phangorn: pml – R documentation

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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.control(epsilon = 1e-08, maxit = 10, trace = 1)

pml(tree, data, bf = NULL, Q = NULL, inv = 0, k = 1, shape = 1,
  rate = 1, model = NULL, site.rate = "gamma", ...)

optim.pml(object, optNni = FALSE, optBf = FALSE, optQ = FALSE,
  optInv = FALSE, optGamma = FALSE, optEdge = TRUE, optRate = FALSE,
  optRooted = FALSE, control = pml.control(epsilon = 1e-08, maxit = 10,
  trace = 1L), model = NULL, rearrangement = ifelse(optNni, "NNI", "none"),
  subs = NULL, ratchet.par = list(iter = 20L, maxit = 100L, prop = 1/2),
  ...)

Arguments

`epsilon`	Stop criterion for optimisation (see details).
`maxit`	Maximum number of iterations (see details).
`trace`	Show output during optimization (see details).
`tree`	A phylogenetic `tree`, object of class `phylo`.
`data`	An alignment, object of class `phyDat`.
`bf`	Base frequencies (see details).
`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`	allows to choose an amino acid models or nucleotide model, see details.
`site.rate`	Indicates what type of gamma distribution to use. Options are "gamma" approach of Yang 1994 (default), "quadrature" after the Laguerre quadrature approach of Felsenstein 2001.
`...`	Further arguments passed to or from other methods.
`object`	An object of class `pml`.
`optNni`	Logical value indicating whether topology 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.
`optRooted`	Logical value indicating if the edge lengths of a rooted tree get optimized.
`control`	A list of parameters for controlling the fitting process.
`rearrangement`	type of tree tree rearrangements to perform, one of "none", "NNI", "stochastic" or "ratchet"
`subs`	A (integer) vector same length as Q to specify the optimization of Q
`ratchet.par`	search parameter for stochastic search

Details

Base frequencies in pml can be supplied in different ways. For amino acid they are usually defined through specifying a model, so the argument bf does not need to be specified. Otherwise if bf=NULL, each state is given equal probability. It can be a numeric vector given the frequencies. Last but not least bf can be string "equal", "empirical" and for codon models additionally "F3x4".

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 optimised, 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:

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. These models are specified as in Posada (2008).

So far 17 amino acid models are supported ("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blosum62", "Dayhoff_DCMut" and "JTT_DCMut") and additionally rate matrices and amino acid frequencies can be supplied.

It is also possible to estimate codon models (e.g. YN98), for details see also the chapter in vignette("phangorn-specials").

If the option 'optRooted' is set to TRUE than the edge lengths of rooted tree are optimized. The tree has to be rooted and by now ultrametric! Optimising rooted trees is generally much slower.

pml.control controls the fitting process. epsilon and maxit are only defined for the most outer loop, this affects pmlCluster, pmlPart and pmlMix. epsilon is defined as (logLik(k)-logLik(k+1))/logLik(k+1), this seems to be a good heuristics which works reasonably for small and large trees or alignments. If trace is set to zero than no out put is shown, if functions are called internally than the trace is decreased by one, so a higher of trace produces more feedback.

If rearrangement is set to stochastic a stochastic search algorithm similar to Nguyen et al. (2015). and for ratchet the likelihood ratchet as in Vos (2003). This should helps often to find better tree topologies, especially for larger trees.

Value

pml or optim.pml return a list of class pml, some are useful for further computations like

`tree`	the phylogenetic tree.
`data`	the alignment.
`logLik`	Log-likelihood of the tree.
`siteLik`	Site log-likelihoods.
`weight`	Weight of the site patterns.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum 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.

Adachi, J., P. J. Waddell, W. Martin, and M. Hasegawa (2000) Plastid genome phylogeny and a model of amino acid substitution for proteins encoded by chloroplast DNA. Journal of Molecular Evolution, 50, 348–358

Rota-Stabelli, O., Z. Yang, and M. Telford. (2009) MtZoa: a general mitochondrial amino acid substitutions model for animal evolutionary studies. Mol. Phyl. Evol, 52(1), 268–72

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

Yang, Z., R. Nielsen, and M. Hasegawa (1998) Models of amino acid substitution and applications to Mitochondrial protein evolution. Molecular Biology and Evolution, 15, 1600–1611

Abascal, F., D. Posada, and R. Zardoya (2007) MtArt: A new Model of amino acid replacement for Arthropoda. Molecular Biology and Evolution, 24, 1–5

Kosiol, C, and Goldman, N (2005) Different versions of the Dayhoff rate matrix - Molecular Biology and Evolution, 22, 193–199

L.-T. Nguyen, H.A. Schmidt, A. von Haeseler, and B.Q. Minh (2015) IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Molecular Biology and Evolution, 32, 268–274.

Vos, R. A. (2003) Accelerated Likelihood Surface Exploration: The Likelihood Ratchet. Systematic Biology, 52(3), 368–373

Yang, Z., and R. Nielsen (1998) Synonymous and nonsynonymous rate variation in nuclear genes of mammals. Journal of Molecular Evolution, 46, 409-418.

Lewis, P.O. (2001) A likelihood approach to estimating phylogeny from discrete morphological character data. Systematic Biology 50, 913–925.

Examples

example(NJ)
# Jukes-Cantor (starting tree from NJ)
  fitJC <- pml(tree, Laurasiatherian)
# optimize edge length parameter
  fitJC <- optim.pml(fitJC)
  fitJC

## Not run: 
# 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, rearrangement = "stochastic",
      optGamma=TRUE, optInv=TRUE, model="GTR")

## End(Not run)


# 2-state data (RY-coded)
  dat <- acgt2ry(Laurasiatherian)
  fit2ST <- pml(tree, dat)
  fit2ST <- optim.pml(fit2ST,optNni=TRUE)
  fit2ST
# show some of the methods available for class pml
  methods(class="pml")

phangorn

Phylogenetic Reconstruction and Analysis

v2.7.0

GPL (>= 2)

Authors

Klaus Schliep [aut, cre] (<https://orcid.org/0000-0003-2941-0161>), Emmanuel Paradis [aut] (<https://orcid.org/0000-0003-3092-2199>), Leonardo de Oliveira Martins [aut] (<https://orcid.org/0000-0001-5247-1320>), Alastair Potts [aut], Tim W. White [aut], Cyrill Stachniss [ctb], Michelle Kendall [ctb], Keren Halabi [ctb], Richel Bilderbeek [ctb], Kristin Winchell [ctb], Liam Revell [ctb], Mike Gilchrist [ctb], Jeremy Beaulieu [ctb], Brian O'Meara [ctb], Long Qu [ctb]

Initial release

pml