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$Corr <- "Correlation matrix is platform dependent, not tested" expect_known_output(print(test_summary), "summary_DFOP_FOCUS_C.txt") test_summary_2 <- summary(f_sfo_sfo_eigen) test_summary_2$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 <- "Correlation matrix is platform dependent, not tested" # The residuals for this method are also platform sensitive test_summary_2$data$residual <- "not tested" 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 <- "Correlation matrix is platform dependent, not tested" 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_res_FOCUS_C_SFO_2 <- function() plot_res(fits[["SFO", "FOCUS_C"]]) 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("plot_res for FOCUS C", plot_res_FOCUS_C_SFO_2) 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) })