Problem with Hypothesis Tests

Hey all!

I am currently writing my master thesis on linear multilevel models. Could someone explain to me how I can manually extract a specific hypothesis for some residual standard deviation of my model? For example, let’s say that I want to make the hypothesis tests in order to find the posterior estimates for the two residual standard deviations of my 2 treatment groups in their actual scale. So, the hypothesis will contain a vector:

c(“exp(sigma_Intercept) = 0”, “exp(sigma_Intercept + sigma_treatment2) = 0”).

I have noticed that the results of the latter hypothesis are slightly different, compared to when I am trying to do it manually through the summary of my model, by setting the posterior estimated parameters and use them in order to compute: exp(sigma_Intercept) and exp(sigma_Intercept+sigma_treatment2). The whole problem comes from me trying to reduce the number of my experiment subjects from a dataset, create a subset of this dataset, sample with replacement n-times, fit the Bayesian linear model n-times and in the end, extract a hypothesis test for my parameters, based on the mean of all the n-hypothesis for my parameters. That is why I am asking if I can somehow do it manually, because the only thing I can certainly achieve is to obtain a vector with the mean of all the n - posterior estimated parameters that I want, but not hypothesis tests for them.

Oh I noticed that

  1. brms has support for a form of hypothesis tests, which I didn’t know about and
  2. this topic is further discussed at Hypothesis Tests with brms where Paul weighs in and he is the ultimate expert on brms

So my answer was probably misleading and I have removed it. Sorry for any confustion.