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phangorn (version 2.12.1)

lli: Internal maximum likelihood functions.

Description

These functions are internally used for the likelihood computations in pml or optim.pml.

Usage

lli(data, tree = NULL, ...)

edQt(Q = c(1, 1, 1, 1, 1, 1), bf = c(0.25, 0.25, 0.25, 0.25))

pml.free()

pml.init(data, k = 1L)

pml.fit(tree, data, bf = rep(1/length(levels), length(levels)), shape = 1, k = 1, Q = rep(1, length(levels) * (length(levels) - 1)/2), levels = attr(data, "levels"), inv = 0, rate = 1, g = NULL, w = NULL, eig = NULL, INV = NULL, ll.0 = NULL, llMix = NULL, wMix = 0, ..., site = FALSE, ASC = FALSE, site.rate = "gamma")

Value

pml.fit returns the log-likelihood.

Arguments

data

An alignment, object of class phyDat.

tree

A phylogenetic tree, object of class phylo.

...

Further arguments passed to or from other methods.

Q

A vector containing the lower triangular part of the rate matrix.

bf

Base frequencies.

k

Number of intervals of the discrete gamma distribution.

shape

Shape parameter of the gamma distribution.

levels

The alphabet used e.g. c("a", "c", "g", "t") for DNA

inv

Proportion of invariable sites.

rate

Rate.

g

vector of quantiles (default is NULL)

w

vector of probabilities (default is NULL)

eig

Eigenvalue decomposition of Q

INV

Sparse representation of invariant sites

ll.0

default is NULL

llMix

default is NULL

wMix

default is NULL

site

return the log-likelihood or vector of sitewise likelihood values

ASC

ascertainment bias correction (ASC), allows to estimate models like Lewis' Mkv.

site.rate

Indicates what type of gamma distribution to use. Options are "gamma" approach of Yang 1994 (default), "gamma_quadrature" after the Laguerre quadrature approach of Felsenstein 2001 and "freerate".

Author

Klaus Schliep klaus.schliep@gmail.com

Details

These functions are exported to be used in different packages so far only in the package coalescentMCMC, but are not intended for end user. Most of the functions call C code and are far less forgiving if the import is not what they expect than pml.

References

Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution, 17, 368--376.

See Also

pml, pml_bb, pmlPart, pmlMix

Examples

Run this code
data(Laurasiatherian)
tree <- NJ(dist.ml(Laurasiatherian))
bf <- rep(0.25, 4)
eig <- edQt()
pml.init(Laurasiatherian)
pml.fit(tree, Laurasiatherian, bf=bf, eig=eig)
pml.free()
pml(tree, Laurasiatherian) |> logLik()

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