, K80, HKY, TrNe, TrN, TPM1, K81, TPM1u, TPM2, TPM2u, TPM3, TPM3u, TIM1e, TIM1, TIM2e, TIM2, TIM3e, TIM3, TVMe, TVM, SYM and GTR. For how these s models are specified see Posada (2008). So far 4 amino acid models are supported ("WAG", "JTT", "Dayhoff" and "LG").

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. Whelan, S. and Goldman, N. (2001) A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular Biology and Evolution, 18, 691--699 Le, S.Q. and Gascuel, O. (2008) LG: An Improved, General Amino-Acid Replacement Matrix Molecular Biology and Evolution 25(7), 1307--1320 Posada, D. (2008) jModelTest: Phylogenetic Model Averaging. Molecular Biology and Evolution 25: 1253-1256

Examples

example(NJ)
# Jukes-Cantor (starting tree from NJ)  
  fitJC <- pml(tree, Laurasiatherian)  
# optimize edge length parameter     
  fitJC <- optim.pml(fitJC)
  fitJC 
  
# search for a better tree using NNI rearrangements     
  fitJC <- optim.pml(fitJC, optNni=TRUE)
  fitJC   
  plot(fitJC$tree)

# JC + Gamma + I - model
  fitJC_GI <- update(fitJC, k=4, inv=.2)
# optimize shape parameter + proportion of invariant sites     
  fitJC_GI <- optim.pml(fitJC_GI, optGamma=TRUE, optInv=TRUE)
# GTR + Gamma + I - model
  fitGTR <- optim.pml(fitJC_GI, optNni=TRUE, optGamma=TRUE, optInv=TRUE, optBf=TRUE, optQ=TRUE)

# 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) 
  fit2ST
  # show some of the methods available for class pml
  methods(class="pml")