Internal maximum likelihood functions.

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")

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".

Value

pml.fit returns the log-likelihood.

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.

Author

Klaus Schliep klaus.schliep@gmail.com

Examples

data(Laurasiatherian)
tree <- NJ(dist.ml(Laurasiatherian))
bf <- rep(0.25, 4)
eig <- edQt()
pml.init(Laurasiatherian)
#> NULL
pml.fit(tree, Laurasiatherian, bf=bf, eig=eig)
#> [1] -54808.83
pml.free()
#> NULL
pml(tree, Laurasiatherian) |> logLik()
#> 'log Lik.' -54808.83 (df=91)