[R-sig-ME] Partitioning species and environment effects MCMCglmm
Demétrius Martins
emaildemetrius at gmail.com
Wed May 31 14:57:54 CEST 2017
Dear
list
,
I'm working on plant traits and I want to partition the variance of each
trait by separating the species effect from the plot effect (i.e. place
where the sample was taken)
and residuals
.
I'm interested in how the traits covary, hence I'm
analysing the traits as a multiresponse model.
I have the following model:
# priors
priorwl.1<-list(R=list(V=diag(17), nu=16.002),G=list(G1=list(V=diag(17),
nu=16.002),
G2= list(V=diag(17),
nu=16.002) ))
#model
mod.bays.wl1<- MCMCglmm( cbind (l_LMA, l_N.mg.g.1, l_C.mg.g.1,
l_Ca.mg.g.1, l_K.mg.g.1, l_Mg.mg.g.1, l_Na.mg.g.1, l_P.mg.g.1,w_WD, w_MC,
w_N.mg.g, w_C.mg.g, w_Ca.mg.g, w_K.mg.g, w_Mg.mg.g, w_Na.mg.g, w_P.mg.g) ~
trait -1
random=~ us(trait):Genus_species +
us(trait):Plot,
rcov = ~ us(trait):units,
prior = priorwl.1, family = rep("gaussian",17),
data=logdata,
burnin = 50000, nitt = 1050000, thin = 100, pr =
TRUE, slice=T)
I’m calculating the variance of each random term of the model as:
l_C.spvar<-mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.Genus_species']
/ (mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.Genus_species'] +
mod.bays.wl1$VCV[,'traitl_C.mg.g.
1:traitl_C.mg.g.1.Plot']+
mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.units'])
l_C.plotvar<-mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.Plot'] /
(mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.Genus_species'] +
mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.Plot']+
mod.bays.wl1$VCV[,'traitl_C.
mg.g.1:traitl_C.mg.g.1.units'])
l_C.resvar<-mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.units'] /
(mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.Genus_species'] +
mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.Plot']+
mod.bays.wl1$VCV[,'traitl_C.mg.g.1:traitl_C.mg.g.1.units'])
in this example
,
I’m calculating the variance of the two random effects and the residual on
the l_C.mg.g.1 trait.
I’ve done this to every single trait specified on the model, however
,
I get very similar effects (species, plot and residual) for every single
trait, for example:
l_C.plotvar
l_C.resvar
l_C.spvar
l_Ca.plotvar
l_Ca.resvar
l_Ca.spvar
l_K.plotvar
l_K.resvar
l_K.spvar
mean.var
0.8222
0.0772
0.1007
0.8181
0.0790
0.1029
0.8142
0.0802
0.1057
lower
0.6481
0.0076
0.0104
0.6418
0.0066
0.0086
0.6283
0.0073
0.0134
upper
0.9823
0.1543
0.2062
0.9777
0.1564
0.2052
0.9734
0.1587
0.2175
Column end
ing
with(plotvar) = plot effect
Column end
ing
with(restvar) = residual
Column end
ing
with(spvar) = species effect
ing
with(spvar) = species effect
I don't know what I could be possibly doing wrong in this case. I
partitioned the variance of each trait by using the lme4 package and
extracted the variance of the random effects of each trait. The
variance
differ drastically one from another
across traits
, hence I have very different results when using MCMCglmm and lme4.
Does anybody know why variances of the traits are so similar?
I appreciate any guidance on this problem.
Best wishes,
Demetrius
*Demétrius Martins*
PhD Student
Imperial College London
**
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