[R-sig-ME] Bivariate MCMCglmm: fixed effect error

[Tara Cox [RPG]]

Dear list,

I am stuck attempting to run a bivariate MCMCglmm that uses personality score and age at first breeding (years) as response variables (both modelled Poisson). I have multiple repeats per individual for personality score, but a single value for age at first breeding. For fixed effects, I have included drivers known to impact personality score (age when tested, age2, colour of room that individual was tested in, and the test number) as well as age at first breeding (natal territory quality and island-wide food availability).

To account for the fact that individuals born more recently in the dataset lived short lives (as per Bouwhuis et al., 2010), I have also included cohort as a fixed effect for age at first breeding.

I've been using the covu method with a parameter expanded prior and model specification outlined below, but I am having issues when trying to run the model with cohort as a fixed effect (error message states: '## Error in MCMCglmm(fitness.stack ~ variable - 1 + at.level(variable, : Mixed model equations singular: use a (stronger) prior'). I have tried setting cohort as both numeric and factor when including it as a fixed effect, but this doesn't make a difference. I know the issue is not with any other part of the model, as it runs successfully when cohort is removed as a fixed effect.


prior.age <- list(G = list(G1 = list(V = diag(1), nu = 1),                            # rand effect for Cohort (fitted for AGE_BREEDING)
                                         G2 = list(V = diag(1), nu = 1)),                           # rand effect for Observer (fitted for Personality)
                           R = list(R1 = list(V = diag(2), nu = 0.002, covu = TRUE),  # 2-way var-cov matrix of ID for Personality, residual for AGE_BREEDING
                                         R2 = list(V = diag(1), nu = 0.002)))                       # residual for Personality


m1  <-   MCMCglmm(fitness.stack ~ variable - 1 +
                            at.level(variable, "Personality"):Personality_Age +
                            at.level(variable, "Personality"):Personality_Age2 +
                            at.level(variable, "Personality"):Personality_RoomColour +
                            at.level(variable, "Personality"):TestNumber +
                            at.level(variable, "AGE_BREEDING"):logNatalTerritoryQuality +
                            at.level(variable, "AGE_BREEDING"):logIslandWideFoodAvailability +
                            at.level(variable, "AGE_BREEDING"):Cohort,
                          random = ~ us(at.level(variable,"Personality")):Observer + us(at.level(variable,"AGE_BREEDING")):Cohort + us(at.level(variable, "Personality")):BirdID,
                          rcov = ~us(at.level(variable, "AGE_BREEDING")):BirdID + idh(at.level(variable, "Personality")):units,
                          family = NULL, # specified already in the data as poisson for both
                          prior = prior.age,
                          nitt=4600000,
                          burnin=60000,
                          thin=500,
                          verbose = FALSE,
                          pr=FALSE,
                          data = stack)


In case the issue is related to sample size or data structure, I have also attached an Rmd containing this info.

If anyone could provide any insight into how I can get my model to run successfully, it would be much appreciated!

Best wishes,
Tara


Tara Cox

Pronouns: she, her, hers

PhD researcher

Dugdale group, School of Biology

Faculty of Biological Sciences

University of Leeds