# codonTest

##### codonTest

Models for detecting positive selection

- Keywords
- cluster

##### Usage

```
codonTest(tree, object, model = c("M0", "M1a", "M2a"),
frequencies = "F3x4", opt_freq = FALSE, codonstart = 1,
control = pml.control(maxit = 20), ...)
```

##### Arguments

- tree
a phylogenetic tree.

- object
an object of class phyDat.

- model
a vector containing the substitution models to compare with each other or "all" to test all available models.

- frequencies
a character string or vector defining how to compute the codon frequencies

- opt_freq
optimize frequencies (so far ignored)

- codonstart
an integer giving where to start the translation. This should be 1, 2, or 3, but larger values are accepted and have for effect to start the translation further within the sequence.

- control
a list of parameters for controlling the fitting process.

- ...
further arguments passed to or from other methods.

##### Details

`codonTest`

allows to test for positive selection similar to programs
like PAML (Yang ) or HyPhy (Kosakovsky Pond et al. 2005).

There are several options for deriving the codon frequencies. Frequencies can be "equal" (1/61), derived from nucleotide frequencies "F1x4" and "F3x4" or "empirical" codon frequencies. The frequencies taken using the empirical frequencies or estimated via maximum likelihood.

So far the M0 model (Goldman and Yang 2002), M1a and M2a are implemented. The M0 model is always computed the other are optional. The convergence may be very slow and sometimes fails.

##### Value

A list whith an element called summary containing a data.frame with the log-likelihood, number of estimated parameters, etc. of all tested models. An object called posterior which contains the posterior probability for the rate class for each sites and the estimates of the defined models.

##### References

Ziheng Yang (2014). *Molecular Evolution: A Statistical
Approach*. Oxford University Press, Oxford

Sergei L. Kosakovsky Pond, Simon D. W. Frost, Spencer V. Muse (2005) HyPhy:
hypothesis testing using phylogenies, *Bioinformatics*, **21(5)**:
676--679, https://doi.org/10.1093/bioinformatics/bti079

Nielsen, R., and Z. Yang. (1998) Likelihood models for detecting positively
selected amino acid sites and applications to the HIV-1 envelope gene.
*Genetics*, **148**: 929--936

##### See Also

##### Examples

```
# NOT RUN {
# }
# NOT RUN {
# load woodmouse data from ape
data(woodmouse)
dat_codon <- dna2codon(as.phyDat(woodmouse))
tree <- NJ(dist.ml(dat_codon))
# optimise the model the old way
fit <- pml(tree, dat_codon, bf="F3x4")
M0 <- optim.pml(fit, model="codon1")
# Now using the codonTest function
fit_codon <- codonTest(tree, dat_codon)
fit_codon
plot(fit_codon, "M1a")
# }
# NOT RUN {
# }
```

*Documentation reproduced from package phangorn, version 2.5.5, License: GPL (>= 2)*