The function ltt computes LTT plot with extant and extinct lineages, and optionally conducts \(\gamma\)-test of Pybus & Harvey (2000). The object returned by ltt can be plotted or re-plotted using plot.
The function gtt computes the value of Pybus & Harvey's \(\gamma\) statistic through time by slice the tree at various points - by default in even intervals from the time above the root at which N = 3 to the present day.
The function mccr performs the MCCR test of Pybus & Harvey (2000) which takes into account incomplete taxon sampling in computing a P-value of the \(\gamma\) statistic.
ltt(tree, plot=TRUE, drop.extinct=FALSE, log.lineages=TRUE, gamma=TRUE, ...)
gtt(tree, n=100, ...)
mccr(obj, rho=1, nsim=100, ...)is a phylogenetic tree in "phylo" format, or an object of class "multiPhylo" containing a list of phylogenetic trees.
a logical value indicating whether or not to create LTT plot.
logical value indicating whether or not to drop extinct tips from the tree.
logical value indicating whether LTT plot should be on log-linear (default) or linear-linear scale.
logical value indicating whether or not to compute eqngamma from Pybus & Harvey (2000; Proc. Roy. Soc. B).
for gtt the number of time intervals to use to track \(\gamma\) through time.
for mccr an object of class "ltt".
for mccr sampling fraction.
for mccr number of simulations to use for the MCCR test.
other arguments to be passed to plotting methods. See plot.default.
ltt returns an object of class "ltt" which includes the following components:
a vector of branching times.
a vector of linages.
optionally, a value for the gamma-statistic.
two-tailed P-value for the gamma-test.
Although it is calculated here, it's unclear how to interpret the \(\gamma\)-statistic if not all the tips in the tree are contemporaneous.
Pybus, O. G., and P. H. Harvey (2000) Testing macro-evolutionary models using incomplete molecular phylogenies. Proc. R. Soc. Lond. B, 267, 2267-2272.
Revell, L. J. (2012) phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol., 3, 217-223.
# NOT RUN {
trees<-pbtree(n=100,scale=100,nsim=10)
obj<-ltt(trees,plot=FALSE)
plot(obj,log="y",log.lineages=FALSE,main="lineage through time plots")
tree<-pbtree(b=1,d=0.25,t=4)
obj<-ltt(tree,gamma=FALSE)
obj
# }
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