From b7901aac76df753ec1213cb02bebea055965ee87 Mon Sep 17 00:00:00 2001 From: Ranke Johannes Date: Mon, 30 Oct 2023 17:09:21 +0100 Subject: Update static docs --- docs/articles/FOCUS_D.html | 17 +- docs/articles/FOCUS_D_files/figure-html/plot-1.png | Bin 80361 -> 79834 bytes .../FOCUS_D_files/figure-html/plot_2-1.png | Bin 25051 -> 24334 bytes docs/articles/prebuilt/2022_cyan_pathway.html | 399 ++++++----------- .../figure-html/unnamed-chunk-13-1.png | Bin 363589 -> 363434 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 364310 -> 363895 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 365199 -> 365048 bytes .../figure-html/unnamed-chunk-20-1.png | Bin 378245 -> 378042 bytes .../figure-html/unnamed-chunk-21-1.png | Bin 371558 -> 371436 bytes .../figure-html/unnamed-chunk-22-1.png | Bin 373392 -> 373322 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 373137 -> 373930 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 373392 -> 373322 bytes 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docs/reference/update.mkinfit-1.png | Bin 44071 -> 42522 bytes docs/reference/update.mkinfit-2.png | Bin 43998 -> 43527 bytes docs/reference/update.mkinfit.html | 5 +- vignettes/web_only/benchmarks.html | 322 ++++++++------ vignettes/web_only/benchmarks.rmd | 7 +- vignettes/web_only/mkin_benchmarks.rda | Bin 1922 -> 2095 bytes vignettes/web_only/saem_benchmarks.rda | Bin 855 -> 982 bytes vignettes/web_only/saem_benchmarks.rmd | 8 +- 236 files changed, 1269 insertions(+), 993 deletions(-) diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index 19367c68..303ddb6f 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6 @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -135,7 +138,7 @@ D Ranke

    Last change 31 January 2019 -(rebuilt 2023-05-19)

    +(rebuilt 2023-10-30) Source: vignettes/FOCUS_D.rmd @@ -237,10 +240,10 @@ the mkinparplot function.

    summary method for mkinfit objects.

     summary(fit)
    -
    ## mkin version used for fitting:    1.2.4 
    -## R version used for fitting:       4.3.0 
    -## Date of fit:     Fri May 19 09:20:23 2023 
    -## Date of summary: Fri May 19 09:20:23 2023 
    +
    ## mkin version used for fitting:    1.2.6 
    +## R version used for fitting:       4.3.1 
    +## Date of fit:     Mon Oct 30 09:40:58 2023 
    +## Date of summary: Mon Oct 30 09:40:58 2023 
     ## 
     ## Equations:
     ## d_parent/dt = - k_parent * parent
    @@ -248,7 +251,7 @@ the mkinparplot function.

    ## ## Model predictions using solution type analytical ## -## Fitted using 401 model solutions performed in 0.048 s +## Fitted using 401 model solutions performed in 0.123 s ## ## Error model: Constant variance ## diff --git a/docs/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/articles/FOCUS_D_files/figure-html/plot-1.png index c0832a1a..f0b51c1f 100644 Binary files a/docs/articles/FOCUS_D_files/figure-html/plot-1.png and b/docs/articles/FOCUS_D_files/figure-html/plot-1.png differ 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 02cfcfb4..f6180470 100644 Binary files a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png and b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png differ diff --git a/docs/articles/prebuilt/2022_cyan_pathway.html b/docs/articles/prebuilt/2022_cyan_pathway.html index c22c6735..c22b07e4 100644 --- a/docs/articles/prebuilt/2022_cyan_pathway.html +++ b/docs/articles/prebuilt/2022_cyan_pathway.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6 @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -135,7 +138,7 @@ residue data on cyantraniliprole Ranke

    Last change on 20 April 2023, -last compiled on 19 Mai 2023

    +last compiled on 30 October 2023 Source: vignettes/prebuilt/2022_cyan_pathway.rmd @@ -155,7 +158,7 @@ be fitted with the mkin package.

    173340 (Application of nonlinear hierarchical models to the kinetic evaluation of chemical degradation data) of the German Environment Agency carried out in 2022 and 2023.

    -

    The mkin package is used in version 1.2.4 which is currently under +

    The mkin package is used in version 1.2.6 which is currently under development. The newly introduced functionality that is used here is a simplification of excluding random effects for a set of fits based on a related set of fits with a reduced model, and the documentation of the @@ -2205,10 +2208,10 @@ Hierarchical SFO path 1 fit with constant variance

    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:27:54 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:03:13 2023 
    +Date of summary: Mon Oct 30 11:18:26 2023 
     
     Equations:
     d_cyan/dt = - k_cyan * cyan
    @@ -2221,7 +2224,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 422.743 s
    +Fitted in 1273.632 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Constant variance 
    @@ -2333,10 +2336,10 @@ Hierarchical SFO path 1 fit with two-component error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:27:49 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 09:58:51 2023 
    +Date of summary: Mon Oct 30 11:18:26 2023 
     
     Equations:
     d_cyan/dt = - k_cyan * cyan
    @@ -2349,7 +2352,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 417.436 s
    +Fitted in 1011.299 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -2463,10 +2466,10 @@ Hierarchical FOMC path 1 fit with constant variance
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:28:29 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:04:48 2023 
    +Date of summary: Mon Oct 30 11:18:26 2023 
     
     Equations:
     d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
    @@ -2481,7 +2484,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 457.122 s
    +Fitted in 1368.338 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Constant variance 
    @@ -2608,10 +2611,10 @@ Hierarchical FOMC path 1 fit with two-component error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:28:21 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:00:40 2023 
    +Date of summary: Mon Oct 30 11:18:26 2023 
     
     Equations:
     d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
    @@ -2626,7 +2629,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 449.531 s
    +Fitted in 1120.168 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -2746,10 +2749,10 @@ Hierarchical DFOP path 1 fit with constant variance
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:29:15 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:02:52 2023 
    +Date of summary: Mon Oct 30 11:18:26 2023 
     
     Equations:
     d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -2768,7 +2771,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 503.737 s
    +Fitted in 1252.502 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Constant variance 
    @@ -2892,10 +2895,10 @@ Hierarchical DFOP path 1 fit with two-component error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:31:24 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:12:10 2023 
    +Date of summary: Mon Oct 30 11:18:26 2023 
     
     Equations:
     d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -2914,7 +2917,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 632.55 s
    +Fitted in 1809.832 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -3038,10 +3041,10 @@ Hierarchical SFORB path 1 fit with constant variance
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:29:23 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:02:30 2023 
    +Date of summary: Mon Oct 30 11:18:26 2023 
     
     Equations:
     d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
    @@ -3059,7 +3062,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 511.715 s
    +Fitted in 1230.946 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Constant variance 
    @@ -3203,10 +3206,10 @@ Hierarchical SFORB path 1 fit with two-component error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:31:23 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:09:13 2023 
    +Date of summary: Mon Oct 30 11:18:26 2023 
     
     Equations:
     d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
    @@ -3224,7 +3227,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 630.627 s
    +Fitted in 1633.433 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -3368,143 +3371,15 @@ Hierarchical HS path 1 fit with constant variance
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:28:57 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:02:52 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
    -d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan
    -d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time <= tb, k1, k2) * cyan -
    -           k_JCZ38 * JCZ38
    -d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time <= tb, k1, k2) * cyan -
    -           k_J9Z38 * J9Z38
    -d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76
    -
    -Data:
    -433 observations of 4 variable(s) grouped in 5 datasets
    -
    -Model predictions using solution type deSolve 
    -
    -Fitted in 485.304 s
    -Using 300, 100 iterations and 10 chains
    -
    -Variance model: Constant variance 
    -
    -Starting values for degradation parameters:
    -        cyan_0    log_k_JCZ38    log_k_J9Z38    log_k_JSE76   f_cyan_ilr_1 
    -      102.8845        -3.4495        -4.9355        -5.6040         0.6468 
    -  f_cyan_ilr_2 f_JCZ38_qlogis         log_k1         log_k2         log_tb 
    -        1.2396         9.7220        -2.9079        -4.1810         1.7813 
    -
    -Fixed degradation parameter values:
    -None
    -
    -Starting values for random effects (square root of initial entries in omega):
    -               cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1
    -cyan_0          5.406        0.00        0.00       0.000       0.0000
    -log_k_JCZ38     0.000        2.33        0.00       0.000       0.0000
    -log_k_J9Z38     0.000        0.00        1.59       0.000       0.0000
    -log_k_JSE76     0.000        0.00        0.00       1.013       0.0000
    -f_cyan_ilr_1    0.000        0.00        0.00       0.000       0.6367
    -f_cyan_ilr_2    0.000        0.00        0.00       0.000       0.0000
    -f_JCZ38_qlogis  0.000        0.00        0.00       0.000       0.0000
    -log_k1          0.000        0.00        0.00       0.000       0.0000
    -log_k2          0.000        0.00        0.00       0.000       0.0000
    -log_tb          0.000        0.00        0.00       0.000       0.0000
    -               f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb
    -cyan_0                0.000           0.00 0.0000 0.0000 0.0000
    -log_k_JCZ38           0.000           0.00 0.0000 0.0000 0.0000
    -log_k_J9Z38           0.000           0.00 0.0000 0.0000 0.0000
    -log_k_JSE76           0.000           0.00 0.0000 0.0000 0.0000
    -f_cyan_ilr_1          0.000           0.00 0.0000 0.0000 0.0000
    -f_cyan_ilr_2          2.038           0.00 0.0000 0.0000 0.0000
    -f_JCZ38_qlogis        0.000          10.33 0.0000 0.0000 0.0000
    -log_k1                0.000           0.00 0.7006 0.0000 0.0000
    -log_k2                0.000           0.00 0.0000 0.8928 0.0000
    -log_tb                0.000           0.00 0.0000 0.0000 0.6773
    -
    -Starting values for error model parameters:
    -a.1 
    -  1 
    -
    -Results:
    -
    -Likelihood computed by importance sampling
    -   AIC  BIC logLik
    -  2427 2419  -1194
    -
    -Optimised parameters:
    -                      est.      lower      upper
    -cyan_0            101.9660  1.005e+02  1.035e+02
    -log_k_JCZ38        -3.4698 -4.716e+00 -2.224e+00
    -log_k_J9Z38        -5.0947 -5.740e+00 -4.450e+00
    -log_k_JSE76        -5.5977 -6.321e+00 -4.875e+00
    -f_cyan_ilr_1        0.6595  3.734e-01  9.456e-01
    -f_cyan_ilr_2        0.5905  1.664e-01  1.015e+00
    -f_JCZ38_qlogis     25.8627 -4.224e+05  4.225e+05
    -log_k1             -3.0884 -3.453e+00 -2.723e+00
    -log_k2             -4.3877 -4.778e+00 -3.998e+00
    -log_tb              2.3057  1.715e+00  2.896e+00
    -a.1                 3.3228         NA         NA
    -SD.log_k_JCZ38      1.4071         NA         NA
    -SD.log_k_J9Z38      0.5774         NA         NA
    -SD.log_k_JSE76      0.6214         NA         NA
    -SD.f_cyan_ilr_1     0.3058         NA         NA
    -SD.f_cyan_ilr_2     0.3470         NA         NA
    -SD.f_JCZ38_qlogis   0.0644         NA         NA
    -SD.log_k1           0.3994         NA         NA
    -SD.log_k2           0.4373         NA         NA
    -SD.log_tb           0.6419         NA         NA
    -
    -Correlation is not available
    -
    -Random effects:
    -                    est. lower upper
    -SD.log_k_JCZ38    1.4071    NA    NA
    -SD.log_k_J9Z38    0.5774    NA    NA
    -SD.log_k_JSE76    0.6214    NA    NA
    -SD.f_cyan_ilr_1   0.3058    NA    NA
    -SD.f_cyan_ilr_2   0.3470    NA    NA
    -SD.f_JCZ38_qlogis 0.0644    NA    NA
    -SD.log_k1         0.3994    NA    NA
    -SD.log_k2         0.4373    NA    NA
    -SD.log_tb         0.6419    NA    NA
    -
    -Variance model:
    -     est. lower upper
    -a.1 3.323    NA    NA
    -
    -Backtransformed parameters:
    -                      est.     lower     upper
    -cyan_0           1.020e+02 1.005e+02 1.035e+02
    -k_JCZ38          3.112e-02 8.951e-03 1.082e-01
    -k_J9Z38          6.129e-03 3.216e-03 1.168e-02
    -k_JSE76          3.706e-03 1.798e-03 7.639e-03
    -f_cyan_to_JCZ38  5.890e-01        NA        NA
    -f_cyan_to_J9Z38  2.318e-01        NA        NA
    -f_JCZ38_to_JSE76 1.000e+00 0.000e+00 1.000e+00
    -k1               4.558e-02 3.164e-02 6.565e-02
    -k2               1.243e-02 8.417e-03 1.835e-02
    -tb               1.003e+01 5.557e+00 1.811e+01
    -
    -Resulting formation fractions:
    -                   ff
    -cyan_JCZ38  5.890e-01
    -cyan_J9Z38  2.318e-01
    -cyan_sink   1.793e-01
    -JCZ38_JSE76 1.000e+00
    -JCZ38_sink  5.861e-12
    -
    -Estimated disappearance times:
    -        DT50   DT90 DT50back DT50_k1 DT50_k2
    -cyan   29.02 158.51    47.72   15.21   55.77
    -JCZ38  22.27  73.98       NA      NA      NA
    -J9Z38 113.09 375.69       NA      NA      NA
    -JSE76 187.01 621.23       NA      NA      NA
    -
    -
    -

    +d_cyan/dt = - ifelse(time +

    +

    Pathway 2 @@ -3514,10 +3389,10 @@ Hierarchical FOMC path 2 fit with two-component error
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:39:30 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:32:26 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
    @@ -3532,7 +3407,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 474.942 s
    +Fitted in 1185.728 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -3680,10 +3555,10 @@ Hierarchical DFOP path 2 fit with constant variance
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:40:29 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:34:49 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -3703,7 +3578,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 533.901 s
    +Fitted in 1329.843 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Constant variance 
    @@ -3871,10 +3746,10 @@ Hierarchical DFOP path 2 fit with two-component error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:43:04 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:41:05 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -3894,7 +3769,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 688.913 s
    +Fitted in 1705.043 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -4062,10 +3937,10 @@ Hierarchical SFORB path 2 fit with constant variance
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:40:32 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:35:39 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
    @@ -4083,7 +3958,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 536.94 s
    +Fitted in 1379.466 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Constant variance 
    @@ -4258,10 +4133,10 @@ Hierarchical SFORB path 2 fit with two-component error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:42:47 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 10:41:39 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
    @@ -4279,7 +4154,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 671.849 s
    +Fitted in 1739.402 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -4459,10 +4334,10 @@ error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:55:35 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:12:56 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan
    @@ -4477,7 +4352,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 748.54 s
    +Fitted in 1872.856 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -4602,10 +4477,10 @@ variance
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:57:10 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:17:06 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -4625,7 +4500,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 843.793 s
    +Fitted in 2122.961 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Constant variance 
    @@ -4702,12 +4577,12 @@ f_JSE76_qlogis     1.9658      NA     NA
     log_k1            -1.9503      NA     NA
     log_k2            -4.4745      NA     NA
     g_qlogis          -0.4967      NA     NA
    -a.1                2.7461 2.59274 2.8994
    +a.1                2.7461 2.59886 2.8932
     SD.log_k_JCZ38     1.3178 0.47602 2.1596
     SD.log_k_J9Z38     0.7022 0.15061 1.2538
    -SD.log_k_JSE76     0.6566 0.15613 1.1570
    +SD.log_k_JSE76     0.6566 0.15614 1.1570
     SD.f_cyan_ilr_1    0.3409 0.11666 0.5652
    -SD.f_cyan_ilr_2    0.4385 0.09482 0.7821
    +SD.f_cyan_ilr_2    0.4385 0.09483 0.7821
     SD.log_k1          0.7381 0.25599 1.2202
     SD.log_k2          0.5133 0.18152 0.8450
     SD.g_qlogis        0.9866 0.35681 1.6164
    @@ -4718,16 +4593,16 @@ Random effects:
                       est.   lower  upper
     SD.log_k_JCZ38  1.3178 0.47602 2.1596
     SD.log_k_J9Z38  0.7022 0.15061 1.2538
    -SD.log_k_JSE76  0.6566 0.15613 1.1570
    +SD.log_k_JSE76  0.6566 0.15614 1.1570
     SD.f_cyan_ilr_1 0.3409 0.11666 0.5652
    -SD.f_cyan_ilr_2 0.4385 0.09482 0.7821
    +SD.f_cyan_ilr_2 0.4385 0.09483 0.7821
     SD.log_k1       0.7381 0.25599 1.2202
     SD.log_k2       0.5133 0.18152 0.8450
     SD.g_qlogis     0.9866 0.35681 1.6164
     
     Variance model:
          est. lower upper
    -a.1 2.746 2.593 2.899
    +a.1 2.746 2.599 2.893
     
     Backtransformed parameters:
                           est. lower upper
    @@ -4768,10 +4643,10 @@ error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:57:32 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:17:59 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -4791,7 +4666,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 865.636 s
    +Fitted in 2175.807 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -4934,10 +4809,10 @@ variance
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:57:01 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:17:04 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
    @@ -4955,7 +4830,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 834.906 s
    +Fitted in 2121.218 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Constant variance 
    @@ -5105,10 +4980,10 @@ error
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.4 
    -R version used for fitting:         4.3.0 
    -Date of fit:     Fri May 19 09:57:17 2023 
    -Date of summary: Fri May 19 09:57:33 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:18:24 2023 
    +Date of summary: Mon Oct 30 11:18:27 2023 
     
     Equations:
     d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound *
    @@ -5126,7 +5001,7 @@ Data:
     
     Model predictions using solution type deSolve 
     
    -Fitted in 850.751 s
    +Fitted in 2200.603 s
     Using 300, 100 iterations and 10 chains
     
     Variance model: Two-component variance function 
    @@ -5275,23 +5150,23 @@ JSE76  25.44  84.51       NA           NA           NA
     

    Session info

    -
    R version 4.3.0 Patched (2023-05-18 r84448)
    +
    R version 4.3.1 (2023-06-16)
     Platform: x86_64-pc-linux-gnu (64-bit)
    -Running under: Debian GNU/Linux 12 (bookworm)
    +Running under: Ubuntu 22.04.3 LTS
     
     Matrix products: default
    -BLAS:   /home/jranke/svn/R/r-patched/build/lib/libRblas.so 
    -LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/liblapack.so.3;  LAPACK version 3.11.0
    +BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
    +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
     
     locale:
    - [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
    - [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
    - [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
    - [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
    + [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
    + [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
    + [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
    + [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
      [9] LC_ADDRESS=C               LC_TELEPHONE=C            
    -[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
    +[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
     
    -time zone: Europe/Berlin
    +time zone: Europe/Zurich
     tzcode source: system (glibc)
     
     attached base packages:
    @@ -5299,32 +5174,32 @@ attached base packages:
     [8] base     
     
     other attached packages:
    -[1] saemix_3.2 npde_3.3   knitr_1.42 mkin_1.2.4
    +[1] saemix_3.2 npde_3.3   knitr_1.44 mkin_1.2.6
     
     loaded via a namespace (and not attached):
    - [1] sass_0.4.6        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
    - [5] lattice_0.21-8    digest_0.6.31     magrittr_2.0.3    evaluate_0.21    
    - [9] grid_4.3.0        fastmap_1.1.1     cellranger_1.1.0  rprojroot_2.0.3  
    -[13] jsonlite_1.8.4    processx_3.8.1    pkgbuild_1.4.0    deSolve_1.35     
    -[17] DBI_1.1.3         mclust_6.0.0      ps_1.7.5          gridExtra_2.3    
    -[21] purrr_1.0.1       fansi_1.0.4       scales_1.2.1      codetools_0.2-19 
    -[25] textshaping_0.3.6 jquerylib_0.1.4   cli_3.6.1         crayon_1.5.2     
    -[29] rlang_1.1.1       munsell_0.5.0     cachem_1.0.8      yaml_2.3.7       
    -[33] inline_0.3.19     tools_4.3.0       memoise_2.0.1     dplyr_1.1.2      
    -[37] colorspace_2.1-0  ggplot2_3.4.2     vctrs_0.6.2       R6_2.5.1         
    -[41] zoo_1.8-12        lifecycle_1.0.3   stringr_1.5.0     fs_1.6.2         
    + [1] sass_0.4.7        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
    + [5] lattice_0.21-9    digest_0.6.33     magrittr_2.0.3    evaluate_0.22    
    + [9] grid_4.3.1        fastmap_1.1.1     cellranger_1.1.0  rprojroot_2.0.3  
    +[13] jsonlite_1.8.7    processx_3.8.2    pkgbuild_1.4.2    deSolve_1.35     
    +[17] mclust_6.0.0      ps_1.7.5          gridExtra_2.3     purrr_1.0.1      
    +[21] fansi_1.0.4       scales_1.2.1      codetools_0.2-19  textshaping_0.3.6
    +[25] jquerylib_0.1.4   cli_3.6.1         crayon_1.5.2      rlang_1.1.1      
    +[29] munsell_0.5.0     cachem_1.0.8      yaml_2.3.7        inline_0.3.19    
    +[33] tools_4.3.1       memoise_2.0.1     dplyr_1.1.2       colorspace_2.1-0 
    +[37] ggplot2_3.4.2     vctrs_0.6.3       R6_2.5.1          zoo_1.8-12       
    +[41] lifecycle_1.0.3   stringr_1.5.0     fs_1.6.3          MASS_7.3-60      
     [45] ragg_1.2.5        callr_3.7.3       pkgconfig_2.0.3   desc_1.4.2       
    -[49] pkgdown_2.0.7     bslib_0.4.2       pillar_1.9.0      gtable_0.3.3     
    -[53] glue_1.6.2        systemfonts_1.0.4 highr_0.10        xfun_0.39        
    -[57] tibble_3.2.1      lmtest_0.9-40     tidyselect_1.2.0  htmltools_0.5.5  
    -[61] nlme_3.1-162      rmarkdown_2.21    compiler_4.3.0    prettyunits_1.1.1
    +[49] pkgdown_2.0.7     bslib_0.5.1       pillar_1.9.0      gtable_0.3.3     
    +[53] glue_1.6.2        systemfonts_1.0.4 xfun_0.40         tibble_3.2.1     
    +[57] lmtest_0.9-40     tidyselect_1.2.0  rstudioapi_0.15.0 htmltools_0.5.6.1
    +[61] nlme_3.1-163      rmarkdown_2.23    compiler_4.3.1    prettyunits_1.2.0
     [65] readxl_1.4.2     

    Hardware info

    -
    CPU model: AMD Ryzen 9 7950X 16-Core Processor
    -
    MemTotal:       64925476 kB
    +
    CPU model: Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz
    +
    MemTotal:       247605564 kB

    diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png index b3d6066b..50f17cf4 100644 Binary files a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png and b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png differ diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png index 341d48b9..31e46086 100644 Binary files a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png and b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png differ diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png index 92df5b6c..daefad97 100644 Binary files a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png and b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png differ diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png index 1a17b3bc..063e8615 100644 Binary files a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png and b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png differ diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png index 15905501..d666672d 100644 Binary files a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png and b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png differ diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png index cc7d0126..55a517f0 100644 Binary files a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png and b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png differ diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png index 48bfc544..2693b983 100644 Binary files a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png and b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png index cc7d0126..55a517f0 100644 Binary files a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png and b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/docs/articles/prebuilt/2022_dmta_parent.html b/docs/articles/prebuilt/2022_dmta_parent.html index 2da41981..9fdf75f7 100644 --- a/docs/articles/prebuilt/2022_dmta_parent.html +++ b/docs/articles/prebuilt/2022_dmta_parent.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6 @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -135,7 +138,7 @@ with residue data on dimethenamid and dimethenamid-P Ranke

    Last change on 5 January -2023, last compiled on 19 Mai 2023

    +2023, last compiled on 30 October 2023 Source: vignettes/prebuilt/2022_dmta_parent.rmd @@ -154,7 +157,7 @@ FOMC, DFOP and HS can be fitted with the mkin package.

    173340 (Application of nonlinear hierarchical models to the kinetic evaluation of chemical degradation data) of the German Environment Agency carried out in 2022 and 2023.

    -

    The mkin package is used in version 1.2.4. It contains the test data +

    The mkin package is used in version 1.2.6. It contains the test data and the functions used in the evaluations. The saemix package is used as a backend for fitting the NLHM, but is also loaded to make the convergence plot function available.

    @@ -1005,7 +1008,7 @@ updated assuming two-component error.

    DFOP OK OK -C +OK OK C OK @@ -1013,7 +1016,7 @@ updated assuming two-component error.

    HS OK -C +OK OK OK OK @@ -1111,9 +1114,9 @@ the best fits.

    FOMC tc 8 -720.4 -718.8 --352.2 +720.7 +719.1 +-352.4 DFOP const @@ -1132,9 +1135,9 @@ the best fits.

    DFOP tc 10 -665.5 -663.4 --322.8 +665.7 +663.6 +-322.9 HS tc @@ -1215,12 +1218,12 @@ achieved with the argument test = TRUE to the kable(format.args = list(digits = 4))
    --++-+ @@ -1238,8 +1241,8 @@ achieved with the argument test = TRUE to the - - + + @@ -1248,12 +1251,12 @@ achieved with the argument test = TRUE to the - - - - + + + + + -
    f_saem_dfop_tc_no_ranef_k2 9663.8661.9663.7661.8 -322.9 NA NA
    f_saem[[“DFOP”, “tc”]] 10665.5663.4-322.80.2809665.7663.6-322.901 10.5961
    @@ -1286,10 +1289,10 @@ Plot of the final NLHM DFOP fit
     summary(f_saem_dfop_tc_no_ranef_k2)
    saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:09 2023 
    -Date of summary: Thu Apr 20 14:07:10 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:19:13 2023 
    +Date of summary: Mon Oct 30 11:19:14 2023 
     
     Equations:
     d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -1301,21 +1304,21 @@ Data:
     
     Model predictions using solution type analytical 
     
    -Fitted in 4.175 s
    +Fitted in 8.975 s
     Using 300, 100 iterations and 9 chains
     
     Variance model: Two-component variance function 
     
     Starting values for degradation parameters:
    -   DMTA_0        k1        k2         g 
    -98.759266  0.087034  0.009933  0.930827 
    +  DMTA_0       k1       k2        g 
    +98.71186  0.08675  0.01374  0.93491 
     
     Fixed degradation parameter values:
     None
     
     Starting values for random effects (square root of initial entries in omega):
            DMTA_0 k1 k2 g
    -DMTA_0  98.76  0  0 0
    +DMTA_0  98.71  0  0 0
     k1       0.00  1  0 0
     k2       0.00  0  1 0
     g        0.00  0  0 1
    @@ -1328,40 +1331,40 @@ Results:
     
     Likelihood computed by importance sampling
         AIC   BIC logLik
    -  663.8 661.9 -322.9
    +  663.7 661.8 -322.9
     
     Optimised parameters:
                    est.     lower     upper
    -DMTA_0    98.228939 96.285869 100.17201
    -k1         0.064063  0.033477   0.09465
    -k2         0.008297  0.005824   0.01077
    -g          0.953821  0.914328   0.99331
    -a.1        1.068479  0.869538   1.26742
    -b.1        0.029424  0.022406   0.03644
    -SD.DMTA_0  2.030437  0.404824   3.65605
    -SD.k1      0.594692  0.256660   0.93272
    -SD.g       1.006754  0.361327   1.65218
    +DMTA_0    98.256267 96.286112 100.22642
    +k1         0.064037  0.033281   0.09479
    +k2         0.008469  0.006002   0.01094
    +g          0.954167  0.914460   0.99387
    +a.1        1.061795  0.863943   1.25965
    +b.1        0.029550  0.022529   0.03657
    +SD.DMTA_0  2.068581  0.427706   3.70946
    +SD.k1      0.598285  0.258235   0.93833
    +SD.g       1.016689  0.360057   1.67332
     
     Correlation: 
        DMTA_0  k1      k2     
    -k1  0.0218                
    -k2  0.0556  0.0355        
    -g  -0.0516 -0.0284 -0.2800
    +k1  0.0213                
    +k2  0.0541  0.0344        
    +g  -0.0521 -0.0286 -0.2744
     
     Random effects:
                 est.  lower  upper
    -SD.DMTA_0 2.0304 0.4048 3.6560
    -SD.k1     0.5947 0.2567 0.9327
    -SD.g      1.0068 0.3613 1.6522
    +SD.DMTA_0 2.0686 0.4277 3.7095
    +SD.k1     0.5983 0.2582 0.9383
    +SD.g      1.0167 0.3601 1.6733
     
     Variance model:
            est.   lower   upper
    -a.1 1.06848 0.86954 1.26742
    -b.1 0.02942 0.02241 0.03644
    +a.1 1.06180 0.86394 1.25965
    +b.1 0.02955 0.02253 0.03657
     
     Estimated disappearance times:
    -      DT50 DT90 DT50back DT50_k1 DT50_k2
    -DMTA 11.45 41.4    12.46   10.82   83.54
    + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.45 41.32 12.44 10.82 81.85

    Alternative check of parameter identifiability @@ -1462,10 +1465,10 @@ Hierarchical mkin fit of the SFO model with error model const
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:02 2023 
    -Date of summary: Thu Apr 20 14:08:16 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:18:56 2023 
    +Date of summary: Mon Oct 30 11:21:30 2023 
     
     Equations:
     d_DMTA/dt = - k_DMTA * DMTA
    @@ -1475,7 +1478,7 @@ Data:
     
     Model predictions using solution type analytical 
     
    -Fitted in 0.982 s
    +Fitted in 1.899 s
     Using 300, 100 iterations and 9 chains
     
     Variance model: Constant variance 
    @@ -1534,10 +1537,10 @@ Hierarchical mkin fit of the SFO model with error model tc
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:03 2023 
    -Date of summary: Thu Apr 20 14:08:16 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:19:00 2023 
    +Date of summary: Mon Oct 30 11:21:30 2023 
     
     Equations:
     d_DMTA/dt = - k_DMTA * DMTA
    @@ -1547,7 +1550,7 @@ Data:
     
     Model predictions using solution type analytical 
     
    -Fitted in 2.398 s
    +Fitted in 5.364 s
     Using 300, 100 iterations and 9 chains
     
     Variance model: Two-component variance function 
    @@ -1608,10 +1611,10 @@ Hierarchical mkin fit of the FOMC model with error model const
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:02 2023 
    -Date of summary: Thu Apr 20 14:08:16 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:18:57 2023 
    +Date of summary: Mon Oct 30 11:21:30 2023 
     
     Equations:
     d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
    @@ -1621,7 +1624,7 @@ Data:
     
     Model predictions using solution type analytical 
     
    -Fitted in 1.398 s
    +Fitted in 2.944 s
     Using 300, 100 iterations and 9 chains
     
     Variance model: Constant variance 
    @@ -1653,7 +1656,7 @@ Optimised parameters:
                   est.   lower   upper
     DMTA_0     98.3435 96.9033  99.784
     alpha       7.2007  2.5889  11.812
    -beta      112.8746 34.8816 190.868
    +beta      112.8745 34.8816 190.867
     a.1         2.0459  1.8054   2.286
     SD.DMTA_0   1.4795  0.2717   2.687
     SD.alpha    0.6396  0.1509   1.128
    @@ -1685,10 +1688,10 @@ Hierarchical mkin fit of the FOMC model with error model tc
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:04 2023 
    -Date of summary: Thu Apr 20 14:08:16 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:19:01 2023 
    +Date of summary: Mon Oct 30 11:21:30 2023 
     
     Equations:
     d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
    @@ -1698,7 +1701,7 @@ Data:
     
     Model predictions using solution type analytical 
     
    -Fitted in 3.044 s
    +Fitted in 6.228 s
     Using 300, 100 iterations and 9 chains
     
     Variance model: Two-component variance function 
    @@ -1724,38 +1727,38 @@ Results:
     
     Likelihood computed by importance sampling
         AIC   BIC logLik
    -  720.4 718.8 -352.2
    +  720.7 719.1 -352.4
     
     Optimised parameters:
                   est.    lower     upper
    -DMTA_0    98.99136 97.26011 100.72261
    -alpha      5.86312  2.57485   9.15138
    -beta      88.55571 29.20889 147.90254
    -a.1        1.51063  1.24384   1.77741
    -b.1        0.02824  0.02040   0.03609
    -SD.DMTA_0  1.57436 -0.04867   3.19739
    -SD.alpha   0.59871  0.17132   1.02611
    -SD.beta    0.72994  0.22849   1.23139
    +DMTA_0    99.10577 97.33296 100.87859
    +alpha      5.46260  2.52199   8.40321
    +beta      81.66080 30.46664 132.85497
    +a.1        1.50219  1.23601   1.76836
    +b.1        0.02893  0.02099   0.03687
    +SD.DMTA_0  1.61887 -0.03636   3.27411
    +SD.alpha   0.58145  0.17364   0.98925
    +SD.beta    0.68205  0.21108   1.15303
     
     Correlation: 
           DMTA_0  alpha  
    -alpha -0.1363        
    -beta  -0.1414  0.2542
    +alpha -0.1321        
    +beta  -0.1430  0.2467
     
     Random effects:
    -            est.    lower upper
    -SD.DMTA_0 1.5744 -0.04867 3.197
    -SD.alpha  0.5987  0.17132 1.026
    -SD.beta   0.7299  0.22849 1.231
    +            est.    lower  upper
    +SD.DMTA_0 1.6189 -0.03636 3.2741
    +SD.alpha  0.5814  0.17364 0.9892
    +SD.beta   0.6821  0.21108 1.1530
     
     Variance model:
    -       est.  lower   upper
    -a.1 1.51063 1.2438 1.77741
    -b.1 0.02824 0.0204 0.03609
    +       est.   lower   upper
    +a.1 1.50219 1.23601 1.76836
    +b.1 0.02893 0.02099 0.03687
     
     Estimated disappearance times:
    -      DT50 DT90 DT50back
    -DMTA 11.11 42.6    12.82
    +      DT50  DT90 DT50back
    +DMTA 11.05 42.81    12.89
     
     

    @@ -1764,10 +1767,10 @@ Hierarchical mkin fit of the DFOP model with error model const
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:02 2023 
    -Date of summary: Thu Apr 20 14:08:16 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:18:57 2023 
    +Date of summary: Mon Oct 30 11:21:30 2023 
     
     Equations:
     d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -1779,7 +1782,7 @@ Data:
     
     Model predictions using solution type analytical 
     
    -Fitted in 1.838 s
    +Fitted in 3.231 s
     Using 300, 100 iterations and 9 chains
     
     Variance model: Constant variance 
    @@ -1810,10 +1813,10 @@ Likelihood computed by importance sampling
     
     Optimised parameters:
                    est.     lower    upper
    -DMTA_0    98.092481 96.573898 99.61106
    +DMTA_0    98.092481 96.573899 99.61106
     k1         0.062499  0.030336  0.09466
     k2         0.009065 -0.005133  0.02326
    -g          0.948967  0.862079  1.03586
    +g          0.948967  0.862080  1.03586
     a.1        1.821671  1.604774  2.03857
     SD.DMTA_0  1.677785  0.472066  2.88350
     SD.k1      0.634962  0.270788  0.99914
    @@ -1848,10 +1851,10 @@ Hierarchical mkin fit of the DFOP model with error model tc
     
     
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:04 2023 
    -Date of summary: Thu Apr 20 14:08:16 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:19:01 2023 
    +Date of summary: Mon Oct 30 11:21:30 2023 
     
     Equations:
     d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
    @@ -1863,21 +1866,21 @@ Data:
     
     Model predictions using solution type analytical 
     
    -Fitted in 3.297 s
    +Fitted in 6.71 s
     Using 300, 100 iterations and 9 chains
     
     Variance model: Two-component variance function 
     
     Starting values for degradation parameters:
    -   DMTA_0        k1        k2         g 
    -98.759266  0.087034  0.009933  0.930827 
    +  DMTA_0       k1       k2        g 
    +98.71186  0.08675  0.01374  0.93491 
     
     Fixed degradation parameter values:
     None
     
     Starting values for random effects (square root of initial entries in omega):
            DMTA_0 k1 k2 g
    -DMTA_0  98.76  0  0 0
    +DMTA_0  98.71  0  0 0
     k1       0.00  1  0 0
     k2       0.00  0  1 0
     g        0.00  0  0 1
    @@ -1890,42 +1893,42 @@ Results:
     
     Likelihood computed by importance sampling
         AIC   BIC logLik
    -  665.5 663.4 -322.8
    +  665.7 663.6 -322.9
     
     Optimised parameters:
                    est.     lower     upper
    -DMTA_0    98.377019 96.447952 100.30609
    -k1         0.064843  0.034607   0.09508
    -k2         0.008895  0.006368   0.01142
    -g          0.949696  0.903815   0.99558
    -a.1        1.065241  0.865754   1.26473
    -b.1        0.029340  0.022336   0.03634
    -SD.DMTA_0  2.007754  0.387982   3.62753
    -SD.k1      0.580473  0.250286   0.91066
    -SD.k2      0.006105 -4.920337   4.93255
    -SD.g       1.097149  0.412779   1.78152
    +DMTA_0    98.347470 96.380815 100.31413
    +k1         0.064524  0.034279   0.09477
    +k2         0.008304  0.005843   0.01076
    +g          0.952128  0.909578   0.99468
    +a.1        1.068907  0.868694   1.26912
    +b.1        0.029265  0.022262   0.03627
    +SD.DMTA_0  2.065796  0.428485   3.70311
    +SD.k1      0.583703  0.251796   0.91561
    +SD.k2      0.004167 -7.832168   7.84050
    +SD.g       1.064450  0.397476   1.73142
     
     Correlation: 
        DMTA_0  k1      k2     
    -k1  0.0235                
    -k2  0.0595  0.0424        
    -g  -0.0470 -0.0278 -0.2731
    +k1  0.0223                
    +k2  0.0568  0.0394        
    +g  -0.0464 -0.0269 -0.2713
     
     Random effects:
                   est.   lower  upper
    -SD.DMTA_0 2.007754  0.3880 3.6275
    -SD.k1     0.580473  0.2503 0.9107
    -SD.k2     0.006105 -4.9203 4.9325
    -SD.g      1.097149  0.4128 1.7815
    +SD.DMTA_0 2.065796  0.4285 3.7031
    +SD.k1     0.583703  0.2518 0.9156
    +SD.k2     0.004167 -7.8322 7.8405
    +SD.g      1.064450  0.3975 1.7314
     
     Variance model:
            est.   lower   upper
    -a.1 1.06524 0.86575 1.26473
    -b.1 0.02934 0.02234 0.03634
    +a.1 1.06891 0.86869 1.26912
    +b.1 0.02927 0.02226 0.03627
     
     Estimated disappearance times:
           DT50  DT90 DT50back DT50_k1 DT50_k2
    -DMTA 11.36 41.32    12.44   10.69   77.92
    +DMTA 11.39 41.36    12.45   10.74   83.48
     
     

    @@ -1934,167 +1937,28 @@ Hierarchical mkin fit of the HS model with error model const
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:03 2023 
    -Date of summary: Thu Apr 20 14:08:16 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:18:59 2023 
    +Date of summary: Mon Oct 30 11:21:30 2023 
     
     Equations:
    -d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA
    -
    -Data:
    -155 observations of 1 variable(s) grouped in 6 datasets
    -
    -Model predictions using solution type analytical 
    -
    -Fitted in 1.972 s
    -Using 300, 100 iterations and 9 chains
    -
    -Variance model: Constant variance 
    -
    -Starting values for degradation parameters:
    -  DMTA_0       k1       k2       tb 
    -97.82176  0.06931  0.02997 11.13945 
    -
    -Fixed degradation parameter values:
    -None
    -
    -Starting values for random effects (square root of initial entries in omega):
    -       DMTA_0 k1 k2 tb
    -DMTA_0  97.82  0  0  0
    -k1       0.00  1  0  0
    -k2       0.00  0  1  0
    -tb       0.00  0  0  1
    -
    -Starting values for error model parameters:
    -a.1 
    -  1 
    -
    -Results:
    -
    -Likelihood computed by importance sampling
    -  AIC   BIC logLik
    -  714 712.1   -348
    -
    -Optimised parameters:
    -              est.    lower    upper
    -DMTA_0    98.16102 96.47747 99.84456
    -k1         0.07876  0.05261  0.10491
    -k2         0.02227  0.01706  0.02747
    -tb        13.99089 -7.40049 35.38228
    -a.1        1.82305  1.60700  2.03910
    -SD.DMTA_0  1.88413  0.56204  3.20622
    -SD.k1      0.34292  0.10482  0.58102
    -SD.k2      0.19851  0.01718  0.37985
    -SD.tb      1.68168  0.58064  2.78272
    -
    -Correlation: 
    -   DMTA_0  k1      k2     
    -k1  0.0142                
    -k2  0.0001 -0.0025        
    -tb  0.0165 -0.1256 -0.0301
    -
    -Random effects:
    -            est.   lower  upper
    -SD.DMTA_0 1.8841 0.56204 3.2062
    -SD.k1     0.3429 0.10482 0.5810
    -SD.k2     0.1985 0.01718 0.3798
    -SD.tb     1.6817 0.58064 2.7827
    -
    -Variance model:
    -     est. lower upper
    -a.1 1.823 1.607 2.039
    -
    -Estimated disappearance times:
    -      DT50  DT90 DT50back DT50_k1 DT50_k2
    -DMTA 8.801 67.91    20.44   8.801   31.13
    -
    -
    -

    +d_DMTA/dt = - ifelse(time +

    Hierarchical mkin fit of the HS model with error model tc
    
     saemix version used for fitting:      3.2 
    -mkin version used for pre-fitting:  1.2.3 
    -R version used for fitting:         4.2.3 
    -Date of fit:     Thu Apr 20 14:07:04 2023 
    -Date of summary: Thu Apr 20 14:08:16 2023 
    +mkin version used for pre-fitting:  1.2.6 
    +R version used for fitting:         4.3.1 
    +Date of fit:     Mon Oct 30 11:19:02 2023 
    +Date of summary: Mon Oct 30 11:21:30 2023 
     
     Equations:
    -d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA
    -
    -Data:
    -155 observations of 1 variable(s) grouped in 6 datasets
    -
    -Model predictions using solution type analytical 
    -
    -Fitted in 3.378 s
    -Using 300, 100 iterations and 9 chains
    -
    -Variance model: Two-component variance function 
    -
    -Starting values for degradation parameters:
    -  DMTA_0       k1       k2       tb 
    -98.45190  0.07525  0.02576 19.19375 
    -
    -Fixed degradation parameter values:
    -None
    -
    -Starting values for random effects (square root of initial entries in omega):
    -       DMTA_0 k1 k2 tb
    -DMTA_0  98.45  0  0  0
    -k1       0.00  1  0  0
    -k2       0.00  0  1  0
    -tb       0.00  0  0  1
    -
    -Starting values for error model parameters:
    -a.1 b.1 
    -  1   1 
    -
    -Results:
    -
    -Likelihood computed by importance sampling
    -    AIC BIC logLik
    -  667.1 665 -323.6
    -
    -Optimised parameters:
    -              est.    lower    upper
    -DMTA_0    97.76570 95.81350 99.71791
    -k1         0.05855  0.03080  0.08630
    -k2         0.02337  0.01664  0.03010
    -tb        31.09638 29.38289 32.80987
    -a.1        1.08835  0.88590  1.29080
    -b.1        0.02964  0.02257  0.03671
    -SD.DMTA_0  2.04877  0.42607  3.67147
    -SD.k1      0.59166  0.25621  0.92711
    -SD.k2      0.30698  0.09561  0.51835
    -SD.tb      0.01274 -0.10914  0.13462
    -
    -Correlation: 
    -   DMTA_0  k1      k2     
    -k1  0.0160                
    -k2 -0.0070 -0.0024        
    -tb -0.0668 -0.0103 -0.2013
    -
    -Random effects:
    -             est.    lower  upper
    -SD.DMTA_0 2.04877  0.42607 3.6715
    -SD.k1     0.59166  0.25621 0.9271
    -SD.k2     0.30698  0.09561 0.5183
    -SD.tb     0.01274 -0.10914 0.1346
    -
    -Variance model:
    -       est.   lower   upper
    -a.1 1.08835 0.88590 1.29080
    -b.1 0.02964 0.02257 0.03671
    -
    -Estimated disappearance times:
    -      DT50  DT90 DT50back DT50_k1 DT50_k2
    -DMTA 11.84 51.71    15.57   11.84   29.66
    -
    -
    -

    +d_DMTA/dt = - ifelse(time +

    +

    Hierarchical model convergence plots @@ -2143,50 +2007,53 @@ Convergence plot for the NLHM HS fit with two-component error

    Session info

    -
    R version 4.2.3 (2023-03-15)
    +
    R version 4.3.1 (2023-06-16)
     Platform: x86_64-pc-linux-gnu (64-bit)
    -Running under: Debian GNU/Linux 12 (bookworm)
    +Running under: Ubuntu 22.04.3 LTS
     
     Matrix products: default
    -BLAS:   /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
    -LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so
    +BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
    +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
     
     locale:
    - [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
    - [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
    - [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
    - [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
    + [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
    + [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
    + [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
    + [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
      [9] LC_ADDRESS=C               LC_TELEPHONE=C            
    -[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
    +[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
    +
    +time zone: Europe/Zurich
    +tzcode source: system (glibc)
     
     attached base packages:
     [1] parallel  stats     graphics  grDevices utils     datasets  methods  
     [8] base     
     
     other attached packages:
    -[1] saemix_3.2 npde_3.3   knitr_1.42 mkin_1.2.3
    +[1] saemix_3.2 npde_3.3   knitr_1.44 mkin_1.2.6
     
     loaded via a namespace (and not attached):
    - [1] highr_0.10        pillar_1.9.0      bslib_0.4.2       compiler_4.2.3   
    - [5] jquerylib_0.1.4   tools_4.2.3       mclust_6.0.0      digest_0.6.31    
    - [9] tibble_3.2.1      jsonlite_1.8.4    evaluate_0.20     memoise_2.0.1    
    -[13] lifecycle_1.0.3   nlme_3.1-162      gtable_0.3.3      lattice_0.21-8   
    -[17] pkgconfig_2.0.3   rlang_1.1.0       DBI_1.1.3         cli_3.6.1        
    -[21] yaml_2.3.7        pkgdown_2.0.7     xfun_0.38         fastmap_1.1.1    
    -[25] gridExtra_2.3     dplyr_1.1.1       stringr_1.5.0     generics_0.1.3   
    -[29] desc_1.4.2        fs_1.6.1          vctrs_0.6.1       sass_0.4.5       
    -[33] systemfonts_1.0.4 tidyselect_1.2.0  rprojroot_2.0.3   lmtest_0.9-40    
    -[37] grid_4.2.3        glue_1.6.2        R6_2.5.1          textshaping_0.3.6
    -[41] fansi_1.0.4       rmarkdown_2.21    purrr_1.0.1       ggplot2_3.4.2    
    -[45] magrittr_2.0.3    codetools_0.2-19  scales_1.2.1      htmltools_0.5.5  
    -[49] colorspace_2.1-0  ragg_1.2.5        utf8_1.2.3        stringi_1.7.12   
    -[53] munsell_0.5.0     cachem_1.0.7      zoo_1.8-12       
    + [1] sass_0.4.7 utf8_1.2.3 generics_0.1.3 stringi_1.7.12 + [5] lattice_0.21-9 digest_0.6.33 magrittr_2.0.3 evaluate_0.22 + [9] grid_4.3.1 fastmap_1.1.1 rprojroot_2.0.3 jsonlite_1.8.7 +[13] mclust_6.0.0 gridExtra_2.3 purrr_1.0.1 fansi_1.0.4 +[17] scales_1.2.1 codetools_0.2-19 textshaping_0.3.6 jquerylib_0.1.4 +[21] cli_3.6.1 rlang_1.1.1 munsell_0.5.0 cachem_1.0.8 +[25] yaml_2.3.7 tools_4.3.1 memoise_2.0.1 dplyr_1.1.2 +[29] colorspace_2.1-0 ggplot2_3.4.2 vctrs_0.6.3 R6_2.5.1 +[33] zoo_1.8-12 lifecycle_1.0.3 stringr_1.5.0 fs_1.6.3 +[37] MASS_7.3-60 ragg_1.2.5 pkgconfig_2.0.3 desc_1.4.2 +[41] pkgdown_2.0.7 bslib_0.5.1 pillar_1.9.0 gtable_0.3.3 +[45] glue_1.6.2 systemfonts_1.0.4 xfun_0.40 tibble_3.2.1 +[49] lmtest_0.9-40 tidyselect_1.2.0 rstudioapi_0.15.0 htmltools_0.5.6.1 +[53] nlme_3.1-163 rmarkdown_2.23 compiler_4.3.1

    Hardware info

    -
    CPU model: AMD Ryzen 9 7950X 16-Core Processor
    -
    MemTotal:       64936316 kB
    +
    CPU model: Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz
    +
    MemTotal:       247605564 kB

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b/docs/articles/prebuilt/2022_dmta_pathway.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6 @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -135,7 +138,7 @@ residue data on dimethenamid and dimethenamid-P Ranke

    Last change on 20 April 2023, -last compiled on 19 Mai 2023

    +last compiled on 30 October 2023 Source: vignettes/prebuilt/2022_dmta_pathway.rmd @@ -155,7 +158,7 @@ can be fitted with the mkin package.

    173340 (Application of nonlinear hierarchical models to the kinetic evaluation of chemical degradation data) of the German Environment Agency carried out in 2022 and 2023.

    -

    The mkin package is used in version 1.2.4, which is currently under +

    The mkin package is used in version 1.2.6, which is currently under development. It contains the test data, and the functions used in the evaluations. The saemix package is used as a backend for fitting the NLHM, but is also loaded to make the convergence plot @@ -1976,23 +1979,23 @@ error

    Session info

    -
    R version 4.3.0 Patched (2023-05-18 r84448)
    +
    R version 4.3.1 (2023-06-16)
     Platform: x86_64-pc-linux-gnu (64-bit)
    -Running under: Debian GNU/Linux 12 (bookworm)
    +Running under: Ubuntu 22.04.3 LTS
     
     Matrix products: default
    -BLAS:   /home/jranke/svn/R/r-patched/build/lib/libRblas.so 
    -LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/liblapack.so.3;  LAPACK version 3.11.0
    +BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
    +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
     
     locale:
    - [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
    - [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
    - [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
    - [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
    + [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
    + [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
    + [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
    + [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
      [9] LC_ADDRESS=C               LC_TELEPHONE=C            
    -[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
    +[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
     
    -time zone: Europe/Berlin
    +time zone: Europe/Zurich
     tzcode source: system (glibc)
     
     attached base packages:
    @@ -2000,31 +2003,31 @@ attached base packages:
     [8] base     
     
     other attached packages:
    -[1] saemix_3.2 npde_3.3   knitr_1.42 mkin_1.2.4
    +[1] saemix_3.2 npde_3.3   knitr_1.44 mkin_1.2.6
     
     loaded via a namespace (and not attached):
    - [1] sass_0.4.6        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
    - [5] lattice_0.21-8    digest_0.6.31     magrittr_2.0.3    evaluate_0.21    
    - [9] grid_4.3.0        fastmap_1.1.1     rprojroot_2.0.3   jsonlite_1.8.4   
    -[13] processx_3.8.1    pkgbuild_1.4.0    deSolve_1.35      DBI_1.1.3        
    -[17] mclust_6.0.0      ps_1.7.5          gridExtra_2.3     purrr_1.0.1      
    -[21] fansi_1.0.4       scales_1.2.1      codetools_0.2-19  textshaping_0.3.6
    -[25] jquerylib_0.1.4   cli_3.6.1         crayon_1.5.2      rlang_1.1.1      
    -[29] munsell_0.5.0     cachem_1.0.8      yaml_2.3.7        inline_0.3.19    
    -[33] tools_4.3.0       memoise_2.0.1     dplyr_1.1.2       colorspace_2.1-0 
    -[37] ggplot2_3.4.2     vctrs_0.6.2       R6_2.5.1          zoo_1.8-12       
    -[41] lifecycle_1.0.3   stringr_1.5.0     fs_1.6.2          ragg_1.2.5       
    + [1] sass_0.4.7        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
    + [5] lattice_0.21-9    digest_0.6.33     magrittr_2.0.3    evaluate_0.22    
    + [9] grid_4.3.1        fastmap_1.1.1     rprojroot_2.0.3   jsonlite_1.8.7   
    +[13] processx_3.8.2    pkgbuild_1.4.2    deSolve_1.35      mclust_6.0.0     
    +[17] ps_1.7.5          gridExtra_2.3     purrr_1.0.1       fansi_1.0.4      
    +[21] scales_1.2.1      codetools_0.2-19  textshaping_0.3.6 jquerylib_0.1.4  
    +[25] cli_3.6.1         crayon_1.5.2      rlang_1.1.1       munsell_0.5.0    
    +[29] cachem_1.0.8      yaml_2.3.7        inline_0.3.19     tools_4.3.1      
    +[33] memoise_2.0.1     dplyr_1.1.2       colorspace_2.1-0  ggplot2_3.4.2    
    +[37] vctrs_0.6.3       R6_2.5.1          zoo_1.8-12        lifecycle_1.0.3  
    +[41] stringr_1.5.0     fs_1.6.3          MASS_7.3-60       ragg_1.2.5       
     [45] callr_3.7.3       pkgconfig_2.0.3   desc_1.4.2        pkgdown_2.0.7    
    -[49] bslib_0.4.2       pillar_1.9.0      gtable_0.3.3      glue_1.6.2       
    -[53] systemfonts_1.0.4 highr_0.10        xfun_0.39         tibble_3.2.1     
    -[57] lmtest_0.9-40     tidyselect_1.2.0  htmltools_0.5.5   nlme_3.1-162     
    -[61] rmarkdown_2.21    compiler_4.3.0    prettyunits_1.1.1
    +[49] bslib_0.5.1 pillar_1.9.0 gtable_0.3.3 glue_1.6.2 +[53] systemfonts_1.0.4 xfun_0.40 tibble_3.2.1 lmtest_0.9-40 +[57] tidyselect_1.2.0 rstudioapi_0.15.0 htmltools_0.5.6.1 nlme_3.1-163 +[61] rmarkdown_2.23 compiler_4.3.1 prettyunits_1.2.0

    Hardware info

    -
    CPU model: AMD Ryzen 9 7950X 16-Core Processor
    -
    MemTotal:       64925476 kB
    +
    CPU model: Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz
    +
    MemTotal:       247605564 kB
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  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -135,7 +138,7 @@ concentrations with mkin Ranke

    Last change 18 September 2019 -(rebuilt 2023-05-19)

    +(rebuilt 2023-10-30) Source: vignettes/twa.rmd diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html index 4cda45e3..f19f59ad 100644 --- a/docs/articles/web_only/FOCUS_Z.html +++ b/docs/articles/web_only/FOCUS_Z.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6 @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -134,7 +137,7 @@ Ranke

    Last change 16 January 2018 -(rebuilt 2023-05-19)

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  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -135,7 +138,7 @@ to the US EPA SOP for the NAFTA guidance Ranke

    26 February 2019 (rebuilt -2023-05-19)

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  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -134,7 +137,7 @@ Ranke

    Last change 17 February 2023 -(rebuilt 2023-05-19)

    +(rebuilt 2023-10-30) Source: vignettes/web_only/benchmarks.rmd @@ -231,7 +234,15 @@ systems. All trademarks belong to their respective owners.

    Constant variance (t1) and two-component error model (t2) for four models fitted to two datasets, i.e. eight fits for each test.

    - +
    ++++++++ @@ -433,6 +444,22 @@ models fitted to two datasets, i.e. eight fits for each test.

    + + + + + + + + + + + + + + + +
    OS CPU 1.386 1.960
    LinuxIntel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz4.3.11.2.52.3693.632
    LinuxIntel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz4.3.11.2.62.8564.960
    @@ -443,6 +470,15 @@ models fitted to two datasets, i.e. eight fits for each test.

    by variable (t5) for three models fitted to one dataset, i.e. three fits for each test.

    +++++++++ @@ -669,6 +705,24 @@ for each test.

    + + + + + + + + + + + + + + + + + +
    OS CPU 2.080 1.106
    LinuxIntel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz4.3.11.2.51.8235.5552.404
    LinuxIntel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz4.3.11.2.61.7615.4052.462
    @@ -678,18 +732,18 @@ for each test.

    Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.

    - +
    +++---------++++++ @@ -992,6 +1046,30 @@ dataset, i.e. one fit for each test.

    + + + + + + + + + + + + + + + + + + + + + + + +
    OS 0.712 0.948
    LinuxIntel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz4.3.11.2.50.7981.0961.2173.1731.6342.271
    LinuxIntel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz4.3.11.2.60.8131.1361.2203.1141.5982.255
    diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html index 3c53b40a..1aa88e05 100644 --- a/docs/articles/web_only/compiled_models.html +++ b/docs/articles/web_only/compiled_models.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6 @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -134,7 +137,7 @@ definitions in mkin

    Johannes Ranke

    -

    2023-05-19

    +

    2023-10-30

    Source: vignettes/web_only/compiled_models.rmd @@ -152,7 +155,7 @@ compiled from autogenerated C code when defining a model using mkinmod. Starting from version 0.9.49.9, the mkinmod() function checks for presence of a compiler using

    -pkgbuild::has_compiler()
    +pkgbuild::has_compiler()

    In previous versions, it used Sys.which("gcc") for this check.

    On Linux, you need to have the essential build tools like make and @@ -213,10 +216,10 @@ solution is also implemented, which is included in the tests below.

    print("R package rbenchmark is not available") }
    ##                    test replications relative elapsed
    -## 4            analytical            1    1.000   0.099
    -## 3     deSolve, compiled            1    1.303   0.129
    -## 2      Eigenvalue based            1    1.697   0.168
    -## 1 deSolve, not compiled            1   21.475   2.126
    +## 4 analytical 1 1.000 0.213 +## 3 deSolve, compiled 1 1.418 0.302 +## 2 Eigenvalue based 1 2.000 0.426 +## 1 deSolve, not compiled 1 23.535 5.013

    We see that using the compiled model is by more than a factor of 10 faster than using deSolve without compiled code.

    @@ -247,15 +250,15 @@ compiled code is available.

    }
    ## Temporary DLL for differentials generated and loaded
    ##                    test replications relative elapsed
    -## 2     deSolve, compiled            1    1.000   0.165
    -## 1 deSolve, not compiled            1   22.673   3.741
    -

    Here we get a performance benefit of a factor of 23 using the version +## 2 deSolve, compiled 1 1.000 0.492 +## 1 deSolve, not compiled 1 20.398 10.036

    +

    Here we get a performance benefit of a factor of 20 using the version of the differential equation model compiled from C code!

    -

    This vignette was built with mkin 1.2.4 on

    -
    ## R version 4.3.0 Patched (2023-05-18 r84448)
    +

    This vignette was built with mkin 1.2.6 on

    +
    ## R version 4.3.1 (2023-06-16)
     ## Platform: x86_64-pc-linux-gnu (64-bit)
    -## Running under: Debian GNU/Linux 12 (bookworm)
    -
    ## CPU model: AMD Ryzen 9 7950X 16-Core Processor
    +## Running under: Ubuntu 22.04.3 LTS
    +
    ## CPU model: Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz
    diff --git a/docs/articles/web_only/dimethenamid_2018.html b/docs/articles/web_only/dimethenamid_2018.html index a89631a2..1cffd561 100644 --- a/docs/articles/web_only/dimethenamid_2018.html +++ b/docs/articles/web_only/dimethenamid_2018.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6 @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -135,7 +138,7 @@ from 2018 Ranke

    Last change 1 July 2022, -built on 19 May 2023

    +built on 30 Oct 2023 Source: vignettes/web_only/dimethenamid_2018.rmd @@ -222,12 +225,12 @@ least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):

    -plot(mixed(f_parent_mkin_const["SFO", ]))
    +plot(mixed(f_parent_mkin_const["SFO", ]))

    Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:

    -plot(mixed(f_parent_mkin_const["DFOP", ]))
    +plot(mixed(f_parent_mkin_const["DFOP", ]))

    The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 @@ -239,7 +242,7 @@ dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:

    -plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)
    +plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)

    While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual @@ -251,7 +254,7 @@ degradation model and the error model (see below).

    predicted residues is reduced by using the two-component error model:

    -plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)
    +plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)

    However, note that in the case of using this error model, the fits to the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by @@ -263,7 +266,7 @@ Status of individual fits: dataset model Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot - DFOP OK OK C OK C OK + DFOP OK OK OK OK C OK C: Optimisation did not converge: iteration limit reached without convergence (10) @@ -319,7 +322,7 @@ indicates that this difference is significant as the p-value is below

                            Model df    AIC    BIC  logLik   Test L.Ratio p-value
     f_parent_nlme_sfo_const     1  5 796.60 811.82 -393.30                       
     f_parent_nlme_sfo_tc        2  6 798.60 816.86 -393.30 1 vs 2    0.00   0.998
    -f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  <.0001
    +f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.95 2 vs 3 134.69 <.0001

    In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these @@ -341,7 +344,7 @@ effects does not improve the fits.

    The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.

    -plot(f_parent_nlme_dfop_tc)
    +plot(f_parent_nlme_dfop_tc)

    @@ -358,8 +361,17 @@ implemented in the saemix package, the convergence plots need to be manually checked for every fit. We define control settings that work well for all the parent data fits shown in this vignette.

    -library(saemix)
    -saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,
    +library(saemix)
    +
    Loading required package: npde
    +
    Package saemix, version 3.2
    +  please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
    +
    
    +Attaching package: 'saemix'
    +
    The following objects are masked from 'package:npde':
    +
    +    kurtosis, skewness
    +
    +saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,
         print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)
     saemix_control_moreiter <- saemixControl(nbiter.saemix = c(1600, 300), nb.chains = 15,
         print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)
    @@ -367,7 +379,7 @@ well for all the parent data fits shown in this vignette.

    print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)

    The convergence plot for the SFO model using constant variance is shown below.

    -
    +
     f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
       control = saemix_control, transformations = "saemix")
     plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")
    @@ -375,19 +387,19 @@ shown below.

    Obviously the selected number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.

    -
    +
     f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
       control = saemix_control, transformations = "saemix")
     plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")

    When fitting the DFOP model with constant variance (see below), parameter convergence is not as unambiguous.

    -
    +
     f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
       control = saemix_control, transformations = "saemix")
     plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")

    -
    +
     print(f_parent_saemix_dfop_const)
    Kinetic nonlinear mixed-effects model fit by SAEM
     Structural model:
    @@ -408,11 +420,11 @@ DMTA_0    97.99583 96.50079 99.4909
     k1         0.06377  0.03432  0.0932
     k2         0.00848  0.00444  0.0125
     g          0.95701  0.91313  1.0009
    -a.1        1.82141  1.65122  1.9916
    -SD.DMTA_0  1.64787  0.45772  2.8380
    +a.1        1.82141  1.60516  2.0377
    +SD.DMTA_0  1.64787  0.45729  2.8384
     SD.k1      0.57439  0.24731  0.9015
    -SD.k2      0.03296 -2.50195  2.5679
    -SD.g       1.10266  0.32369  1.8816
    +SD.k2 0.03296 -2.50524 2.5712 +SD.g 1.10266 0.32354 1.8818

    While the other parameters converge to credible values, the variance of k2 (omega2.k2) converges to a very small value. The printout of the saem.mmkin model shows that the estimated @@ -423,14 +435,14 @@ this model.

    also observe that the estimated variance of k2 becomes very small, while being ill-defined, as illustrated by the excessive confidence interval of SD.k2.

    -
    +
     f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
       control = saemix_control, transformations = "saemix")
     f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
       control = saemix_control_moreiter, transformations = "saemix")
     plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence")

    -
    +
     print(f_parent_saemix_dfop_tc)
    Kinetic nonlinear mixed-effects model fit by SAEM
     Structural model:
    @@ -446,17 +458,17 @@ Likelihood computed by importance sampling
       666 664   -323
     
     Fitted parameters:
    -          estimate    lower    upper
    -DMTA_0    98.27617  96.3088 100.2436
    -k1         0.06437   0.0337   0.0950
    -k2         0.00880   0.0063   0.0113
    -g          0.95249   0.9100   0.9949
    -a.1        1.06161   0.8625   1.2607
    -b.1        0.02967   0.0226   0.0367
    -SD.DMTA_0  2.06075   0.4187   3.7028
    -SD.k1      0.59357   0.2561   0.9310
    -SD.k2      0.00292 -10.2960  10.3019
    -SD.g       1.05725   0.3808   1.7337
    + estimate lower upper +DMTA_0 98.24165 96.29190 100.1914 +k1 0.06421 0.03352 0.0949 +k2 0.00866 0.00617 0.0111 +g 0.95340 0.91218 0.9946 +a.1 1.06463 0.86503 1.2642 +b.1 0.02964 0.02259 0.0367 +SD.DMTA_0 2.03611 0.40416 3.6681 +SD.k1 0.59534 0.25692 0.9338 +SD.k2 0.00042 -73.01372 73.0146 +SD.g 1.04234 0.37189 1.7128

    Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with @@ -472,7 +484,7 @@ message.

    The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and the version with increased iterations can be compared using the model comparison function of the saemix package:

    -
    +
     AIC_parent_saemix <- saemix::compare.saemix(
       f_parent_saemix_sfo_const$so,
       f_parent_saemix_sfo_tc$so,
    @@ -480,7 +492,7 @@ comparison function of the saemix package:

    f_parent_saemix_dfop_tc$so, f_parent_saemix_dfop_tc_moreiter$so)
    Likelihoods calculated by importance sampling
    -
    +
     rownames(AIC_parent_saemix) <- c(
       "SFO const", "SFO tc", "DFOP const", "DFOP tc", "DFOP tc more iterations")
     print(AIC_parent_saemix)
    @@ -488,13 +500,13 @@ comparison function of the saemix package:

    SFO const 796.38 795.34 SFO tc 798.38 797.13 DFOP const 705.75 703.88 -DFOP tc 665.65 663.57 -DFOP tc more iterations 665.88 663.80
    +DFOP tc 665.67 663.59 +DFOP tc more iterations 665.85 663.76

    In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.

    -
    +
     f_parent_saemix_dfop_tc$so <-
       saemix::llgq.saemix(f_parent_saemix_dfop_tc$so)
     AIC_parent_saemix_methods <- c(
    @@ -504,7 +516,7 @@ the three methods are compared.

    ) print(AIC_parent_saemix_methods)
        is     gq    lin 
    -665.65 665.68 665.11 
    +665.67 665.74 665.13

    The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value.

    @@ -518,7 +530,7 @@ iterations makes a lot of difference. When using the LAPACK version coming with Debian Bullseye, the AIC based on Gaussian quadrature is almost the same as the one obtained with the other methods, also when using defaults for the fit.

    -
    +
     f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc["DFOP", ])
     f_parent_saemix_dfop_tc_defaults$so <-
       saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so)
    @@ -529,7 +541,7 @@ using defaults for the fit.

    ) print(AIC_parent_saemix_methods_defaults)
        is     gq    lin 
    -669.77 669.36 670.95 
    +670.09 669.37 671.29
    @@ -538,7 +550,7 @@ using defaults for the fit.

    The following table gives the AIC values obtained with both backend packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).

    -
    +
     AIC_all <- data.frame(
       check.names = FALSE,
       "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"),
    @@ -549,7 +561,7 @@ iterations second phase, 15 chains).

    saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "is") ) -kable(AIC_all)
    +kable(AIC_all)
    @@ -577,15 +589,15 @@ iterations second phase, 15 chains).

    - + - - + +
    Degradation model DFOP const NA709.26704.95 705.75
    DFOP tc 671.91665.11665.65665.13665.67
    @@ -612,48 +624,48 @@ satisfactory precision.

    Session Info

    -
    +
    -
    R version 4.3.0 Patched (2023-05-18 r84448)
    +
    R version 4.3.1 (2023-06-16)
     Platform: x86_64-pc-linux-gnu (64-bit)
    -Running under: Debian GNU/Linux 12 (bookworm)
    +Running under: Ubuntu 22.04.3 LTS
     
     Matrix products: default
    -BLAS:   /home/jranke/svn/R/r-patched/build/lib/libRblas.so 
    -LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/liblapack.so.3;  LAPACK version 3.11.0
    +BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
    +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
     
     locale:
    - [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
    - [3] LC_TIME=C                  LC_COLLATE=de_DE.UTF-8    
    - [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
    - [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
    + [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
    + [3] LC_TIME=C                  LC_COLLATE=en_US.UTF-8    
    + [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
    + [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
      [9] LC_ADDRESS=C               LC_TELEPHONE=C            
    -[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
    +[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
     
    -time zone: Europe/Berlin
    +time zone: Europe/Zurich
     tzcode source: system (glibc)
     
     attached base packages:
     [1] stats     graphics  grDevices utils     datasets  methods   base     
     
     other attached packages:
    -[1] nlme_3.1-162 mkin_1.2.4   knitr_1.42  
    +[1] saemix_3.2   npde_3.3     nlme_3.1-163 mkin_1.2.6   knitr_1.44  
     
     loaded via a namespace (and not attached):
    - [1] sass_0.4.6        utf8_1.2.3        generics_0.1.3    saemix_3.2       
    - [5] stringi_1.7.12    lattice_0.21-8    digest_0.6.31     magrittr_2.0.3   
    - [9] evaluate_0.21     grid_4.3.0        fastmap_1.1.1     rprojroot_2.0.3  
    -[13] jsonlite_1.8.4    DBI_1.1.3         mclust_6.0.0      gridExtra_2.3    
    -[17] purrr_1.0.1       fansi_1.0.4       scales_1.2.1      textshaping_0.3.6
    -[21] jquerylib_0.1.4   cli_3.6.1         rlang_1.1.1       munsell_0.5.0    
    -[25] cachem_1.0.8      yaml_2.3.7        tools_4.3.0       parallel_4.3.0   
    -[29] memoise_2.0.1     dplyr_1.1.2       colorspace_2.1-0  ggplot2_3.4.2    
    -[33] vctrs_0.6.2       R6_2.5.1          zoo_1.8-12        lifecycle_1.0.3  
    -[37] stringr_1.5.0     fs_1.6.2          ragg_1.2.5        pkgconfig_2.0.3  
    -[41] desc_1.4.2        pkgdown_2.0.7     bslib_0.4.2       pillar_1.9.0     
    -[45] gtable_0.3.3      glue_1.6.2        systemfonts_1.0.4 xfun_0.39        
    -[49] tibble_3.2.1      lmtest_0.9-40     tidyselect_1.2.0  npde_3.3         
    -[53] htmltools_0.5.5   rmarkdown_2.21    compiler_4.3.0   
    + [1] sass_0.4.7 utf8_1.2.3 generics_0.1.3 stringi_1.7.12 + [5] lattice_0.21-9 digest_0.6.33 magrittr_2.0.3 evaluate_0.22 + [9] grid_4.3.1 fastmap_1.1.1 rprojroot_2.0.3 jsonlite_1.8.7 +[13] mclust_6.0.0 gridExtra_2.3 purrr_1.0.1 fansi_1.0.4 +[17] scales_1.2.1 codetools_0.2-19 textshaping_0.3.6 jquerylib_0.1.4 +[21] cli_3.6.1 rlang_1.1.1 munsell_0.5.0 cachem_1.0.8 +[25] yaml_2.3.7 tools_4.3.1 parallel_4.3.1 memoise_2.0.1 +[29] dplyr_1.1.2 colorspace_2.1-0 ggplot2_3.4.2 vctrs_0.6.3 +[33] R6_2.5.1 zoo_1.8-12 lifecycle_1.0.3 stringr_1.5.0 +[37] fs_1.6.3 MASS_7.3-60 ragg_1.2.5 pkgconfig_2.0.3 +[41] desc_1.4.2 pkgdown_2.0.7 bslib_0.5.1 pillar_1.9.0 +[45] gtable_0.3.3 glue_1.6.2 systemfonts_1.0.4 xfun_0.40 +[49] tibble_3.2.1 lmtest_0.9-40 tidyselect_1.2.0 rstudioapi_0.15.0 +[53] htmltools_0.5.6.1 rmarkdown_2.23 compiler_4.3.1

    References diff --git a/docs/articles/web_only/multistart.html b/docs/articles/web_only/multistart.html index b9224bb0..dff087e4 100644 --- a/docs/articles/web_only/multistart.html +++ b/docs/articles/web_only/multistart.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6

    @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -134,7 +137,7 @@ Ranke

    Last change 20 April 2023 -(rebuilt 2023-05-19)

    +(rebuilt 2023-10-30) Source: vignettes/web_only/multistart.rmd diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png index f41dc889..8be6ba61 100644 Binary files a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png and b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png differ diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png index 9e206791..bc518c0c 100644 Binary files a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png and b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png index c8e918cd..9ecdf4c9 100644 Binary files a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png and b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/docs/articles/web_only/saem_benchmarks.html b/docs/articles/web_only/saem_benchmarks.html index a9637876..095a37e2 100644 --- a/docs/articles/web_only/saem_benchmarks.html +++ b/docs/articles/web_only/saem_benchmarks.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6
    @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
  • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
  • @@ -134,7 +137,7 @@ Ranke

    Last change 17 February 2023 -(rebuilt 2023-05-19)

    +(rebuilt 2023-10-30) Source: vignettes/web_only/saem_benchmarks.rmd @@ -190,7 +193,7 @@ explanation of the following preprocessing.

     anova(
       sfo_const, dfop_const, sforb_const, hs_const,
    -  sfo_tc, dfop_tc, sforb_tc, hs_tc) |> kable(, digits = 1)
    + sfo_tc, dfop_tc, sforb_tc, hs_tc) |> kable(, digits = 1)
    @@ -330,7 +333,17 @@ systems. All trademarks belong to their respective owners.

    Parent only

    Constant variance for SFO, DFOP, SFORB and HS.

    -
    +
    ++++++++++ @@ -412,10 +425,30 @@ systems. All trademarks belong to their respective owners.

    + + + + + + + + + +
    CPU OS 1.987 2.055
    Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHzLinux1.2.63.22.9986.5236.1264.721

    Two-component error fits for SFO, DFOP, SFORB and HS.

    - +
    ++++++++++ @@ -497,6 +530,16 @@ systems. All trademarks belong to their respective owners.

    + + + + + + + + + +
    CPU OS 3.433 3.595
    Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHzLinux1.2.63.25.0708.4648.5257.599
    @@ -505,6 +548,14 @@ systems. All trademarks belong to their respective owners.

    Two-component error for DFOP-SFO and SFORB-SFO.

    ++++++++ @@ -570,6 +621,14 @@ systems. All trademarks belong to their respective owners.

    + + + + + + + +
    CPU OS 12.160 265.934
    Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHzLinux1.2.63.230.168748.675
    @@ -577,7 +636,14 @@ systems. All trademarks belong to their respective owners.

    Three metabolites

    Two-component error for SFORB-SFO3-plus

    - +
    +++++++ @@ -635,6 +701,13 @@ systems. All trademarks belong to their respective owners.

    + + + + + + +
    CPU OS 3.2 456.252
    Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHzLinux1.2.63.21235.028
    diff --git a/docs/index.html b/docs/index.html index 79dadd3b..eb3fe7f9 100644 --- a/docs/index.html +++ b/docs/index.html @@ -242,6 +242,7 @@

    Thanks to everyone involved for collaboration and support!

    Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for her interest and support for using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data.

    +

    Regarding the application of nonlinear mixed-effects models to degradation data, von Götz et al (1999) have already proposed to use this technique in the context of environmental risk assessments of pesticides. However, this work was apparently not followed up, which is why we had to independently arrive at the idea and missed to cite this previous work on the topic in our first publications.

    References @@ -249,6 +250,11 @@ + + + @@ -262,6 +268,11 @@ Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical D Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data Environmental Sciences Europe30 17 doi:10.1186/s12302-018-0145-1 + + +
    +Ranke J (2023) Application of nonlinear hierarchical models to the kinetic evaluation of chemical degradation data - Guidance for the use of an R markdown template file. Umweltbundesamt TEXTE 151/2023 +
    Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. Environments 8 (8) 71 doi:10.3390/environments8080071
    +Von Götz N, Nörtersheuser P, Richter O (1999) Population based analysis of pesticide kinetics Chemosphere 38 7 doi:10.1016/S0045-6535(98)00388-9 +

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