# ==================================================================== #
# TITLE:                                                               #
# AMR: An R Package for Working with Antimicrobial Resistance Data     #
#                                                                      #
# SOURCE CODE:                                                         #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# PLEASE CITE THIS SOFTWARE AS:                                        #
# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
# Journal of Statistical Software, 104(3), 1-31.                       #
# https://doi.org/10.18637/jss.v104.i03                                #
#                                                                      #
# Developed at the University of Groningen and the University Medical  #
# Center Groningen in The Netherlands, in collaboration with many      #
# colleagues from around the world, see our website.                   #
#                                                                      #
# This R package is free software; you can freely use and distribute   #
# it for both personal and commercial purposes under the terms of the  #
# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
# the Free Software Foundation.                                        #
# We created this package for both routine data analysis and academic  #
# research and it was publicly released in the hope that it will be    #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
#                                                                      #
# Visit our website for the full manual and a complete tutorial about  #
# how to conduct AMR data analysis: https://amr-for-r.org              #
# ==================================================================== #

test_that("test-g.test.R", {
  skip_on_cran()

  # GOODNESS-OF-FIT

  # example 1: clearfield rice vs. red rice
  x <- c(772, 1611, 737)
  expect_equal(g.test(x, p = c(0.25, 0.50, 0.25))$p.value,
    0.12574,
    tolerance = 0.0001
  )

  # example 2: red crossbills
  x <- c(1752, 1895)
  expect_equal(g.test(x)$p.value,
    0.017873,
    tolerance = 0.0001
  )

  expect_error(g.test(0))
  expect_error(g.test(c(0, 1), 0))
  expect_error(g.test(c(1, 2, 3, 4), p = c(0.25, 0.25)))
  expect_error(g.test(c(1, 2, 3, 4), p = c(0.25, 0.25, 0.25, 0.24)))
  # expect_warning(g.test(c(1, 2, 3, 4), p = c(0.25, 0.25, 0.25, 0.24), rescale.p = TRUE))

  # INDEPENDENCE

  x <- as.data.frame(
    matrix(
      data = round(runif(4) * 100000, 0),
      ncol = 2,
      byrow = TRUE
    )
  )

  # fisher.test() is always better for 2x2 tables:
  # expect_warning(g.test(x))
  expect_true(suppressWarnings(g.test(x)$p.value) < 1)

  # expect_warning(g.test(x = c(772, 1611, 737), y = c(780, 1560, 780), rescale.p = TRUE))

  expect_error(g.test(matrix(data = c(-1, -2, -3, -4), ncol = 2, byrow = TRUE)))
  expect_error(g.test(matrix(data = c(0, 0, 0, 0), ncol = 2, byrow = TRUE)))
})