From 70591022c07f0e8fb4dd67789b7c8d78af8ebc18 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 2 May 2019 13:17:05 +0200 Subject: Better initials for error model parameters - Also make it possible to specify initial values for error model parameters. - Run tests - Rebuild docs --- docs/articles/FOCUS_D.html | 14 +- .../FOCUS_D_files/figure-html/plot_2-1.png | Bin 14288 -> 14288 bytes docs/articles/FOCUS_L.html | 154 ++++++++++----------- .../figure-html/unnamed-chunk-10-1.png | Bin 29157 -> 29150 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 55027 -> 54997 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 38740 -> 38711 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 14866 -> 14858 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 23974 -> 23974 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 27988 -> 27981 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 28412 -> 28407 bytes docs/articles/mkin.html | 2 +- .../mkin_files/figure-html/unnamed-chunk-2-1.png | Bin 114757 -> 114726 bytes docs/articles/twa.html | 2 +- docs/articles/web_only/FOCUS_Z.html | 18 +-- .../figure-html/FOCUS_2006_Z_fits_1-1.png | Bin 85595 -> 85592 bytes .../figure-html/FOCUS_2006_Z_fits_11-1.png | Bin 127712 -> 127690 bytes .../figure-html/FOCUS_2006_Z_fits_11a-1.png | Bin 96892 -> 96509 bytes .../figure-html/FOCUS_2006_Z_fits_11b-1.png | Bin 22308 -> 22308 bytes .../figure-html/FOCUS_2006_Z_fits_2-1.png | Bin 86200 -> 86198 bytes .../figure-html/FOCUS_2006_Z_fits_3-1.png | Bin 85813 -> 85812 bytes docs/articles/web_only/NAFTA_examples.html | 120 ++++++++-------- .../NAFTA_examples_files/figure-html/p10-1.png | Bin 53961 -> 53933 bytes .../NAFTA_examples_files/figure-html/p11-1.png | Bin 51343 -> 51316 bytes .../NAFTA_examples_files/figure-html/p12a-1.png | Bin 54599 -> 54573 bytes .../NAFTA_examples_files/figure-html/p12b-1.png | Bin 47216 -> 47194 bytes .../NAFTA_examples_files/figure-html/p13-1.png | Bin 51514 -> 51486 bytes .../NAFTA_examples_files/figure-html/p14-1.png | Bin 54065 -> 54038 bytes .../NAFTA_examples_files/figure-html/p15a-1.png | Bin 51221 -> 51194 bytes .../NAFTA_examples_files/figure-html/p15b-1.png | Bin 53121 -> 53096 bytes .../NAFTA_examples_files/figure-html/p16-1.png | Bin 62569 -> 62542 bytes .../NAFTA_examples_files/figure-html/p5a-1.png | Bin 55413 -> 55386 bytes .../NAFTA_examples_files/figure-html/p5b-1.png | Bin 53575 -> 53549 bytes .../NAFTA_examples_files/figure-html/p6-1.png | Bin 54970 -> 54945 bytes .../NAFTA_examples_files/figure-html/p7-1.png | Bin 66810 -> 66786 bytes .../NAFTA_examples_files/figure-html/p8-1.png | Bin 61563 -> 61538 bytes .../NAFTA_examples_files/figure-html/p9a-1.png | Bin 53089 -> 53061 bytes .../NAFTA_examples_files/figure-html/p9b-1.png | Bin 50039 -> 50012 bytes docs/articles/web_only/benchmarks.html | 112 +++++++-------- docs/articles/web_only/compiled_models.html | 18 +-- 39 files changed, 218 insertions(+), 222 deletions(-) (limited to 'docs/articles') diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index e63feb07..0ae6a391 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -88,7 +88,7 @@

Example evaluation of FOCUS Example Dataset D

Johannes Ranke

-

2019-04-10

+

2019-05-02

@@ -167,9 +167,9 @@

A comprehensive report of the results is obtained using the summary method for mkinfit objects.

summary(fit)
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:31 2019 
-## Date of summary: Wed Apr 10 10:11:31 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:29 2019 
+## Date of summary: Thu May  2 12:40:29 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
@@ -177,10 +177,10 @@
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted with method using 396 model solutions performed in 1.048 s
+## Fitted using 396 model solutions performed in 1.048 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##                    value   type
@@ -235,7 +235,7 @@
 ## k_m1_sink      0.005261   7.510 6.165e-09  0.004012 6.898e-03
 ## sigma          3.126000   8.718 2.235e-10  2.396000 3.855e+00
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   6.398       4 15
 ## parent     6.827       3  6
diff --git a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
index 79ec3aaf..64da1d2e 100644
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diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index 8af99f6c..b05963ae 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -88,7 +88,7 @@
       
Example evaluation of FOCUS Laboratory Data L1 to L3

                         
Johannes Ranke

             
-            
2019-04-10

+            
2019-05-02

       
       
       
@@ -113,19 +113,19 @@
 
m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE)
 summary(m.L1.SFO)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:33 2019 
-## Date of summary: Wed Apr 10 10:11:33 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:31 2019 
+## Date of summary: Thu May  2 12:40:31 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 133 model solutions performed in 0.344 s
+## Fitted using 133 model solutions performed in 0.289 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##                   value   type
@@ -163,7 +163,7 @@
 ## k_parent_sink  0.09561   26.57 2.487e-14  0.08824  0.1036
 ## sigma          2.78000    6.00 1.216e-05  1.79200  3.7670
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   3.424       2  7
 ## parent     3.424       2  7
@@ -214,9 +214,9 @@
 
## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
 ## finite result is doubtful

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:34 2019 
-## Date of summary: Wed Apr 10 10:11:34 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:33 2019 
+## Date of summary: Thu May  2 12:40:33 2019 
 ## 
 ## 
 ## Warning: Optimisation did not converge:
@@ -228,24 +228,24 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 344 model solutions performed in 0.778 s
+## Fitted using 599 model solutions performed in 1.277 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##             value   type
-## parent_0 89.85000  state
-## alpha     1.00000 deparm
-## beta     10.00000 deparm
-## sigma     2.77987  error
+##              value   type
+## parent_0 89.850000  state
+## alpha     1.000000 deparm
+## beta     10.000000 deparm
+## sigma     2.779868  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##               value lower upper
 ## parent_0  89.850000  -Inf   Inf
 ## log_alpha  0.000000  -Inf   Inf
 ## log_beta   2.302585  -Inf   Inf
-## sigma      2.779870     0   Inf
+## sigma      2.779868     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -253,16 +253,16 @@
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##           Estimate Std. Error  Lower  Upper
 ## parent_0     92.47     1.2810 89.720 95.220
-## log_alpha    10.60        NaN    NaN    NaN
-## log_beta     12.95        NaN    NaN    NaN
-## sigma         2.78     0.4554  1.803  3.757
+## log_alpha    10.66        NaN    NaN    NaN
+## log_beta     13.01        NaN    NaN    NaN
+## sigma         2.78     0.4599  1.794  3.766
 ## 
 ## Parameter correlation:
 ##           parent_0 log_alpha log_beta    sigma
-## parent_0  1.000000       NaN      NaN 0.008714
+## parent_0  1.000000       NaN      NaN 0.003475
 ## log_alpha      NaN         1      NaN      NaN
 ## log_beta       NaN       NaN        1      NaN
-## sigma     0.008714       NaN      NaN 1.000000
+## sigma     0.003475       NaN      NaN 1.000000
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -270,11 +270,11 @@
 ## for estimators of untransformed parameters.
 ##           Estimate  t value    Pr(>t)  Lower  Upper
 ## parent_0     92.47 72.13000 1.052e-19 89.720 95.220
-## alpha     40090.00  0.02388 4.906e-01     NA     NA
-## beta     419300.00  0.02388 4.906e-01     NA     NA
-## sigma         2.78  6.00000 1.628e-05  1.803  3.757
+## alpha     42700.00  0.02298 4.910e-01     NA     NA
+## beta     446600.00  0.02298 4.910e-01     NA     NA
+## sigma         2.78  6.00000 1.628e-05  1.794  3.766
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   3.619       3  6
 ## parent     3.619       3  6
@@ -318,19 +318,19 @@
 

 
summary(m.L2.FOMC, data = FALSE)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:35 2019 
-## Date of summary: Wed Apr 10 10:11:35 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:34 2019 
+## Date of summary: Thu May  2 12:40:34 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 240 model solutions performed in 0.564 s
+## Fitted using 240 model solutions performed in 0.506 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -373,7 +373,7 @@
 ## beta        1.234   4.012 1.942e-03  0.6945  2.192
 ## sigma       2.276   4.899 5.977e-04  1.2050  3.347
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   6.205       3  3
 ## parent     6.205       3  3
@@ -393,9 +393,9 @@
 

 
summary(m.L2.DFOP, data = FALSE)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:37 2019 
-## Date of summary: Wed Apr 10 10:11:37 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:35 2019 
+## Date of summary: Thu May  2 12:40:35 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -404,10 +404,10 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 585 model solutions performed in 1.296 s
+## Fitted using 587 model solutions performed in 1.273 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -431,18 +431,18 @@
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##          Estimate Std. Error      Lower     Upper
 ## parent_0  93.9500  9.998e-01    91.5900   96.3100
-## log_k1     3.1350  2.336e+03 -5522.0000 5528.0000
+## log_k1     3.1330  2.265e+03 -5354.0000 5360.0000
 ## log_k2    -1.0880  6.285e-02    -1.2370   -0.9394
 ## g_ilr     -0.2821  7.033e-02    -0.4484   -0.1158
 ## sigma      1.4140  2.886e-01     0.7314    2.0960
 ## 
 ## Parameter correlation:
 ##            parent_0     log_k1     log_k2      g_ilr      sigma
-## parent_0  1.000e+00  5.247e-07 -1.026e-10  2.665e-01 -8.076e-11
-## log_k1    5.247e-07  1.000e+00  8.592e-05 -1.690e-04 -7.938e-06
-## log_k2   -1.026e-10  8.592e-05  1.000e+00 -7.903e-01  5.048e-10
-## g_ilr     2.665e-01 -1.690e-04 -7.903e-01  1.000e+00 -6.476e-10
-## sigma    -8.076e-11 -7.938e-06  5.048e-10 -6.476e-10  1.000e+00
+## parent_0  1.000e+00  5.434e-07 -9.989e-11  2.665e-01 -3.978e-10
+## log_k1    5.434e-07  1.000e+00  8.888e-05 -1.748e-04 -8.207e-06
+## log_k2   -9.989e-11  8.888e-05  1.000e+00 -7.903e-01  5.751e-10
+## g_ilr     2.665e-01 -1.748e-04 -7.903e-01  1.000e+00 -7.109e-10
+## sigma    -3.978e-10 -8.207e-06  5.751e-10 -7.109e-10  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -450,19 +450,19 @@
 ## for estimators of untransformed parameters.
 ##          Estimate   t value    Pr(>t)   Lower   Upper
 ## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100
-## k1        23.0000 4.377e-04 4.998e-01  0.0000     Inf
+## k1        22.9300 4.514e-04 4.998e-01  0.0000     Inf
 ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909
 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591
 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data    2.53       4  2
 ## parent      2.53       4  2
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50_k1 DT50_k2
-## parent 0.5335 5.311 0.03014   2.058

+## parent 0.5335 5.311 0.03023   2.058

 
Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.

 
 
@@ -492,9 +492,9 @@
 
We can extract the summary and plot for e.g. the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.

 
summary(mm.L3[["DFOP", 1]])

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:39 2019 
-## Date of summary: Wed Apr 10 10:11:39 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:37 2019 
+## Date of summary: Thu May  2 12:40:37 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -503,10 +503,10 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 372 model solutions performed in 0.809 s
+## Fitted using 372 model solutions performed in 0.777 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -537,11 +537,11 @@
 ## 
 ## Parameter correlation:
 ##           parent_0     log_k1     log_k2      g_ilr      sigma
-## parent_0 1.000e+00  1.732e-01  2.282e-02  4.009e-01  1.656e-07
-## log_k1   1.732e-01  1.000e+00  4.945e-01 -5.809e-01  6.759e-08
-## log_k2   2.282e-02  4.945e-01  1.000e+00 -6.812e-01  3.867e-07
-## g_ilr    4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -3.839e-07
-## sigma    1.656e-07  6.759e-08  3.867e-07 -3.839e-07  1.000e+00
+## parent_0 1.000e+00  1.732e-01  2.282e-02  4.009e-01  1.660e-07
+## log_k1   1.732e-01  1.000e+00  4.945e-01 -5.809e-01  6.635e-08
+## log_k2   2.282e-02  4.945e-01  1.000e+00 -6.812e-01  3.880e-07
+## g_ilr    4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -3.822e-07
+## sigma    1.660e-07  6.635e-08  3.880e-07 -3.822e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -554,7 +554,7 @@
 ## g         0.45660  34.920 2.581e-05  0.41540   0.49850
 ## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   2.225       4  4
 ## parent     2.225       4  4
@@ -597,19 +597,19 @@
 
The \(\chi^2\) error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the \(\chi^2\) test passes is slightly lower for the FOMC model. However, the difference appears negligible.

 
summary(mm.L4[["SFO", 1]], data = FALSE)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:39 2019 
-## Date of summary: Wed Apr 10 10:11:40 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:38 2019 
+## Date of summary: Thu May  2 12:40:38 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 146 model solutions performed in 0.306 s
+## Fitted using 146 model solutions performed in 0.298 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##                  value   type
@@ -634,9 +634,9 @@
 ## 
 ## Parameter correlation:
 ##                    parent_0 log_k_parent_sink      sigma
-## parent_0          1.000e+00         5.938e-01  5.612e-10
-## log_k_parent_sink 5.938e-01         1.000e+00 -4.994e-10
-## sigma             5.612e-10        -4.994e-10  1.000e+00
+## parent_0          1.000e+00         5.938e-01  4.256e-10
+## log_k_parent_sink 5.938e-01         1.000e+00 -7.280e-10
+## sigma             4.256e-10        -7.280e-10  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -647,7 +647,7 @@
 ## k_parent_sink  0.006541   14.17 1.578e-05  0.005455 7.842e-03
 ## sigma          3.162000    4.00 5.162e-03  1.130000 5.194e+00
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   3.287       2  6
 ## parent     3.287       2  6
@@ -661,19 +661,19 @@
 ## parent  106  352

 
summary(mm.L4[["FOMC", 1]], data = FALSE)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:40 2019 
-## Date of summary: Wed Apr 10 10:11:40 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:38 2019 
+## Date of summary: Thu May  2 12:40:38 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 224 model solutions performed in 0.478 s
+## Fitted using 224 model solutions performed in 0.458 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -701,10 +701,10 @@
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.460e-07
-## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.351e-08
-## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.079e-08
-## sigma     -2.460e-07  2.351e-08  5.079e-08  1.000e+00
+## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.473e-07
+## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.429e-08
+## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.183e-08
+## sigma     -2.473e-07  2.429e-08  5.183e-08  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -716,7 +716,7 @@
 ## beta      64.9800   2.540 3.201e-02 21.7800 193.900
 ## sigma      1.8300   4.000 8.065e-03  0.5598   3.100
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   2.029       3  5
 ## parent     2.029       3  5
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diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html
index 2e5cd759..bf52f364 100644
--- a/docs/articles/mkin.html
+++ b/docs/articles/mkin.html
@@ -88,7 +88,7 @@
       
Introduction to mkin

                         
Johannes Ranke

             
-            
2019-04-10

+            
2019-05-02

       
       
       
diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
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diff --git a/docs/articles/twa.html b/docs/articles/twa.html
index cdcea4d3..7d9d1c4e 100644
--- a/docs/articles/twa.html
+++ b/docs/articles/twa.html
@@ -88,7 +88,7 @@
       
Calculation of time weighted average concentrations with mkin

                         
Johannes Ranke

             
-            
2019-04-10

+            
2019-05-02

       
       
       
diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html
index 33d0c90f..950e8eab 100644
--- a/docs/articles/web_only/FOCUS_Z.html
+++ b/docs/articles/web_only/FOCUS_Z.html
@@ -88,7 +88,7 @@
       
Example evaluation of FOCUS dataset Z

                         
Johannes Ranke

             
-            
2019-04-10

+            
2019-05-02

       
       
       
@@ -132,11 +132,11 @@
 

 
summary(m.Z.2a, data = FALSE)$bpar

 
##             Estimate se_notrans    t value     Pr(>t)    Lower    Upper
-## Z0_0      9.7015e+01    3.39373 2.8587e+01 6.4606e-21 91.66556 102.3642
-## k_Z0_sink 4.0181e-10    0.22534 1.7831e-09 5.0000e-01  0.00000      Inf
-## k_Z0_Z1   2.2360e+00    0.15915 1.4050e+01 1.1387e-13  1.95303   2.5600
-## k_Z1_sink 4.8212e-01    0.06547 7.3641e+00 5.1396e-08  0.40341   0.5762
-## sigma     4.8041e+00    0.63763 7.5343e+00 3.4444e-08  3.52677   6.0815

+## Z0_0      9.7015e+01   3.394776 2.8578e+01 6.5093e-21 91.66556 102.3642
+## k_Z0_sink 4.0301e-10   0.225510 1.7871e-09 5.0000e-01  0.00000      Inf
+## k_Z0_Z1   2.2360e+00   0.159161 1.4049e+01 1.1412e-13  1.95303   2.5600
+## k_Z1_sink 4.8212e-01   0.065499 7.3608e+00 5.1791e-08  0.40341   0.5762
+## sigma     4.8041e+00   0.637657 7.5340e+00 3.4468e-08  3.52677   6.0815

 
As obvious from the parameter summary (the  component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.

 
A similar result can be obtained when formation fractions are used in the model formulation:

 
Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"),
@@ -335,14 +335,14 @@
 ## 
 ## $SFORB
 ##     Z0_b1     Z0_b2     Z3_b1     Z3_b2 
-## 2.4471355 0.0075125 0.0800068 0.0000000 
+## 2.4471329 0.0075123 0.0800074 0.0000000 
 ## 
 ## $distimes
 ##      DT50   DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2
-## Z0 0.3043 1.1848    0.28325     92.266         NA         NA
+## Z0 0.3043 1.1848    0.28325     92.268         NA         NA
 ## Z1 1.5148 5.0320         NA         NA         NA         NA
 ## Z2 1.6414 5.4526         NA         NA         NA         NA
-## Z3     NA     NA         NA         NA     8.6636        Inf

+## Z3     NA     NA         NA         NA     8.6635        Inf

 
It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.

 
 

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diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
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diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html
index 0bcfca27..0ae02f3c 100644
--- a/docs/articles/web_only/NAFTA_examples.html
+++ b/docs/articles/web_only/NAFTA_examples.html
@@ -88,7 +88,7 @@
       
Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance

                         
Johannes Ranke

             
-            
2019-04-10

+            
2019-05-02

       
       
       
@@ -153,7 +153,7 @@
 ## DFOP 55.5 3.70e+11 2.03e+11
 ## 
 ## Representative half-life:
-## [1] 321.5119

+## [1] 321.51

 
 

 

@@ -189,7 +189,7 @@
 ##          Estimate   Pr(>t)   Lower   Upper
 ## parent_0 9.84e+01 1.24e-27 97.8078 98.9187
 ## k1       1.55e-02 4.10e-04  0.0143  0.0167
-## k2       1.09e-11 5.00e-01  0.0000     Inf
+## k2       8.28e-12 5.00e-01  0.0000     Inf
 ## g        6.89e-01 2.92e-03  0.6626  0.7142
 ## sigma    6.48e-01 2.38e-05  0.4147  0.8813
 ## 
@@ -198,10 +198,10 @@
 ##      DT50     DT90 DT50_rep
 ## SFO  86.6 2.88e+02 8.66e+01
 ## IORE 85.5 7.17e+02 2.16e+02
-## DFOP 83.6 1.04e+11 6.34e+10
+## DFOP 83.6 1.37e+11 8.37e+10
 ## 
 ## Representative half-life:
-## [1] 215.8655
+## [1] 215.87

@@ -237,7 +237,7 @@ ## Estimate Pr(>t) Lower Upper ## parent_0 9.66e+01 1.57e-25 95.3476 97.8979 ## k1 2.55e-02 7.33e-06 0.0233 0.0278 -## k2 3.88e-11 5.00e-01 0.0000 Inf +## k2 3.87e-11 5.00e-01 0.0000 Inf ## g 8.61e-01 7.55e-06 0.8314 0.8867 ## sigma 1.46e+00 6.93e-06 0.9661 1.9483 ## @@ -246,10 +246,10 @@ ## DT50 DT90 DT50_rep ## SFO 38.6 1.28e+02 3.86e+01 ## IORE 34.0 1.77e+02 5.32e+01 -## DFOP 34.1 8.42e+09 1.79e+10 +## DFOP 34.1 8.43e+09 1.79e+10 ## ## Representative half-life: -## [1] 53.16582 +## [1] 53.17

@@ -285,7 +285,7 @@ ## Estimate Pr(>t) Lower Upper ## parent_0 9.89e+01 9.44e-49 95.4640 102.2573 ## k1 1.81e-02 1.75e-01 0.0116 0.0281 -## k2 2.57e-10 5.00e-01 0.0000 Inf +## k2 2.46e-10 5.00e-01 0.0000 Inf ## g 6.06e-01 2.19e-01 0.4826 0.7178 ## sigma 7.40e+00 2.97e-15 6.0201 8.7754 ## @@ -294,10 +294,10 @@ ## DT50 DT90 DT50_rep ## SFO 94.3 3.13e+02 9.43e+01 ## IORE 96.7 1.51e+03 4.55e+02 -## DFOP 96.4 5.32e+09 2.69e+09 +## DFOP 96.4 5.58e+09 2.82e+09 ## ## Representative half-life: -## [1] 454.5528 +## [1] 454.55

@@ -357,7 +357,7 @@ ## DFOP 55.6 517 253.0 ## ## Representative half-life: -## [1] 201.0316 +## [1] 201.03
@@ -409,23 +409,19 @@ ## DFOP 10.5 2.17e+12 1.21e+12 ## ## Representative half-life: -## [1] 101.4264 +## [1] 101.43

In this example, the residuals of the SFO indicate a lack of fit of this model, so even if it was an abiotic experiment, the data do not suggest a simple exponential decline.

Example on page 9, lower panel

p9b <- nafta(NAFTA_SOP_Attachment[["p9b"]])
-
## Warning in sqrt(diag(covar)): NaNs wurden erzeugt
## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt
-
## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt
-
## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
-## finite result is doubtful
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p9b)
+
plot(p9b)

-
print(p9b)
+
print(p9b)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 35.64867 23.22334 35.64867 
@@ -450,9 +446,9 @@
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower   Upper
 ## parent_0  94.7123 1.61e-16 93.1355 96.2891
-## k1         0.0389      NaN  0.0316  0.0478
-## k2         0.0389 1.13e-08  0.0203  0.0743
-## g          0.7599      NaN      NA      NA
+## k1         0.0389      NaN  0.0306  0.0495
+## k2         0.0389 1.10e-06  0.0186  0.0812
+## g          0.7598      NaN  0.0000  1.0000
 ## sigma      1.5957 2.50e-04  0.9135  2.2779
 ## 
 ## 
@@ -463,18 +459,18 @@
 ## DFOP 17.8 59.2     17.8
 ## 
 ## Representative half-life:
-## [1] 14.80013
+## [1] 14.8

Here, mkin gives a longer slow DT50 for the DFOP model (17.8 days) than PestDF (13.5 days). Presumably, this is related to the fact that PestDF gives a negative value for the proportion of the fast degradation which should be between 0 and 1, inclusive. This parameter is called f in PestDF and g in mkin. In mkin, it is restricted to the interval from 0 to 1.

Example on page 10

-
p10 <- nafta(NAFTA_SOP_Attachment[["p10"]])
+
p10 <- nafta(NAFTA_SOP_Attachment[["p10"]])
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p10)
+
plot(p10)

-
print(p10)
+
print(p10)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 899.4089 336.4348 899.4089 
@@ -512,7 +508,7 @@
 ## DFOP 14.0 46.5    14.00
 ## 
 ## Representative half-life:
-## [1] 8.862193
+## [1] 8.86

Here, a value below N is given for the IORE model, because the data suggests a faster decline towards the end of the experiment, which appears physically rather unlikely in the case of a photolysis study. It seems PestDF does not constrain N to values above zero, thus the slight difference in IORE model parameters between PestDF and mkin.

@@ -522,12 +518,12 @@

Example on page 11

-
p11 <- nafta(NAFTA_SOP_Attachment[["p11"]])
+
p11 <- nafta(NAFTA_SOP_Attachment[["p11"]])
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p11)
+
plot(p11)

-
print(p11)
+
print(p11)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 579.6805 204.7932 144.7783 
@@ -576,7 +572,7 @@
 

 

 Example on page 12, upper panel

-
p12a <- nafta(NAFTA_SOP_Attachment[["p12a"]])

+
p12a <- nafta(NAFTA_SOP_Attachment[["p12a"]])

 
## Warning in summary.mkinfit(x): Could not estimate covariance matrix;
 ## singular system.

 
## Warning in sqrt(diag(covar)): NaNs wurden erzeugt

@@ -586,9 +582,9 @@
 ## finite result is doubtful
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p12a)
+
plot(p12a)

-
print(p12a)
+
print(p12a)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 695.4440 220.0685 695.4440 
@@ -626,12 +622,12 @@
 ## DFOP 5.58 18.5     5.58
 ## 
 ## Representative half-life:
-## [1] 3.987308
+## [1] 3.99

Example on page 12, lower panel

-
p12b <- nafta(NAFTA_SOP_Attachment[["p12b"]])
+
p12b <- nafta(NAFTA_SOP_Attachment[["p12b"]])
## Warning in sqrt(diag(covar)): NaNs wurden erzeugt
## Warning in qt(alpha/2, rdf): NaNs wurden erzeugt
## Warning in qt(1 - alpha/2, rdf): NaNs wurden erzeugt
@@ -642,9 +638,9 @@ ## finite result is doubtful 
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p12b)
+
plot(p12b)

-
print(p12b)
+
print(p12b)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 58.90242 19.06353 58.90242 
@@ -682,17 +678,17 @@
 ## DFOP 11.8 39.1    11.80
 ## 
 ## Representative half-life:
-## [1] 9.461912
+## [1] 9.46

Example on page 13

-
p13 <- nafta(NAFTA_SOP_Attachment[["p13"]])
+
p13 <- nafta(NAFTA_SOP_Attachment[["p13"]])
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p13)
+
plot(p13)

-
print(p13)
+
print(p13)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 174.5971 142.3951 174.5971 
@@ -717,7 +713,7 @@
 ## $DFOP
 ##          Estimate   Pr(>t)    Lower    Upper
 ## parent_0 92.73500 9.25e-15 8.95e+01 9.59e+01
-## k1        0.00258 4.28e-01 1.38e-08 4.82e+02
+## k1        0.00258 4.28e-01 1.25e-08 5.31e+02
 ## k2        0.00258 3.69e-08 2.20e-03 3.03e-03
 ## g         0.00442 5.00e-01 0.00e+00 1.00e+00
 ## sigma     3.41172 1.35e-04 2.02e+00 4.80e+00
@@ -730,22 +726,22 @@
 ## DFOP  269  892      269
 ## 
 ## Representative half-life:
-## [1] 168.5123
+## [1] 168.51

DT50 not observed in the study and DFOP problems in PestDF

-
p14 <- nafta(NAFTA_SOP_Attachment[["p14"]])
+
p14 <- nafta(NAFTA_SOP_Attachment[["p14"]])
## Warning in sqrt(diag(covar)): NaNs wurden erzeugt
## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt
## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
 ## finite result is doubtful
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p14)
+
plot(p14)

-
print(p14)
+
print(p14)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 48.43249 28.67746 27.26248 
@@ -771,7 +767,7 @@
 ##          Estimate   Pr(>t)    Lower    Upper
 ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768
 ## k1       9.53e-03 1.20e-01  0.00638   0.0143
-## k2       6.17e-12 5.00e-01  0.00000      Inf
+## k2       5.42e-12 5.00e-01  0.00000      Inf
 ## g        3.98e-01 2.19e-01  0.30481   0.4998
 ## sigma    1.17e+00 7.68e-06  0.77406   1.5610
 ## 
@@ -780,16 +776,16 @@
 ##          DT50     DT90 DT50_rep
 ## SFO  2.48e+02 8.25e+02 2.48e+02
 ## IORE 4.34e+02 2.22e+04 6.70e+03
-## DFOP 3.00e+10 2.91e+11 1.12e+11
+## DFOP 3.41e+10 3.31e+11 1.28e+11
 ## 
 ## Representative half-life:
-## [1] 6697.437
+## [1] 6697.44

The slower rate constant reported by PestDF is negative, which is not physically realistic, and not possible in mkin. The other fits give the same results in mkin and PestDF.

N is less than 1 and DFOP fraction parameter is below zero

-
p15a <- nafta(NAFTA_SOP_Attachment[["p15a"]])
+
p15a <- nafta(NAFTA_SOP_Attachment[["p15a"]])
## Warning in sqrt(diag(covar)): NaNs wurden erzeugt
## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt
## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt
@@ -797,9 +793,9 @@ ## finite result is doubtful 
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p15a)
+
plot(p15a)

-
print(p15a)
+
print(p15a)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 245.5248 135.0132 245.5248 
@@ -837,13 +833,13 @@
 ## DFOP 72.8  242     72.8
 ## 
 ## Representative half-life:
-## [1] 41.32749
-
p15b <- nafta(NAFTA_SOP_Attachment[["p15b"]])
+## [1] 41.33 +
p15b <- nafta(NAFTA_SOP_Attachment[["p15b"]])
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The half-life obtained from the IORE model may be used
-
plot(p15b)
+
plot(p15b)

-
print(p15b)
+
print(p15b)
## Sums of squares:
 ##       SFO      IORE      DFOP 
 ## 106.91629  68.55574 106.91629 
@@ -881,20 +877,20 @@
 ## DFOP  143  474    143.0
 ## 
 ## Representative half-life:
-## [1] 71.18014
+## [1] 71.18

In mkin, only the IORE fit is affected (deemed unrealistic), as the fraction parameter of the DFOP model is restricted to the interval between 0 and 1 in mkin. The SFO fits give the same results for both mkin and PestDF.

The DFOP fraction parameter is greater than 1

-
p16 <- nafta(NAFTA_SOP_Attachment[["p16"]])
+
p16 <- nafta(NAFTA_SOP_Attachment[["p16"]])
## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
## The representative half-life of the IORE model is longer than the one corresponding
## to the terminal degradation rate found with the DFOP model.
## The representative half-life obtained from the DFOP model may be used
-
plot(p16)
+
plot(p16)

-
print(p16)
+
print(p16)
## Sums of squares:
 ##      SFO     IORE     DFOP 
 ## 3831.804 2062.008 1550.980 
@@ -919,7 +915,7 @@
 ## $DFOP
 ##          Estimate   Pr(>t)   Lower  Upper
 ## parent_0  88.5333 7.40e-18 79.9836 97.083
-## k1        18.6317 5.00e-01  0.0000    Inf
+## k1        18.6315 5.00e-01  0.0000    Inf
 ## k2         0.0776 1.41e-05  0.0518  0.116
 ## g          0.4733 1.41e-09  0.3674  0.582
 ## sigma      7.1902 2.11e-08  5.2785  9.102
@@ -932,7 +928,7 @@
 ## DFOP 0.67 21.4     8.93
 ## 
 ## Representative half-life:
-## [1] 8.932679
+## [1] 8.93

In PestDF, the DFOP fit seems to have stuck in a local minimum, as mkin finds a solution with a much lower \(\chi^2\) error level. As the half-life from the slower rate constant of the DFOP model is larger than the IORE derived half-life, the NAFTA recommendation obtained with mkin is to use the DFOP representative half-life of 8.9 days.

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Benchmark timings for mkin on various systems

Johannes Ranke

-

2019-04-10

+

2019-05-02

@@ -115,32 +115,32 @@ }
# Parent only
 t1 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D)))[["elapsed"]]
-t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D), error_model = "tc"))[["elapsed"]]
-
-# One metabolite
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
+t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D), error_model = "tc"))[["elapsed"]] +
## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge:
+## false convergence (8)
+
# One metabolite
+SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
## Successfully compiled differential equation model from auto-generated C code.
-
FOMC_SFO <- mkinmod(
-  parent = mkinsub("FOMC", "m1"),
-  m1 = mkinsub("SFO"))
-
## Successfully compiled differential equation model from auto-generated C code.
-
DFOP_SFO <- mkinmod(
+
 
## Successfully compiled differential equation model from auto-generated C code.

-
t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D)))[["elapsed"]]

+
+
## Successfully compiled differential equation model from auto-generated C code.

+
t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D)))[["elapsed"]]

+
## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
+## Observations with value of zero were removed from the data

 
## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data
-
-## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
 ## Observations with value of zero were removed from the data
 
 ## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
 ## Observations with value of zero were removed from the data

-
t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(subset(FOCUS_2006_D, value != 0)), error_model = "tc"))[["elapsed"]]
-t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D), error_model = "obs"))[["elapsed"]]

+
t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(subset(FOCUS_2006_D, value != 0)), error_model = "tc"))[["elapsed"]]
+t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D), error_model = "obs"))[["elapsed"]]

 
## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
 ## Observations with value of zero were removed from the data
 
@@ -149,32 +149,32 @@
 
 ## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
 ## Observations with value of zero were removed from the data

-
# Two metabolites, synthetic data
-m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
-                           M1 = mkinsub("SFO", "M2"),
-                           M2 = mkinsub("SFO"),
-                           use_of_ff = "max", quiet = TRUE)
-
-m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
-                           M1 = mkinsub("SFO"),
-                           M2 = mkinsub("SFO"),
-                           use_of_ff = "max", quiet = TRUE)
-
-SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data
-
-DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
-
-t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))["elapsed"]
-t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))["elapsed"]
-
-t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "tc"))["elapsed"]
-t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "tc"))["elapsed"]
-
-t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "obs"))["elapsed"]
-t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "obs"))["elapsed"]
-
-mkin_benchmarks[system_string, paste0("t", 1:11)] <- c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
-mkin_benchmarks

+
# Two metabolites, synthetic data
+m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
+                           M1 = mkinsub("SFO", "M2"),
+                           M2 = mkinsub("SFO"),
+                           use_of_ff = "max", quiet = TRUE)
+
+m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
+                           M1 = mkinsub("SFO"),
+                           M2 = mkinsub("SFO"),
+                           use_of_ff = "max", quiet = TRUE)
+
+SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data
+
+DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
+
+t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))["elapsed"]
+t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))["elapsed"]
+
+t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "tc"))["elapsed"]
+t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "tc"))["elapsed"]
+
+t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "obs"))["elapsed"]
+t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "obs"))["elapsed"]
+
+mkin_benchmarks[system_string, paste0("t", 1:11)] <- c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
+mkin_benchmarks

 
##                                                                                                       CPU
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 AMD Ryzen 7 1700 Eight-Core Processor
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 AMD Ryzen 7 1700 Eight-Core Processor
@@ -198,68 +198,68 @@
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 8.184
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 7.064
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.296
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 6.303
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 5.936
 ##                                                                         t2
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 11.019
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 22.889
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 12.558
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 21.239
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 21.837
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 20.545
 ##                                                                        t3
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.764
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.649
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.786
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.510
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.487
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.446
 ##                                                                         t4
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 14.347
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 13.789
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  8.461
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 13.805
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 14.162
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 15.335
 ##                                                                        t5
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 9.495
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 6.395
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 5.675
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.386
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 6.021
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 6.002
 ##                                                                        t6
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 2.623
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 2.542
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 2.723
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 2.643
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 2.657
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 2.635
 ##                                                                        t7
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.523
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259
 ##                                                                        t8
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 7.525
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.632
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.862
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.02
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  4.72
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.737
 ##                                                                         t9
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 16.621
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  8.171
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  7.618
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 11.124
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  8.364
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  7.763
 ##                                                                       t10
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 8.576
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 3.676
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 3.579
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 5.388
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 3.623
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 3.427
 ##                                                                        t11
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 31.267
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  5.636
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.574
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.365
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4   5.95

-
save(mkin_benchmarks, file = "~/git/mkin/inst/benchmark_data/mkin_benchmarks.rda")

+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  5.626
+
save(mkin_benchmarks, file = "~/git/mkin/inst/benchmark_data/mkin_benchmarks.rda")
diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html index 597b7c55..ee279f51 100644 --- a/docs/articles/web_only/compiled_models.html +++ b/docs/articles/web_only/compiled_models.html @@ -88,7 +88,7 @@

Performance benefit by using compiled model definitions in mkin

Johannes Ranke

-

2019-04-10

+

2019-05-02

@@ -163,14 +163,14 @@ ## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet ## = TRUE): Observations with value of zero were removed from the data 
##                    test replications elapsed relative user.self sys.self
-## 3     deSolve, compiled            3   3.215    1.000     3.213        0
-## 1 deSolve, not compiled            3  42.468   13.209    42.445        0
-## 2      Eigenvalue based            3   4.666    1.451     4.663        0
+## 3     deSolve, compiled            3   3.178    1.000     3.176        0
+## 1 deSolve, not compiled            3  28.700    9.031    28.685        0
+## 2      Eigenvalue based            3   4.401    1.385     4.398        0
 ##   user.child sys.child
 ## 3          0         0
 ## 1          0         0
 ## 2          0         0
-

We see that using the compiled model is by a factor of around 13 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.

+

We see that using the compiled model is by a factor of around 9 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.

@@ -214,14 +214,14 @@ ## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): Observations with ## value of zero were removed from the data 
##                    test replications elapsed relative user.self sys.self
-## 2     deSolve, compiled            3   4.906    1.000     4.902        0
-## 1 deSolve, not compiled            3  70.459   14.362    70.421        0
+## 2     deSolve, compiled            3   4.549    1.000     4.547        0
+## 1 deSolve, not compiled            3  49.752   10.937    49.729        0
 ##   user.child sys.child
 ## 2          0         0
 ## 1          0         0
-

Here we get a performance benefit of a factor of 14 using the version of the differential equation model compiled from C code!

+

Here we get a performance benefit of a factor of 11 using the version of the differential equation model compiled from C code!

This vignette was built with mkin 0.9.49.4 on

-
## R version 3.5.3 (2019-03-11)
+
## R version 3.6.0 (2019-04-26)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
 ## Running under: Debian GNU/Linux 9 (stretch)

 
## CPU model: AMD Ryzen 7 1700 Eight-Core Processor

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