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

[Jarrod Hadfield]

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]]
>
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