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/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 ++-- 26 files changed, 132 insertions(+), 136 deletions(-) (limited to 'docs/articles/web_only') 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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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
-- cgit v1.2.1