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<h1>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018</h1>
<small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/dimethenamid_2018.R'><code>R/dimethenamid_2018.R</code></a></small>
<div class="hidden name"><code>dimethenamid_2018.Rd</code></div>
</div>
<div class="ref-description">
<p>The datasets were extracted from the active substance evaluation dossier
published by EFSA. Kinetic evaluations shown for these datasets are intended
to illustrate and advance kinetic modelling. The fact that these data and
some results are shown here does not imply a license to use them in the
context of pesticide registrations, as the use of the data may be
constrained by data protection regulations.</p>
</div>
<pre class="usage"><span class='va'>dimethenamid_2018</span></pre>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<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)
Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour
Rev. 2 - November 2017
<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
in the 'dataset_generation' directory. In the code, page numbers are given for
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 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
#> 3 7 7 7 4
#> Time normalisation factors $f_time_norm:
#> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938
#> Meta information $meta:
#> study usda_soil_type study_moisture_ref_type rel_moisture
#> Calke Unsworth 2014 Sandy loam pF2 1.00
#> Borstel Staudenmaier 2009 Sand pF1 0.50
#> Elliot 1 Wendt 1997 Clay loam pF2.5 0.75
#> Elliot 2 Wendt 1997 Clay loam pF2.5 0.75
#> Flaach König 1996 Sandy clay loam pF1 0.40
#> BBA 2.2 König 1995 Loamy sand pF1 0.40
#> BBA 2.3 König 1995 Sandy loam pF1 0.40
#> study_ref_moisture temperature
#> Calke NA 20
#> Borstel 23.00 20
#> Elliot 1 33.37 23
#> Elliot 2 33.37 23
#> Flaach NA 20
#> BBA 2.2 NA 20
#> BBA 2.3 NA 20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>7</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span>
<span class='va'>ds_i</span> <span class='op'><-</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</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'>"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 = 98.7132391714013
#> eta.DMTA_0 ~ 2.32692496033921
#> log_k_M23 = -3.92162409637283
#> eta.log_k_M23 ~ 0.549278519419884
#> 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 = -2.17879574608926
#> eta.f_DMTA_tffm0_2_qlogis ~ 0.3
#> f_DMTA_tffm0_3_qlogis = -2.14036526460782
#> eta.f_DMTA_tffm0_3_qlogis ~ 0.3
#> sigma_low_DMTA = 0.700117227383809
#> rsd_high_DMTA = 0.0257724286053519
#> sigma_low_M23 = 0.700117227383809
#> rsd_high_M23 = 0.0257724286053519
#> sigma_low_M27 = 0.700117227383809
#> rsd_high_M27 = 0.0257724286053519
#> sigma_low_M31 = 0.700117227383809
#> 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: 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>
<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 1.1.1 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
#> 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
#> 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 *
#> 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:
#> 563 observations of 4 variable(s) grouped in 6 datasets
#>
#> Degradation model predictions using RxODE
#>
#> 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
#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388
#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis
#> -1.7554 -2.2352 -3.7758 0.4363
#>
#> Mean of starting values for error model parameters:
#> sigma_low rsd_high
#> 0.70012 0.02577
#>
#> Fixed degradation parameter values:
#> None
#>
#> Results:
#>
#> Likelihood calculated by focei
#> AIC BIC logLik
#> 1918 2014 -937.2
#>
#> Optimised parameters:
#> est. lower upper
#> 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
#> 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 2.327 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 0.8552 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
#> 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 0.901 0.000 0.000
#> eta.log_k2 0.000 1.577 0.000
#> 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
#> 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
#> 0.70012 0.02577
#>
#> Backtransformed parameters:
#> est. lower upper
#> DMTA_0 98.71324 98.68012 98.74636
#> k_M23 0.01981 0.01977 0.01985
#> 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 0.10993
#> DMTA_M27 0.09049
#> DMTA_M31 0.08414
#> DMTA_sink 0.71543
#>
#> Estimated disappearance times:
#> DT50 DT90 DT50back DT50_k1 DT50_k2
#> DMTA 10.72 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] "Thu Sep 16 14:06:56 2021"
#> ....
#> Minimisation finished
#> [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>
<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: 98.3400 -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: 2.0.5
#> 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 *
#> 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:
#> 563 observations of 4 variable(s) grouped in 6 datasets
#>
#> Degradation model predictions using RxODE
#>
#> 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
#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388
#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis
#> -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
#> 0.70012 0.02577 0.70012 0.02577 0.70012
#> rsd_high_M27 sigma_low_M31 rsd_high_M31
#> 0.02577 0.70012 0.02577
#>
#> Fixed degradation parameter values:
#> None
#>
#> Results:
#>
#> Likelihood calculated by focei
#> AIC BIC logLik
#> 1953 2074 -948.3
#>
#> Optimised parameters:
#> est. lower upper
#> 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 -0.019
#> 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
#> log_k_M31
#> log_k1
#> log_k2
#> g_qlogis
#> f_DMTA_tffm0_1_qlogis
#> 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 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
#> 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 0.4814 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
#> 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 0.1508 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
#> 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 0.3155
#>
#> Variance model:
#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27
#> 0.95572 0.03325 0.47871 0.10733 0.68264
#> rsd_high_M27 sigma_low_M31 rsd_high_M31
#> 0.07072 0.78486 0.03557
#>
#> Backtransformed parameters:
#> est. lower upper
#> 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 0.10668
#> DMTA_M27 0.09481
#> DMTA_M31 0.08908
#> DMTA_sink 0.70943
#>
#> Estimated disappearance times:
#> DT50 DT90 DT50back DT50_k1 DT50_k2
#> 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>
</div></pre>
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