`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.

## Usage

```
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)
```

## Arguments

- x
An object of class

`phyDat`

- 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.

## Details

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.

## References

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.

## Author

Klaus Schliep klaus.schliep@gmail.com

## Examples

```
data(Laurasiatherian)
dm1 <- dist.hamming(Laurasiatherian)
tree1 <- NJ(dm1)
dm2 <- dist.logDet(Laurasiatherian)
tree2 <- NJ(dm2)
treedist(tree1,tree2)
#> symmetric.difference branch.score.difference path.difference
#> 4.00000000 0.05705091 30.95157508
#> quadratic.path.difference
#> 0.80097967
# JC model
dm3 <- dist.ml(Laurasiatherian)
tree3 <- NJ(dm3)
treedist(tree1,tree3)
#> symmetric.difference branch.score.difference path.difference
#> 6.0000000 0.0412520 30.3644529
#> quadratic.path.difference
#> 0.6106899
# F81 + Gamma
dm4 <- dist.ml(Laurasiatherian, model="F81", k=4, shape=.4)
tree4 <- NJ(dm4)
treedist(tree1,tree4)
#> symmetric.difference branch.score.difference path.difference
#> 12.0000000 0.1356107 40.7676342
#> quadratic.path.difference
#> 2.0709714
treedist(tree3,tree4)
#> symmetric.difference branch.score.difference path.difference
#> 8.00000000 0.09494752 39.52214569
#> quadratic.path.difference
#> 1.46345381
```