mirror of
https://github.com/msberends/AMR.git
synced 2026-11-16 00:10:57 +01:00
dr. M.S. (Matthijs) Berends
53d0704271
- New sir_combine() to combine SIR results of multiple antimicrobials into one variable; antibiogram(), wisca() and the count/proportion functions now use the same engine (antibiogram() and wisca() output unchanged) - With multiple antimicrobials, count_*() and proportion_*() now follow the documented combination therapy rules: resistance() is the proportion of failing combinations, count_all() excludes isolates with an unknown combination result, and WT/NWT/NS/NI are handled correctly - where() is now decided at runtime instead of at installation, fixing antibiogram() and wisca() without tidyselect (e.g. CRAN binaries without dplyr) - Imports from data.table and cli moved to .onLoad(); data.table functions are only used while it is loaded; the like() example no longer unloads data.table, which crashed R - Static test that fails on code outside functions depending on suggested packages - New check-without-dplyr workflow: installs AMR with all suggested packages, removes dplyr (and tidyselect), then runs all unit tests and examples against the installed package - g.test() now returns the degrees of freedom as 'parameter', so they are printed - Fix R CMD check NOTEs (hidden .claude folder, lost braces in inherited g.test and ggplot_pca docs)
78 lines
3.5 KiB
R
78 lines
3.5 KiB
R
# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
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# AMR: An R Package for Working with Antimicrobial Resistance Data. #
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# Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in the Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://amr-for-r.org #
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# ==================================================================== #
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test_that("test-g.test.R", {
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skip_on_cran()
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# GOODNESS-OF-FIT
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# example 1: clearfield rice vs. red rice
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x <- c(772, 1611, 737)
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expect_equal(g.test(x, p = c(0.25, 0.50, 0.25))$p.value,
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0.12574,
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tolerance = 0.0001
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)
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# example 2: red crossbills
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x <- c(1752, 1895)
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expect_equal(g.test(x)$p.value,
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0.017873,
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tolerance = 0.0001
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)
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# degrees of freedom are returned as the standard 'htest' component, so print() shows them
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expect_identical(g.test(x)$parameter, c(df = 1))
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expect_identical(names(g.test(matrix(c(762, 327, 468, 484, 239, 477), nrow = 2))$parameter), "df")
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expect_equal(unname(g.test(matrix(c(762, 327, 468, 484, 239, 477), nrow = 2))$parameter), 2)
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expect_error(g.test(0))
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expect_error(g.test(c(0, 1), 0))
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expect_error(g.test(c(1, 2, 3, 4), p = c(0.25, 0.25)))
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expect_error(g.test(c(1, 2, 3, 4), p = c(0.25, 0.25, 0.25, 0.24)))
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# expect_warning(g.test(c(1, 2, 3, 4), p = c(0.25, 0.25, 0.25, 0.24), rescale.p = TRUE))
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# INDEPENDENCE
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x <- as.data.frame(
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matrix(
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data = round(runif(4) * 100000, 0),
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ncol = 2,
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byrow = TRUE
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)
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)
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# fisher.test() is always better for 2x2 tables:
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# expect_warning(g.test(x))
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expect_true(suppressWarnings(g.test(x)$p.value) < 1)
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# expect_warning(g.test(x = c(772, 1611, 737), y = c(780, 1560, 780), rescale.p = TRUE))
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expect_error(g.test(matrix(data = c(-1, -2, -3, -4), ncol = 2, byrow = TRUE)))
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expect_error(g.test(matrix(data = c(0, 0, 0, 0), ncol = 2, byrow = TRUE)))
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})
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