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Diffstat (limited to 'docs/dev/reference/dimethenamid_2018.html')
| -rw-r--r-- | docs/dev/reference/dimethenamid_2018.html | 320 |
1 files changed, 157 insertions, 163 deletions
diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index a77cf0f4..919e9363 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -162,7 +162,7 @@ constrained by data protection regulations.</p> <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> - <p>An <a href='mkindsg.html'>mkindsg</a> object grouping eight datasets with some meta information</p> + <p>An <a href='mkindsg.html'>mkindsg</a> object grouping seven datasets with some meta information</p> <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) @@ -177,42 +177,36 @@ specific pieces of information in the comments.</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>)</span> -</div><div class='output co'>#> <mkindsg> holding 8 mkinds objects +</div><div class='output co'>#> <mkindsg> holding 7 mkinds objects #> Title $title: Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 #> Occurrence of observed compounds $observed_n: #> DMTAP M23 M27 M31 DMTA -#> 4 7 7 7 4 +#> 3 7 7 7 4 #> Time normalisation factors $f_time_norm: -#> [1] 1.0000000 0.9706477 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 -#> [8] 0.6733938 +#> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938 #> Meta information $meta: -#> study usda_soil_type study_moisture_ref_type -#> Calke Unsworth 2014 Sandy loam pF2 -#> Borstel 1 Staudenmaier 2013 Sand pF1 -#> Borstel 2 Staudenmaier 2009 Sand pF1 -#> Elliot 1 Wendt 1997 Clay loam pF2.5 -#> Elliot 2 Wendt 1997 Clay loam pF2.5 -#> Flaach König 1996 Sandy clay loam pF1 -#> BBA 2.2 König 1995 Loamy sand pF1 -#> BBA 2.3 König 1995 Sandy loam pF1 -#> rel_moisture study_ref_moisture temperature -#> Calke 1.00 NA 20 -#> Borstel 1 0.50 23.00 20 -#> Borstel 2 0.50 23.00 20 -#> Elliot 1 0.75 33.37 23 -#> Elliot 2 0.75 33.37 23 -#> Flaach 0.40 NA 20 -#> BBA 2.2 0.40 NA 20 -#> BBA 2.3 0.40 NA 20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span> +#> study usda_soil_type study_moisture_ref_type rel_moisture +#> Calke Unsworth 2014 Sandy loam pF2 1.00 +#> Borstel Staudenmaier 2009 Sand pF1 0.50 +#> Elliot 1 Wendt 1997 Clay loam pF2.5 0.75 +#> Elliot 2 Wendt 1997 Clay loam pF2.5 0.75 +#> Flaach König 1996 Sandy clay loam pF1 0.40 +#> BBA 2.2 König 1995 Loamy sand pF1 0.40 +#> BBA 2.3 König 1995 Sandy loam pF1 0.40 +#> study_ref_moisture temperature +#> Calke NA 20 +#> Borstel 23.00 20 +#> Elliot 1 33.37 23 +#> Elliot 2 33.37 23 +#> Flaach NA 20 +#> BBA 2.2 NA 20 +#> BBA 2.3 NA 20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>7</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span> <span class='va'>ds_i</span> <span class='op'><-</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>ds</span><span class='op'>[[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span> <span class='va'>ds_i</span><span class='op'>[</span><span class='va'>ds_i</span><span class='op'>$</span><span class='va'>name</span> <span class='op'>==</span> <span class='st'>"DMTAP"</span>, <span class='st'>"name"</span><span class='op'>]</span> <span class='op'><-</span> <span class='st'>"DMTA"</span> <span class='va'>ds_i</span><span class='op'>$</span><span class='va'>time</span> <span class='op'><-</span> <span class='va'>ds_i</span><span class='op'>$</span><span class='va'>time</span> <span class='op'>*</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>f_time_norm</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span> <span class='va'>ds_i</span> <span class='op'>}</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>sapply</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>ds</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>ds</span><span class='op'>)</span> <span class='va'>ds</span><span class='op'>$</span><span class='va'>title</span><span class='op'>)</span> -<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 1"</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 2"</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span> -<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 1"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> -<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 2"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 1"</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 2"</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span> <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 1"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 2"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> @@ -231,33 +225,33 @@ specific pieces of information in the comments.</p> </div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> function () #> { #> ini({ -#> DMTA_0 = 98.7697627680706 -#> eta.DMTA_0 ~ 2.35171765917765 +#> DMTA_0 = 98.7132391714013 +#> eta.DMTA_0 ~ 2.32692496033921 #> log_k_M23 = -3.92162409637283 #> eta.log_k_M23 ~ 0.549278519419884 -#> log_k_M27 = -4.33774620773911 -#> eta.log_k_M27 ~ 0.864474956685295 -#> log_k_M31 = -4.24767627688461 -#> eta.log_k_M31 ~ 0.750297149164171 -#> log_k1 = -2.2341008812259 -#> eta.log_k1 ~ 0.902976221565793 -#> log_k2 = -3.7762779983269 -#> eta.log_k2 ~ 1.57684519529298 -#> g_qlogis = 0.450175725479389 -#> eta.g_qlogis ~ 3.0851335687675 -#> f_DMTA_tffm0_1_qlogis = -2.09240906629456 +#> log_k_M27 = -4.33057580082049 +#> eta.log_k_M27 ~ 0.855184233768426 +#> log_k_M31 = -4.24415516780733 +#> eta.log_k_M31 ~ 0.745746058085877 +#> log_k1 = -2.23515804885306 +#> eta.log_k1 ~ 0.901033446532357 +#> log_k2 = -3.77581484944379 +#> eta.log_k2 ~ 1.57682329638124 +#> g_qlogis = 0.436302910942805 +#> eta.g_qlogis ~ 3.10190528862808 +#> f_DMTA_tffm0_1_qlogis = -2.0914852208395 #> eta.f_DMTA_tffm0_1_qlogis ~ 0.3 -#> f_DMTA_tffm0_2_qlogis = -2.18057573598794 +#> f_DMTA_tffm0_2_qlogis = -2.17879574608926 #> eta.f_DMTA_tffm0_2_qlogis ~ 0.3 -#> f_DMTA_tffm0_3_qlogis = -2.14267187609763 +#> f_DMTA_tffm0_3_qlogis = -2.14036526460782 #> eta.f_DMTA_tffm0_3_qlogis ~ 0.3 -#> sigma_low_DMTA = 0.697933852349996 +#> sigma_low_DMTA = 0.700117227383809 #> rsd_high_DMTA = 0.0257724286053519 -#> sigma_low_M23 = 0.697933852349996 +#> sigma_low_M23 = 0.700117227383809 #> rsd_high_M23 = 0.0257724286053519 -#> sigma_low_M27 = 0.697933852349996 +#> sigma_low_M27 = 0.700117227383809 #> rsd_high_M27 = 0.0257724286053519 -#> sigma_low_M31 = 0.697933852349996 +#> sigma_low_M31 = 0.700117227383809 #> rsd_high_M31 = 0.0257724286053519 #> }) #> model({ @@ -295,7 +289,7 @@ specific pieces of information in the comments.</p> #> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) #> }) #> } -#> <environment: 0x555559ac3820></div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span> +#> <environment: 0x555559e97ac0></div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span> <span class='va'>f_dmta_nlmixr_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/foceiControl.html'>foceiControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span><span class='op'>)</span><span class='op'>)</span> @@ -308,7 +302,7 @@ specific pieces of information in the comments.</p> #> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 #> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 -#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>RxODE 1.1.0 using 8 threads (see ?getRxThreads)</span> +#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>RxODE 1.1.1 using 8 threads (see ?getRxThreads)</span> #> <span class='message'> no cache: create with `rxCreateCache()`</span></div><div class='output co'>#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation #> F: Forward difference gradient approximation #> C: Central difference gradient approximation @@ -324,12 +318,12 @@ specific pieces of information in the comments.</p> #> <span style='text-decoration: underline;'>|.....................| o9 | o10 |...........|...........|</span> #> calculating covariance matrix #> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: S matrix non-positive definite</span></div><div class='output co'>#> <span class='warning'>Warning: using R matrix to calculate covariance</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='output co'>#> user system elapsed -#> 232.621 14.126 246.850 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> -</div><div class='output co'>#> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.0.5 -#> R version used for fitting: 4.1.0 -#> Date of fit: Wed Aug 4 15:53:54 2021 -#> Date of summary: Wed Aug 4 15:53:54 2021 +#> 230.015 8.962 238.957 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> +</div><div class='output co'>#> nlmixr version used for fitting: 2.0.5 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.1 +#> Date of fit: Thu Sep 16 14:06:55 2021 +#> Date of summary: Thu Sep 16 14:06:55 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -346,23 +340,23 @@ specific pieces of information in the comments.</p> #> exp(-k2 * time))) * DMTA - k_M31 * M31 #> #> Data: -#> 568 observations of 4 variable(s) grouped in 6 datasets +#> 563 observations of 4 variable(s) grouped in 6 datasets #> #> Degradation model predictions using RxODE #> -#> Fitted in 246.669 s +#> Fitted in 238.792 s #> #> Variance model: Two-component variance function #> #> Mean of starting values for individual parameters: #> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 -#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 #> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis -#> -1.7571 -2.2341 -3.7763 0.4502 +#> -1.7554 -2.2352 -3.7758 0.4363 #> #> Mean of starting values for error model parameters: #> sigma_low rsd_high -#> 0.69793 0.02577 +#> 0.70012 0.02577 #> #> Fixed degradation parameter values: #> None @@ -371,20 +365,20 @@ specific pieces of information in the comments.</p> #> #> Likelihood calculated by focei #> AIC BIC logLik -#> 1936 2031 -945.9 +#> 1918 2014 -937.2 #> #> Optimised parameters: #> est. lower upper -#> DMTA_0 98.7698 98.7356 98.8039 -#> log_k_M23 -3.9216 -3.9235 -3.9197 -#> log_k_M27 -4.3377 -4.3398 -4.3357 -#> log_k_M31 -4.2477 -4.2497 -4.2457 -#> log_k1 -2.2341 -2.2353 -2.2329 -#> log_k2 -3.7763 -3.7781 -3.7744 -#> g_qlogis 0.4502 0.4496 0.4507 -#> f_DMTA_tffm0_1_qlogis -2.0924 -2.0936 -2.0912 -#> f_DMTA_tffm0_2_qlogis -2.1806 -2.1818 -2.1794 -#> f_DMTA_tffm0_3_qlogis -2.1427 -2.1439 -2.1415 +#> DMTA_0 98.7132 98.6801 98.7464 +#> log_k_M23 -3.9216 -3.9235 -3.9198 +#> log_k_M27 -4.3306 -4.3326 -4.3286 +#> log_k_M31 -4.2442 -4.2461 -4.2422 +#> log_k1 -2.2352 -2.2364 -2.2340 +#> log_k2 -3.7758 -3.7776 -3.7740 +#> g_qlogis 0.4363 0.4358 0.4368 +#> f_DMTA_tffm0_1_qlogis -2.0915 -2.0926 -2.0903 +#> f_DMTA_tffm0_2_qlogis -2.1788 -2.1800 -2.1776 +#> f_DMTA_tffm0_3_qlogis -2.1404 -2.1415 -2.1392 #> #> Correlation: #> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs @@ -410,10 +404,10 @@ specific pieces of information in the comments.</p> #> #> Random effects (omega): #> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 -#> eta.DMTA_0 2.352 0.0000 0.0000 0.0000 +#> eta.DMTA_0 2.327 0.0000 0.0000 0.0000 #> eta.log_k_M23 0.000 0.5493 0.0000 0.0000 -#> eta.log_k_M27 0.000 0.0000 0.8645 0.0000 -#> eta.log_k_M31 0.000 0.0000 0.0000 0.7503 +#> eta.log_k_M27 0.000 0.0000 0.8552 0.0000 +#> eta.log_k_M31 0.000 0.0000 0.0000 0.7457 #> eta.log_k1 0.000 0.0000 0.0000 0.0000 #> eta.log_k2 0.000 0.0000 0.0000 0.0000 #> eta.g_qlogis 0.000 0.0000 0.0000 0.0000 @@ -425,9 +419,9 @@ specific pieces of information in the comments.</p> #> eta.log_k_M23 0.000 0.000 0.000 #> eta.log_k_M27 0.000 0.000 0.000 #> eta.log_k_M31 0.000 0.000 0.000 -#> eta.log_k1 0.903 0.000 0.000 +#> eta.log_k1 0.901 0.000 0.000 #> eta.log_k2 0.000 1.577 0.000 -#> eta.g_qlogis 0.000 0.000 3.085 +#> eta.g_qlogis 0.000 0.000 3.102 #> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.000 #> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.000 #> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.000 @@ -456,44 +450,44 @@ specific pieces of information in the comments.</p> #> #> Variance model: #> sigma_low rsd_high -#> 0.69793 0.02577 +#> 0.70012 0.02577 #> #> Backtransformed parameters: #> est. lower upper -#> DMTA_0 98.76976 98.73563 98.80390 +#> DMTA_0 98.71324 98.68012 98.74636 #> k_M23 0.01981 0.01977 0.01985 -#> k_M27 0.01307 0.01304 0.01309 -#> k_M31 0.01430 0.01427 0.01433 -#> f_DMTA_to_M23 0.10984 NA NA -#> f_DMTA_to_M27 0.09036 NA NA -#> f_DMTA_to_M31 0.08399 NA NA -#> k1 0.10709 0.10696 0.10722 -#> k2 0.02291 0.02287 0.02295 -#> g 0.61068 0.61055 0.61081 +#> k_M27 0.01316 0.01313 0.01319 +#> k_M31 0.01435 0.01432 0.01438 +#> f_DMTA_to_M23 0.10993 NA NA +#> f_DMTA_to_M27 0.09049 NA NA +#> f_DMTA_to_M31 0.08414 NA NA +#> k1 0.10698 0.10685 0.10710 +#> k2 0.02292 0.02288 0.02296 +#> g 0.60738 0.60725 0.60751 #> #> Resulting formation fractions: #> ff -#> DMTA_M23 0.10984 -#> DMTA_M27 0.09036 -#> DMTA_M31 0.08399 -#> DMTA_sink 0.71581 +#> DMTA_M23 0.10993 +#> DMTA_M27 0.09049 +#> DMTA_M31 0.08414 +#> DMTA_sink 0.71543 #> #> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> DMTA 10.66 59.78 18 6.473 30.26 -#> M23 34.99 116.24 NA NA NA -#> M27 53.05 176.23 NA NA NA -#> M31 48.48 161.05 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> DMTA 10.72 60.1 18.09 6.48 30.24 +#> M23 34.99 116.2 NA NA NA +#> M27 52.67 175.0 NA NA NA +#> M31 48.31 160.5 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> </div><div class='img'><img src='dimethenamid_2018-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># Using saemix takes about 18 minutes</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span> <span class='va'>f_dmta_saemix</span> <span class='op'><-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 15:53:55 2021" +#> [1] "Thu Sep 16 14:06:56 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:12:40 2021"</div><div class='output co'>#> user system elapsed -#> 1192.021 0.064 1192.182 </div><div class='input'> +#> [1] "Thu Sep 16 14:25:28 2021"</div><div class='output co'>#> user system elapsed +#> 1176.278 0.021 1176.388 </div><div class='input'> <span class='co'># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span> <span class='co'># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span> <span class='co'># The likelihood calculated for the nlmixr fit is much lower than that found by saemix</span> @@ -504,15 +498,15 @@ specific pieces of information in the comments.</p> <span class='va'>f_dmta_nlmixr_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/saemControl.html'>saemControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span>, logLik <span class='op'>=</span> <span class='cn'>TRUE</span>, nmc <span class='op'>=</span> <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 98.3427 -3.5148 -3.3187 -3.7728 -2.1163 -2.8457 0.9482 -2.8064 -2.7412 -2.8745 2.7912 0.6805 0.8213 0.8055 0.8578 1.4980 2.9309 0.2850 0.2854 0.2850 4.0990 0.3821 3.5349 0.6537 5.4143 0.0002 4.5093 0.1905 -#> 500: 97.8277 -4.3506 -4.0318 -4.1520 -3.0553 -3.5843 1.1326 -2.0873 -2.0421 -2.0751 0.2960 1.2515 0.2531 0.3807 0.7928 0.8863 6.5211 0.1433 0.1082 0.3353 0.8960 0.0470 0.7501 0.0475 0.9527 0.0281 0.7321 0.0594</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> user system elapsed -#> 813.299 3.736 151.935 </div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 98.3400 -3.5096 -3.3392 -3.7596 -2.2055 -2.7755 1.0281 -2.7872 -2.7223 -2.8341 2.6422 0.7027 0.8124 0.7085 0.8560 1.4980 3.2777 0.3063 0.2850 0.2850 4.1120 0.3716 4.4582 0.3994 4.4820 0.4025 3.7803 0.5780 +#> 500: 97.8212 -4.4030 -4.0872 -4.1289 -2.8278 -4.3505 2.6614 -2.1252 -2.1308 -2.0749 2.9463 1.2933 0.2802 0.3467 0.4814 0.7877 3.0743 0.1508 0.1523 0.3155 0.9557 0.0333 0.4787 0.1073 0.6826 0.0707 0.7849 0.0356</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> user system elapsed +#> 800.784 3.715 149.687 </div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> </div><div class='output co'>#> <span class='error'>Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> -</div><div class='output co'>#> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.0.5 -#> R version used for fitting: 4.1.0 -#> Date of fit: Wed Aug 4 16:16:18 2021 -#> Date of summary: Wed Aug 4 16:16:18 2021 +</div><div class='output co'>#> nlmixr version used for fitting: 2.0.5 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.1 +#> Date of fit: Thu Sep 16 14:29:02 2021 +#> Date of summary: Thu Sep 16 14:29:02 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -529,25 +523,25 @@ specific pieces of information in the comments.</p> #> exp(-k2 * time))) * DMTA - k_M31 * M31 #> #> Data: -#> 568 observations of 4 variable(s) grouped in 6 datasets +#> 563 observations of 4 variable(s) grouped in 6 datasets #> #> Degradation model predictions using RxODE #> -#> Fitted in 151.67 s +#> Fitted in 149.421 s #> #> Variance model: Two-component variance function #> #> Mean of starting values for individual parameters: #> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 -#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 #> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis -#> -1.7571 -2.2341 -3.7763 0.4502 +#> -1.7554 -2.2352 -3.7758 0.4363 #> #> Mean of starting values for error model parameters: #> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 -#> 0.69793 0.02577 0.69793 0.02577 0.69793 +#> 0.70012 0.02577 0.70012 0.02577 0.70012 #> rsd_high_M27 sigma_low_M31 rsd_high_M31 -#> 0.02577 0.69793 0.02577 +#> 0.02577 0.70012 0.02577 #> #> Fixed degradation parameter values: #> None @@ -556,32 +550,32 @@ specific pieces of information in the comments.</p> #> #> Likelihood calculated by focei #> AIC BIC logLik -#> 2036 2157 -989.8 +#> 1953 2074 -948.3 #> #> Optimised parameters: #> est. lower upper -#> DMTA_0 97.828 96.121 99.535 -#> log_k_M23 -4.351 -5.300 -3.401 -#> log_k_M27 -4.032 -4.470 -3.594 -#> log_k_M31 -4.152 -4.689 -3.615 -#> log_k1 -3.055 -3.785 -2.325 -#> log_k2 -3.584 -4.517 -2.651 -#> g_qlogis 1.133 -2.165 4.430 -#> f_DMTA_tffm0_1_qlogis -2.087 -2.407 -1.768 -#> f_DMTA_tffm0_2_qlogis -2.042 -2.336 -1.748 -#> f_DMTA_tffm0_3_qlogis -2.075 -2.557 -1.593 +#> DMTA_0 97.821 95.862 99.780 +#> log_k_M23 -4.403 -5.376 -3.430 +#> log_k_M27 -4.087 -4.545 -3.629 +#> log_k_M31 -4.129 -4.639 -3.618 +#> log_k1 -2.828 -3.389 -2.266 +#> log_k2 -4.351 -5.472 -3.229 +#> g_qlogis 2.661 0.824 4.499 +#> f_DMTA_tffm0_1_qlogis -2.125 -2.449 -1.801 +#> f_DMTA_tffm0_2_qlogis -2.131 -2.468 -1.794 +#> f_DMTA_tffm0_3_qlogis -2.075 -2.540 -1.610 #> #> Correlation: #> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs -#> log_k_M23 -0.031 -#> log_k_M27 -0.050 0.004 -#> log_k_M31 -0.032 0.003 0.078 -#> log_k1 0.014 -0.002 -0.002 -0.001 -#> log_k2 0.059 0.006 -0.001 0.002 -0.037 -#> g_qlogis -0.077 0.005 0.009 0.004 0.035 -0.201 -#> f_DMTA_tffm0_1_qlogis -0.104 0.066 0.009 0.006 0.000 -0.011 0.014 -#> f_DMTA_tffm0_2_qlogis -0.120 0.013 0.081 -0.033 -0.002 -0.013 0.017 -#> f_DMTA_tffm0_3_qlogis -0.086 0.010 0.060 0.078 -0.002 -0.005 0.010 +#> log_k_M23 -0.019 +#> log_k_M27 -0.028 0.004 +#> log_k_M31 -0.019 0.003 0.075 +#> log_k1 0.038 -0.004 -0.006 -0.003 +#> log_k2 0.046 0.011 0.008 0.009 0.068 +#> g_qlogis -0.067 0.004 0.006 0.001 -0.076 -0.409 +#> f_DMTA_tffm0_1_qlogis -0.062 0.055 0.006 0.004 -0.008 -0.004 0.012 +#> f_DMTA_tffm0_2_qlogis -0.062 0.010 0.058 -0.034 -0.008 -0.007 0.014 +#> f_DMTA_tffm0_3_qlogis -0.052 0.009 0.056 0.071 -0.006 -0.001 0.008 #> f_DMTA_0_1 f_DMTA_0_2 #> log_k_M23 #> log_k_M27 @@ -590,15 +584,15 @@ specific pieces of information in the comments.</p> #> log_k2 #> g_qlogis #> f_DMTA_tffm0_1_qlogis -#> f_DMTA_tffm0_2_qlogis 0.026 -#> f_DMTA_tffm0_3_qlogis 0.019 0.002 +#> f_DMTA_tffm0_2_qlogis 0.017 +#> f_DMTA_tffm0_3_qlogis 0.014 -0.005 #> #> Random effects (omega): #> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 -#> eta.DMTA_0 0.296 0.000 0.0000 0.0000 -#> eta.log_k_M23 0.000 1.252 0.0000 0.0000 -#> eta.log_k_M27 0.000 0.000 0.2531 0.0000 -#> eta.log_k_M31 0.000 0.000 0.0000 0.3807 +#> eta.DMTA_0 2.946 0.000 0.0000 0.0000 +#> eta.log_k_M23 0.000 1.293 0.0000 0.0000 +#> eta.log_k_M27 0.000 0.000 0.2802 0.0000 +#> eta.log_k_M31 0.000 0.000 0.0000 0.3467 #> eta.log_k1 0.000 0.000 0.0000 0.0000 #> eta.log_k2 0.000 0.000 0.0000 0.0000 #> eta.g_qlogis 0.000 0.000 0.0000 0.0000 @@ -610,9 +604,9 @@ specific pieces of information in the comments.</p> #> eta.log_k_M23 0.0000 0.0000 0.000 #> eta.log_k_M27 0.0000 0.0000 0.000 #> eta.log_k_M31 0.0000 0.0000 0.000 -#> eta.log_k1 0.7928 0.0000 0.000 -#> eta.log_k2 0.0000 0.8863 0.000 -#> eta.g_qlogis 0.0000 0.0000 6.521 +#> eta.log_k1 0.4814 0.0000 0.000 +#> eta.log_k2 0.0000 0.7877 0.000 +#> eta.g_qlogis 0.0000 0.0000 3.074 #> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.0000 0.000 #> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.0000 0.000 #> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 0.000 @@ -624,8 +618,8 @@ specific pieces of information in the comments.</p> #> eta.log_k1 0.0000 0.0000 #> eta.log_k2 0.0000 0.0000 #> eta.g_qlogis 0.0000 0.0000 -#> eta.f_DMTA_tffm0_1_qlogis 0.1433 0.0000 -#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1082 +#> eta.f_DMTA_tffm0_1_qlogis 0.1508 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1523 #> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 #> eta.f_DMTA_tffm0_3_qlogis #> eta.DMTA_0 0.0000 @@ -637,40 +631,40 @@ specific pieces of information in the comments.</p> #> eta.g_qlogis 0.0000 #> eta.f_DMTA_tffm0_1_qlogis 0.0000 #> eta.f_DMTA_tffm0_2_qlogis 0.0000 -#> eta.f_DMTA_tffm0_3_qlogis 0.3353 +#> eta.f_DMTA_tffm0_3_qlogis 0.3155 #> #> Variance model: #> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 -#> 0.89603 0.04704 0.75015 0.04753 0.95265 +#> 0.95572 0.03325 0.47871 0.10733 0.68264 #> rsd_high_M27 sigma_low_M31 rsd_high_M31 -#> 0.02810 0.73212 0.05942 +#> 0.07072 0.78486 0.03557 #> #> Backtransformed parameters: #> est. lower upper -#> DMTA_0 97.82774 96.120503 99.53498 -#> k_M23 0.01290 0.004991 0.03334 -#> k_M27 0.01774 0.011451 0.02749 -#> k_M31 0.01573 0.009195 0.02692 -#> f_DMTA_to_M23 0.11033 NA NA -#> f_DMTA_to_M27 0.10218 NA NA -#> f_DMTA_to_M31 0.08784 NA NA -#> k1 0.04711 0.022707 0.09773 -#> k2 0.02775 0.010918 0.07056 -#> g 0.75632 0.102960 0.98823 +#> DMTA_0 97.82122 95.862233 99.78020 +#> k_M23 0.01224 0.004625 0.03239 +#> k_M27 0.01679 0.010615 0.02654 +#> k_M31 0.01610 0.009664 0.02683 +#> f_DMTA_to_M23 0.10668 NA NA +#> f_DMTA_to_M27 0.09481 NA NA +#> f_DMTA_to_M31 0.08908 NA NA +#> k1 0.05914 0.033731 0.10370 +#> k2 0.01290 0.004204 0.03958 +#> g 0.93471 0.695081 0.98900 #> #> Resulting formation fractions: #> ff -#> DMTA_M23 0.11033 -#> DMTA_M27 0.10218 -#> DMTA_M31 0.08784 -#> DMTA_sink 0.69965 +#> DMTA_M23 0.10668 +#> DMTA_M27 0.09481 +#> DMTA_M31 0.08908 +#> DMTA_sink 0.70943 #> #> Estimated disappearance times: #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> DMTA 16.59 57.44 17.29 14.71 24.97 -#> M23 53.74 178.51 NA NA NA -#> M27 39.07 129.78 NA NA NA -#> M31 44.06 146.36 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> +#> DMTA 12.57 45.43 13.67 11.72 53.73 +#> M23 56.63 188.11 NA NA NA +#> M27 41.29 137.18 NA NA NA +#> M31 43.05 143.01 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> </div><div class='img'><img src='dimethenamid_2018-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> 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