anc.Bayes | R Documentation |

This function uses Bayesian MCMC to sample from the posterior distribution for the states at internal nodes in the tree.

```
anc.Bayes(tree, x, ngen=10000, control=list(), ...)
## S3 method for class 'anc.Bayes'
plot(x, ...)
## S3 method for class 'anc.Bayes'
density(x, ...)
```

`tree` |
an object of class |

`x` |
a vector of tip values for species; |

`ngen` |
a integer indicating the number of generations for the MCMC. |

`control` |
a list of control parameters containing the following elements: |

`...` |
optional arguments, including to be passed to |

`plot.anc.Bayes`

generates a likelihood profile plot of the MCMC by default, but can also create a profile plot for any of the sampled variables by using the optional argument `what`

. For instance, `what=40`

(or, equivalently, `what="40"`

) will create a profile plot of the MCMC for node `40`

. Additional arguments are passed to `plot`

.

`density.anc.Bayes`

computes a posterior density from the MCMC sample. Like `plot.anc.Bayes`

takes the optional argument `what`

, but unlike `plot.anc.Bayes`

computes the posterior density for the root node by default. The object computed by this function is of class `"density"`

and can be visualized using `plot.density`

. Burn-in (in generations) can be set using the optional argument `burnin`

, otherwise it will be assumed to be 20

The `print`

and `summary`

methods for this object class also return (invisibly) a vector of estimated ancestral states based on a user-supplied burn-in (or 20% of the number of generations of MCMC, if no burn-in is provided). Burn-in can be specified with the optional argument `burnin`

.

`anc.Bayes`

returns an object of class `"anc.Bayes"`

including at least two components:

`mcmc` |
a data frame with rows |

`tree` |
our input phylogeny. |

Liam Revell liam.revell@umb.edu

Revell, L. J. (2012) phytools: An R package for phylogenetic comparative biology (and other things). *Methods Ecol. Evol.*, **3**, 217-223.

`ace`

, `anc.ML`

, `anc.trend`

, `evol.rate.mcmc`

, `fastAnc`

```
## set seed
set.seed(77)
## load data from Garland et al. (1992)
data(mammal.tree)
data(mammal.data)
## extract character of interest
ln.bodyMass<-log(setNames(mammal.data$bodyMass,
rownames(mammal.data)))
## run MCMC (should be run at least 1e6 generations)
mcmc<-anc.Bayes(mammal.tree,ln.bodyMass,
ngen=50000)
print(mcmc,printlen=20) ## estimates
par(mfrow=c(2,1))
plot(mcmc,bty="l",main="Likelihood-profile from MCMC",
font.main=3) ## likelihood-profile
plot(density(mcmc,what=Ntip(mammal.tree)+1,
burnin=20000),bty="l",
main="Posterior density for root state of log(body mass)",
font.main=3)
par(mfrow=c(1,1)) ## reset par to default
```

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