Improve tests

- Improve control of the number of cores
- Reduce the precision of the correlation matrix in the test summary
  output, as the exact results are platform dependent

5 files changed, 45 insertions, 34 deletions

diff --git a/check.log b/check.log
index 445fb66b..325b690c 100644
--- a/check.log
+++ b/check.log
@@ -1,11 +1,11 @@
 * using log directory ‘/home/jranke/git/mkin/mkin.Rcheck’
-* using R version 3.5.2 (2018-12-20)
+* using R version 3.6.0 (2019-04-26)
 * using platform: x86_64-pc-linux-gnu (64-bit)
 * using session charset: UTF-8
 * using options ‘--no-tests --as-cran’
 * checking for file ‘mkin/DESCRIPTION’ ... OK
 * checking extension type ... Package
-* this is package ‘mkin’ version ‘0.9.48.1’
+* this is package ‘mkin’ version ‘0.9.49.4’
 * package encoding: UTF-8
 * checking CRAN incoming feasibility ... Note_to_CRAN_maintainers
 Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’
@@ -17,10 +17,10 @@ Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’
 * checking for hidden files and directories ... OK
 * checking for portable file names ... OK
 * checking for sufficient/correct file permissions ... OK
-* checking serialization versions ... OK
 * checking whether package ‘mkin’ can be installed ... OK
 * checking installed package size ... OK
 * checking package directory ... OK
+* checking for future file timestamps ... OK
 * checking ‘build’ directory ... OK
 * checking DESCRIPTION meta-information ... OK
 * checking top-level files ... OK
diff --git a/test.log b/test.log
index 04c7f1ce..b1e9b9f3 100644
--- a/test.log
+++ b/test.log
@@ -1,24 +1,24 @@
 Loading mkin
 Testing mkin
-✔ | OK F W S | Context
-
⠏ |  0       | Export dataset for reading into CAKE
⠋ |  1       | Export dataset for reading into CAKE
✔ |  1       | Export dataset for reading into CAKE
-
⠏ |  0       | Error model fitting
⠋ |  1       | Error model fitting
⠙ |  2       | Error model fitting
⠹ |  3       | Error model fitting
⠸ |  4       | Error model fitting
⠼ |  5       | Error model fitting
⠴ |  6       | Error model fitting
⠦ |  7       | Error model fitting
⠧ |  8       | Error model fitting
⠇ |  9       | Error model fitting
⠏ | 10       | Error model fitting
⠋ | 11       | Error model fitting
⠙ | 12       | Error model fitting
✔ | 12       | Error model fitting [148.0 s]
-
⠏ |  0       | Calculation of FOCUS chi2 error levels
⠋ |  1       | Calculation of FOCUS chi2 error levels
⠙ |  2       | Calculation of FOCUS chi2 error levels
⠹ |  3       | Calculation of FOCUS chi2 error levels
✔ |  3       | Calculation of FOCUS chi2 error levels [2.3 s]
-
⠏ |  0       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠋ |  1       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠙ |  2       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠹ |  3       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠸ |  4       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠼ |  5       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠴ |  6       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠦ |  7       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠧ |  8       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠇ |  9       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠏ | 10       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠋ | 11       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠙ | 12       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠹ | 13       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
✔ | 13       | Results for FOCUS D established in expertise for UBA (Ranke 2014) [3.7 s]
-
⠏ |  0       | Test fitting the decline of metabolites from their maximum
⠋ |  1       | Test fitting the decline of metabolites from their maximum
⠙ |  2       | Test fitting the decline of metabolites from their maximum
⠹ |  3       | Test fitting the decline of metabolites from their maximum
⠸ |  4       | Test fitting the decline of metabolites from their maximum
⠼ |  5       | Test fitting the decline of metabolites from their maximum
⠴ |  6       | Test fitting the decline of metabolites from their maximum
✔ |  6       | Test fitting the decline of metabolites from their maximum [0.9 s]
-
⠏ |  0       | Fitting the logistic model
⠋ |  1       | Fitting the logistic model
✔ |  1       | Fitting the logistic model [0.9 s]
-
⠏ |  0       | Test dataset class mkinds used in gmkin
⠋ |  1       | Test dataset class mkinds used in gmkin
✔ |  1       | Test dataset class mkinds used in gmkin
-
⠏ |  0       | Special cases of mkinfit calls
⠋ |  1       | Special cases of mkinfit calls
⠙ |  2       | Special cases of mkinfit calls
⠹ |  3       | Special cases of mkinfit calls
⠸ |  4       | Special cases of mkinfit calls
⠼ |  5       | Special cases of mkinfit calls
⠴ |  6       | Special cases of mkinfit calls
⠦ |  7       | Special cases of mkinfit calls
⠧ |  8       | Special cases of mkinfit calls
⠇ |  9       | Special cases of mkinfit calls
⠏ | 10       | Special cases of mkinfit calls
⠋ | 11       | Special cases of mkinfit calls
⠙ | 12       | Special cases of mkinfit calls
✔ | 12       | Special cases of mkinfit calls [2.7 s]
-
⠏ |  0       | mkinmod model generation and printing
⠋ |  1       | mkinmod model generation and printing
⠙ |  2       | mkinmod model generation and printing
⠹ |  3       | mkinmod model generation and printing
⠸ |  4       | mkinmod model generation and printing
⠼ |  5       | mkinmod model generation and printing
⠴ |  6       | mkinmod model generation and printing
⠦ |  7       | mkinmod model generation and printing
⠧ |  8       | mkinmod model generation and printing
⠇ |  9       | mkinmod model generation and printing
✔ |  9       | mkinmod model generation and printing [0.2 s]
-
⠏ |  0       | Model predictions with mkinpredict
⠋ |  1       | Model predictions with mkinpredict
⠙ |  2       | Model predictions with mkinpredict
⠹ |  3       | Model predictions with mkinpredict
✔ |  3       | Model predictions with mkinpredict [0.3 s]
-
⠏ |  0       | Evaluations according to 2015 NAFTA guidance
⠋ |  1       | Evaluations according to 2015 NAFTA guidance
⠙ |  2       | Evaluations according to 2015 NAFTA guidance
⠹ |  3       | Evaluations according to 2015 NAFTA guidance
⠸ |  4       | Evaluations according to 2015 NAFTA guidance
⠼ |  5       | Evaluations according to 2015 NAFTA guidance
⠴ |  6       | Evaluations according to 2015 NAFTA guidance
⠦ |  7       | Evaluations according to 2015 NAFTA guidance
⠧ |  8       | Evaluations according to 2015 NAFTA guidance
⠇ |  9       | Evaluations according to 2015 NAFTA guidance
⠏ | 10       | Evaluations according to 2015 NAFTA guidance
⠋ | 11       | Evaluations according to 2015 NAFTA guidance
⠙ | 12       | Evaluations according to 2015 NAFTA guidance
⠹ | 13       | Evaluations according to 2015 NAFTA guidance
⠸ | 14       | Evaluations according to 2015 NAFTA guidance
⠼ | 15       | Evaluations according to 2015 NAFTA guidance
⠴ | 16       | Evaluations according to 2015 NAFTA guidance
✔ | 16       | Evaluations according to 2015 NAFTA guidance [3.9 s]
-
⠏ |  0       | Fitting of parent only models
⠋ |  1       | Fitting of parent only models
⠙ |  2       | Fitting of parent only models
⠹ |  3       | Fitting of parent only models
⠸ |  4       | Fitting of parent only models
⠼ |  5       | Fitting of parent only models
⠴ |  6       | Fitting of parent only models
⠦ |  7       | Fitting of parent only models
⠧ |  8       | Fitting of parent only models
⠇ |  9       | Fitting of parent only models
⠏ | 10       | Fitting of parent only models
⠋ | 11       | Fitting of parent only models
⠙ | 12       | Fitting of parent only models
⠹ | 13       | Fitting of parent only models
⠸ | 14       | Fitting of parent only models
⠼ | 15       | Fitting of parent only models
⠴ | 16       | Fitting of parent only models
⠦ | 17       | Fitting of parent only models
⠧ | 18       | Fitting of parent only models
⠇ | 19       | Fitting of parent only models
⠏ | 20       | Fitting of parent only models
⠋ | 21       | Fitting of parent only models
✔ | 21       | Fitting of parent only models [40.0 s]
-
⠏ |  0       | Calculation of maximum time weighted average concentrations (TWAs)
⠋ |  1       | Calculation of maximum time weighted average concentrations (TWAs)
⠙ |  2       | Calculation of maximum time weighted average concentrations (TWAs)
⠹ |  3       | Calculation of maximum time weighted average concentrations (TWAs)
⠸ |  4       | Calculation of maximum time weighted average concentrations (TWAs)
✔ |  4       | Calculation of maximum time weighted average concentrations (TWAs) [2.3 s]
-
⠏ |  0       | Summary
⠋ |  1       | Summary
✔ |  1       | Summary
-
⠏ |  0       | Plotting
⠋ |  1       | Plotting
⠙ |  2       | Plotting
⠹ |  3       | Plotting
⠸ |  4       | Plotting
✔ |  4       | Plotting [0.3 s]
-
⠏ |  0       | AIC calculation
⠋ |  1       | AIC calculation
⠙ |  2       | AIC calculation
✔ |  2       | AIC calculation
-
⠏ |  0       | Complex test case from Schaefer et al. (2007) Piacenza paper
⠋ |  1       | Complex test case from Schaefer et al. (2007) Piacenza paper
⠙ |  2       | Complex test case from Schaefer et al. (2007) Piacenza paper
✔ |  2       | Complex test case from Schaefer et al. (2007) Piacenza paper [5.3 s]
-
⠏ |  0       | Results for synthetic data established in expertise for UBA (Ranke 2014)
⠋ |  1       | Results for synthetic data established in expertise for UBA (Ranke 2014)
⠙ |  2       | Results for synthetic data established in expertise for UBA (Ranke 2014)
⠹ |  3       | Results for synthetic data established in expertise for UBA (Ranke 2014)
⠸ |  4       | Results for synthetic data established in expertise for UBA (Ranke 2014)
✔ |  4       | Results for synthetic data established in expertise for UBA (Ranke 2014) [7.1 s]
+✔ |  OK F W S | Context
+
⠏ |   0       | Export dataset for reading into CAKE
✔ |   1       | Export dataset for reading into CAKE
+
⠏ |   0       | Error model fitting
⠋ |   1       | Error model fitting
⠹ |   3       | Error model fitting
⠸ |   4       | Error model fitting
⠼ |   5       | Error model fitting
⠴ |   6       | Error model fitting
⠧ |   8       | Error model fitting
⠏ |  10       | Error model fitting
⠋ |  11       | Error model fitting
✔ |  12       | Error model fitting [147.8 s]
+
⠏ |   0       | Calculation of FOCUS chi2 error levels
⠋ |   1       | Calculation of FOCUS chi2 error levels
⠹ |   3       | Calculation of FOCUS chi2 error levels
✔ |   3       | Calculation of FOCUS chi2 error levels [2.3 s]
+
⠏ |   0       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠋ |   1       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠙ |   2       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠸ |   4       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠇ |   9       | Results for FOCUS D established in expertise for UBA (Ranke 2014)
✔ |  13       | Results for FOCUS D established in expertise for UBA (Ranke 2014) [3.7 s]
+
⠏ |   0       | Test fitting the decline of metabolites from their maximum
⠋ |   1       | Test fitting the decline of metabolites from their maximum
⠹ |   3       | Test fitting the decline of metabolites from their maximum
⠼ |   5       | Test fitting the decline of metabolites from their maximum
✔ |   6       | Test fitting the decline of metabolites from their maximum [0.9 s]
+
⠏ |   0       | Fitting the logistic model
⠋ |   1       | Fitting the logistic model
✔ |   1       | Fitting the logistic model [0.9 s]
+
⠏ |   0       | Test dataset class mkinds used in gmkin
✔ |   1       | Test dataset class mkinds used in gmkin
+
⠏ |   0       | Special cases of mkinfit calls
⠋ |   1       | Special cases of mkinfit calls
⠇ |   9       | Special cases of mkinfit calls
⠏ |  10       | Special cases of mkinfit calls
⠋ |  11       | Special cases of mkinfit calls
⠙ |  12       | Special cases of mkinfit calls
✔ |  12       | Special cases of mkinfit calls [2.7 s]
+
⠏ |   0       | mkinmod model generation and printing
⠇ |   9       | mkinmod model generation and printing
✔ |   9       | mkinmod model generation and printing [0.2 s]
+
⠏ |   0       | Model predictions with mkinpredict
⠋ |   1       | Model predictions with mkinpredict
✔ |   3       | Model predictions with mkinpredict [0.3 s]
+
⠏ |   0       | Evaluations according to 2015 NAFTA guidance
⠙ |   2       | Evaluations according to 2015 NAFTA guidance
⠇ |   9       | Evaluations according to 2015 NAFTA guidance
⠏ |  10       | Evaluations according to 2015 NAFTA guidance
⠴ |  16       | Evaluations according to 2015 NAFTA guidance
✔ |  16       | Evaluations according to 2015 NAFTA guidance [3.9 s]
+
⠏ |   0       | Fitting of parent only models
⠋ |   1       | Fitting of parent only models
⠙ |   2       | Fitting of parent only models
⠹ |   3       | Fitting of parent only models
⠸ |   4       | Fitting of parent only models
⠼ |   5       | Fitting of parent only models
⠴ |   6       | Fitting of parent only models
⠦ |   7       | Fitting of parent only models
⠧ |   8       | Fitting of parent only models
⠇ |   9       | Fitting of parent only models
⠏ |  10       | Fitting of parent only models
⠋ |  11       | Fitting of parent only models
⠙ |  12       | Fitting of parent only models
⠹ |  13       | Fitting of parent only models
⠴ |  16       | Fitting of parent only models
⠧ |  18       | Fitting of parent only models
⠏ |  20       | Fitting of parent only models
✔ |  21       | Fitting of parent only models [40.1 s]
+
⠏ |   0       | Calculation of maximum time weighted average concentrations (TWAs)
⠋ |   1       | Calculation of maximum time weighted average concentrations (TWAs)
⠙ |   2       | Calculation of maximum time weighted average concentrations (TWAs)
⠹ |   3       | Calculation of maximum time weighted average concentrations (TWAs)
⠸ |   4       | Calculation of maximum time weighted average concentrations (TWAs)
✔ |   4       | Calculation of maximum time weighted average concentrations (TWAs) [2.2 s]
+
⠏ |   0       | Summary
✔ |   1       | Summary
+
⠏ |   0       | Plotting
⠹ |   3       | Plotting
✔ |   4       | Plotting [0.3 s]
+
⠏ |   0       | AIC calculation
✔ |   2       | AIC calculation
+
⠏ |   0       | Complex test case from Schaefer et al. (2007) Piacenza paper
⠋ |   1       | Complex test case from Schaefer et al. (2007) Piacenza paper
✔ |   2       | Complex test case from Schaefer et al. (2007) Piacenza paper [5.3 s]
+
⠏ |   0       | Results for synthetic data established in expertise for UBA (Ranke 2014)
⠋ |   1       | Results for synthetic data established in expertise for UBA (Ranke 2014)
⠹ |   3       | Results for synthetic data established in expertise for UBA (Ranke 2014)
✔ |   4       | Results for synthetic data established in expertise for UBA (Ranke 2014) [7.1 s]
 
 ══ Results ═════════════════════════════════════════════════════════════════════
 Duration: 223.4 s
diff --git a/tests/testthat/summary_DFOP_FOCUS_C.txt b/tests/testthat/summary_DFOP_FOCUS_C.txt
index c4800f35..fb8051c5 100644
--- a/tests/testthat/summary_DFOP_FOCUS_C.txt
+++ b/tests/testthat/summary_DFOP_FOCUS_C.txt
@@ -43,12 +43,12 @@ g_ilr      1.2490    0.05811  1.0870  1.4100
 sigma      0.6962    0.16410  0.2406  1.1520
 
 Parameter correlation:
-           parent_0     log_k1     log_k2      g_ilr      sigma
-parent_0  1.000e+00  4.393e-01  8.805e-02 -3.176e-02  5.407e-07
-log_k1    4.393e-01  1.000e+00  4.821e-01 -6.716e-01  4.399e-07
-log_k2    8.805e-02  4.821e-01  1.000e+00 -7.532e-01 -1.143e-07
-g_ilr    -3.176e-02 -6.716e-01 -7.532e-01  1.000e+00 -2.196e-08
-sigma     5.407e-07  4.399e-07 -1.143e-07 -2.196e-08  1.000e+00
+         parent_0 log_k1 log_k2  g_ilr  sigma
+parent_0    1e+00  4e-01  9e-02 -3e-02  5e-07
+log_k1      4e-01  1e+00  5e-01 -7e-01  4e-07
+log_k2      9e-02  5e-01  1e+00 -8e-01 -1e-07
+g_ilr      -3e-02 -7e-01 -8e-01  1e+00 -2e-08
+sigma       5e-07  4e-07 -1e-07 -2e-08  1e+00
 
 Backtransformed parameters:
 Confidence intervals for internally transformed parameters are asymmetric.
diff --git a/tests/testthat/test_error_models.R b/tests/testthat/test_error_models.R
index 5a7aa4e8..1ec48605 100644
--- a/tests/testthat/test_error_models.R
+++ b/tests/testthat/test_error_models.R
@@ -72,9 +72,6 @@ test_that("Error model 'tc' works", {
 
 test_that("Reweighting method 'tc' produces reasonable variance estimates", {
 
-  # I need to make the tc method more robust against that
-  # skip_on_cran()
-
   # Check if we can approximately obtain the parameters and the error model
   # components that were used in the data generation
 
@@ -94,15 +91,25 @@ test_that("Reweighting method 'tc' produces reasonable variance estimates", {
     sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
     n = 1, reps = 100, digits = 5, LOD = -Inf, seed = 123456)
 
+  # Per default (on my box) use all cores minus one
+  n_cores <- parallel::detectCores() - 1
+
+  # We are only allowed one core on travis
+  if (Sys.getenv("TRAVIS") != "") n_cores = 1
+
+  # Also on Windows we would need to make a cluster first,
+  # and I do not know how this would work on winbuilder or CRAN, so
+  if (Sys.info()["sysname"] == "Windows") n_cores = 1
+
   # Unweighted fits
   f_2_10 <- mmkin("DFOP", d_2_10, error_model = "const", quiet = TRUE,
-    cores = if (Sys.getenv("TRAVIS") != "") 1 else 15)
+    cores = n_cores)
   parms_2_10 <- apply(sapply(f_2_10, function(x) x$bparms.optim), 1, mean)
   parm_errors_2_10 <- (parms_2_10 - parms_DFOP_optim) / parms_DFOP_optim
   expect_true(all(abs(parm_errors_2_10) < 0.12))
 
   f_2_10_tc <- mmkin("DFOP", d_2_10, error_model = "tc", quiet = TRUE,
-    cores = if (Sys.getenv("TRAVIS") != "") 1 else 15)
+    cores = n_cores)
   parms_2_10_tc <- apply(sapply(f_2_10_tc, function(x) x$bparms.optim), 1, mean)
   parm_errors_2_10_tc <- (parms_2_10_tc - parms_DFOP_optim) / parms_DFOP_optim
   expect_true(all(abs(parm_errors_2_10_tc) < 0.05))
@@ -153,7 +160,7 @@ test_that("Reweighting method 'tc' produces reasonable variance estimates", {
   # Doing more takes a lot of computing power
   skip_on_travis()
   f_met_2_15_tc_e4 <- mmkin(list(m_synth_DFOP_lin), d_met_2_15, quiet = TRUE,
-                            error_model = "tc", cores = 15)
+                            error_model = "tc", cores = n_cores)
 
   parms_met_2_15_tc_e4 <- apply(sapply(f_met_2_15_tc_e4, function(x) x$bparms.optim), 1, mean)
   parm_errors_met_2_15_tc_e4 <- (parms_met_2_15_tc_e4[names(parms_DFOP_lin_optim)] -
diff --git a/tests/testthat/test_plots_summary_twa.R b/tests/testthat/test_plots_summary_twa.R
index cf5715fa..43036f1a 100644
--- a/tests/testthat/test_plots_summary_twa.R
+++ b/tests/testthat/test_plots_summary_twa.R
@@ -58,6 +58,10 @@ test_that("The summary is reproducible", {
   test_summary$date.summary <- "Dummy date for testing"
   test_summary$calls <- "test 0"
   test_summary$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$Corr <- signif(test_summary$Corr, 1)
   expect_known_output(print(test_summary), "summary_DFOP_FOCUS_C.txt")
 })