AMR/inst/tinytest/test-resistance_predict.R

# ==================================================================== #
# TITLE:                                                               #
# AMR: An R Package for Working with Antimicrobial Resistance Data     #
#                                                                      #
# SOURCE CODE:                                                         #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# PLEASE CITE THIS SOFTWARE AS:                                        #
# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
# Journal of Statistical Software, 104(3), 1-31.                       #
# https://doi.org/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/   #
# ==================================================================== #

if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
  expect_stdout(AMX_R <- example_isolates %>%
    filter(mo == "B_ESCHR_COLI") %>%
    sir_predict(
      col_ab = "AMX",
      col_date = "date",
      model = "binomial",
      minimum = 10,
      info = TRUE
    ) %>%
    pull("value"))
  # 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,
  col_ab = "AMX",
  year_min = 2010,
  model = "binomial",
  info = TRUE
)))
pdf(NULL) # prevent Rplots.pdf being created
expect_silent(plot(x))
if (AMR:::pkg_is_available("ggplot2")) {
  expect_silent(ggplot_sir_predict(x))
  expect_silent(ggplot2::autoplot(x))
  expect_error(ggplot_sir_predict(example_isolates))
}
expect_stdout(sir_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  model = "binomial",
  col_ab = "AMX",
  col_date = "date",
  info = TRUE
))
expect_stdout(sir_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  model = "loglin",
  col_ab = "AMX",
  col_date = "date",
  info = TRUE
))
expect_stdout(sir_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  model = "lin",
  col_ab = "AMX",
  col_date = "date",
  info = TRUE
))

expect_error(sir_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  model = "INVALID MODEL",
  col_ab = "AMX",
  col_date = "date",
  info = TRUE
))
expect_error(sir_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  model = "binomial",
  col_ab = "NOT EXISTING COLUMN",
  col_date = "date",
  info = TRUE
))
expect_error(sir_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  model = "binomial",
  col_ab = "AMX",
  col_date = "NOT EXISTING COLUMN",
  info = TRUE
))
expect_error(sir_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  col_ab = "AMX",
  col_date = "NOT EXISTING COLUMN",
  info = TRUE
))
expect_error(sir_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  col_ab = "AMX",
  col_date = "date",
  info = TRUE
))
# almost all E. coli are MEM S in the Netherlands :)
expect_error(resistance_predict(
  x = subset(example_isolates, mo == "B_ESCHR_COLI"),
  model = "binomial",
  col_ab = "MEM",
  col_date = "date",
  info = TRUE
))