I've been running a glm with a binomial distribution and two predictors which are date and a categorical variable named 'pond':
glm(yes/no ~ date + pond, family=binomial(link="logit"), data=dat)
However, running into trouble as 'pond has 15 levels (leading to quasi separation). Would running a bayglm be appropriate to deal with this issue (I cant reduce the number of levels)? If so, how would I select appropriate priors, given that I don't have any prior/expert data. Are there certain 'default' priors I should use, considering it's a binomial glm?