[R-sig-ME] Unclear output from MCMCglmm with categorical predictors

[HADFIELD Jarrod]

Hi,

Most likely the phylogenetic heritability (the phylogenetic variance / the phylogenetic +residual variance) is approaching one resulting in numerical difficulties. Probably the best thing is to assume that the phylogenetic heritability equals 1 and use the reduced phylogenetic mixed model implementation. This allows the phylogenetic heritability to be equal to 1 without causing numerical issues. At some point I will integrate these models into the main MCMCglmm package, but for now you can download it from here: http://jarrod.bio.ed.ac.uk/MCMCglmmRAM_2.24.tar.gz.

Change the name of the ‘’Binomial’ column to ‘animal’ and fit:

T1 <- MCMCglmm(Activity ~ -1 + log(Mass) + Max.Temp * Annual.Precip,
                               random = ~ animal
                               prior = list(R = list(fix=1, V=1e-15), G = list(G1 = list(V=1, nu=0.002))),
                               pedigree = tree,
       reduced=TRUE,
                               burnin = 150000, nitt = 2650001, thin = 2500,
                               family = "threshold",  data = Tdata,
                               pl = TRUE, pr = TRUE, saveX = TRUE, saveZ = TRUE,
                               verbose = FALSE)

You should need fewer iterations.

Cheers,

Jarrod


On 20 Nov 2018, at 18:08, roee maor <roeemaor using gmail.com<mailto:roeemaor using gmail.com>> wrote:

Dear Jarrod (and list),

Following your previous comment I added "random = ~ Binomial" to my model to allow for a phylogenetic analysis.
This causes convergence problems: the trace plots show increasing oscillations along each chain (although no directional trends, so it's not a burn-in issue). Also, the posterior samples are highly correlated,  residual variance estimates are >10^3 and threshold estimates are high (>20 on the latent scale).
Surprisingly (to me), predictors that are strongly significant in the non-phylogenetic model lose their effect in the phylogenetic model (I tried several alternative parameter configurations).

It seems that this model attributes the explained variance to phylogeny alone.
Can anyone explain what is going on here?  Am I specifying the model poorly or just asking my data more than it can answer?

I tried to overcome this issue by using a fully resolved variant of the phylogeny, which only improved things slightly.
I also changed the random effect to "random=~Family" or "random=~Order", which reduced the variance and threshold estimates to more acceptable levels (<10), but still no significant predictors (and I'm not sure how the algorithm calculates covariance between higher taxa in the phylogeny).
Separately I tried parameter expanded prior: "prior = list(R = list(V=1, fix=1), G = list(G1 = list(V=1, nu=1, alpha.mu<http://alpha.mu/>=0, alpha.V=1000)))". That didn't help, and messing with priors for this reason feels like poor practice.

This is the model:
T1 <- MCMCglmm(Activity ~ -1 + log(Mass) + Max.Temp * Annual.Precip,
                               random = ~ Binomial,
                               prior = list(R = list(fix=1, V=1), G = list(G1 = list(V=1, nu=0.002))),
                               ginverse = list(Binomial=INphylo$Ainv),
                               burnin = 150000, nitt = 2650001, thin = 2500,
                               family = "threshold",  data = Tdata,
                               pl = TRUE, pr = TRUE, saveX = TRUE, saveZ = TRUE,
                               verbose = FALSE)

The data I use looks like this (not all variables appear in each model):

str(Tdata)
'data.frame': 1389 obs. of  10 variables:
 $ Binomial           : Factor w/ 1421 levels "Abrocoma_bennettii",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ Order                : Factor w/ 27 levels "Afrosoricida",..: 24 24 24 24 24 3 24 24 2 2 ...
 $ Family               : Factor w/ 126 levels "Abrocomidae",..: 1 26 26 26 26 46 74 87 10 10 ...
 $ Activity              : Factor w/ 3 levels "1","2","3": 1 3 2 2 2 3 2 3 1 3 ...
 $ Habitat              : Factor w/ 6 levels "Aqua","Arbo",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ Diet                   : Factor w/ 3 levels "Faun","Herb",..: 2 3 3 3 3 1 3 2 2 2 ...
 $ Mass                 : num  250.5 24.9 34.5 38.9 24.5 ...
 $ Max.Temp         : num  22 16.6 19.1 19.8 17.2 ...
 $ Annual.Precip   : num  166 645 558 903 1665 ...

Any advice would be much appreciated!
Many thanks,


--
Roi Maor
PhD candidate
School of Zoology, Tel Aviv University
Centre for Biodiversity and Environment Research, UCL


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