# Single Chain ------------------------------------------------------------

test_that("drop burn-in works on a single chain", {
  iters = 200
  burnin = 25
  model <- drop_burnin(model = example_model, burn_in = burnin)
  
  # number of clusters and writers
  K <- model$rjags_data$G
  W <- model$rjags_data$W
  
  # check that model is an mcmc object
  expect_true(coda::is.mcmc(model$fitted_model[[1]]))
  
  # check dimensions
  expect_length(model$fitted_model, 1)
  expect_equal(dim(model$fitted_model[[1]]), c(iters-burnin, 2*K + 3*K*W))
})

test_that("about variable works on a single chain", {
  expect_equal(about_variable(variable = "pi[1,3]", model = example_model), 
               "Pi is the cluster fill probability for writer ID w0009 and cluster 3")
  
  expect_equal(about_variable(variable = "mu[2,5]", model = example_model), 
               "Mu is the location parameter of a wrapped-Cauchy distribution for writer ID w0030 and cluster 5")
  
  expect_equal(about_variable(variable = "tau[3,5]", model = example_model), 
               "Tau is the scale parameter of a wrapped-Cauchy distribution for writer ID w0238 and cluster 5")
  
  expect_equal(about_variable(variable = "gamma[4]", model = example_model), 
               "Gamma is the mean cluster fill probability across all writers for cluster 4")
  
  expect_equal(about_variable(variable = "eta[3]", 
                              model = example_model), 
               "Eta is the mean, or the location parameter, of the hyper prior for mu for cluster 3")
})