[R-sig-ME] Continuous vs. categorical correlated group effects

[Ben Bolker]

  Can you show us the summary() of your data?
  Is it possible you have complete separation in your continuous predictor?

On 18-01-02 02:38 PM, Drager, Andrea Pilar wrote:
> 
> Hi All,
> 
> I am having trouble running a Bayesian mixed model in MCMCglmm where I
> have individual-level data for my response variable, and species-level
> data as the random effect (such as "species"), plus any other
> species-level continuous variable, such as abundance, in the model. But
> if the the other species-level variable is categorical--whether because
> I make it a random effect or because it is in fact categorical--the
> model runs! Could someone please explain the stats behind this?
> 
> 
> prior = list(R = list(V = 1, nu = 0, fix = 1),  G = list(G1=list(V =
> 1,nu = 0.002)))
> 
> Won't run-->MCMCglmm(binary_individual_repsonse ~ species_abund_continuous,
>                      random = ~ species_id_categorical, family =
> "categorical")
> 
>             Error : Mixed model equations singular: use a (stronger) prior
> 
> 
> Runs-->MCMCglmm(binary_individual_response ~ 1,
>                 random = ~ species_abund_categorical +
> species_id_categorical, family = "categorical")
> 
> Runs-->MCMCglmm(binary_individual_response  ~ species_id_categorical,
>                 random = ~ species_abund_categorical, family=
> "categorical")
> 
> 
> Thanks in advance!
> Andrea Pilar Drager
> PhD. student
> Ecology and Evolutionary Biology, Rice University
> 
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