motmot.2.0 (version 1.1.2)

timeSliceSummary: Identify shifts in the rate of trait diversification through time

Description

Summarises phenotypic rate variation on phylogenies through

Usage

timeSliceSummary(timeSliceObject, cutoff = 4, AICc = TRUE,
  lowerBound = 1e-08, upperBound = 1000, plot.phylo = TRUE, cex.tip = 1,
  tip.offset = 1, phylo.width = 1, tip.colour = "grey50",
  colour.ramp = c("blue", "red"))

Arguments

timeSliceObject

Output of a timeSlice analysis in transformPhylo.ML

cutoff

Cutoff value for differences in AIC scores when comparing models. More complex models with an AIC score more than this number of units lower than simpler models are retained (as per runMedusa in the geiger package).

AICc

If true, AICc is used instead of AIC.

lowerBound

Minimum value for parameter estimates.

upperBound

Maximum value for parameter estimates.

plot.phylo

Logical. If TRUE, the phylogeny is plotted

cex.tip

The character size of tip labels

tip.offset

the distance tip labels should be printed from pendant edges

phylo.width

The width of edges on the phylogeny

tip.colour

Colour of species' tip labels

colour.ramp

The colours signifying different rates from low (first colour) to high (second colour)

Value

ModelFit Summary of the best optimal rate shift model.

Rates Summary of the rate parameters from the best rate shift model.

optimalTree A phylo object with branch lengths scaled relative to rate.

Details

This functions summarises the output of a "timeSlice" model in transformPhylo.ML (see below). The best overall model is chosen based on AIC (or AICc if AICc=TRUE). The cut-off point for improvement in AIC score between successively more complex models can be defined using cutoff. The default cutoff is 4 but this is somewhat arbitrary and a "good" cut-off may well vary between data sets so it may well be worth exploring different cutoffs.

References

To Add

Examples

# NOT RUN {
data(anolis.tree)
data(anolis.data)
attach(anolis.data)
male.length <- matrix(Male_SVL, dimnames=list(rownames(anolis.data)))
sortedData <- sortTraitData(anolis.tree, male.length)
phy <- sortedData$phy
male.length <- sortedData$trait
phy.clade <- extract.clade(phy, 182)
male.length.clade <- as.matrix(male.length[match(phy.clade$tip.label, 
rownames(male.length)),])
timeSlice.10.ml <- transformPhylo.ML(y=male.length.clade, phy=phy.clade, model="timeSlice", 
splitTime=c(10))
outputSummary <- timeSliceSummary(timeSlice.10.ml, cutoff=0.001, cex.tip=0.5, 
phylo.width=2, colour.ramp=c("blue", "red"))
# }