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96 lines
5.0 KiB
R
96 lines
5.0 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2021 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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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://msberends.github.io/AMR/ #
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# ==================================================================== #
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if (suppressWarnings(require("dplyr"))) {
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expect_stdout(AMX_R <- example_isolates %>%
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filter(mo == "B_ESCHR_COLI") %>%
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rsi_predict(col_ab = "AMX",
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col_date = "date",
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model = "binomial",
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minimum = 10,
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info = TRUE) %>%
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pull("value"))
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# AMX resistance will increase according to data set `example_isolates`
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expect_true(AMX_R[3] < AMX_R[20])
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}
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expect_stdout(x <- suppressMessages(resistance_predict(example_isolates,
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col_ab = "AMX",
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year_min = 2010,
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model = "binomial",
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info = TRUE)))
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pdf(NULL) # prevent Rplots.pdf being created
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expect_silent(plot(x))
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if (suppressWarnings(require("ggplot2"))) {
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expect_silent(ggplot_rsi_predict(x))
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expect_silent(ggplot(x))
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expect_error(ggplot_rsi_predict(example_isolates))
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}
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expect_stdout(rsi_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "binomial",
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col_ab = "AMX",
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col_date = "date",
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info = TRUE))
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expect_stdout(rsi_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "loglin",
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col_ab = "AMX",
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col_date = "date",
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info = TRUE))
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expect_stdout(rsi_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "lin",
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col_ab = "AMX",
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col_date = "date",
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info = TRUE))
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expect_error(rsi_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "INVALID MODEL",
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col_ab = "AMX",
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col_date = "date",
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info = TRUE))
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expect_error(rsi_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "binomial",
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col_ab = "NOT EXISTING COLUMN",
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col_date = "date",
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info = TRUE))
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expect_error(rsi_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "binomial",
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col_ab = "AMX",
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col_date = "NOT EXISTING COLUMN",
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info = TRUE))
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expect_error(rsi_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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col_ab = "AMX",
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col_date = "NOT EXISTING COLUMN",
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info = TRUE))
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expect_error(rsi_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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col_ab = "AMX",
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col_date = "date",
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info = TRUE))
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# almost all E. coli are MEM S in the Netherlands :)
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expect_error(resistance_predict(x = subset(example_isolates, mo == "B_ESCHR_COLI"),
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model = "binomial",
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col_ab = "MEM",
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col_date = "date",
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info = TRUE))
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