Hi everyone — It seems like the solution is that I was looking at the wrong p-value, as is often the case!
>From Liam: "The issue may be that you need to look at the p-value from the internal print-out of aov.phylo not from the summary." I was using the summary() function to examine the model output from geiger. When applying this function to the phyl.aov output object, the p-values appear to be from a regular anova, not the phylogenetic anova. To access the phylogenetic anova p value, you have to look inside the object generated by phyl.aov: attr(obj, ’summary’) This will display the appropriate "Pr(>F) given phy" Jake > On Nov 15, 2018, at 12:53 PM, Liam J. Revell <liam.rev...@umb.edu> wrote: > > Hi Jacob. > > As far as I know, aov.phylo and phylANOVA should be doing more or less the > same thing. With random data if I run enough simulations for the null > distribution of F the P-values of the two different implementations come out > almost exactly the same. One difference that I noted is that if you give > either method data vectors without taxon names both work, but only phylANOVA > gives a warning. > > Please send along the data that has generated this incongruency if you are > unable to figure it out. > > All the best, Liam > > Liam J. Revell > Associate Professor, University of Massachusetts Boston > Profesor Asistente, Universidad Católica de la Ssma Concepción > web: http://faculty.umb.edu/liam.revell/, http://www.phytools.org > > On 11/15/2018 2:30 PM, Jacob Berv wrote: >> Dear R-sig-phylo, >> I was wondering if anyone on here might be able to help me understand the >> difference between phytool’s implementation of phylogenetic ANOVA and >> geiger’s implementation. From the respective documentation, it seems that >> both approaches rely on and cite the same reference: >> Garland T Jr, AW Dickerman, CM Janis, and JA Jones. 1993. Phylogenetic >> analysis of covariance by computer simulation. Systematic Biology >> 42(3):265-292. >> Both seem to have a similar approach, at least as it is described in their >> respective documentations, and both seem to rely on character simulations to >> derive their p values. It seems aov.phylo uses sim.char() and phylANOVA uses >> fastBM() for their simulations internally. >> On Liam’s blog, he indicates that these tests are the same, except that >> phylANOVA additionally performs post-hoc tests. >> http://blog.phytools.org/2013/02/updated-phylanova.html >> However, running some of my data through both of these tests is generating >> totally different results (aov.phylo detecting significant differences where >> phylANOVA does not, with p values differing by 5 orders of magnitude. >> Running my same test data~group through a pgls also generates a result >> comparable to what I get from phylANOVA — so it seems like perhaps aov.phylo >> is the outlier? >> Best, >> Jake Berv >> _______________________________________________ >> R-sig-phylo mailing list - R-sig-phylo@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo >> Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/ _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/
