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

70 lines
3.0 KiB
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)))