context('check ability')

library(dplyr)


test_that('inconsistencies between data and parms are handled correctly',{

  db = open_project('../verbAggression.db')
  
  f1 = fit_enorm(db)
  f2 = fit_enorm(db, item_id != 'S4DoShout')
  f3 = fit_enorm(db, !(item_id=='S4DoShout' & item_score == 1))
  
  # params must cover all item, item_score combinations 
  expect_no_error({p1 = ability(db,f1)})
  expect_error({p2 = ability(db,f2)}, regexp='parameters.+items')
  expect_error({p3 = ability(db,f3)}, regexp='parameters.+scores')
  
  # of course the reverse is not necessary
  expect_no_error({p4 = ability(db,f1,item_score !=2 )})
  
  dbDisconnect(db)
})  


test_that('verbAgg abilities', {

  db = open_project('../verbAggression.db')
  f = fit_enorm(db)
  
  
  # check ability mle is inverse of expected_score
  es = expected_score(f)
  expect_lt(
    ability_tables(f) %>%
      filter(is.finite(theta)) %>%
      mutate(error = abs(booklet_score - es(theta))) %>%
      pull(error) %>%
      mean(),
    0.00001,
    label = "ability_tables mle on average estimated to within .00001 of test_score")
  
  
  nscores = get_rules(db) %>%
    group_by(item_id) %>%
    summarize(m=max(item_score)) %>%
    ungroup() %>%
    pull(m) %>%
    sum() + 1
  
  test_cases = list(MLE = c('MLE','normal'), WLE = c('WLE','normal'), EAP_normal = c('EAP','normal'), EAP_J = c('EAP','Jeffreys'))
  
  res = lapply(test_cases, function(s){ ability_tables(f, method = s[1], prior = s[2])})
  
  expect_false(any(sapply(lapply(res,'[[','theta'), is.unsorted)), info='abilities not increasing verbAgg')
  
  theta = do.call(cbind,lapply(res,'[[','theta'))
  expect_true(sum(!apply(theta,1,is.finite)) == 2 && !any(is.finite(theta[c(1,nscores),1])), info='inifinity only in MLE')
  theta[!is.finite(theta)] = NA
  r = cor(theta,use='pairwise')
  expect_true(all(r >= .99), info='high correlation ability estimates one booklet')
  expect_true(all(r[upper.tri(r)] < 1), info='different abnility methods are different')
  

  dbDisconnect(db)
  
})