AMR/inst/tinytest/test-g.test.R

# ==================================================================== #
# TITLE                                                                #
# AMR: An R Package for Working with Antimicrobial Resistance Data     #
#                                                                      #
# SOURCE                                                               #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# CITE AS                                                              #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
# Data. Journal of Statistical Software, 104(3), 1-31.                 #
# doi: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://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)))