From af7c6de4db9981ac814362c441fbac22c8faa2d7 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 24 Nov 2022 09:02:26 +0100 Subject: Start online docs of the development version --- docs/dev/reference/dimethenamid_2018.html | 126 +++++++++++++++++------------- 1 file changed, 70 insertions(+), 56 deletions(-) (limited to 'docs/dev/reference/dimethenamid_2018.html') diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index 2454a609..96ec73c6 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -22,7 +22,7 @@ constrained by data protection regulations."> mkin - 1.1.2 + 1.2.2 @@ -49,11 +49,14 @@ constrained by data protection regulations.">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models
  • - Example evaluation of FOCUS Example Dataset Z + Short demo of the multistart method
  • Performance benefit by using compiled model definitions in mkin
    • +
  • + Example evaluation of FOCUS Example Dataset Z +
  • Calculation of time weighted average concentrations with mkin
    • @@ -61,7 +64,10 @@ constrained by data protection regulations.">Example evaluation of NAFTA SOP Attachment examples
  • - Some benchmark timings + Benchmark timings for mkin +
    • +
  • + Benchmark timings for saem.mmkin
  • @@ -180,17 +186,15 @@ specific pieces of information in the comments.

    # influence of ill-defined rate constants that have # extremely small values: plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = FALSE) - # If we disregards ill-defined rate constants, the results # look more plausible, but the truth is likely to be in # between these variants plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE) - + # We can also specify a default value for the failing # log parameters, to mimic FOCUS guidance plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE, default_log_parms = log(2)/1000) - # As these attempts are not satisfying, we use nonlinear mixed-effects models # f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc) # nlme reaches maxIter = 50 without convergence @@ -200,11 +204,11 @@ specific pieces of information in the comments.

    # graphics device used) #saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence") summary(f_dmta_saem_tc) -#> saemix version used for fitting: 3.1 -#> mkin version used for pre-fitting: 1.1.2 -#> R version used for fitting: 4.2.1 -#> Date of fit: Fri Sep 16 10:29:07 2022 -#> Date of summary: Fri Sep 16 10:29:07 2022 +#> saemix version used for fitting: 3.2 +#> mkin version used for pre-fitting: 1.2.2 +#> R version used for fitting: 4.2.2 +#> Date of fit: Thu Nov 24 08:05:16 2022 +#> Date of summary: Thu Nov 24 08:05:16 2022 #> #> Equations: #> d_DMTA/dt = - k_DMTA * DMTA @@ -217,7 +221,7 @@ specific pieces of information in the comments.

    #> #> Model predictions using solution type deSolve #> -#> Fitted in 797.539 s +#> Fitted in 819.725 s #> Using 300, 100 iterations and 9 chains #> #> Variance model: Two-component variance function @@ -235,69 +239,79 @@ specific pieces of information in the comments.

    #> #> Likelihood computed by importance sampling #> AIC BIC logLik -#> 2276 2272 -1120 +#> 2276 2273 -1120 #> #> Optimised parameters: -#> est. lower upper -#> DMTA_0 88.5943 84.3961 92.7925 -#> log_k_DMTA -3.0466 -3.5609 -2.5322 -#> log_k_M23 -4.0684 -4.9340 -3.2029 -#> log_k_M27 -3.8628 -4.2627 -3.4628 -#> log_k_M31 -3.9803 -4.4804 -3.4801 -#> f_DMTA_ilr_1 0.1304 -0.2186 0.4795 -#> f_DMTA_ilr_2 0.1490 -0.2559 0.5540 -#> f_DMTA_ilr_3 -1.3970 -1.6976 -1.0964 +#> est. lower upper +#> DMTA_0 88.3192 83.8656 92.7729 +#> log_k_DMTA -3.0530 -3.5686 -2.5373 +#> log_k_M23 -4.0620 -4.9202 -3.2038 +#> log_k_M27 -3.8633 -4.2668 -3.4598 +#> log_k_M31 -3.9731 -4.4763 -3.4699 +#> f_DMTA_ilr_1 0.1346 -0.2150 0.4841 +#> f_DMTA_ilr_2 0.1449 -0.2593 0.5491 +#> f_DMTA_ilr_3 -1.3882 -1.7011 -1.0753 +#> a.1 0.9156 0.8229 1.0084 +#> b.1 0.1383 0.1215 0.1551 +#> SD.DMTA_0 3.7280 -0.6951 8.1511 +#> SD.log_k_DMTA 0.6431 0.2781 1.0080 +#> SD.log_k_M23 1.0096 0.3782 1.6409 +#> SD.log_k_M27 0.4583 0.1541 0.7625 +#> SD.log_k_M31 0.5738 0.1942 0.9533 +#> SD.f_DMTA_ilr_1 0.4119 0.1528 0.6709 +#> SD.f_DMTA_ilr_2 0.4780 0.1806 0.7754 +#> SD.f_DMTA_ilr_3 0.3657 0.1383 0.5931 #> #> Correlation: #> DMTA_0 l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2 -#> log_k_DMTA 0.0309 -#> log_k_M23 -0.0231 -0.0031 -#> log_k_M27 -0.0381 -0.0048 0.0039 -#> log_k_M31 -0.0251 -0.0031 0.0021 0.0830 -#> f_DMTA_ilr_1 -0.0046 -0.0006 0.0417 -0.0437 0.0328 -#> f_DMTA_ilr_2 -0.0008 -0.0002 0.0214 -0.0270 -0.0909 -0.0361 -#> f_DMTA_ilr_3 -0.1832 -0.0135 0.0434 0.0804 0.0395 -0.0070 0.0059 +#> log_k_DMTA 0.0303 +#> log_k_M23 -0.0229 -0.0032 +#> log_k_M27 -0.0372 -0.0049 0.0041 +#> log_k_M31 -0.0245 -0.0032 0.0022 0.0815 +#> f_DMTA_ilr_1 -0.0046 -0.0006 0.0415 -0.0433 0.0324 +#> f_DMTA_ilr_2 -0.0008 -0.0002 0.0214 -0.0267 -0.0893 -0.0361 +#> f_DMTA_ilr_3 -0.1755 -0.0135 0.0423 0.0775 0.0377 -0.0066 0.0060 #> #> Random effects: #> est. lower upper -#> SD.DMTA_0 3.3651 -0.9649 7.6951 -#> SD.log_k_DMTA 0.6415 0.2774 1.0055 -#> SD.log_k_M23 1.0176 0.3809 1.6543 -#> SD.log_k_M27 0.4538 0.1522 0.7554 -#> SD.log_k_M31 0.5684 0.1905 0.9464 -#> SD.f_DMTA_ilr_1 0.4111 0.1524 0.6699 -#> SD.f_DMTA_ilr_2 0.4788 0.1808 0.7768 -#> SD.f_DMTA_ilr_3 0.3501 0.1316 0.5685 +#> SD.DMTA_0 3.7280 -0.6951 8.1511 +#> SD.log_k_DMTA 0.6431 0.2781 1.0080 +#> SD.log_k_M23 1.0096 0.3782 1.6409 +#> SD.log_k_M27 0.4583 0.1541 0.7625 +#> SD.log_k_M31 0.5738 0.1942 0.9533 +#> SD.f_DMTA_ilr_1 0.4119 0.1528 0.6709 +#> SD.f_DMTA_ilr_2 0.4780 0.1806 0.7754 +#> SD.f_DMTA_ilr_3 0.3657 0.1383 0.5931 #> #> Variance model: -#> est. lower upper -#> a.1 0.9349 0.8409 1.029 -#> b.1 0.1344 0.1178 0.151 +#> est. lower upper +#> a.1 0.9156 0.8229 1.0084 +#> b.1 0.1383 0.1215 0.1551 #> #> Backtransformed parameters: #> est. lower upper -#> DMTA_0 88.59431 84.396147 92.79246 -#> k_DMTA 0.04752 0.028413 0.07948 -#> k_M23 0.01710 0.007198 0.04064 -#> k_M27 0.02101 0.014084 0.03134 -#> k_M31 0.01868 0.011329 0.03080 -#> f_DMTA_to_M23 0.14498 NA NA -#> f_DMTA_to_M27 0.12056 NA NA -#> f_DMTA_to_M31 0.11015 NA NA +#> DMTA_0 88.31924 83.865625 92.77286 +#> k_DMTA 0.04722 0.028196 0.07908 +#> k_M23 0.01721 0.007298 0.04061 +#> k_M27 0.02100 0.014027 0.03144 +#> k_M31 0.01882 0.011375 0.03112 +#> f_DMTA_to_M23 0.14608 NA NA +#> f_DMTA_to_M27 0.12077 NA NA +#> f_DMTA_to_M31 0.11123 NA NA #> #> Resulting formation fractions: #> ff -#> DMTA_M23 0.1450 -#> DMTA_M27 0.1206 -#> DMTA_M31 0.1101 -#> DMTA_sink 0.6243 +#> DMTA_M23 0.1461 +#> DMTA_M27 0.1208 +#> DMTA_M31 0.1112 +#> DMTA_sink 0.6219 #> #> Estimated disappearance times: #> DT50 DT90 -#> DMTA 14.59 48.45 -#> M23 40.52 134.62 -#> M27 32.99 109.60 -#> M31 37.11 123.26 +#> DMTA 14.68 48.76 +#> M23 40.27 133.76 +#> M27 33.01 109.65 +#> M31 36.84 122.38 # As the confidence interval for the random effects of DMTA_0 # includes zero, we could try an alternative model without # such random effects -- cgit v1.2.1