# ==================================================================== #
# TITLE                                                                #
# Antimicrobial Resistance (AMR) Data Analysis for R                   #
#                                                                      #
# SOURCE                                                               #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# LICENCE                                                              #
# (c) 2018-2022 Berends MS, Luz CF et al.                              #
# Developed at the University of Groningen, the Netherlands, in        #
# collaboration with non-profit organisations Certe Medical            #
# Diagnostics & Advice, and University Medical Center Groningen.       # 
#                                                                      #
# 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://msberends.github.io/AMR/   #
# ==================================================================== #

# 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)))
