AMR/inst/tinytest/test-resistance_predict.R

121 lines
4.3 KiB
R
Raw Normal View History

2021-05-15 21:36:22 +02:00
# ==================================================================== #
# TITLE #
2022-10-05 09:12:22 +02:00
# AMR: An R Package for Working with Antimicrobial Resistance Data #
2021-05-15 21:36:22 +02:00
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
2022-10-05 09:12:22 +02:00
# 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 #
# #
2022-12-27 15:16:15 +01:00
# 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. #
2021-05-15 21:36:22 +02:00
# #
# 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/ #
# ==================================================================== #
2023-02-18 14:56:06 +01:00
if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
2021-05-15 21:36:22 +02:00
expect_stdout(AMX_R <- example_isolates %>%
2022-08-28 10:31:50 +02:00
filter(mo == "B_ESCHR_COLI") %>%
2023-01-21 23:47:20 +01:00
sir_predict(
2022-08-28 10:31:50 +02:00
col_ab = "AMX",
col_date = "date",
model = "binomial",
minimum = 10,
info = TRUE
) %>%
pull("value"))
2021-05-15 21:36:22 +02:00
# AMX resistance will increase according to data set `example_isolates`
expect_true(AMX_R[3] < AMX_R[20])
}
expect_stdout(x <- suppressMessages(resistance_predict(example_isolates,
2022-08-28 10:31:50 +02:00
col_ab = "AMX",
year_min = 2010,
model = "binomial",
info = TRUE
)))
2021-05-15 21:36:22 +02:00
pdf(NULL) # prevent Rplots.pdf being created
expect_silent(plot(x))
2021-05-21 20:20:51 +02:00
if (AMR:::pkg_is_available("ggplot2")) {
2023-01-21 23:47:20 +01:00
expect_silent(ggplot_sir_predict(x))
2023-02-18 14:56:06 +01:00
expect_silent(ggplot2::autoplot(x))
2023-01-21 23:47:20 +01:00
expect_error(ggplot_sir_predict(example_isolates))
2021-05-15 21:36:22 +02:00
}
2023-01-21 23:47:20 +01:00
expect_stdout(sir_predict(
2022-08-28 10:31:50 +02:00
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
model = "binomial",
col_ab = "AMX",
col_date = "date",
info = TRUE
))
2023-01-21 23:47:20 +01:00
expect_stdout(sir_predict(
2022-08-28 10:31:50 +02:00
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
model = "loglin",
col_ab = "AMX",
col_date = "date",
info = TRUE
))
2023-01-21 23:47:20 +01:00
expect_stdout(sir_predict(
2022-08-28 10:31:50 +02:00
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
model = "lin",
col_ab = "AMX",
col_date = "date",
info = TRUE
))
2021-05-15 21:36:22 +02:00
2023-01-21 23:47:20 +01:00
expect_error(sir_predict(
2022-08-28 10:31:50 +02:00
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
model = "INVALID MODEL",
col_ab = "AMX",
col_date = "date",
info = TRUE
))
2023-01-21 23:47:20 +01:00
expect_error(sir_predict(
2022-08-28 10:31:50 +02:00
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
model = "binomial",
col_ab = "NOT EXISTING COLUMN",
col_date = "date",
info = TRUE
))
2023-01-21 23:47:20 +01:00
expect_error(sir_predict(
2022-08-28 10:31:50 +02:00
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
model = "binomial",
col_ab = "AMX",
col_date = "NOT EXISTING COLUMN",
info = TRUE
))
2023-01-21 23:47:20 +01:00
expect_error(sir_predict(
2022-08-28 10:31:50 +02:00
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
col_ab = "AMX",
col_date = "NOT EXISTING COLUMN",
info = TRUE
))
2023-01-21 23:47:20 +01:00
expect_error(sir_predict(
2022-08-28 10:31:50 +02:00
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
col_ab = "AMX",
col_date = "date",
info = TRUE
))
2021-05-15 21:36:22 +02:00
# almost all E. coli are MEM S in the Netherlands :)
2022-08-28 10:31:50 +02:00
expect_error(resistance_predict(
x = subset(example_isolates, mo == "B_ESCHR_COLI"),
model = "binomial",
col_ab = "MEM",
col_date = "date",
info = TRUE
))