I just tried this using the code below, and I can't see a trend. I calculated 
the mean tip value across the species in each simulation, and made a box plot 
over 10000 simulations. Although sim.char() doesn't do this, one can simply 
draw these numbers from a multivariate normal distribution - and so their 
expected mean is zero.

rr<-c(0.001, 0.01, 0.1, 1, 10, 100, 1000)
phy<-birthdeath.tree(b=1, d=0, taxa.stop=20)

allMeans<-matrix(nrow=10000, ncol=length(rr))
for(i in 1:length(rr)) {
        mm<-rr[i]
        ss<-sim.char(phy, as.matrix(rr[i]), nsims=10000)
        allMeans[,i]<-apply(ss, 3, FUN=mean)
        
}

meanmeans<-apply(allMeans, 2, FUN=mean)

cc<-as.numeric(allMeans)
xx<-rep(rr, each=10000)

plot(as.factor(xx), cc)

On May 20, 2011, at 12:32 PM, Dean Adams wrote:

> Hi all,
> 
> I'm observing a curious pattern in continuous trait data simulated on a 
> phylogeny that I cannot explain. I thought I'd throw it out to the group for 
> ideas. My apologies if this was addressed in an earlier thread.
> 
> I'm exploring four different R functions for simulating continuous data under 
> Brownian Motion: 'rTraitCont' in Ape, 'sim.char' in Geiger, 
> 'transformPhylo.sim' from MotMot, and 'fastBM' from Liam Revell's 
> PhyTools-beta.  For these simulations I generate a tree (in this case a 
> perfectly-balanced tree) and simulate 100 data sets on the same phylogeny 
> using a particular initial BM rate parameter (sigma).  For each simulation, I 
> calculate the mean & variance of the simulated tips data, and then summarize 
> these across 100 simulations to understand some general properties of the 4 
> data-generating functions.  I'm using a wide range of initial sigmas to see 
> how things fall out (sigma = 0.0001, 0.001. 0.01 ... 1000).
> 
> At sigma = 1.0, the mean of the variance in tips data across simulations is 
> similar for all 4 methods.  And as expected, as sigma increases or decreases, 
> the mean variation among tips values also increases or decreases.  For each 
> sigma value, mean levels of variation are also similar for 3 of the 4 
> methods: with rTraitCont having much larger relative levels of variation as 
> sigma >>1 , and much smaller relative levels of variation as sigma << 1.   (I 
> suspect this might be due to a scaling difference in the functions: sim.char, 
> fastBM, and transformPhylo.sim all use ~sqrt(sigma*branchlengths) when 
> generating random normal data, but I'm not certain what rTraitCont uses.)
> 
> However, the most curious finding is that for all methods, as sigma 
> increases, so too does the mean trait value across the tips (and the converse 
> occurs as sigma decreases).  This observation is curious to me, as one should 
> not see a predictable shift in the mean under Brownian motion.  I thought 
> this might be due to simulating too few data sets and taking the mean of the 
> mean, but the pattern remained when 10,000 simulated data sets were generated 
> (though obviously, the effect was smaller, with mean values closer to zero). 
> I then repeated the simulations for different numbers of taxa (N=16, 32, 64, 
> 128) and the pattern was still present.  So I think this observation is 
> robust.
> 
> Presently, I am at a loss to explain this observation, though I have a 
> nagging suspicion that I'm missing something obvious. For instance, it may be 
> related to the Central limit theorem, as increasing the number of simulated 
> data sets decreased the magnitude of the mean effect across them. But that 
> doesn't fully explain why the mean deviation from zero increases 
> systematically with an increasing rate parameter (sigma).
> 
> Any thoughts on this would be greatly appreciated.
> 
> Best,
> 
> Dean
> 
> -- 
> Dr. Dean C. Adams
> Associate Professor
> Department of Ecology, Evolution, and Organismal Biology
> Department of Statistics
> Iowa State University
> Ames, Iowa
> 50011
> www.public.iastate.edu/~dcadams/
> phone: 515-294-3834
> 
> _______________________________________________
> R-sig-phylo mailing list
> R-sig-phylo@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo

Luke Harmon
Assistant Professor
Biological Sciences
University of Idaho
208-885-0346
lu...@uidaho.edu




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