Description Usage Arguments Details Value Author(s) References See Also Examples

Fits a model of evolution for categorical traits, allowing for multi-state characters, polymorphisms, missing data, and incompletely resolved trees

1 2 3 |

`phy` |
a phylogenetic tree, in |

`data` |
a data matrix containing species information (see Details). |

`ntraits` |
specifies the number of traits to included in the analysis. |

`charnum` |
specified the character to analyze. |

`rate.mat` |
a user-supplied rate matrix index of parameters to be optimized. |

`model` |
specifies the underlying model. |

`node.states` |
method used to calculate ancestral states at internal nodes. |

`p` |
a vector of transition rates. Allows the user to calculate the likelihood given a specified set of parameter values to specified as fixed and calculate the likelihood. |

`root.p` |
a vector used to fix the probabilities at the root, but “maddfitz” can also be supplied to use the method of Maddison et al (2007) and FitzJohn et al (2009) (see details). |

`ip` |
initial values used for the likelihood search. Can be a single value or a vector of unique values for each parameter. The default is |

`lb` |
lower bound for the likelihood search. The default is |

`ub` |
upper bound for the likelihood search. The default is |

`diagn` |
logical indicating whether diagnostic tests should be performed. The default is |

The function takes a tree and a trait file and estimates transition rates and ancestral states for binary or multistate characters. The first column of the trait file must contain the species labels to match to the tree, with the second, third, fourth, and so on, corresponding to the traits of interest. Use the `charnum`

variable to select the trait for analysis. Also, the input phylogeny need not be bifurcating as the algorithm is implemented to handle multifucations. Polytomies are allowed by generalizing Felsenstein's (1981) pruning algorithm to be the product of the probability of observing the tip states of n descendant nodes, rather than two, as in the completely bifurcating case.

The user can fix the root state probabilities by supplying a vector to the `root.p`

. If user supplies the flag `root.p`

=“maddfitz” calls the same procedure described by Maddison et al (2007) and FitzJohn et al (2009) is used. Note, however, that the default `root.p=NULL`

assumes equal weighting among all possible states.

Ambiguities (polymorphic taxa or taxa missing data) are assigned likelihoods following Felsenstein (2004, p. 255). Polymorphic taxa are coded “&” with all states observed at a tip. For example, if a trait has four states and taxonA is observed to be in state 1 and 3, the character would be coded as “1&3”. rayDISC then uses this information to assign a likelihood of 1.0 to both states. Missing data are treated as ambiguous for all states, thus all states for taxa missing data are assigned a likelihood of 1.0. For example, for a four-state character (i.e. DNA), a taxon missing data will have likelihoods of all four states equal to 1.0 [e.g. L(A)=1.0, L(C)=1.0, L(G)=1.0, L(T)=1.0].

`rayDISC`

returns an object of class `rayDISC`

. This is a list with elements:

`$loglik` |
the maximum negative log-likelihood. |

`$AIC` |
Akaike information criterion. |

`$AICc` |
Akaike information criterion corrected for sample size. |

`$ntraits` |
The number of traits specified. |

`$solution` |
a matrix containing the maximum likelihood estimates of the transition rates. |

`$solution.se` |
a matrix containing the approximate standard errors of the transition rates. The standard error is calculated as the square root of the diagonal of the inverse of the Hessian matrix. |

`$index.mat` |
The indices of the parameters being estimated are returned. The numbers correspond to the row in the |

`$opts` |
Internal settings of the likelihood search. |

`$data` |
User-supplied dataset. |

`$phy` |
User-supplied tree. |

`$states` |
The likeliest states at each internal node. |

`$tip.states` |
NULL |

`$iterations` |
The number of iterations used by the optimization routine. |

`$eigval` |
The eigenvalues from the decomposition of the Hessian of the likelihood function. If any |

`$eigvect` |
The eigenvectors from the decomposition of the Hessian of the likelihood function is returned. |

`$bound.hit` |
A logical for diagnosing if rate parameters were constrained by |

`$message.tree` |
A list of taxa which were listed in the data matrix, but were not present in the passed |

`$message.data` |
A list of taxa which were present in the passed |

Jeffrey C. Oliver and Jeremy M. Beaulieu

Felsenstein, J. 1981. A likelihood approach to character weighting and what it tells us about parsimony and compatibility. Biological Journal of the Linnean Society 16: 183-196.

Felsenstein J. 2004. Inferring phylogenies. Sunderland MA: Sinauer Associates.

FitzJohn, R.G., W.P. Maddison, and S.P. Otto. 2009. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Systematic Biology 58:595-611.

Maddison, W.P., P.E. Midford, and S.P. Otto. 2007. Estimating a binary characters effect on speciation and extinction. Systematic Biology 56:701-710.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# Not run
### Example 1
# Load data
# data(rayDISC.example)
## Perform ancestral state estimation, using an asymmetric model of evolution and marginal
## reconstruction of ancestral states
# recon <- rayDISC(rayDISC.example$tree,rayDISC.example$trait,model="ARD",
# node.states="marginal")
## Plot reconstructions on tree
# plotRECON(rayDISC.example$tree,recon$states)
### Example 2
## Perform ancestral state estimation on second character, using a single-rate model of
## evolution, marginal reconstruction of ancestral states, and setting the lower bound for
##parameter estimates to 0.01
# recon <- rayDISC(rayDISC.example$tree,rayDISC.example$trait,charnum=2,model="ER",
# node.states="marginal",lb=0.01)
### Example 3
## Perform ancestral state estimation on third character, using a single-rate model of
## evolution and joint reconstruction of ancestral states
# recon <- rayDISC(rayDISC.example$tree,rayDISC.example$trait,charnum=3,
# model="ER",node.states="joint")
``` |

```
Loading required package: ape
Loading required package: nloptr
Loading required package: GenSA
```

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