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-rw-r--r--test.log214
-rw-r--r--tests/testthat/test_FOMC_ill-defined.R2
-rw-r--r--tests/testthat/test_parent_only.R60
-rw-r--r--tests/testthat/test_twa.R4
4 files changed, 243 insertions, 37 deletions
diff --git a/test.log b/test.log
index 935df857..3593b2a7 100644
--- a/test.log
+++ b/test.log
@@ -9,14 +9,218 @@ Testing mkin
Calculation of FOCUS chi2 error levels: ..
Results for FOCUS D established in expertise for UBA (Ranke 2014): ......
The t-test for significant difference from zero: ..
-Fitting the FOMC model with large parameter correlation: S
+Fitting the FOMC model with large parameter correlation: Model cost at call 1 : 2154.97
+Model cost at call 2 : 2154.97
+Model cost at call 4 : 2154.97
+Model cost at call 5 : 388.1874
+Model cost at call 9 : 198.5808
+Model cost at call 10 : 198.5806
+Model cost at call 13 : 167.315
+Model cost at call 15 : 167.315
+Model cost at call 17 : 122.5165
+Model cost at call 21 : 110.3697
+Model cost at call 25 : 99.37279
+Model cost at call 26 : 99.37279
+Model cost at call 29 : 85.86611
+Model cost at call 30 : 85.86609
+Model cost at call 33 : 83.79827
+Model cost at call 35 : 83.79825
+Model cost at call 37 : 77.76253
+Model cost at call 39 : 77.76252
+Model cost at call 40 : 77.76252
+Model cost at call 41 : 74.85232
+Model cost at call 45 : 71.0713
+Model cost at call 49 : 69.0689
+Model cost at call 53 : 65.69063
+Model cost at call 54 : 65.69063
+Model cost at call 57 : 63.81175
+Model cost at call 58 : 63.81174
+Model cost at call 61 : 61.77925
+Model cost at call 65 : 60.68903
+Model cost at call 69 : 58.787
+Model cost at call 73 : 57.79498
+Model cost at call 77 : 56.72184
+Model cost at call 78 : 56.72184
+Model cost at call 81 : 56.27078
+Model cost at call 82 : 56.27077
+Model cost at call 85 : 55.76643
+Model cost at call 89 : 55.06746
+Model cost at call 93 : 54.77276
+Model cost at call 97 : 54.46435
+Model cost at call 101 : 53.97206
+Model cost at call 105 : 53.7466
+Model cost at call 109 : 53.45331
+Model cost at call 113 : 53.15008
+Model cost at call 117 : 52.8698
+Model cost at call 122 : 52.79044
+Model cost at call 126 : 52.64587
+Model cost at call 127 : 52.56412
+Model cost at call 131 : 52.37304
+Model cost at call 135 : 52.29005
+Model cost at call 139 : 52.20652
+Model cost at call 143 : 52.05187
+Model cost at call 147 : 51.95367
+Model cost at call 151 : 51.80296
+Model cost at call 152 : 51.80296
+Model cost at call 153 : 51.80296
+Model cost at call 155 : 51.79287
+Model cost at call 156 : 51.79286
+Model cost at call 157 : 51.79286
+Model cost at call 159 : 51.71749
+Model cost at call 163 : 51.69885
+Model cost at call 164 : 51.66416
+Model cost at call 169 : 51.61289
+Model cost at call 173 : 51.5739
+Model cost at call 177 : 51.5122
+Model cost at call 181 : 51.44598
+Model cost at call 185 : 51.42107
+Model cost at call 189 : 51.37519
+Model cost at call 193 : 51.32105
+Model cost at call 194 : 51.32105
+Model cost at call 195 : 51.32105
+Model cost at call 197 : 51.29233
+Model cost at call 199 : 51.29233
+Model cost at call 200 : 51.29233
+Model cost at call 202 : 51.27541
+Model cost at call 206 : 51.24732
+Model cost at call 210 : 51.20348
+Model cost at call 214 : 51.1884
+Model cost at call 218 : 51.15654
+Model cost at call 222 : 51.12473
+Model cost at call 226 : 51.1093
+Model cost at call 230 : 51.09604
+Model cost at call 234 : 51.07288
+Model cost at call 238 : 51.04678
+Model cost at call 243 : 51.03801
+Model cost at call 247 : 51.02297
+Model cost at call 248 : 51.01588
+Model cost at call 252 : 50.99353
+Model cost at call 253 : 50.99353
+Model cost at call 254 : 50.99353
+Model cost at call 256 : 50.98775
+Model cost at call 257 : 50.97537
+Model cost at call 258 : 50.97537
+Model cost at call 259 : 50.97537
+Model cost at call 260 : 50.97537
+Model cost at call 262 : 50.97142
+Model cost at call 266 : 50.96632
+Model cost at call 272 : 50.9654
+Model cost at call 276 : 50.96385
+Model cost at call 279 : 50.96385
+Model cost at call 284 : 50.96354
+Model cost at call 286 : 50.96354
+Model cost at call 290 : 50.96292
+Model cost at call 293 : 50.96292
+Model cost at call 298 : 50.9628
+Model cost at call 300 : 50.9628
+Model cost at call 305 : 50.96277
+Model cost at call 307 : 50.96277
+Model cost at call 312 : 50.96277
+Model cost at call 314 : 50.96277
+Model cost at call 318 : 50.96276
+Model cost at call 321 : 50.96276
+Model cost at call 326 : 50.96276
+Model cost at call 328 : 50.96276
+Model cost at call 332 : 50.96275
+Model cost at call 333 : 50.96274
+Model cost at call 336 : 50.96274
+Model cost at call 337 : 50.96274
+Model cost at call 342 : 50.96274
+Model cost at call 344 : 50.96274
+Model cost at call 348 : 50.96274
+Model cost at call 351 : 50.96274
+Model cost at call 357 : 50.96274
+Model cost at call 359 : 50.96274
+Model cost at call 363 : 50.96274
+Model cost at call 364 : 50.96274
+Model cost at call 367 : 50.96274
+Model cost at call 375 : 50.96274
+Model cost at call 381 : 50.96274
+Model cost at call 388 : 50.96274
+Model cost at call 452 : 50.96274
+.Model cost at call 1 : 2154.97
+Model cost at call 3 : 2154.97
+Model cost at call 5 : 2154.97
+Model cost at call 6 : 716.4083
+Model cost at call 8 : 716.4082
+Model cost at call 11 : 136.1354
+Model cost at call 12 : 136.1354
+Model cost at call 15 : 64.35183
+Model cost at call 16 : 64.35183
+Model cost at call 20 : 61.48715
+Model cost at call 22 : 61.48715
+Model cost at call 24 : 59.59792
+Model cost at call 26 : 59.59792
+Model cost at call 28 : 57.47603
+Model cost at call 30 : 57.47603
+Model cost at call 32 : 57.10469
+Model cost at call 34 : 57.10468
+Model cost at call 36 : 54.78942
+Model cost at call 38 : 54.78942
+Model cost at call 40 : 54.29399
+Model cost at call 42 : 54.29399
+Model cost at call 44 : 53.55295
+Model cost at call 46 : 53.55294
+Model cost at call 49 : 53.10239
+Model cost at call 51 : 53.10239
+Model cost at call 53 : 52.95095
+Model cost at call 55 : 52.95095
+Model cost at call 57 : 52.41198
+Model cost at call 59 : 52.41198
+Model cost at call 61 : 52.24816
+Model cost at call 63 : 52.24816
+Model cost at call 65 : 51.99669
+Model cost at call 67 : 51.99669
+Model cost at call 69 : 51.82092
+Model cost at call 71 : 51.82092
+Model cost at call 74 : 51.69389
+Model cost at call 76 : 51.69389
+Model cost at call 78 : 51.64468
+Model cost at call 80 : 51.64468
+Model cost at call 82 : 51.46367
+Model cost at call 84 : 51.46366
+Model cost at call 86 : 51.407
+Model cost at call 88 : 51.407
+Model cost at call 90 : 51.30871
+Model cost at call 92 : 51.30871
+Model cost at call 94 : 51.23556
+Model cost at call 96 : 51.23556
+Model cost at call 98 : 51.17829
+Model cost at call 100 : 51.17829
+Model cost at call 103 : 51.13498
+Model cost at call 105 : 51.13498
+Model cost at call 107 : 51.11419
+Model cost at call 109 : 51.11419
+Model cost at call 111 : 51.05405
+Model cost at call 113 : 51.05405
+Model cost at call 115 : 51.0096
+Model cost at call 117 : 51.0096
+Model cost at call 119 : 50.97496
+Model cost at call 121 : 50.97496
+Model cost at call 125 : 50.96419
+Model cost at call 126 : 50.96419
+Model cost at call 129 : 50.96365
+Model cost at call 131 : 50.96365
+Model cost at call 134 : 50.96354
+Model cost at call 136 : 50.96354
+Model cost at call 138 : 50.96333
+Model cost at call 140 : 50.96333
+Model cost at call 142 : 50.9629
+Model cost at call 144 : 50.9629
+Model cost at call 147 : 50.96282
+Model cost at call 149 : 50.96282
+Model cost at call 152 : 50.9628
+Model cost at call 156 : 50.96277
+Model cost at call 161 : 50.96276
+Model cost at call 165 : 50.96275
+Model cost at call 170 : 50.96274
+Model cost at call 175 : 50.96274
+Optimisation by method Marq successfully terminated.
+.
Model predictions with mkinpredict: ...
-Fitting of parent only models: .....................
+Fitting of parent only models: ......................
Complex test case from Schaefer et al. (2007) Piacenza paper: ..
Results for synthetic data established in expertise for UBA (Ranke 2014): ....
Calculation of maximum time weighted average concentrations (TWAs): ...
-Skipped ------------------------------------------------------------------------
-1. Fitting with large parameter correlation gives warnings (@test_FOMC_ill-defined.R#30) - Skip test for warnings triggered by large parameter correlation as it failed on r-forge
-
DONE ===========================================================================
diff --git a/tests/testthat/test_FOMC_ill-defined.R b/tests/testthat/test_FOMC_ill-defined.R
index 3ca8a99e..ee3d2b68 100644
--- a/tests/testthat/test_FOMC_ill-defined.R
+++ b/tests/testthat/test_FOMC_ill-defined.R
@@ -27,7 +27,7 @@ FOMC_test <- data.frame(
test_that("Fitting with large parameter correlation gives warnings", {
- skip("Skip test for warnings triggered by large parameter correlation as it failed on r-forge")
+ #skip("Skip test for warnings triggered by large parameter correlation as it failed on r-forge")
# When fitting from the maximum, the Port algorithm does not converge (with
# default settings)
diff --git a/tests/testthat/test_parent_only.R b/tests/testthat/test_parent_only.R
index 5dcf297c..1d100cca 100644
--- a/tests/testthat/test_parent_only.R
+++ b/tests/testthat/test_parent_only.R
@@ -23,7 +23,7 @@ calc_dev.percent <- function(fitlist, reference, endpoints = TRUE, round_results
for (i in 1:length(fitlist)) {
fit <- fitlist[[i]]
if (endpoints) {
- results <- c(fit$bparms.optim,
+ results <- c(fit$bparms.optim,
endpoints(fit)$distimes$DT50,
endpoints(fit)$distimes$DT90)
} else {
@@ -44,21 +44,23 @@ SFORB <- mkinmod(parent = list(type = "SFORB"))
test_that("Fits for FOCUS A deviate less than 0.1% from median of values from FOCUS report", {
fit.A.SFO <- list(mkinfit("SFO", FOCUS_2006_A, quiet = TRUE))
- median.A.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F,
- dataset == "A",
+ median.A.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F,
+ dataset == "A",
c(M0, k, DT50, DT90)), "median"))
dev.percent.A.SFO <- calc_dev.percent(fit.A.SFO, median.A.SFO)
expect_equivalent(dev.percent.A.SFO[[1]] < 0.1, rep(TRUE, 4))
# Fitting FOCUS A with FOMC is possible, but the correlation between
- # alpha and beta, when obtained, is 1.0000, and the fit sometimes failed on
- # Windows, as the Port algorithm did not converge (winbuilder, 2015-05-15)
- fit.A.FOMC <- try(list(mkinfit("FOMC", FOCUS_2006_A, quiet = TRUE)))
+ # alpha and beta, when obtained, is 1.0000, and the fit does not
+ # converge using the Port algorithm, which yields a warning
+ expect_warning(
+ fit.A.FOMC <- try(list(mkinfit("FOMC", FOCUS_2006_A, quiet = TRUE))))
+
if (!inherits(fit.A.FOMC, "try-error")) {
- median.A.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F,
- dataset == "A",
+ median.A.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F,
+ dataset == "A",
c(M0, alpha, beta, DT50, DT90)), "median"))
dev.percent.A.FOMC <- calc_dev.percent(fit.A.FOMC, median.A.FOMC)
@@ -68,8 +70,8 @@ test_that("Fits for FOCUS A deviate less than 0.1% from median of values from FO
fit.A.DFOP <- list(mkinfit("DFOP", FOCUS_2006_A, quiet = TRUE))
- median.A.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B,
- dataset == "A",
+ median.A.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B,
+ dataset == "A",
c(M0, k1, k2, f, DT50, DT90)), "median"))
dev.percent.A.DFOP <- calc_dev.percent(fit.A.DFOP, median.A.DFOP)
@@ -78,8 +80,8 @@ test_that("Fits for FOCUS A deviate less than 0.1% from median of values from FO
fit.A.HS <- list(mkinfit("HS", FOCUS_2006_A, quiet = TRUE))
- median.A.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F,
- dataset == "A",
+ median.A.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F,
+ dataset == "A",
c(M0, k1, k2, tb, DT50, DT90)), "median"))
dev.percent.A.HS <- calc_dev.percent(fit.A.HS, median.A.HS)
@@ -89,8 +91,8 @@ test_that("Fits for FOCUS A deviate less than 0.1% from median of values from FO
test_that("Fits for FOCUS B deviate less than 0.1% from median of values from FOCUS report", {
fit.B.SFO <- list(mkinfit("SFO", FOCUS_2006_B, quiet = TRUE))
- median.B.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F,
- dataset == "B",
+ median.B.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F,
+ dataset == "B",
c(M0, k, DT50, DT90)), "median"))
dev.percent.B.SFO <- calc_dev.percent(fit.B.SFO, median.B.SFO)
@@ -98,8 +100,8 @@ test_that("Fits for FOCUS B deviate less than 0.1% from median of values from FO
fit.B.FOMC <- list(mkinfit("FOMC", FOCUS_2006_B, quiet = TRUE))
- median.B.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F,
- dataset == "B",
+ median.B.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F,
+ dataset == "B",
c(M0, alpha, beta, DT50, DT90)), "median"))
dev.percent.B.FOMC <- calc_dev.percent(fit.B.FOMC, median.B.FOMC)
@@ -107,8 +109,8 @@ test_that("Fits for FOCUS B deviate less than 0.1% from median of values from FO
fit.B.DFOP <- list(mkinfit("DFOP", FOCUS_2006_B, quiet = TRUE))
- median.B.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B,
- dataset == "B",
+ median.B.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B,
+ dataset == "B",
c(M0, k1, k2, f, DT50, DT90)), "median"))
dev.percent.B.DFOP <- calc_dev.percent(fit.B.DFOP, median.B.DFOP)
@@ -117,9 +119,9 @@ test_that("Fits for FOCUS B deviate less than 0.1% from median of values from FO
fit.B.HS <- list(mkinfit("HS", FOCUS_2006_B, quiet = TRUE))
- median.B.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F,
- dataset == "B",
- c(M0, k1, k2, tb, DT50, DT90)),
+ median.B.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F,
+ dataset == "B",
+ c(M0, k1, k2, tb, DT50, DT90)),
"median", na.rm = TRUE))
dev.percent.B.HS <- calc_dev.percent(fit.B.HS, median.B.HS)
@@ -133,8 +135,8 @@ test_that("Fits for FOCUS B deviate less than 0.1% from median of values from FO
test_that("Fits for FOCUS C deviate less than 0.1% from median of values from FOCUS report", {
fit.C.SFO <- list(mkinfit("SFO", FOCUS_2006_C, quiet = TRUE))
- median.C.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F,
- dataset == "C",
+ median.C.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F,
+ dataset == "C",
c(M0, k, DT50, DT90)), "median"))
dev.percent.C.SFO <- calc_dev.percent(fit.C.SFO, median.C.SFO)
@@ -142,18 +144,18 @@ test_that("Fits for FOCUS C deviate less than 0.1% from median of values from FO
fit.C.FOMC <- list(mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE))
- median.C.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F,
- dataset == "C",
+ median.C.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F,
+ dataset == "C",
c(M0, alpha, beta, DT50, DT90)), "median"))
- dev.percent.C.FOMC <- calc_dev.percent(fit.C.FOMC, median.C.FOMC,
+ dev.percent.C.FOMC <- calc_dev.percent(fit.C.FOMC, median.C.FOMC,
round_results = 2) # Not enough precision in FOCUS results
expect_equivalent(dev.percent.C.FOMC[[1]] < 0.1, rep(TRUE, 5))
fit.C.HS <- list(mkinfit("HS", FOCUS_2006_C, quiet = TRUE))
- median.C.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F,
- dataset == "C",
+ median.C.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F,
+ dataset == "C",
c(M0, k1, k2, tb, DT50, DT90)), "median"))
dev.percent.C.HS <- calc_dev.percent(fit.C.HS, median.C.HS, round_results = c(2, 4, 6, 2, 2))
@@ -202,7 +204,7 @@ test_that("DFOP fits give approximately (0.001%) equal results with different so
test_that("SFORB fits give approximately (0.002%) equal results with different solution methods", {
fit.B.SFORB.default <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE)$bparms.optim
- fits.B.SFORB <- list()
+ fits.B.SFORB <- list()
fits.B.SFORB[[1]] <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE, solution_type = "eigen")
fits.B.SFORB[[2]] <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE, solution_type = "deSolve")
dev.percent <- calc_dev.percent(fits.B.SFORB, fit.B.SFORB.default, endpoints = FALSE)
diff --git a/tests/testthat/test_twa.R b/tests/testthat/test_twa.R
index 3a28e0c1..19e706e1 100644
--- a/tests/testthat/test_twa.R
+++ b/tests/testthat/test_twa.R
@@ -1,4 +1,4 @@
-# Copyright (C) 2016 Johannes Ranke
+# Copyright (C) 2016,2017 Johannes Ranke
# Contact: jranke@uni-bremen.de
# This file is part of the R package mkin
@@ -37,7 +37,7 @@ test_that("Time weighted average concentrations are correct", {
outtimes = outtimes_7)
twa_num <- mean(pred_7$parent)
names(twa_num) <- 7
- twa_ana <- twa(fit, 7)
+ twa_ana <- max_twa_parent(fit, 7)
# Test for absolute difference (scale = 1)
expect_equal(twa_num, twa_ana, tolerance = 0.001, scale = 1)

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