gmyc: Optimizes genetic clusters using the generalized mixed yule...

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

Description

This function optimizes either the single threshold (Pons et al. 2006; Fontaneto et al. 2007) or multiple threshold (Monaghan et al. Submitted) versions of the generalized mixed Yule coalescent. Note that the multiple threshold version is in development and can take a long time to run. We recommend use of the single threshold version. Results are returned as a list object of class 'gmyc'.

Usage

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gmyc(tr, method = "single", interval = c(0, 5), quiet = FALSE)

Arguments

tr

An ultrametric, dichotomous tree object in ape format.

method

Method of analysis, either "single" for single-threshold version or "multiple" for multiple-threshold version.

interval

Upper and lower limit of estimation of scaling parameters, e.g. c(0,10)

quiet

By default shows progress on console. Use quiet = FALSE to disable.

Details

The function optimizes the likelihood function described in Pons et al. 2006, which specifies the likelihood of branching intervals assuming: i) between species branching according to a Yule model or assuming evenly increasing or decreasing net speciation rates towards the present; ii) coalescence within species according to a neutral coalescent or with evenly increasing or decreasing rates of coalescent branching towards the ancestral node. The single threshold version uses a single threshold to specify the transition from between- to within-species branching. The multiple threshold version starts with a single threshold, but uses an iterative algorithm to test whether relaxation of the single threshold leads to a significant improvement in model fit (described in Monghan et al. submitted). After completing optimization, use plot to see the likelihood surface, a log-lineage through time plot and a tree with clusters indicated. Use summary to summarize the output statistics, including a log-likelihood ratio test of the fitted model against a null model of no distinct species clusters.

Value

gmyc returns an object of class "gmyc": a list with the follow elements

method

method used for an analysis

likelihood

likelihood values for each optimization

parameters

estimated parameters for each optimization. (lambda1, lambda2, pp1, pp2)

entity

numbers of entities

cluster

numbers of clusters

MRCA

index of MRCA nodes, i.e. ancestral node of each delimited cluster

threshold.time

The optimized threshold times.

tree

The tree.

Warning

The multiple threshold version works, but is very experimental (highly sensitive to initial conditions) and takes a long time to run.

The likelihood ratio test by summary.gmyc is now revised with a new interpretion of threshold time, and results using older splits packages (<= 1.0-14) need revisions. Any result using former versions with a P-value <= 0.112 will be significant.

Note

Important: Having the input tree in the correct format - ultrametric, fully dichotomous - is very important as the code assumes these properties. You can use r8s by Sanderson to derive ultrametric trees or simply fit a molecular clock model in PAUP. TreeEdit is useful for manipulating trees in terms of rooting and converting polytomies to dichotomies with zero branch lengths. Use read.tree or read.nexus to read trees into R using ape (>=1.9).

Author(s)

Tomochika Fujisawa [email protected] and Tim Barraclough [email protected]

References

Fontaneto, D. E. Herniou, C. Boschetti, M. Caprioli, G. Melone, C. Ricci and T.G. Barraclough. 2007. Independently evolving species in asexual bdelloid rotifers. PLoS Biology. 5:e87

Monaghan, M.T., Wild, R., Elliot, M., Fujisawa, T., Balke, M., Inward, D.J.G., Lees, D.C., Ranaivosolo, R., Eggleton, P., Barraclough, T.G., Vogler, AP. 2009. Accelerated species inventory on Madagascar using coalescent-based models of species delineation. Systematic Biology. 58:298-311.

Pons, J, Barraclough, TG, Gomez-Zurita, J, Cardoso, A, Duran, DP, Hazell, S, Kamoun, S, Sumlin, WD, Vogler, AP,2006. Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Systematic Biology. 55:595-609.

Fujisawa, T and Barraclough T.G. in press. Delimiting species using single locus data and the generalized mixed Yule coalescent approach. Systematic Biology.

See Also

spec.list, test.tr, gmyc.support

Examples

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data(test.tr)
##run the single threshold model
test1 <- gmyc(test.tr, method="single", interval=c(0, 10))
summary(test1)   ##show summary results
spec.list(test1) ##show species-sample associations

plot(test1)

##plot results
##un-comment to run the multiple threshold model, NOTE it takes a long time
#test2 <- gmyc(test.tr, method="multiple", interval= c(0, 10))
#summary(test2)
#plot(test2)

splits documentation built on May 31, 2017, 1:45 a.m.