set.seed(444)
record<-simFossilRecord(p=0.1, q=0.1, nruns=1,
nTotalTaxa=c(30,40), nExtant=0)
taxa<-fossilRecord2fossilTaxa(record)
rangesCont <- sampleRanges(taxa, r=0.5)
rangesDisc <- binTimeData(rangesCont, int.length=1)
cladogram<-taxa2cladogram(taxa, plot=TRUE)
#using multiDiv with very different data types
ttree <- timePaleoPhy(cladogram, rangesCont, type="basic", add.term=TRUE, plot=FALSE)
input <- list(rangesCont, rangesDisc, ttree)
multiDiv(input, plot=TRUE)
#using fixed interval times
multiDiv(input, int.times=rangesDisc[[1]], plot=TRUE)
#using multiDiv with samples of trees
ttrees <- timePaleoPhy(cladogram, rangesCont, type="basic",
randres=TRUE, ntrees=10, add.term=TRUE)
multiDiv(ttrees)
#uncertainty in diversity history is solely due to
#the random resolution of polytomies
#multiDiv can also take output from simFossilRecord, via fossilRecord2fossilTaxa
#what do many simulations run under some set of conditions 'look' like on average?
set.seed(444)
records<-simFossilRecord(p=0.1, q=0.1, nruns=10,
totalTime=30, plot=TRUE)
taxa<-sapply(records,fossilRecord2fossilTaxa)
multiDiv(taxa)
#increasing cone of diversity!
#Even better on a log scale:
multiDiv(taxa, plotLogRich=TRUE)
#pure-birth example with simFossilRecord
#note that conditioning is tricky
set.seed(444)
recordsPB<-simFossilRecord(p=0.1, q=0, nruns=10,
totalTime=30,plot=TRUE)
taxaPB<-sapply(recordsPB,fossilRecord2fossilTaxa)
multiDiv(taxaPB,plotLogRich=TRUE)
#compare many discrete diversity curves
discreteRanges<-lapply(taxa,function(x)
binTimeData(sampleRanges(x, r=0.5,
min.taxa=1), int.length=7))
multiDiv(discreteRanges)
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