So I've built a (long computational time) jags mixed model that includes an interaction term in the fixed effects. After the fact I want to multiply two of the posterior means for the regression coefficients - with credible intervals.
library(lme4) library(runjags) model <- template.jags(total.fruits ~ reg + amd*gen + (1 | reg), data=Arabidopsis, family = "poisson") # can ignore warning for this demo ml.jags <- run.jags(model) summary(ml.jags)
So I want to work out the value of amd*gen for level 2 of amd afterwards. # For the Mean or Median I can multiply the terms
> results[row.names(results) == "amd_effect", "Mean"] * results[row.names(results) == "gen_coefficient_amd_level", "Mean"]  0.0001745244
But how do I put credible intervals around this since multiplying the credible intervals directly is wrong? I know in a frequentist framework I can do this using the standard errors but I was not sure if that is true also in Bayesian stats?