[R-meta] phylogenetic information in both moderator and random part of rma.mv?

-----Original Message-----
From: Sigurd Einum [mailto:sigurd.einum at ntnu.no]
Sent: Friday, 30 September, 2022 10:26
To: Viechtbauer, Wolfgang (NP); r-sig-meta-analysis at r-project.org
Subject: RE: phylogenetic information in both moderator and random part of
rma.mv?

Dear Wolfgang, thank you for these insights! This clarified for me the way
effects of class and phylogeny (within class) is partitioned in the model!
When I fit the two models you suggested (see below), AICc is lower for mod2 (with
delta AICc > 2). My interpretation of your reply, i.e. that the effect of
phylogeny is an empirical question, is that since there is little evidence for a
phylogenetic signal within classes for this data set, mod2 (i.e. treating the
species as independent observations as long as I account for the random species
effect (1|species)) is appropriate when estimating the class effect.

mod1 <- rma.mv(lambda, sampvar,mods = ~ Class, random = list(~ 1 | study, ~ 1 |
species, ~ 1 | phylo),
              R = list(phylo = A), data = Final.data, sparse = TRUE, method =
"REML")

mod2 <- rma.mv(lambda, sampvar,mods = ~ Class, random = list(~ 1 | study, ~ 1 |
species),
                                  data = Final.data, sparse = TRUE, method =
"REML")

Best wishes,
Sigurd

-----Original Message-----
From: Viechtbauer, Wolfgang (NP)
<wolfgang.viechtbauer at maastrichtuniversity.nl>
Sent: Thursday, September 29, 2022 4:59 PM
To: r-sig-meta-analysis at r-project.org
Cc: Sigurd Einum <sigurd.einum at ntnu.no>
Subject: RE: phylogenetic information in both moderator and random part of
rma.mv?

Dear Sigurd,

I do not know enough about the specifics of the application to say whether
comparing the model with versus without phylogeny is sufficient to conclude
something about evolutionary divergence.

However, let me make a few comments (I am also basing some comments here
on what you wrote to me initially in an email before redirecting you to this
mailing list for further discussions):

So you fitted a mixed-effects model with lme() of the form:

lme(Y ~ Class, random = ~ 1 | species, data=Final.data, method="ML")

(or maybe including weights = varFixed(~ sampvar)) and found 'Class' to be
relevant (regardless of whether this means: large coefficient, statistically
significant, sufficiently larger value of the information criterion compared to

a

null model). Then you fitted the model below (where you are accounting for
phylogeny) and now the support for the relevance of 'Class' disappears or is
considerably weaker. I hope this summarizes the issue.

First, I would ask: Have you compared the lme() model with this?

rma.mv(Y, sampvar, mods = ~ Class, random = ~ 1 | species, data = Final.data,
sparse = TRUE, method = "ML")

Note that this is not exactly the same model as what lme() fits as explained

here:

https://www.metafor-project.org/doku.php/tips:rma_vs_lm_lme_lmer

This aside, it is quite possible that the relevance / effect / significance of

'Class'

changes when accounting for the phylogeny, because this accounts for the
potential dependence of the outcome due to similarities between species in a
different way than just including species itself as a random effect.

Now does it make sense to include Class as a moderator while also including
random effects for species? I would say yes. Class is a broader category, so the
species random effect accounts for heterogeneity within classes (and the fixed
effect for class allows the average of all species belonging to the same class

to

differ from that of other classes). And whether the values of this random effect
are correlated or not (as predicted by the phylogeny) is an empirical question.

So

if the model with phylogeny fits better, then I would go with that.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis
[mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Sigurd
Einum
Sent: Wednesday, 28 September, 2022 21:04
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] phylogenetic information in both moderator and random
part of rma.mv?

Dear list,

I want to test for evolutionary divergence (among ectothermic animals)
in a single trait Y. My first approach to this was to test for
divergence among taxonomic classes (specifically amphibians, reptiles,
insects, fish, and crustaceans). I compiled data on several species per
class, and multiple estimates of Y per species (from different
experiments), and analysed the data using a traditional mixed effects
model (lme), with species as a random effect and taxonomic class as fixed

effect.

However, one reviewer suggested I should control for phylogeny (without
being more specific). So I built a tree for these species (using
package rotl), made it ultrametric (using compute.brlen in package
ape), and computed the variance- covariance matrix A from this (using
vcv from package ape). I then created a variable phylo to distinguish
the phylogenetic component from the non- phlylogenetic species random

effect, and used metafor to fit the model:

rma.mv(Y, sampvar,mods = ~ Class, random = list(~ 1 | species, ~ 1 | phylo),
              R = list(phylo = A), data = Final.data, sparse = TRUE,
method =
"ML")

(sampvar is the sampling variance associated with each estimate of Y)

However, now I have started doubting whether this model makes sense,
i.e. to estimate an effect of taxonomic class (which in essence is a
phylogenetic effect) while simultaneously modelling a random effect of
phylogeny. Would an appropriate alternative be to not have class as a
moderator, but rather compare fits of models with and without the
phylogenetic variance-covariance matrix. If the model including
phylogeny is better than one without it, can I conclude that there is

evolutionary

divergence in this trait?

Any advice anyone might have on this would be much appreciated!

Best wishes
Sigurd Einum