context("check accuracy of elliptical.test a = 0.5")
set.seed(10)
data(nydf)
coords <- nydf[, c("longitude", "latitude")]
pop <- nydf$population
cases <- floor(nydf$cases)
shape <- c(1, 1.5, 2, 3, 4, 5)
nangle <- c(1, 4, 6, 9, 12, 15)
ex <- sum(cases) / sum(pop) * pop
ubpop <- 0.1
cl <- NULL
min.cases <- 2
alpha <- 1


nsim <- 19
pbapply::pboptions(type = "none")
out0.5 <- elliptic.test(coords, cases, pop,
  nsim = nsim,
  alpha = 1, a = 0.5, ubpop = 0.1
)
locids0.51 <- c(
  52, 50, 53, 38, 49, 15, 48, 39, 1, 37, 16, 44,
  14, 47, 40, 2, 13, 43, 51, 45, 17, 11, 12, 3, 46
)
locids0.52 <- c(87, 88, 86, 89, 92, 85, 90)
locids0.53 <- c(
  115, 116, 114, 111, 117, 113, 123, 120, 110,
  122, 112, 118, 121, 124, 220, 133, 131, 119,
  130, 125, 132, 219, 126, 127, 135
)
locids0.54 <- c(170, 171, 166, 167)

test_that("check accuracy of elliptical.test a = 0.5", {
  expect_equal(
    locids0.51,
    out0.5$clusters[[1]]$locids
  )
  expect_equal(
    0.058,
    round(out0.5$clusters[[1]]$semiminor_axis, 3)
  )
  expect_equal(
    0.087,
    round(out0.5$clusters[[1]]$semimajor_axis, 3)
  )
  expect_equal(90, out0.5$clusters[[1]]$angle - 90)
  expect_equal(1.5, out0.5$clusters[[1]]$shape)
  expect_equal(99685, out0.5$clusters[[1]]$pop)
  expect_equal(93, out0.5$clusters[[1]]$cases)
  expect_equal(52.03, round(out0.5$clusters[[1]]$ex, 2))
  expect_equal(1.79, round(out0.5$clusters[[1]]$smr, 2))
  expect_equal(1.95, round(out0.5$clusters[[1]]$rr, 2))
  expect_equal(14.772705, round(out0.5$clusters[[1]]$loglikrat, 6))
  expect_equal(14.474236, round(out0.5$clusters[[1]]$test_statistic, 6))
  # true p-value 0.00019
  expect_equal(0.05, out0.5$clusters[[1]]$pvalue)

  expect_equal(
    locids0.52,
    out0.5$clusters[[2]]$locids
  )
  expect_equal(
    locids0.53,
    out0.5$clusters[[3]]$locids
  )
  expect_equal(
    locids0.54,
    out0.5$clusters[[4]]$locids
  )
})