*The*phylogenetic ANOVA is generally meant to refer to the simulation approach of Garland et al. (1993). Under this method, we fit the ANOVA model in the typical way - but then we conduct Brownian numerical simulations on the tree to obtain a null distribution of the model test statistic (

*F*). This Monte Carlo simulated null distribution is used for hypothesis testing. The Garland et al. (1993) phylogenetic ANOVA is implemented in the function phy.anova from the geiger package, as well as in the phytools function phylANOVA, which adds post-hoc tests.

The second approach is what I'm going to refer to as the phylogenetic

*generalized least squares*(GLS) ANOVA. Under this model we used the mechanics of GLS to fit a linear model in which the residual error of the model has phylogenetic autocorrelation. This is analogous, and mathematically equivalent, to fitting a phylogenetic regression (sensu Grafen 1989), as described in Rohlf (2001).

The disadvantage of the former approach is that it cannot be used to estimate the coefficients of the fitted ANOVA model - only the p-value of this fit. This is because the OLS estimators (although statistically unbiased) are not minimum variance - and may have very high variance (probably depending on the phylogenetic structure of the independent variable). By contrast, GLS has the advantage of giving unbiased and minimum variance estimators of the fitted model coefficients - that is, conditioned on our model for the residual error being correct.

In spite of the similarity of the goals of these two different approaches, to my knowledge no prior study has compared their type I error or power. The code below can be used to estimate the type I error of each approach, as well as standard OLS (i.e., ignoring the phylogeny):

# load required packages

require(phytools)

require(nlme)

# number of replicates

N<-200

# balanced tree

tree<-compute.brlen(stree(n=128,type="balanced"))

# transition matrix for simulation

Q<-matrix(c(-2,1,1,1,-2,1,1,1,-2),3,3)

rownames(Q)<-colnames(Q)<-c(0,1,2)

# simulate discrete character

mtrees<-replicate(N,sim.history(tree,Q),simplify=FALSE)

class(mtrees)<-"multiPhylo"

# plot (just for fun)

cols<-c("white","blue","red"); names(cols)<-0:2

foo<-function(x){

plotTree(x,ftype="off",lwd=5)

plotSimmap(x,cols,pts=FALSE,ftype="off",lwd=3,add=TRUE)

}

lapply(mtrees,foo)

# conduct simulations under the null

anovaOLS<-anovaPGLS<-anovaGarland<-list()

for(i in 1:N){

b<-c(0,0,0)

x1<-factor(mtrees[[i]]$states,levels=c(0,1,2))

e<-fastBM(mtrees[[i]])

y<-as.vector(b%*%t(model.matrix(~0+x1))+e)

anovaOLS[[i]]<-anova(gls(y~x1,data.frame(y,x1)))

anovaPGLS[[i]]<-anova(gls(y~x1,data.frame(y,x1), correlation=corBrownian(1,mtrees[[i]])))

anovaGarland[[i]]<-phylANOVA(mtrees[[i]],x1,y,posthoc=F)

print(paste("replicate",i))

}

# extract the p-values of the fitted models

pOLS<-sapply(anovaOLS,function(x) x$'p-value'[2])

pGLS<-sapply(anovaPGLS,function(x) x$'p-value'[2])

pGarland<-sapply(anovaGarland,function(x) x$Pf)

require(phytools)

require(nlme)

# number of replicates

N<-200

# balanced tree

tree<-compute.brlen(stree(n=128,type="balanced"))

# transition matrix for simulation

Q<-matrix(c(-2,1,1,1,-2,1,1,1,-2),3,3)

rownames(Q)<-colnames(Q)<-c(0,1,2)

# simulate discrete character

mtrees<-replicate(N,sim.history(tree,Q),simplify=FALSE)

class(mtrees)<-"multiPhylo"

# plot (just for fun)

cols<-c("white","blue","red"); names(cols)<-0:2

foo<-function(x){

plotTree(x,ftype="off",lwd=5)

plotSimmap(x,cols,pts=FALSE,ftype="off",lwd=3,add=TRUE)

}

lapply(mtrees,foo)

# conduct simulations under the null

anovaOLS<-anovaPGLS<-anovaGarland<-list()

for(i in 1:N){

b<-c(0,0,0)

x1<-factor(mtrees[[i]]$states,levels=c(0,1,2))

e<-fastBM(mtrees[[i]])

y<-as.vector(b%*%t(model.matrix(~0+x1))+e)

anovaOLS[[i]]<-anova(gls(y~x1,data.frame(y,x1)))

anovaPGLS[[i]]<-anova(gls(y~x1,data.frame(y,x1), correlation=corBrownian(1,mtrees[[i]])))

anovaGarland[[i]]<-phylANOVA(mtrees[[i]],x1,y,posthoc=F)

print(paste("replicate",i))

}

# extract the p-values of the fitted models

pOLS<-sapply(anovaOLS,function(x) x$'p-value'[2])

pGLS<-sapply(anovaPGLS,function(x) x$'p-value'[2])

pGarland<-sapply(anovaGarland,function(x) x$Pf)

From here it is simple to get the type I error rate for each method:

> # get type I error

> typeI.OLS<-mean(pOLS<=0.05)

> typeI.GLS<-mean(pGLS<=0.05)

> typeI.Garland<-mean(pGarland<=0.05)

> typeI.OLS

[1] 0.85

> typeI.GLS

[1] 0.04

> typeI.Garland

[1] 0.06

> typeI.OLS<-mean(pOLS<=0.05)

> typeI.GLS<-mean(pGLS<=0.05)

> typeI.Garland<-mean(pGarland<=0.05)

> typeI.OLS

[1] 0.85

> typeI.GLS

[1] 0.04

> typeI.Garland

[1] 0.06

So, it looks like Garland et al.'s simulation method and phylogenetic GLS both have appropriate type I error rates when there is no effect of the independent variable - but ignoring the phylogeny entirely can result in very high type I error. I should note that the simulation approach - using phylogenetic simulation of

*x*and Grafen's branch lengths - is kind of the "worst case scenario" for ignoring phylogeny.

We can loop around this code and ask whether the power to detect an effect of various sizes differs between PGLS-ANOVA and Garland et al.'s simulation approach. Here is the code I used as well as the result. To be able to run through this a little quicker, I decided to use only the first 100 trees from my prior simulation.

# power analysis

N<-100

power.GLS<-typeI.GLS; power.Garland<-typeI.Garland

anovaPGLS<-anovaGarland<-list()

for(i in 1:10){

for(j in 1:N){

# simulate an effect

# note that this relative to a variance between tips

# separated by the root of 1.0

b<-c(0,-0.1*i,0.1*i)

x1<-factor(mtrees[[j]]$states,levels=c(0,1,2))

e<-fastBM(mtrees[[j]])

y<-as.vector(b%*%t(model.matrix(~0+x1))+e)

anovaPGLS[[j]]<-anova(gls(y~x1,data.frame(y,x1), correlation=corBrownian(1,mtrees[[j]])))

anovaGarland[[j]]<-phylANOVA(mtrees[[j]],x1,y, posthoc=F)

print(paste("effect",i*0.1,"- replicate",j))

}

# extract the p-values of the fitted models

pGLS<-sapply(anovaPGLS,function(x) x$'p-value'[2])

pGarland<-sapply(anovaGarland,function(x) x$Pf)

# get power

power.GLS[i+1]<-mean(pGLS<=0.05)

power.Garland[i+1]<-mean(pGarland<=0.05)

}

plot(0:10/10,power.GLS,type="b",ylim=c(0,1), xlab="effect size",ylab="power")

lines(0:10/10,power.Garland,type="b")

lines(c(0,1),c(0.05,0.05),lty=2)

text(x=1,y=0.95,"PGLS",pos=2,offset=0)

text(x=1,y=0.7,"Garland et al.",pos=2,offset=0)

So, there you have it. If I've done this properly, it suggests that the Garland et al. simulation method has the correct type I error - but relatively low power compared to PGLS. Cool.N<-100

power.GLS<-typeI.GLS; power.Garland<-typeI.Garland

anovaPGLS<-anovaGarland<-list()

for(i in 1:10){

for(j in 1:N){

# simulate an effect

# note that this relative to a variance between tips

# separated by the root of 1.0

b<-c(0,-0.1*i,0.1*i)

x1<-factor(mtrees[[j]]$states,levels=c(0,1,2))

e<-fastBM(mtrees[[j]])

y<-as.vector(b%*%t(model.matrix(~0+x1))+e)

anovaPGLS[[j]]<-anova(gls(y~x1,data.frame(y,x1), correlation=corBrownian(1,mtrees[[j]])))

anovaGarland[[j]]<-phylANOVA(mtrees[[j]],x1,y, posthoc=F)

print(paste("effect",i*0.1,"- replicate",j))

}

# extract the p-values of the fitted models

pGLS<-sapply(anovaPGLS,function(x) x$'p-value'[2])

pGarland<-sapply(anovaGarland,function(x) x$Pf)

# get power

power.GLS[i+1]<-mean(pGLS<=0.05)

power.Garland[i+1]<-mean(pGarland<=0.05)

}

plot(0:10/10,power.GLS,type="b",ylim=c(0,1), xlab="effect size",ylab="power")

lines(0:10/10,power.Garland,type="b")

lines(c(0,1),c(0.05,0.05),lty=2)

text(x=1,y=0.95,"PGLS",pos=2,offset=0)

text(x=1,y=0.7,"Garland et al.",pos=2,offset=0)

One caveat that I'd like to attach to all of this is that I've always found the presumed generating process - one in which the mean structure effectively evolves 'saltationally' (changing state spontaneously whenever the discrete character changes state) but the error structure evolves by Brownian evolution - to feel more than a little artificial.

That being said, I still feel that this result might interest some people. Could this be a published 'note?' I'm not sure, but feedback is welcome.

Publish this.

ReplyDeletethis is really cool Liam. Seems to be somewhat in line with what Freckleton et al. showed recently comparing GLS approaches to PVR? Though not entirely I guess, because the phyloANOVA still assumes an evolutionary model for the null distribution, just not for estimating parameters. (link http://www.jstor.org/stable/10.1086/660272)

ReplyDeleteI agree that this should be of sufficient interest to be brought to general attention through a published note.

Cheers!

I agree, its worth publishing a note on this. I'd add that another difference between the two methods, at least in comparison with phy.anova in geiger (sorry I'm still not familiar with the function in phytools), is that using gls the analysis can be run with the estimated lambda parameter, allowing to adjust for deviation from a purely Brownian model. I would be curious to see if under an anova scenario, comparing among groups, deviance from a Brownian model is more common...

ReplyDeleteCheers!

Alejandro

Cool stuff, thanks for sharing :)

ReplyDeleteI agree with the other comments: this is cool and definetly worth of being published in some more "formal" outlet!

ReplyDeleteThe Garland et al. (1993) phylogenetic ANOVA is implemented in the function phy.anova from the geiger package, as well as in the phytools function phylANOVA, which adds post-hoc tests.

ReplyDeleteM.Cortez

This may reveal my flaky stats background.. but can you do a post hoc tukey test with a pgls ANOVA? I used lsmeans on a gls model (with the phylogenetic autocorrelation included) and got an output... but I'm not sure this analysis is correct. Thanks

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