Hi,
If you want iid species effects then put them in the random part. You will
need to rename them so that they are not associated with the ginverse. For
example,
dataset$species.ide<-dataset$species
and fit species.ide as a random effect.
You only have 17 species. This is not enough to get precise estimates of
the variance components and so you should expect prior sensitivity,
particularly with respect to the phylogenetic part. I would try and
simplify your model. I'm not sure how many of the fixed effects are
species-level or individual-level but I would try and reduce the complexity
of the fixed part of the model too (for example is the 4-way interaction
needed?). The warning usually indicates that the model is overparameterised.
Cheers,
Jarrod
Quoting Tricia Markle <markl033 at umn.edu> on Wed, 29 Apr 2015 00:24:53
-0500:
Hello,
I have a working MCMCglmm model with phylogenetic consideration and repeat
measures. I realized after the fact that ?species? wasn?t properly
included
in the model. When I added this additional factor (of 17 levels), however,
I received the following error message:
--------
Error in MCMCglmm(LVO2 ~ 1 + Temp + Acclm + Lat_Ext + LMass + Sex +
species
+ : ill-conditioned G/R structure: use proper priors if you haven't or
rescale data if you have
In addition: Warning message:
In MCMCglmm(LVO2 ~ 1 + Temp + Acclm + Lat_Ext + LMass + Sex + species + :
some fixed effects are not estimable and have been removed. Use
singular.ok=TRUE to sample these effects, but use an informative prior!
----------
I had some help with my priors originally so I am not sure the best way to
now tweak them to make them work? Could different priors help or is there
something ?wrong? with adding species as another factor?
My hypothesis centers around the remaining significant interaction term
Acclm:Lat_Ext ? whether the relationship of acclimation on oxygen
consumption (VO2) is differs depending on the latitudinal extent of a
species (while also considering a number of covariates).
Here is my code:
library(ape)
library(MCMCglmm)
dataset<-read.csv(file="RespData.csv", head=TRUE)
attach(dataset)
str(dataset) # confirming that sex, range, species, and ID are all factors
#Phylogeny Component
tree<-read.tree("Plethodontidae_comb61_PL.phy")
species<-c("D._carolinensis_KHK103", "D._fuscus_KHK142",
"D._ochrophaeus_WKS05", "D._ocoee_B_KHK62", "D._orestes_KHK129",
"D._monticola_A", "D._santeetlah_11775", "P_cinereus", "P_cylindraceus",
"P_glutinosus", "P_hubrichti", "P_montanus", "P_punctatus", "P_richmondi",
"P_teyahalee", "P_virginia", "P_wehrlei")
pruned.tree<-drop.tip(tree,tree$tip.label[-match(species,
tree$tip.label)])# Prune tree to just include species of interest
sptree<-makeNodeLabel(pruned.tree, method="number", prefix="node")
treeAinv<-inverseA(sptree, nodes="TIPS")$Ainv
random=~us(1+Temp):species
prior<-list(G=list(G1=list(V=diag(2), nu=2, alpha.mu=c(0,0),
alpha.V=diag(2)*1000)), R=list(V=diag(1), nu=0.002))
#Model 1
model1<MCMCglmm(LVO2~1+Temp+Acclm+Lat_Ext+LMass+Sex+species+Temp*Acclm+Temp*Lat_Ext+Acclm*Lat_Ext+Temp*Acclm*Lat*Ext,
random=random, data=dataset, family="gaussian",
ginverse=list(species=treeAinv), prior=prior, nitt=300000, burnin=25000,
thin = 1000, verbose=FALSE)
Thank you kindly for your help!
Tricia
[[alternative HTML version deleted]]