test_that("make sure margCompareErrorCheckF is catching errors", {
  
  skip_on_cran()
  skip_if_not_installed('rstanarm')
  
  set.seed(500)
  
  modelData       <- rstanarm::wells
  modelData$assoc <- ifelse(modelData$assoc==1, 'Y', 'N')
  
  rowMiss <- sample(1:nrow(modelData), size=10, replace=F)
  colMiss <- sample(1:ncol(modelData), size=10, replace=T)
  
  for(i in 1:10){
    
    modelData[rowMiss[[i]], colMiss[[i]]] <- NA
    
  }
  
  logitModel  <- suppressWarnings(rstanarm::stan_glm(switch ~ dist*educ + arsenic + I(arsenic^2) + assoc, data=modelData, family=binomial, refresh=0, chains=2, iter=500))
  
  margTestError   <- bayesMargEffF(logitModel, marginal_effect='educ', start_value=5, end_value=0, digits=4)
  margTestNoError <- bayesMargEffF(logitModel, marginal_effect='educ', start_value=5, end_value=0, digits=4, at=list(dist=c(20, 30)))
  
  expect_error(margCompareErrorCheckF(marg_list=1, ci=.95, hdi_interval=T, centrality='mean'), regexp="The 'marg_list' argument must have class 'bayesmeanscale_marg'!")
  expect_error(margCompareErrorCheckF(margTestNoError, ci=1.1, hdi_interval=T, centrality='mean'), regexp="The credible interval level must be between 0 and 1!")
  expect_error(margCompareErrorCheckF(margTestNoError, ci=.95, hdi_interval="W", centrality='mean'), regexp="This is a logical argument!")
  expect_no_error(margCompareErrorCheckF(margTestNoError, ci=.95, hdi_interval=T, centrality='mean'))
  expect_error(margCompareErrorCheckF(margTestError, ci=.95, hdi_interval=T, centrality='mean'), regexp="There is only 1 marginal effect, so nothing to compare to!")
  
})