Hello, I’ve ran into a weird issue with my model that I can’t make sense of.
I have a poisson regression model that I has a lot of predictors (21). The weird thing is, is that if I include all 21 predictors the parameters become highly correlated (.98 - .99), with accompanying warning that Bulk and Tail ESS are too small. However, if I remove any predictor (just one is enough), this behavior disappears and the model estimates normally (without any excessive correlation of the parameters).
I am, quite frankly, not sure what to make of this. All the dependent variables are standardized to mean=0; sd=1, with Normal(0, 1) priors on all coefficients.
The data set is quite large with thousands of observations.
None of the predictors are highly correlated between themselves, so multicollinearity should not be an issue.
Here’s my code:
test_model <- brm( dependent_var ~ independent_var_1 + independent_var_2 + independent_var_3 + ... independent_var_20 + independent_var_21, data = test_data, family = poisson(), prior = my_prior, chains = 6, cores = 6, iter = 3000, control = list(adapt_delta = 0.9999, max_treedepth = 15))