pml {phangorn}R Documentation

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.

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:
model optBf optQ
Jukes-Cantor FALSE FALSE
F81 TRUE FALSE
GTR TRUE TRUE

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.

Author(s)

Klaus Schliep K.P.Schliep@massey.ac.nz

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)

## Not run:     
# 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)
## End(Not run)

# 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")  

[Package phangorn version 0.0-2 Index]