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

The function implements the K-clades parametric rate comparison test. This function compares rate estimates among defined subtrees and evaluates various groupings from 1 to k groups for these subtrees.

1 | ```
comp.fit.subs(trees, focal, k, mod.id = c(1, 0, 0, 0), min.val = 0.01)
``` |

`trees` |
A list from from function |

`focal` |
A vector indicating the subtrees to compare |

`k` |
A value indicting the maximum number of groupings of subtrees to examine |

`mod.id` |
A vector with four elements of 0 or 1 indicating which models to consider. 1 indicates that the model should be considered. 0 indicates the model is not considered. These for elements refer to an exponential, Weibel, lognormal, and rate variable, respectively. |

`min.val` |
A value for determining the minimum edge length for a tree scaled against the longest edge length. A value of 0.01 (the default) rescales the minimum edge length to 1 |

The list of possible subtrees is provided by the function `id.subtrees`

. The function will explore all possible groupings of subtrees into k defined groups choosing the best fit model for each partition from among the models identified by mod.id.

A dataframe that consists of the following:

`k` |
The number of groups |

`Groups` |
The groupings for each subtree numbered as 1 to the number of subtrees indicated. The numbering corresponds to the order in which subtrees are identified by focal. Groups are separated with vs. |

`gi_Pj` |
The jth parameter value for the ith group in the analysis |

`gi_mod.id` |
The best model chosen for the ith group |

`gi_n.param` |
The number of parameters in the best model for the ith group |

`AIC` |
Akaike information criterion score for the entire model for a grouping scheme |

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

`dAICc` |
The delta AIC across all grouping schemes and k values relative to the best fit model |

The output can get very large as k increases. Function tab.summary is useful for reducing the size of the result table.

Premal Shah, Benjamin Fitzpatrick and James Fordyce.

Shah, P., B. M. Fitzpatrick, and J. A. Fordyce. 2013. A parametric method for assessing diversification rate variation in phylogenetic trees. Evolution 67:368-377.

1 2 3 4 5 6 7 8 | ```
data(hivtree.newick)
cat(hivtree.newick, file = "hivtree.phy", sep = "\n")
tree.hiv <- read.tree("hivtree.phy") # load tree
unlink("hivtree.phy") # delete the file "hivtree.phy"
idHIV<-id.subtrees(tree.hiv)
plot(idHIV$tree,show.node.label=TRUE)
cfsHIV<-comp.fit.subs(idHIV$subtree,focal=c(153,119,96,5),k=4)
``` |

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