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# Copyright (C) 2016-2019 Johannes Ranke
# Contact: jranke@uni-bremen.de
# This file is part of the R package mkin
# mkin is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <http://www.gnu.org/licenses/>
context("Calculation of maximum time weighted average concentrations (TWAs)")
test_that("Time weighted average concentrations are correct", {
skip_on_cran()
outtimes_10 <- seq(0, 10, length.out = 10000)
ds <- "FOCUS_C"
for (model in models) {
fit <- fits[[model, ds]]
bpar <- summary(fit)$bpar[, "Estimate"]
pred_10 <- mkinpredict(fit$mkinmod,
odeparms = bpar[2:length(bpar)],
odeini = c(parent = bpar[[1]]),
outtimes = outtimes_10)
twa_num <- mean(pred_10$parent)
names(twa_num) <- 10
twa_ana <- max_twa_parent(fit, 10)
# Test for absolute difference (scale = 1)
# The tolerance can be reduced if the length of outtimes is increased,
# but this needs more computing time so we stay with lenght.out = 10k
expect_equal(twa_num, twa_ana, tolerance = 0.003, scale = 1)
}
})
context("Summary")
test_that("Summaries are reproducible", {
fit <- fits[["DFOP", "FOCUS_C"]]
test_summary <- summary(fit)
test_summary$fit_version <- "Dummy 0.0 for testing"
test_summary$fit_Rversion <- "Dummy R version for testing"
test_summary$date.fit <- "Dummy date for testing"
test_summary$date.summary <- "Dummy date for testing"
test_summary$calls <- "test 0"
test_summary$Corr <- signif(test_summary$Corr, 1)
test_summary$time <- c(elapsed = "test time 0")
# The correlation matrix is quite platform dependent
# It differs between i386 and amd64 on Windows
# and between Travis and my own Linux system
test_summary$Corr <- NULL
expect_known_output(print(test_summary), "summary_DFOP_FOCUS_C.txt")
test_summary_2 <- summary(f_sfo_sfo_eigen)
test_summary_2$fit_version <- "Dummy 0.0 for testing"
test_summary_2$fit_Rversion <- "Dummy R version for testing"
test_summary_2$date.fit <- "Dummy date for testing"
test_summary_2$date.summary <- "Dummy date for testing"
test_summary_2$calls <- "test 0"
test_summary_2$time <- c(elapsed = "test time 0")
# The correlation matrix is quite platform dependent
# It differs between i386 and amd64 on Windows
# and between Travis and my own Linux system
# Even more so when using the Eigen method
test_summary_2$Corr <- NULL
expect_known_output(print(test_summary_2), "summary_DFOP_FOCUS_D_eigen.txt")
test_summary_3 <- summary(f_sfo_sfo_desolve)
test_summary_3$fit_version <- "Dummy 0.0 for testing"
test_summary_3$fit_Rversion <- "Dummy R version for testing"
test_summary_3$date.fit <- "Dummy date for testing"
test_summary_3$date.summary <- "Dummy date for testing"
test_summary_3$calls <- "test 0"
test_summary_3$time <- c(elapsed = "test time 0")
# The correlation matrix is quite platform dependent
# It differs between i386 and amd64 on Windows
# and between Travis and my own Linux system
test_summary_3$Corr <- NULL
expect_known_output(print(test_summary_3), "summary_DFOP_FOCUS_D_deSolve.txt")
})
context("Plotting")
test_that("Plotting mkinfit and mmkin objects is reproducible", {
skip_on_cran()
plot_default_FOCUS_C_SFO <- function() plot(fits[["SFO", "FOCUS_C"]])
plot_res_FOCUS_C_SFO <- function() plot(fits[["SFO", "FOCUS_C"]], show_residuals = TRUE)
plot_sep_FOCUS_C_SFO <- function() plot_sep(fits[["SFO", "FOCUS_C"]])
mkinparplot_FOCUS_C_SFO <- function() mkinparplot(fits[["SFO", "FOCUS_C"]])
mkinerrplot_FOCUS_C_SFO <- function() mkinerrplot(fits[["SFO", "FOCUS_C"]])
mmkin_FOCUS_C <- function() plot(fits[, "FOCUS_C"])
mmkin_SFO <- function() plot(fits["SFO",])
fit_D_obs_eigen <- suppressWarnings(mkinfit(SFO_SFO, FOCUS_2006_D, error_model = "obs", quiet = TRUE))
fit_C_tc <- mkinfit("SFO", FOCUS_2006_C, error_model = "tc", quiet = TRUE)
plot_errmod_fit_D_obs_eigen <- function() plot_err(fit_D_obs_eigen, sep_obs = FALSE)
plot_errmod_fit_C_tc <- function() plot_err(fit_C_tc)
plot_res_sfo_sfo <- function() plot_res(f_sfo_sfo_desolve)
plot_err_sfo_sfo <- function() plot_err(f_sfo_sfo_desolve)
plot_errmod_fit_obs_1 <- function() plot_err(fit_obs_1, sep_obs = FALSE)
plot_errmod_fit_tc_1 <- function() plot_err(fit_tc_1, sep_obs = FALSE)
vdiffr::expect_doppelganger("mkinfit plot for FOCUS C with defaults", plot_default_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mkinfit plot for FOCUS C with residuals like in gmkin", plot_res_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mkinfit plot for FOCUS C with sep = TRUE", plot_sep_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mkinparplot for FOCUS C SFO", mkinparplot_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mkinerrplot for FOCUS C SFO", mkinerrplot_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mmkin plot for FOCUS C", mmkin_FOCUS_C)
vdiffr::expect_doppelganger("mmkin plot for SFO (FOCUS C and D)", mmkin_SFO)
vdiffr::expect_doppelganger("plot_errmod with FOCUS C tc", plot_errmod_fit_C_tc)
skip_on_travis() # Still not working on Travis, maybe because of deSolve producing
# different results when not working with a compiled model or eigenvalues
vdiffr::expect_doppelganger("plot_errmod with FOCUS D obs eigen", plot_errmod_fit_D_obs_eigen)
vdiffr::expect_doppelganger("plot_res for FOCUS D", plot_res_sfo_sfo)
vdiffr::expect_doppelganger("plot_err for FOCUS D", plot_err_sfo_sfo)
vdiffr::expect_doppelganger("plot_errmod with SFO_lin_a_tc", plot_errmod_fit_tc_1)
vdiffr::expect_doppelganger("plot_errmod with SFO_lin_a_obs", plot_errmod_fit_obs_1)
})
context("AIC calculation")
test_that("The AIC is reproducible", {
expect_equivalent(AIC(fits[["SFO", "FOCUS_C"]]), 59.3, scale = 1, tolerance = 0.1)
expect_equivalent(AIC(fits[, "FOCUS_C"]),
data.frame(df = c(3, 4, 5, 5), AIC = c(59.3, 44.7, 29.0, 39.2)),
scale = 1, tolerance = 0.1)
expect_error(AIC(fits["SFO", ]), "column object")
expect_equivalent(BIC(fits[, "FOCUS_C"]),
data.frame(df = c(3, 4, 5, 5), AIC = c(59.9, 45.5, 30.0, 40.2)),
scale = 1, tolerance = 0.1)
})
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