# pml.control

##### Likelihood of a tree.

`pml`

computes the likelihood of a phylogenetic tree given a sequence
alignment and a model. `optim.pml`

optimizes the different model
parameters.

- Keywords
- cluster

##### 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, ...)

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.

- …
Further arguments passed to or from other methods.

- 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.

- 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 probabilty. 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 |

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", "Blossum62", "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

the phylogenetic tree.

the alignment.

Log-likelihood of the tree.

Site log-likelihoods.

Weight of the site patterns.

##### 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.

##### See Also

`bootstrap.pml`

, `modelTest`

,
`pmlPart`

, `pmlMix`

, `plot.phylo`

,
`SH.test`

, `ancestral.pml`

##### Examples

```
# NOT RUN {
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")
# }
# 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")
# }
```

*Documentation reproduced from package phangorn, version 2.5.5, License: GPL (>= 2)*