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Priors for us(trait):units structure in MCMCglmm model. Error message - help needed.

2 messages · Tricia Markle, Jarrod Hadfield

Original
Tricia Markle
# 
Hello,



I am hoping that someone could provide some thoughts on an appropriate
prior set-up for my model which uses a ?us(trait):units? structure in an
MCMCglmm model with repeat measures and a phylogeny component.



I am assuming that I need to use uninformative proper priors with a set-up
something along the lines of:



prior<-list(G=list(G1=list(V=diag(#), nu=#)), R=list(V=diag(#), n=#))



I have spent a considerable amount of time working on this (looking at help
guides, posted examples etc.) and regardless of what numbers I try, I
continue to get the following error message:



Error in priorformat(if (NOpriorG) { :

  V is the wrong dimension for some prior$G/prior$R elements



Data Details: I have 308 individual salamanders, each acclimated at 3
different temperatures (6,14,22C). Then for each acclimation temperature
metabolic rate is measured at 3 test temperatures (5, 15, 25C) (so total of
9 trials per individual).



I am attempting to compare slopes of the test temperatures between
acclimation temperatures. There are 18 species, but my main question is
whether large ranging species have greater differences in slope between
acclimation temps than narrow ranging species (species are divided into
those with small (1) versus large (2) ranges).



Here is the rest of my code:



dataset<-read.csv(file="RespData.csv", head=TRUE)

dataset$Range<-as.factor(dataset$Range)

str(dataset)

#Phylogeny Component

tree<-read.tree("Plethodontidae_comb61_PL.phy")

species<-c("D._carolinensis", "D._fuscus", "D._imitator", "D._ochrophaeus",
"D._ocoee", "D._orestes",  "D._monticola_A",  "D._santeetlah",
"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") #rename
nodes to be unique



treeAinv<-inverseA(sptree, nodes="TIPS")$Ainv



random=~us(1+Temp):Range



#note, I could alternatively use random=~us(1+Temp):species, but results
are likely harder to interpret



prior<-list(G=list(G1=list(V=diag(#), nu=#)), R=list(V=diag(#), n=#))



model1<-MCMCglmm(LVO2~1+Acclm+Temp+LMass+Sex+Range+Acclm*Temp*Range,
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
Jarrod Hadfield
# 
Hi,

If temp is continuous, you are trying to estimate a 2X2 covariance  
matrix for the random effects and a scalar variance for the residuals.  
In general, I use priors of the form:

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))

If temp is categorical then you are trying to estimate a 3X3  
covariance matrix for the random effects. Personally, I would opt for  
continuous temp, at least in the random effect part of the model.

You almost certainly want random=~us(1+Temp):species rather than  
random=~us(1+Temp):Range. It sounds like Temp:Range in the fixed part  
of the model is the key term you want to test (do large ranging  
species respond differently to temp). random=~us(1+Temp):species deals  
with the fact that species may vary in their response to temperature  
independently of anything to do with their ranges.

You don't have many species so don't expect to get very precise  
estimates of the covariance matrix, particularly given the species  
effects are assumed to be correlated due to their shared phylogenetic  
history.

Cheers,

Jarrod




Quoting Tricia Markle <markl033 at umn.edu> on Mon, 26 Jan 2015 00:05:20 -0600: