[R-sig-ME] Adding additional factor to previously working MCMCglmm model kills it. Problem with priors? Help?

[Tricia Markle]

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

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