(v1.6.0.9031) tinytest unit tests

tests/testthat/test-g.test.R

@@ -1,71 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Data Analysis for R                   #
-#                                                                      #
-# SOURCE                                                               #
-# https://github.com/msberends/AMR                                     #
-#                                                                      #
-# LICENCE                                                              #
-# (c) 2018-2021 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/   #
-# ==================================================================== #
-
-context("g.test.R")
-
-test_that("G-test works", {
-  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))$p.value,
-               expected = 0.12574,
-               tolerance = 0.00001)
-
-  # example 2: red crossbills
-  x <- c(1752, 1895)
-  expect_equal(g.test(x)$p.value,
-               expected = 0.01787343,
-               tolerance = 0.00000001)
-
-  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_lt(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)))
-
-})