Likelihood of a tree.

pml_bb for pml black box infers a phylogenetic tree infers a tree using maximum likelihood (ML).

Usage

pml_bb(x, model = NULL, rearrangement = "stochastic",
  method = "unrooted", start = NULL, tip.dates = NULL, ...)

Arguments

x: An alignment of class (either class phyDat, DNAbin or AAbin) or an object of class modelTest.
model: A string providing model (e.g. "GTR+G(4)+I"). Not necessary if a modelTest object is supplied.
rearrangement: Type of tree tree rearrangements to perform, one of "none", "NNI", "stochastic" or "ratchet"
method: One of "unrooted", "ultrametric" or "tiplabeled".
start: A starting tree can be supplied.
tip.dates: A named vector of sampling times associated to the tips / sequences.
...: Further arguments passed to or from other methods.

Value

pml_bb returns an object of class pml.

Details

pml_bb is a convenience function combining pml and optim.pml. If no tree is supplied, the function will generate a starting tree. If a modelTest object is supplied the model will be chosen according to BIC.

tip.dates should be a named vector of sampling times, in any time unit, with time increasing toward the present. For example, this may be in units of “days since study start” or “years since 10,000 BCE”, but not “millions of years ago”.

model takes a string and tries to extract the model. When an modelTest object the best BIC model is chosen by default. The string should contain a substitution model (e.g. JC, GTR, WAG) and can additional have a term "+I" for invariant sites, "+G(4)" for a discrete gamma model, "+R(4)" for a free rate model. In case of amino acid models a term "+F" for estimating the amino acid frequencies. Whether nucleotide frequencies are estimated is defined by pml.control.

Currently very experimental and likely to change.

Author

Klaus Schliep klaus.schliep@gmail.com

Examples


data(woodmouse)
tmp <- pml_bb(woodmouse, model="HKY+I", rearrangement="NNI")
#> optimize edge weights:  -1811.409 --> -1810.473 
#> optimize rate matrix:  -1810.473 --> -1758.757 
#> optimize invariant sites:  -1758.757 --> -1744.355 
#> optimize edge weights:  -1744.355 --> -1744.199 
#> optimize topology:  -1744.199 --> -1744.199  NNI moves:  0 
#> optimize rate matrix:  -1744.199 --> -1744.186 
#> optimize invariant sites:  -1744.186 --> -1744.186 
#> optimize edge weights:  -1744.186 --> -1744.186 
#> optimize rate matrix:  -1744.186 --> -1744.186 
#> optimize invariant sites:  -1744.186 --> -1744.186 
#> optimize edge weights:  -1744.186 --> -1744.186 

if (FALSE) {
data(Laurasiatherian)
mt <- modelTest(Laurasiatherian)
fit <- pml_bb(mt)

# estimate free rate model with 2 rate categories
fit_HKY_R2 <- pml_bb(woodmouse, model="HKY+R(2)")
}