Sunday, March 19, 2017

Simulation method for testing if the rate of phenotypic evolution differs among trees using phytools::ratebytree

As noted in some previous posts (e.g., 1, 2 3) I have written a function, ratebytree, to compare the rate of trait evolution under Brownian motion between different trees.

As I have previously pointed out this is essentially the censored rate test of described in an article by Brian O'Meara & colleagues from 2006.

I've been working on this as part of a project that is being conducted with members of Andrew Crawford's lab here at la Universidad de los Andes.

Prior versions of the method used a χ2 distribution to obtain a P-value for the comparison of one- and multiple rate models. A limitation of this approach is that though the theory of likelihoods tells as the (two-times-the) likelihood ratio should approach a χ2 distribution with degrees of freedom equal to the difference in parameterization of the two models, this property is asymptotic - meaning that for phylogenies that are relatively modest in size, we may not be able to realistically assume the likelihood-ratio to have a χ2 distribution under the null.

One relatively straightforward way to circumvent this issue, and one that is already implemented for other phytools function (such as the closely related function brownie.lite) is to use numerical simulation under the fitted null model to generate a null-distribution of the likelihood-ratio.

I have now implemented this method for ratebytree. The update can be obtained by installing phytools from GitHub.

Here's a quick demo:

library(phytools)
packageVersion("phytools")
## [1] '0.5.84'
## here is our trees & data:
trees
## 3 phylogenetic trees
x
## [[1]]
##         t1         t2         t7         t8         t5         t6 
## -1.6596753 -1.6202389 -1.3149799 -1.5414064 -1.3301366 -2.0684157 
##         t3         t9        t10         t4 
## -1.8276196 -0.8308954 -0.6392167 -1.8945305 
## 
## [[2]]
##          t4         t10         t11          t5         t13         t14 
##  2.48011289  0.36885303  0.28941190 -2.48173563 -2.08700280 -2.00920263 
##         t12         t19         t20          t7         t17         t18 
## -1.63965832 -0.79959572 -1.12513496 -1.63145651  0.71196801  0.48481364 
##          t6         t15         t16          t3          t8          t9 
## -1.23720659 -2.75963479 -2.01630598  0.01276553 -3.34797694 -4.18915934 
##          t1          t2 
## -1.56362471  0.80736136 
## 
## [[3]]
##          t1          t8          t9         t14         t15          t7 
##  0.96676584  0.99311776 -0.39647851  0.38995149  0.45217977  0.05880620 
##          t2         t19         t20         t18         t13         t10 
##  2.03242066  0.25253907  0.59278690  0.49438127 -2.55757363 -0.03192012 
##         t11          t6          t3         t16         t17         t12 
##  0.36683176  2.18782243  2.13977282  1.65146078  1.56860812  0.50089411 
##          t4          t5 
##  0.72104497  0.47858242
## plotted:
ylim<-range(x)
par(mfrow=c(1,3))
phenogram(trees[[1]],x[[1]],ylim=ylim,spread.cost=c(1,0))
phenogram(trees[[2]],x[[2]],ylim=ylim,spread.cost=c(1,0))
phenogram(trees[[3]],x[[3]],ylim=ylim,spread.cost=c(1,0))

plot of chunk unnamed-chunk-1

## now let's fit our models:
fit.chisq<-ratebytree(trees,x)
fit.chisq
## ML common-rate model:
##  s^2  a[1]   a[2]    a[3]    k   logL
## value    1.2177  -1.5456 -0.4248 0.9155  4   -69.8154    
## 
## ML multi-rate model:
##   s^2[1] s^2[2]  s^2[3]   a[1]   a[2]    a[3]    k   logL
## value    0.337   1.3276  1.5482  -1.5456 -0.4248 0.9155  6   -66.6573    
## 
## Likelihood ratio: 6.3163 
## P-value (based on X^2): 0.0425 
## 
## R thinks it has found the ML solution.
fit.sim<-ratebytree(trees,x,test="simulation")
## Generating null distribution via simulation -> |...........|
## Done!
fit.sim
## ML common-rate model:
##  s^2  a[1]   a[2]    a[3]    k   logL
## value    1.2177  -1.5456 -0.4248 0.9155  4   -69.8154    
## 
## ML multi-rate model:
##   s^2[1] s^2[2]  s^2[3]   a[1]   a[2]    a[3]    k   logL
## value    0.337   1.3276  1.5482  -1.5456 -0.4248 0.9155  6   -66.6573    
## 
## Likelihood ratio: 6.3163 
## P-value (based on simulation): 0.099 
## 
## R thinks it has found the ML solution.

For reference, the data for this demo were simulated as follows:

t1<-pbtree(n=10)
t2<-pbtree(n=20)
t3<-pbtree(n=20)
x<-fastBM(t1,sig2=1)
y<-fastBM(t2,sig2=2)
z<-fastBM(t3,sig2=2)
trees<-c(t1,t2,t3)
x<-list(x,y,z)

In a subsequent post I will employ simulation to examine whether using the χ2 test actually results in elevated type I error (as we might expect in theory for small trees) compared to the practice of obtaining a null-distribution via simulation, as we have used above.

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