As a beginner in bayesian stats, I have the following question. In my model, I test how speakers of RP language discriminate a particular vowel contrast compared to speakers of CG language (reference level). Here’s my model’s formula:
fit <- brm(response ~ 0 + Intercept + language + (1 | subject), data = test, family = bernoulli("logit"), prior = prior, sample_prior = "yes")
Family: bernoulli Links: mu = logit Formula: response ~ 0 + Intercept + language + (1 | subject) Data: test (Number of observations: 320) Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1; total post-warmup draws = 4000 Group-Level Effects: ~subject (Number of levels: 20) Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS sd(Intercept) 0.87 0.30 0.42 1.58 1.00 1373 2192 Population-Level Effects: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS Intercept -0.06 0.32 -0.68 0.58 1.00 1694 1964 languageRP 2.76 0.62 1.59 4.07 1.00 2503 1846 Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS and Tail_ESS are effective sample size measures, and Rhat is the potential scale reduction factor on split chains (at convergence, Rhat = 1).
Now, I want to do a hypothesis testing to find the evidence of above chance performance (i.e., > 50%) in the discrimination of the contrast by the two populations. I would like to know the code for this testing
Your help will be much appreciated!