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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