diff options
Diffstat (limited to 'tests')
25 files changed, 344 insertions, 401 deletions
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12.00px; font-family: sans;' textLength='80.04px' lengthAdjust='spacingAndGlyphs'>Original results</text> <text x='126.22' y='470.11' style='font-size: 12.00px; font-family: sans;' textLength='75.36px' lengthAdjust='spacingAndGlyphs'>Multistart runs</text> </g> </svg> diff --git a/tests/testthat/anova_sfo_saem.txt b/tests/testthat/anova_sfo_saem.txt index 9e4bf71f..0ccd6d5c 100644 --- a/tests/testthat/anova_sfo_saem.txt +++ b/tests/testthat/anova_sfo_saem.txt @@ -1,7 +1,7 @@ -Data: 262 observations of 1 variable(s) grouped in 15 datasets +Data: 263 observations of 1 variable(s) grouped in 15 datasets npar AIC BIC Lik Chisq Df Pr(>Chisq) -sfo_saem_1_reduced 5 1310 1313 -650 -sfo_saem_1_reduced_mkin 5 1310 1313 -650 0 0 -sfo_saem_1 6 1312 1316 -650 0 1 1 -sfo_saem_1_mkin 6 1312 1316 -650 0 0 +sfo_saem_1_reduced 5 1302 1306 -646 +sfo_saem_1_reduced_mkin 5 1302 1306 -646 0 0 +sfo_saem_1 6 1304 1308 -646 0 1 1 +sfo_saem_1_mkin 6 1303 1308 -646 1 0 diff --git a/tests/testthat/illparms_hfits_synth.txt b/tests/testthat/illparms_hfits_synth.txt index affd1318..7a69645b 100644 --- a/tests/testthat/illparms_hfits_synth.txt +++ b/tests/testthat/illparms_hfits_synth.txt @@ -1,8 +1,4 @@ error -degradation const - SFO - FOMC sd(log_alpha), sd(log_beta) - error -degradation tc - SFO sd(parent_0) - FOMC sd(parent_0), sd(log_alpha), sd(log_beta) +degradation const tc + SFO sd(parent_0) sd(parent_0) + FOMC sd(log_beta) sd(parent_0), sd(log_beta) diff --git a/tests/testthat/illparms_hfits_synth_no_ranef_auto.txt b/tests/testthat/illparms_hfits_synth_no_ranef_auto.txt deleted file mode 100644 index a64ed222..00000000 --- a/tests/testthat/illparms_hfits_synth_no_ranef_auto.txt +++ /dev/null @@ -1,4 +0,0 @@ - error -degradation const tc - SFO sd(parent_0) - FOMC b.1 diff --git a/tests/testthat/print_dfop_saemix_1.txt b/tests/testthat/print_dfop_saem_1.txt index f6fda37c..bdc40065 100644 --- a/tests/testthat/print_dfop_saemix_1.txt +++ b/tests/testthat/print_dfop_saem_1.txt @@ -9,16 +9,15 @@ Data: Likelihood computed by importance sampling AIC BIC logLik - 1409 1415 -695 + 1409 1415 -696 Fitted parameters: - estimate lower upper -parent_0 100.09 98.94 101.25 -log_k1 -2.68 -2.91 -2.45 -log_k2 -4.12 -4.24 -4.00 -g_qlogis -0.41 -0.63 -0.20 -a.1 0.91 0.67 1.15 -b.1 0.05 0.04 0.06 -SD.log_k1 0.36 0.21 0.50 -SD.log_k2 0.22 0.13 0.30 -SD.g_qlogis 0.15 -0.09 0.40 + estimate lower upper +parent_0 99.92 98.77 101.06 +log_k1 -2.72 -2.95 -2.50 +log_k2 -4.14 -4.27 -4.01 +g_qlogis -0.35 -0.53 -0.16 +a.1 0.92 0.68 1.16 +b.1 0.05 0.04 0.06 +SD.log_k1 0.37 0.23 0.51 +SD.log_k2 0.23 0.14 0.31 diff --git a/tests/testthat/print_fits_synth_const.txt b/tests/testthat/print_fits_synth_const.txt index 2ea1f133..5d076d3d 100644 --- a/tests/testthat/print_fits_synth_const.txt +++ b/tests/testthat/print_fits_synth_const.txt @@ -4,8 +4,6 @@ Status of individual fits: dataset model 1 2 3 4 5 6 SFO OK OK OK OK OK OK - FOMC C C OK OK OK OK + FOMC OK OK OK OK OK OK -C: Optimisation did not converge: -false convergence (8) OK: No warnings diff --git a/tests/testthat/print_hfits_synth_no_ranef_auto.txt b/tests/testthat/print_hfits_synth_no_ranef_auto.txt deleted file mode 100644 index 9af1cbcd..00000000 --- a/tests/testthat/print_hfits_synth_no_ranef_auto.txt +++ /dev/null @@ -1,9 +0,0 @@ -<mhmkin> object -Status of individual fits: - - error -degradation const tc - SFO OK OK - FOMC OK OK - -OK: Fit terminated successfully diff --git a/tests/testthat/print_mmkin_sfo_1_mixed.txt b/tests/testthat/print_mmkin_sfo_1_mixed.txt index 33e5bf5c..c12cfe2b 100644 --- a/tests/testthat/print_mmkin_sfo_1_mixed.txt +++ b/tests/testthat/print_mmkin_sfo_1_mixed.txt @@ -3,7 +3,7 @@ Structural model: d_parent/dt = - k_parent * parent Data: -262 observations of 1 variable(s) grouped in 15 datasets +263 observations of 1 variable(s) grouped in 15 datasets <mmkin> object Status of individual fits: @@ -16,4 +16,4 @@ OK: No warnings Mean fitted parameters: parent_0 log_k_parent - 99.9 -3.3 + 100.0 -3.4 diff --git a/tests/testthat/print_multistart_biphasic.txt b/tests/testthat/print_multistart_dfop_sfo.txt index b4344f22..b4344f22 100644 --- a/tests/testthat/print_multistart_biphasic.txt +++ b/tests/testthat/print_multistart_dfop_sfo.txt diff --git a/tests/testthat/print_sfo_saem_1_reduced.txt b/tests/testthat/print_sfo_saem_1_reduced.txt index bac8848e..1c7fb588 100644 --- a/tests/testthat/print_sfo_saem_1_reduced.txt +++ b/tests/testthat/print_sfo_saem_1_reduced.txt @@ -3,16 +3,16 @@ Structural model: d_parent/dt = - k_parent * parent Data: -262 observations of 1 variable(s) grouped in 15 datasets +263 observations of 1 variable(s) grouped in 15 datasets Likelihood computed by importance sampling AIC BIC logLik - 1310 1313 -650 + 1302 1306 -646 Fitted parameters: estimate lower upper -parent_0 1e+02 99.08 1e+02 -k_parent 4e-02 0.03 4e-02 -a.1 9e-01 0.75 1e+00 +parent_0 1e+02 99.03 1e+02 +k_parent 3e-02 0.03 4e-02 +a.1 9e-01 0.71 1e+00 b.1 5e-02 0.04 5e-02 -SD.k_parent 3e-01 0.20 4e-01 +SD.k_parent 2e-01 0.14 3e-01 diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R index 777c998a..4e2f76ab 100644 --- a/tests/testthat/setup_script.R +++ b/tests/testthat/setup_script.R @@ -2,17 +2,23 @@ require(mkin) require(testthat) # Per default (on my box where I set NOT_CRAN in .Rprofile) use all cores minus one +# Otherwise (CRAN check systems) use the allowed maximum of two cores if (identical(Sys.getenv("NOT_CRAN"), "true")) { n_cores <- parallel::detectCores() - 1 } else { - n_cores <- 1 + n_cores <- 2 } # Use the two available cores on travis if (Sys.getenv("TRAVIS") != "") n_cores = 2 -# On Windows we would need to make a cluster first -if (Sys.info()["sysname"] == "Windows") n_cores = 1 +# On Windows we need to make a cluster, or use one core +if (Sys.info()["sysname"] == "Windows") { + cl <- parallel::makePSOCKcluster(n_cores) + n_cores = 1 +} else { + cl <- parallel::makeForkCluster(n_cores) +} # Very simple example fits f_1_mkin_trans <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE) @@ -24,7 +30,7 @@ models <- c("SFO", "FOMC", "DFOP", "HS") fits <- suppressWarnings( # FOCUS A FOMC was, it seems, in testthat output mmkin(models, list(FOCUS_A = FOCUS_2006_A, FOCUS_C = FOCUS_2006_C, FOCUS_D = FOCUS_2006_D), - quiet = TRUE, cores = n_cores)) + quiet = TRUE, cluster = cl)) # One metabolite SFO_SFO <- mkinmod(parent = mkinsub("SFO", to = "m1"), @@ -81,112 +87,26 @@ fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TR fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE, error_model_algorithm = "threestep") -# Mixed models data and fits -sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) -n <- n_biphasic <- 15 -log_sd <- 0.3 -err_1 = list(const = 1, prop = 0.05) -tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop) -const <- function(value) 2 - -set.seed(123456) -SFO <- mkinmod(parent = mkinsub("SFO")) -k_parent = rlnorm(n, log(0.03), log_sd) -set.seed(123456) -ds_sfo <- lapply(1:n, function(i) { - ds_mean <- mkinpredict(SFO, c(k_parent = k_parent[i]), - c(parent = 100), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -FOMC <- mkinmod(parent = mkinsub("FOMC")) -fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8) -fomc_parms <- as.matrix(data.frame( - alpha = rlnorm(n, log(fomc_pop$alpha), 0.4), - beta = rlnorm(n, log(fomc_pop$beta), 0.2))) -set.seed(123456) -ds_fomc <- lapply(1:3, function(i) { - ds_mean <- mkinpredict(FOMC, fomc_parms[i, ], - c(parent = 100), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -DFOP <- mkinmod(parent = mkinsub("DFOP")) -dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4) -dfop_parms <- as.matrix(data.frame( - k1 = rlnorm(n, log(dfop_pop$k1), log_sd), - k2 = rlnorm(n, log(dfop_pop$k2), log_sd), - g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd)))) -set.seed(123456) -ds_dfop <- lapply(1:n, function(i) { - ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], - c(parent = dfop_pop$parent_0), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -HS <- mkinmod(parent = mkinsub("HS")) -hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15) -hs_parms <- as.matrix(data.frame( - k1 = rlnorm(n, log(hs_pop$k1), log_sd), - k2 = rlnorm(n, log(hs_pop$k2), log_sd), - tb = rlnorm(n, log(hs_pop$tb), 0.1))) -set.seed(123456) -ds_hs <- lapply(1:10, function(i) { - ds_mean <- mkinpredict(HS, hs_parms[i, ], - c(parent = hs_pop$parent_0), sampling_times) - add_err(ds_mean, const, n = 1)[[1]] -}) - -set.seed(123456) -DFOP_SFO <- mkinmod( - parent = mkinsub("DFOP", "m1"), - m1 = mkinsub("SFO"), - quiet = TRUE) -dfop_sfo_pop <- list(parent_0 = 100, - k_m1 = 0.007, f_parent_to_m1 = 0.5, - k1 = 0.1, k2 = 0.02, g = 0.5) -syn_biphasic_parms <- as.matrix(data.frame( - k1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k1), log_sd), - k2 = rlnorm(n_biphasic, log(dfop_sfo_pop$k2), log_sd), - g = plogis(rnorm(n_biphasic, qlogis(dfop_sfo_pop$g), log_sd)), - f_parent_to_m1 = plogis(rnorm(n_biphasic, - qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)), - k_m1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k_m1), log_sd))) -ds_biphasic_mean <- lapply(1:n_biphasic, - function(i) { - mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ], - c(parent = 100, m1 = 0), sampling_times) - } -) -set.seed(123456) -ds_biphasic <- lapply(ds_biphasic_mean, function(ds) { - add_err(ds, - sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), - n = 1, secondary = "m1")[[1]] -}) - # Mixed model fits -mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = n_cores) -mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cores = n_cores, +mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cluster = cl) +mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cluster = cl, error_model = "tc") -mmkin_biphasic <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, quiet = TRUE, cores = n_cores, +DFOP_SFO <- mkinmod(parent = mkinsub("DFOP", "m1"), + m1 = mkinsub("SFO"), quiet = TRUE) +mmkin_dfop_sfo <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_dfop_sfo, quiet = TRUE, + cluster = cl, control = list(eval.max = 500, iter.max = 400), error_model = "tc") # nlme dfop_nlme_1 <- suppressWarnings(nlme(mmkin_dfop_1)) -nlme_biphasic <- suppressWarnings(nlme(mmkin_biphasic)) +nlme_dfop_sfo <- suppressWarnings(nlme(mmkin_dfop_sfo)) # saemix sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix") sfo_saem_1_reduced <- update(sfo_saem_1, no_random_effect = "parent_0") -dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", - no_random_effect = "parent_0") - -saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE) -saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE) +dfop_saem_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", + no_random_effect = c("parent_0", "g_qlogis")) +parallel::stopCluster(cl) diff --git a/tests/testthat/summary_hfit_sfo_tc.txt b/tests/testthat/summary_hfit_sfo_tc.txt index 41743091..0618c715 100644 --- a/tests/testthat/summary_hfit_sfo_tc.txt +++ b/tests/testthat/summary_hfit_sfo_tc.txt @@ -8,7 +8,7 @@ Equations: d_parent/dt = - k_parent * parent Data: -106 observations of 1 variable(s) grouped in 6 datasets +95 observations of 1 variable(s) grouped in 6 datasets Model predictions using solution type analytical @@ -17,26 +17,35 @@ Using 300, 100 iterations and 9 chains Variance model: Two-component variance function -Mean of starting values for individual parameters: +Starting values for degradation parameters: parent_0 log_k_parent - 101 -3 + 94 -2 Fixed degradation parameter values: None +Starting values for random effects (square root of initial entries in omega): + parent_0 log_k_parent +parent_0 4 0.0 +log_k_parent 0 0.7 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + Results: Likelihood computed by importance sampling AIC BIC logLik - 533 531 -261 + 542 541 -266 Optimised parameters: - est. lower upper -parent_0 101.02 99.58 102.46 -log_k_parent -3.32 -3.53 -3.11 -a.1 0.91 0.64 1.17 -b.1 0.05 0.04 0.06 -SD.log_k_parent 0.27 0.11 0.42 + est. lower upper +parent_0 92.52 89.11 95.9 +log_k_parent -1.66 -2.07 -1.3 +a.1 2.03 1.60 2.5 +b.1 0.09 0.07 0.1 +SD.log_k_parent 0.51 0.22 0.8 Correlation: pr_0 @@ -44,18 +53,18 @@ log_k_parent 0.1 Random effects: est. lower upper -SD.log_k_parent 0.3 0.1 0.4 +SD.log_k_parent 0.5 0.2 0.8 Variance model: est. lower upper -a.1 0.91 0.64 1.17 -b.1 0.05 0.04 0.06 +a.1 2.03 1.60 2.5 +b.1 0.09 0.07 0.1 Backtransformed parameters: - est. lower upper -parent_0 1e+02 1e+02 1e+02 -k_parent 4e-02 3e-02 4e-02 + est. lower upper +parent_0 92.5 89.1 95.9 +k_parent 0.2 0.1 0.3 Estimated disappearance times: DT50 DT90 -parent 19 64 +parent 4 12 diff --git a/tests/testthat/summary_saem_biphasic_s.txt b/tests/testthat/summary_saem_dfop_sfo_s.txt index 7c337843..6468ff17 100644 --- a/tests/testthat/summary_saem_biphasic_s.txt +++ b/tests/testthat/summary_saem_dfop_sfo_s.txt @@ -22,7 +22,7 @@ Using 300, 100 iterations and 4 chains Variance model: Two-component variance function -Mean of starting values for individual parameters: +Starting values for degradation parameters: parent_0 k_m1 f_parent_to_m1 k1 k2 1e+02 7e-03 5e-01 1e-01 2e-02 g @@ -31,6 +31,19 @@ Mean of starting values for individual parameters: Fixed degradation parameter values: None +Starting values for random effects (square root of initial entries in omega): + parent_0 k_m1 f_parent_to_m1 k1 k2 g +parent_0 101 0 0 0 0 0 +k_m1 0 1 0 0 0 0 +f_parent_to_m1 0 0 1 0 0 0 +k1 0 0 0 1 0 0 +k2 0 0 0 0 1 0 +g 0 0 0 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + Results: Likelihood computed by importance sampling diff --git a/tests/testthat/test_AIC.R b/tests/testthat/test_AIC.R index 57b9a673..7e97904d 100644 --- a/tests/testthat/test_AIC.R +++ b/tests/testthat/test_AIC.R @@ -8,6 +8,6 @@ test_that("The AIC is reproducible", { expect_error(AIC(fits["SFO", ]), "column object") expect_error(BIC(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)), + data.frame(df = c(3, 4, 5, 5), BIC = c(59.9, 45.5, 30.0, 40.2)), scale = 1, tolerance = 0.1) }) diff --git a/tests/testthat/test_dmta.R b/tests/testthat/test_dmta.R index 5cfc61d2..825c6e80 100644 --- a/tests/testthat/test_dmta.R +++ b/tests/testthat/test_dmta.R @@ -11,13 +11,13 @@ names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL -# mkin -dmta_dfop <- mmkin("DFOP", dmta_ds, quiet = TRUE, cores = n_cores) -dmta_dfop_tc <- mmkin("DFOP", dmta_ds, error_model = "tc", quiet = TRUE, cores = n_cores) - test_that("Different backends get consistent results for DFOP tc, dimethenamid data", { skip_on_cran() # Time constraints + # mkin + dmta_dfop <- mmkin("DFOP", dmta_ds, quiet = TRUE, cores = n_cores) + dmta_dfop_tc <- mmkin("DFOP", dmta_ds, error_model = "tc", quiet = TRUE, cores = n_cores) + # nlme expect_warning( nlme_dfop_tc <- nlme(dmta_dfop_tc), diff --git a/tests/testthat/test_mhmkin.R b/tests/testthat/test_mhmkin.R index 93333ac1..da063326 100644 --- a/tests/testthat/test_mhmkin.R +++ b/tests/testthat/test_mhmkin.R @@ -3,8 +3,11 @@ context("Batch fitting and diagnosing hierarchical kinetic models") test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", { skip_on_cran() - fits_synth_const <- suppressWarnings( - mmkin(c("SFO", "FOMC"), ds_sfo[1:6], cores = n_cores, quiet = TRUE)) + fits_synth_const <- mmkin(c("SFO", "FOMC"), ds_fomc[1:6], cores = n_cores, quiet = TRUE) + + expect_known_output( + print(fits_synth_const), + "print_fits_synth_const.txt") fits_synth_tc <- suppressWarnings( update(fits_synth_const, error_model = "tc")) @@ -19,8 +22,8 @@ test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", { print(illparms(hfits)), "illparms_hfits_synth.txt") - expect_equal(which.min(AIC(hfits)), 3) - expect_equal(which.min(BIC(hfits)), 3) + expect_equal(which.min(AIC(hfits)), 4) + expect_equal(which.min(BIC(hfits)), 4) hfit_sfo_tc <- update(hfits[["SFO", "tc"]], covariance.model = diag(c(0, 1))) @@ -38,22 +41,19 @@ test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", { expect_known_output(print(test_summary, digits = 1), "summary_hfit_sfo_tc.txt") - # It depends on the platform exactly which of the datasets fail to converge - # with FOMC, because they were generated to be SFO - skip_on_travis() - - expect_known_output( - print(fits_synth_const), - "print_fits_synth_const.txt") - - hfits_no_ranef_auto <- update(hfits, no_random_effect = "auto", auto_ranef_threshold = 2) - - expect_known_output( - print(hfits_no_ranef_auto), - "print_hfits_synth_no_ranef_auto.txt") - - expect_known_output( - print(illparms(hfits_no_ranef_auto)), - "illparms_hfits_synth_no_ranef_auto.txt") - + hfits_sfo_reduced <- update(hfits, + no_random_effect = illparms(hfits)) + expect_equal( + as.character(illparms(hfits_sfo_reduced)), + rep("", 4)) + + # We can also manually set up an object specifying random effects to be + # excluded. Entries in the inital list have to be by column + no_ranef <- list("parent_0", "log_beta", "parent_0", c("parent_0", "log_beta")) + dim(no_ranef) <- c(2, 2) + + hfits_sfo_reduced_2 <- update(hfits, + no_random_effect = no_ranef) + expect_equivalent(round(anova(hfits_sfo_reduced), 0), + round(anova(hfits_sfo_reduced_2), 0)) }) diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R index 646b6110..39a332f5 100644 --- a/tests/testthat/test_mixed.R +++ b/tests/testthat/test_mixed.R @@ -1,17 +1,24 @@ context("Nonlinear mixed-effects models") + # Round error model parameters as they are not rounded in print methods dfop_nlme_1$modelStruct$varStruct$const <- signif(dfop_nlme_1$modelStruct$varStruct$const, 3) dfop_nlme_1$modelStruct$varStruct$prop <- signif(dfop_nlme_1$modelStruct$varStruct$prop, 4) +dfop_sfo_pop <- attr(ds_dfop_sfo, "pop") + test_that("Print methods work", { expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt") expect_known_output(print(mixed(mmkin_sfo_1), digits = 2), "print_mmkin_sfo_1_mixed.txt") expect_known_output(print(dfop_nlme_1, digits = 1), "print_dfop_nlme_1.txt") + expect_known_output(print(sfo_saem_1_reduced, digits = 1), "print_sfo_saem_1_reduced.txt") - expect_known_output(print(dfop_saemix_1, digits = 1), "print_dfop_saemix_1.txt") + skip_on_cran() # The following test is platform dependent and fails on + # win-builder with current (18 Nov 2022) R-devel, on the Linux R-devel CRAN check systems + # and also using R-devel locally + expect_known_output(print(dfop_saem_1, digits = 1), "print_dfop_saem_1.txt") }) test_that("nlme results are reproducible to some degree", { @@ -31,17 +38,19 @@ test_that("nlme results are reproducible to some degree", { # k1 and k2 just fail the first test (lower bound of the ci), so we need to exclude it dfop_no_k1_k2 <- c("parent_0", "k_m1", "f_parent_to_m1", "g") dfop_sfo_pop_no_k1_k2 <- as.numeric(dfop_sfo_pop[dfop_no_k1_k2]) - dfop_sfo_pop <- as.numeric(dfop_sfo_pop) # to remove names - ci_dfop_sfo_n <- summary(nlme_biphasic)$confint_back + ci_dfop_sfo_n <- summary(nlme_dfop_sfo)$confint_back expect_true(all(ci_dfop_sfo_n[dfop_no_k1_k2, "lower"] < dfop_sfo_pop_no_k1_k2)) - expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop)) + expect_true(all(ci_dfop_sfo_n[, "upper"] > as.numeric(dfop_sfo_pop))) }) test_that("saemix results are reproducible for biphasic fits", { - test_summary <- summary(saem_biphasic_s) + skip_on_cran() + saem_dfop_sfo_s <- saem(mmkin_dfop_sfo, transformations = "saemix", quiet = TRUE) + + test_summary <- summary(saem_dfop_sfo_s) test_summary$saemixversion <- "Dummy 0.0 for testing" test_summary$mkinversion <- "Dummy 0.0 for testing" test_summary$Rversion <- "Dummy R version for testing" @@ -49,33 +58,28 @@ test_that("saemix results are reproducible for biphasic fits", { test_summary$date.summary <- "Dummy date for testing" test_summary$time <- c(elapsed = "test time 0") - expect_known_output(print(test_summary, digits = 1), "summary_saem_biphasic_s.txt") + expect_known_output(print(test_summary, digits = 1), "summary_saem_dfop_sfo_s.txt") dfop_sfo_pop <- as.numeric(dfop_sfo_pop) no_k1 <- c(1, 2, 3, 5, 6) no_k2 <- c(1, 2, 3, 4, 6) no_k1_k2 <- c(1, 2, 3, 6) - ci_dfop_sfo_s_s <- summary(saem_biphasic_s)$confint_back + ci_dfop_sfo_s_s <- summary(saem_dfop_sfo_s)$confint_back expect_true(all(ci_dfop_sfo_s_s[, "lower"] < dfop_sfo_pop)) expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop)) - # k2 is not fitted well - ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$confint_back - expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2])) - expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1])) - # I tried to only do few iterations in routine tests as this is so slow # but then deSolve fails at some point (presumably at the switch between # the two types of iterations) - #saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", + #saem_dfop_sfo_2 <- saem(mmkin_biphasic, solution_type = "deSolve", # control = list(nbiter.saemix = c(10, 5), nbiter.burn = 5), quiet = TRUE) skip("Fitting with saemix takes around 10 minutes when using deSolve") - saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", quiet = TRUE) + saem_dfop_sfo_2 <- saem(mmkin_dfop_sfo, solution_type = "deSolve", quiet = TRUE) # As with the analytical solution, k1 and k2 are not fitted well - ci_dfop_sfo_s_d <- summary(saem_biphasic_2)$confint_back + ci_dfop_sfo_s_d <- summary(saem_dfop_sfo_2)$confint_back expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2])) expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1])) }) diff --git a/tests/testthat/test_multistart.R b/tests/testthat/test_multistart.R index c1a10d10..dda0ea23 100644 --- a/tests/testthat/test_multistart.R +++ b/tests/testthat/test_multistart.R @@ -1,41 +1,57 @@ context("Multistart method for saem.mmkin models") test_that("multistart works for saem.mmkin models", { + skip_on_cran() # Save CRAN time set.seed(123456) - saem_sfo_s_multi <- multistart(sfo_saem_1_reduced, n = 8, cores = n_cores, - no_random_effect = "parent_0") + saem_sfo_s_multi <- multistart(sfo_saem_1_reduced, n = 8, cores = n_cores) + anova_sfo <- anova(sfo_saem_1, sfo_saem_1_reduced, best(saem_sfo_s_multi), test = TRUE ) - expect_true(anova_sfo[3, "Pr(>Chisq)"] > 0.5) + expect_equal(round(anova_sfo, 1)["sfo_saem_1_reduced", "AIC"], 1302.2) + expect_equal(round(anova_sfo, 1)["best(saem_sfo_s_multi)", "AIC"], 1302.2) + expect_true(anova_sfo[3, "Pr(>Chisq)"] > 0.2) # Local: 1, win-builder: 0.4 + + saem_dfop_sfo_m <- saem(mmkin_dfop_sfo, transformations = "mkin", quiet = TRUE) + dfop_sfo_pop <- attr(ds_dfop_sfo, "pop") + + # k2 is not fitted well (compare saem_dfop_sfo_s in test_mixed.R) + ci_dfop_sfo_s_m <- summary(saem_dfop_sfo_m)$confint_back + no_k1 <- c(1, 2, 3, 5, 6) + no_k2 <- c(1, 2, 3, 4, 6) + expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2])) + expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1])) + - skip_on_cran() # Save CRAN time set.seed(123456) - saem_biphasic_m_multi <- multistart(saem_biphasic_m, n = 8, + saem_dfop_sfo_m_multi <- multistart(saem_dfop_sfo_m, n = 8, cores = n_cores) - expect_known_output(print(saem_biphasic_m_multi), - file = "print_multistart_biphasic.txt") + expect_known_output(print(saem_dfop_sfo_m_multi), + file = "print_multistart_dfop_sfo.txt") - anova_biphasic <- anova(saem_biphasic_m, - best(saem_biphasic_m_multi)) + anova_dfop_sfo <- anova(saem_dfop_sfo_m, + best(saem_dfop_sfo_m_multi)) # With the new starting parameters we do not improve # with multistart any more - expect_equal(anova_biphasic[2, "AIC"], anova_biphasic[1, "AIC"], + expect_equal(anova_dfop_sfo[2, "AIC"], anova_dfop_sfo[1, "AIC"], tolerance = 1e-4) skip_on_travis() # Plots are platform dependent + plot_dfop_sfo_saem_m <- function() plot(saem_dfop_sfo_m) + vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_dfop_sfo_saem_m) + llhist_sfo <- function() llhist(saem_sfo_s_multi) parplot_sfo <- function() parplot(saem_sfo_s_multi, ylim = c(0.5, 2)) vdiffr::expect_doppelganger("llhist for sfo fit", llhist_sfo) vdiffr::expect_doppelganger("parplot for sfo fit", parplot_sfo) - llhist_biphasic <- function() llhist(saem_biphasic_m_multi) - parplot_biphasic <- function() parplot(saem_biphasic_m_multi, - ylim = c(0.5, 2)) + llhist_dfop_sfo <- function() llhist(saem_dfop_sfo_m_multi) + parplot_dfop_sfo <- function() parplot(saem_dfop_sfo_m_multi, + ylim = c(0.5, 2), llquant = 0.5) - vdiffr::expect_doppelganger("llhist for biphasic saemix fit", llhist_biphasic) - vdiffr::expect_doppelganger("parplot for biphasic saemix fit", parplot_biphasic) + vdiffr::expect_doppelganger("llhist for dfop sfo fit", llhist_dfop_sfo) + vdiffr::expect_doppelganger("parplot for dfop sfo fit", parplot_dfop_sfo) }) diff --git a/tests/testthat/test_nafta.R b/tests/testthat/test_nafta.R index 8eb052c5..b89ea342 100644 --- a/tests/testthat/test_nafta.R +++ b/tests/testthat/test_nafta.R @@ -4,6 +4,7 @@ test_that("Data for more than one compound are rejected", expect_error(nafta(FOCUS_2006_D, cores = 1))) test_that("Test data from Appendix B are correctly evaluated", { + skip_on_cran() expect_message(res <- nafta(NAFTA_SOP_Appendix_B, "aerobic aquatic", cores = 1)) # From Figure D.1 @@ -25,6 +26,7 @@ test_that("Test data from Appendix B are correctly evaluated", { }) test_that("Test data from Appendix D are correctly evaluated", { + skip_on_cran() # We are not interested in the warnings about non-normal residuals here suppressWarnings( res <- nafta(NAFTA_SOP_Appendix_D, "MRID 555555", diff --git a/tests/testthat/test_plot.R b/tests/testthat/test_plot.R index 13058c00..f5da5982 100644 --- a/tests/testthat/test_plot.R +++ b/tests/testthat/test_plot.R @@ -41,27 +41,23 @@ test_that("Plotting mkinfit, mmkin and mixed model objects is reproducible", { f_uba_mmkin <- mmkin(list("DFOP-SFO" = dfop_sfo_uba), ds_uba, quiet = TRUE, cores = n_cores) f_uba_dfop_sfo_mixed <- mixed(f_uba_mmkin["DFOP-SFO", ]) - f_uba_dfop_sfo_saem <- saem(f_uba_mmkin["DFOP-SFO", ], quiet = TRUE, transformations = "saemix") - plot_biphasic_mmkin <- function() plot(f_uba_dfop_sfo_mixed, pop_curve = TRUE) - vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_biphasic_mmkin) + plot_dfop_sfo_mmkin <- function() plot(f_uba_dfop_sfo_mixed, pop_curve = TRUE) + vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_dfop_sfo_mmkin) - plot_biphasic_saem_s <- function() plot(f_uba_dfop_sfo_saem) - vdiffr::expect_doppelganger("mixed model fit for saem object with saemix transformations", plot_biphasic_saem_s) + plot_dfop_sfo_saem_s <- function() plot(f_uba_dfop_sfo_saem) + vdiffr::expect_doppelganger("mixed model fit for saem object with saemix transformations", plot_dfop_sfo_saem_s) skip_on_travis() - plot_biphasic_nlme <- function() plot(dfop_nlme_1) - vdiffr::expect_doppelganger("mixed model fit for nlme object", plot_biphasic_nlme) + plot_dfop_sfo_nlme <- function() plot(dfop_nlme_1) + vdiffr::expect_doppelganger("mixed model fit for nlme object", plot_dfop_sfo_nlme) - #plot_biphasic_mmkin <- function() plot(mixed(mmkin_biphasic)) + #plot_dfop_sfo_mmkin <- function() plot(mixed(mmkin_dfop_sfo)) # Biphasic fits with lots of data and fits have lots of potential for differences - plot_biphasic_nlme <- function() plot(nlme_biphasic) - #plot_biphasic_saem_s <- function() plot(saem_biphasic_s) - plot_biphasic_saem_m <- function() plot(saem_biphasic_m) - - vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_biphasic_saem_m) + plot_dfop_sfo_nlme <- function() plot(nlme_dfop_sfo) + #plot_dfop_sfo_saem_s <- function() plot(saem_dfop_sfo_s) # different results when working with eigenvalues plot_errmod_fit_D_obs_eigen <- function() plot_err(fit_D_obs_eigen, sep_obs = FALSE) diff --git a/tests/testthat/test_saemix_parent.R b/tests/testthat/test_saemix_parent.R index 20889c6c..7fbecd0c 100644 --- a/tests/testthat/test_saemix_parent.R +++ b/tests/testthat/test_saemix_parent.R @@ -15,7 +15,7 @@ test_that("Parent fits using saemix are correctly implemented", { expect_silent(print(illparms(sfo_saem_1_reduced))) # We cannot currently do the fit with completely fixed initial values - mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100)) + mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100), cluster = NULL, cores = n_cores) sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters") # We get an error if we do not supply a suitable model specification @@ -38,11 +38,11 @@ test_that("Parent fits using saemix are correctly implemented", { s_sfo_nlme_1 <- summary(sfo_nlme_1) # Compare with input - expect_equal(round(s_sfo_saem_1$confint_ranef["SD.k_parent", "est."], 1), 0.3) - expect_equal(round(s_sfo_saem_1_mkin$confint_ranef["SD.log_k_parent", "est."], 1), 0.3) + expect_equal(round(s_sfo_saem_1$confint_ranef["SD.k_parent", "est."], 1), 0.3, tol = 0.1) + expect_equal(round(s_sfo_saem_1_mkin$confint_ranef["SD.log_k_parent", "est."], 1), 0.3, tol = 0.1) # k_parent is a bit different from input 0.03 here - expect_equal(round(s_sfo_saem_1$confint_back["k_parent", "est."], 3), 0.035) - expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), 0.035) + expect_equal(round(s_sfo_saem_1$confint_back["k_parent", "est."], 3), 0.033) + expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), 0.033) # But the result is pretty unanimous between methods expect_equal(round(s_sfo_saem_1_reduced$confint_back["k_parent", "est."], 3), @@ -74,7 +74,7 @@ test_that("Parent fits using saemix are correctly implemented", { mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores) fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE, transformations = "saemix", no_random_effect = "parent_0") - fomc_pop <- as.numeric(fomc_pop) + fomc_pop <- as.numeric(attr(ds_fomc, "pop")) ci_fomc_s1 <- summary(fomc_saem_1)$confint_back expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop)) expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop)) @@ -87,14 +87,14 @@ test_that("Parent fits using saemix are correctly implemented", { expect_equal(endpoints(fomc_saem_1), endpoints(fomc_saem_2), tol = 0.01) # DFOP - dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", + dfop_saem_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", no_random_effect = "parent_0") - s_dfop_s1 <- summary(dfop_saemix_1) # mkin transformations - s_dfop_s2 <- summary(dfop_saemix_2) # saemix transformations + s_dfop_s1 <- summary(dfop_saem_1) # mkin transformations + s_dfop_s2 <- summary(dfop_saem_2) # saemix transformations s_dfop_n <- summary(dfop_nlme_1) - dfop_pop <- as.numeric(dfop_pop) + dfop_pop <- as.numeric(attr(ds_dfop, "pop")) expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop)) expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop)) @@ -111,18 +111,18 @@ test_that("Parent fits using saemix are correctly implemented", { # SFORB mmkin_sforb_1 <- mmkin("SFORB", ds_dfop, quiet = TRUE, cores = n_cores) - sforb_saemix_1 <- saem(mmkin_sforb_1, quiet = TRUE, + sforb_saem_1 <- saem(mmkin_sforb_1, quiet = TRUE, no_random_effect = c("parent_free_0"), transformations = "mkin") - sforb_saemix_2 <- saem(mmkin_sforb_1, quiet = TRUE, + sforb_saem_2 <- saem(mmkin_sforb_1, quiet = TRUE, no_random_effect = c("parent_free_0"), transformations = "saemix") expect_equal( - log(endpoints(dfop_saemix_1)$distimes[1:2]), - log(endpoints(sforb_saemix_1)$distimes[1:2]), tolerance = 0.01) + log(endpoints(dfop_saem_1)$distimes[1:2]), + log(endpoints(sforb_saem_1)$distimes[1:2]), tolerance = 0.01) expect_equal( - log(endpoints(sforb_saemix_1)$distimes[1:2]), - log(endpoints(sforb_saemix_2)$distimes[1:2]), tolerance = 0.01) + log(endpoints(sforb_saem_1)$distimes[1:2]), + log(endpoints(sforb_saem_2)$distimes[1:2]), tolerance = 0.01) mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores) hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE, no_random_effect = "parent_0") @@ -131,7 +131,7 @@ test_that("Parent fits using saemix are correctly implemented", { expect_equal(endpoints(hs_saem_1), endpoints(hs_saem_2), tol = 0.01) ci_hs_s1 <- summary(hs_saem_1)$confint_back - hs_pop <- as.numeric(hs_pop) + hs_pop <- as.numeric(attr(ds_hs, "pop")) #expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated expect_true(all(ci_hs_s1[, "upper"] > hs_pop)) }) @@ -141,10 +141,10 @@ test_that("We can also use mkin solution methods for saem", { "saemix transformations is only supported if an analytical solution is implemented" ) skip("This still takes almost 2.5 minutes although we do not solve ODEs") - dfop_saemix_3 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", - solution_type = "analytical", no_random_effect = "parent_0") - distimes_dfop <- endpoints(dfop_saemix_1)$distimes - distimes_dfop_analytical <- endpoints(dfop_saemix_3)$distimes + dfop_saem_3 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", + solution_type = "analytical", no_random_effect = c("parent_0", "g_qlogis")) + distimes_dfop <- endpoints(dfop_saem_1)$distimes + distimes_dfop_analytical <- endpoints(dfop_saem_3)$distimes rel_diff <- abs(distimes_dfop_analytical - distimes_dfop) / distimes_dfop expect_true(all(rel_diff < 0.01)) }) |
