Hi all,
I’m having a strange issue with my model that I’m hoping someone here can help me troubleshoot. I’m trying to put my first Bayesian models together on a study that I just finished, and being that they’re my first models, I’m running a “frequentist” model alongside it to make sure the coefficient estimates are roughly the same. I’m going into this using a weakly-informative prior and not trying to push the results in the slightest; yet the results of the two models are dramatically different. I don’t get any errors or warnings so I can’t figure out why.
Code is here:
Priors_MEmodel<- student_t(df=5, location = c(0, 0, 0),scale = c(2, 2, 2), autoscale = FALSE)
Main_EffectsModel=stan_glm(Accept_Reject~Discount+Floor,
family = binomial(link = "logit"),
data=sonadata_clean,
prior = Priors_MEmodel,
#prior_intercept = normal(),
prior_PD = TRUE,
algorithm = c("sampling"),
#mean_PPD = TRUE,
#adapt_delta = 0.95,
#QR = FALSE,
#sparse = FALSE,
chains=3,iter=5000,cores=3)
library(jmv)
frequentist_model=logRegBin(data = sonadata_clean, dep = Accept_Reject,
covs = NULL,
factors = vars(Floor, Discount),
blocks = list(list("Discount","Floor")),
refLevels = list(list(var="Accept_Reject",ref="0"),
list(var="Discount",ref="0"),
list(var="Floor",ref="0")),
pseudoR2 = c("r2n"),
ci=TRUE,
OR = TRUE)
describe_posterior(Main_EffectsModel)
frequentist_model
The results of the two models are attached in a picture. If anyone has any suggestions on how to diagnose and fix this that would be amazing. Nothing I’ve tried so far (enabling/disabling various stan_glm settings; any adjustments to the prior) brings the Bayesian estimates closer to Model1.
