#Simulate some fossil ranges with simFossilTaxa()
set.seed(444)
taxa<-simFossilTaxa(p=0.1,q=0.1,nruns=1,mintaxa=20,maxtaxa=30,maxtime=1000,nExtant=0)
#simulate a fossil record with imperfect sampling with sampleRanges()
rangesCont<-sampleRanges(taxa,r=0.5)
#Now let's use binTimeData() to bin in intervals of 1 time unit
rangesDisc<-binTimeData(rangesCont,int.length=1)
#now, get an estimate of the sampling rate (we set it to 0.5 above)
#we can do something very similar for the discrete time data (can be a bit slow)
SPres1<-getSampProbDisc(rangesDisc)
sProb<-SPres1[[2]][2]
print(sProb)
#note that that's the sampling PROBABILITY per bin, not the instantaneous rate of change
#we want the rate for srcTimePaleoPhy()
#we can use sProb2sRate() to get the rate. We'll need to also tell it the int.length
sRate<-sProb2sRate(sProb,int.length=1)
print(sRate)
#estimates that r=0.3... kind of low... (simulated sampling rate is 0.5)
#Note: for real data, you may need to use an average int.length (no constant length)
#this data was simulated under homogenous sampling rates, extinction rates
#if we fit a model with random groups and allow for multiple timebins, AIC should be higher (less informative models)
randomgroup<-sample(1:2,nrow(rangesDisc[[2]]),replace=TRUE)
SPres2<-getSampProbDisc(rangesDisc,grp1=randomgroup)
SPres3<-getSampProbDisc(rangesDisc,n_tbins=2)
print(c(SPres1$AICc,SPres2$AICc,SPres3$AICc))
#and we can see the most simple model has the lowest AICc (most informative model)Run the code above in your browser using DataLab