dist.hamming | R Documentation |
dist.hamming
, dist.ml
and dist.logDet
compute pairwise
distances for an object of class phyDat
. dist.ml
uses DNA /
AA sequences to compute distances under different substitution models.
dist.hamming(x, ratio = TRUE, exclude = "none")
dist.ml(x, model = "JC69", exclude = "none", bf = NULL, Q = NULL,
k = 1L, shape = 1, ...)
dist.logDet(x)
x |
An object of class |
ratio |
Compute uncorrected ('p') distance or character difference. |
exclude |
One of "none", "all", "pairwise" indicating whether to delete the sites with missing data (or ambiguous states). The default is handle missing data as in pml. |
model |
One of "JC69", "F81" or one of 17 amino acid models see details. |
bf |
A vector of base frequencies. |
Q |
A vector containing the lower triangular part of the rate matrix. |
k |
Number of intervals of the discrete gamma distribution. |
shape |
Shape parameter of the gamma distribution. |
... |
Further arguments passed to or from other methods. |
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 additional rate matrices and frequencies can be supplied.
The "F81" model uses empirical base frequencies, the "JC69" equal base frequencies. This is even the case if the data are not nucleotides.
an object of class dist
Klaus Schliep klaus.schliep@gmail.com
Lockhart, P. J., Steel, M. A., Hendy, M. D. and Penny, D. (1994) Recovering evolutionary trees under a more realistic model of sequence evolution. Molecular Biology and Evolution, 11, 605–602.
Jukes TH and Cantor CR (1969). Evolution of Protein Molecules. New York: Academic Press. 21–132.
McGuire, G., Prentice, M. J. and Wright, F. (1999). Improved error bounds for genetic distances from DNA sequences. Biometrics, 55, 1064–1070.
For more distance methods for nucleotide data see
dist.dna
and dist.p
for pairwise
polymorphism p-distances. writeDist
for export and import
distances.
data(Laurasiatherian)
dm1 <- dist.hamming(Laurasiatherian)
tree1 <- NJ(dm1)
dm2 <- dist.logDet(Laurasiatherian)
tree2 <- NJ(dm2)
treedist(tree1,tree2)
# JC model
dm3 <- dist.ml(Laurasiatherian)
tree3 <- NJ(dm3)
treedist(tree1,tree3)
# F81 + Gamma
dm4 <- dist.ml(Laurasiatherian, model="F81", k=4, shape=.4)
tree4 <- NJ(dm4)
treedist(tree1,tree4)
treedist(tree3,tree4)
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