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Diffstat (limited to 'docs/dev/reference/dimethenamid_2018.html')
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1 files changed, 472 insertions, 3 deletions
diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index 21dea623..160dcaa3 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -168,7 +168,7 @@ constrained by data protection regulations.</p> <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour Rev. 2 - November 2017 -<a href='http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211'>http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211</a></p> +<a href='https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716'>https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a></p> <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> <p>The R code used to create this data object is installed with this package @@ -203,7 +203,476 @@ specific pieces of information in the comments.</p> #> 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></pre> +#> 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> + <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> +<span class='co'># \dontrun{</span> +<span class='va'>dfop_sfo3_plus</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> + DMTA <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M23"</span>, <span class='st'>"M27"</span>, <span class='st'>"M31"</span><span class='op'>)</span><span class='op'>)</span>, + M23 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, + M27 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, + M31 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"M27"</span>, sink <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>, + quiet <span class='op'>=</span> <span class='cn'>TRUE</span> +<span class='op'>)</span> +<span class='va'>f_dmta_mkin_tc</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span> + <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"DFOP-SFO3+"</span> <span class='op'>=</span> <span class='va'>dfop_sfo3_plus</span><span class='op'>)</span>, + <span class='va'>dmta_ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> +<span class='fu'><a href='nlmixr.mmkin.html'>nlmixr_model</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</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'>#> function () +#> { +#> ini({ +#> DMTA_0 = 98.7697627680706 +#> eta.DMTA_0 ~ 2.35171765917765 +#> 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 +#> eta.f_DMTA_tffm0_1_qlogis ~ 0.3 +#> f_DMTA_tffm0_2_qlogis = -2.18057573598794 +#> eta.f_DMTA_tffm0_2_qlogis ~ 0.3 +#> f_DMTA_tffm0_3_qlogis = -2.14267187609763 +#> eta.f_DMTA_tffm0_3_qlogis ~ 0.3 +#> sigma_low_DMTA = 0.697933852349996 +#> rsd_high_DMTA = 0.0257724286053519 +#> sigma_low_M23 = 0.697933852349996 +#> rsd_high_M23 = 0.0257724286053519 +#> sigma_low_M27 = 0.697933852349996 +#> rsd_high_M27 = 0.0257724286053519 +#> sigma_low_M31 = 0.697933852349996 +#> rsd_high_M31 = 0.0257724286053519 +#> }) +#> model({ +#> DMTA_0_model = DMTA_0 + eta.DMTA_0 +#> DMTA(0) = DMTA_0_model +#> k_M23 = exp(log_k_M23 + eta.log_k_M23) +#> k_M27 = exp(log_k_M27 + eta.log_k_M27) +#> k_M31 = exp(log_k_M31 + eta.log_k_M31) +#> k1 = exp(log_k1 + eta.log_k1) +#> k2 = exp(log_k2 + eta.log_k2) +#> g = expit(g_qlogis + eta.g_qlogis) +#> f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) +#> f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) +#> f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) +#> f_DMTA_to_M23 = f_DMTA_tffm0_1 +#> f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1) +#> f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * +#> (1 - f_DMTA_tffm0_1) +#> d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - +#> g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - +#> g) * exp(-k2 * time))) * DMTA +#> d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + +#> k_M31 * M31 +#> d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31 +#> DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA) +#> M23 ~ add(sigma_low_M23) + prop(rsd_high_M23) +#> M27 ~ add(sigma_low_M27) + prop(rsd_high_M27) +#> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) +#> }) +#> } +#> <environment: 0x555559c00ce8></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> +<span class='op'>)</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'>→ 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'>→ 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'>→ calculate jacobian</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:02 +#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:04 +#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 +#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:08 +#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#> </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> +#> <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 +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 | +#> |.....................| log_k1 | log_k2 | g_qlogis |f_DMTA_tffm0_1_qlogis | +#> |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high | +#> |.....................| o1 | o2 | o3 | o4 | +#> |.....................| o5 | o6 | o7 | o8 | +#> <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 +#> 227.879 9.742 237.728 </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: 1.1.0 +#> R version used for fitting: 4.1.0 +#> Date of fit: Tue Jul 27 16:02:33 2021 +#> Date of summary: Tue Jul 27 16:02:34 2021 +#> +#> Equations: +#> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) +#> * DMTA +#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 +#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M31 * M31 +#> +#> Data: +#> 568 observations of 4 variable(s) grouped in 6 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 237.547 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 +#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis +#> -1.7571 -2.2341 -3.7763 0.4502 +#> +#> Mean of starting values for error model parameters: +#> sigma_low rsd_high +#> 0.69793 0.02577 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 1936 2031 -945.9 +#> +#> 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 +#> +#> Correlation: +#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs +#> log_k_M23 0 +#> log_k_M27 0 0 +#> log_k_M31 0 0 0 +#> log_k1 0 0 0 0 +#> log_k2 0 0 0 0 0 +#> g_qlogis 0 0 0 0 0 0 +#> f_DMTA_tffm0_1_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_tffm0_2_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_tffm0_3_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_0_1 f_DMTA_0_2 +#> log_k_M23 +#> log_k_M27 +#> log_k_M31 +#> log_k1 +#> log_k2 +#> g_qlogis +#> f_DMTA_tffm0_1_qlogis +#> f_DMTA_tffm0_2_qlogis 0 +#> f_DMTA_tffm0_3_qlogis 0 0 +#> +#> 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.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_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 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.log_k1 eta.log_k2 eta.g_qlogis +#> eta.DMTA_0 0.000 0.000 0.000 +#> 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_k2 0.000 1.577 0.000 +#> eta.g_qlogis 0.000 0.000 3.085 +#> 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 +#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis +#> eta.DMTA_0 0.0 0.0 +#> eta.log_k_M23 0.0 0.0 +#> eta.log_k_M27 0.0 0.0 +#> eta.log_k_M31 0.0 0.0 +#> eta.log_k1 0.0 0.0 +#> eta.log_k2 0.0 0.0 +#> eta.g_qlogis 0.0 0.0 +#> eta.f_DMTA_tffm0_1_qlogis 0.3 0.0 +#> eta.f_DMTA_tffm0_2_qlogis 0.0 0.3 +#> eta.f_DMTA_tffm0_3_qlogis 0.0 0.0 +#> eta.f_DMTA_tffm0_3_qlogis +#> eta.DMTA_0 0.0 +#> eta.log_k_M23 0.0 +#> eta.log_k_M27 0.0 +#> eta.log_k_M31 0.0 +#> eta.log_k1 0.0 +#> eta.log_k2 0.0 +#> eta.g_qlogis 0.0 +#> eta.f_DMTA_tffm0_1_qlogis 0.0 +#> eta.f_DMTA_tffm0_2_qlogis 0.0 +#> eta.f_DMTA_tffm0_3_qlogis 0.3 +#> +#> Variance model: +#> sigma_low rsd_high +#> 0.69793 0.02577 +#> +#> Backtransformed parameters: +#> est. lower upper +#> DMTA_0 98.76976 98.73563 98.80390 +#> 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 +#> +#> Resulting formation fractions: +#> ff +#> DMTA_M23 0.10984 +#> DMTA_M27 0.09036 +#> DMTA_M31 0.08399 +#> DMTA_sink 0.71581 +#> +#> 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> +</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] "Tue Jul 27 16:02:34 2021" +#> .... +#> Minimisation finished +#> [1] "Tue Jul 27 16:21:39 2021"</div><div class='output co'>#> user system elapsed +#> 1213.394 0.087 1213.578 </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> +<span class='co'># Also, the trace plot and the plot of the individual predictions is not</span> +<span class='co'># convincing for the parent. It seems we are fitting an overparameterised</span> +<span class='co'># model, so the result we get strongly depends on starting parameters and control settings.</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_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 +#> 818.782 3.808 154.926 </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: 1.1.0 +#> R version used for fitting: 4.1.0 +#> Date of fit: Tue Jul 27 16:25:23 2021 +#> Date of summary: Tue Jul 27 16:25:23 2021 +#> +#> Equations: +#> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) +#> * DMTA +#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 +#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M31 * M31 +#> +#> Data: +#> 568 observations of 4 variable(s) grouped in 6 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 154.632 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 +#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis +#> -1.7571 -2.2341 -3.7763 0.4502 +#> +#> 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 +#> rsd_high_M27 sigma_low_M31 rsd_high_M31 +#> 0.02577 0.69793 0.02577 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 2036 2157 -989.8 +#> +#> 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 +#> +#> 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 +#> f_DMTA_0_1 f_DMTA_0_2 +#> log_k_M23 +#> log_k_M27 +#> log_k_M31 +#> log_k1 +#> 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 +#> +#> 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.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 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.log_k1 eta.log_k2 eta.g_qlogis +#> eta.DMTA_0 0.0000 0.0000 0.000 +#> 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.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 +#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis +#> eta.DMTA_0 0.0000 0.0000 +#> eta.log_k_M23 0.0000 0.0000 +#> eta.log_k_M27 0.0000 0.0000 +#> eta.log_k_M31 0.0000 0.0000 +#> 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_3_qlogis 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis +#> eta.DMTA_0 0.0000 +#> eta.log_k_M23 0.0000 +#> eta.log_k_M27 0.0000 +#> eta.log_k_M31 0.0000 +#> eta.log_k1 0.0000 +#> eta.log_k2 0.0000 +#> 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 +#> +#> 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 +#> rsd_high_M27 sigma_low_M31 rsd_high_M31 +#> 0.02810 0.73212 0.05942 +#> +#> 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 +#> +#> Resulting formation fractions: +#> ff +#> DMTA_M23 0.11033 +#> DMTA_M27 0.10218 +#> DMTA_M31 0.08784 +#> DMTA_sink 0.69965 +#> +#> 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> +</div><div class='img'><img src='dimethenamid_2018-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> +</div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"> |