Continuous vs. categorical correlated group effects

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