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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'>#&gt; &lt;mkindsg&gt; holding 7 mkinds objects
#&gt; Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 
#&gt; Occurrence of observed compounds $observed_n:
#&gt; DMTAP   M23   M27   M31  DMTA 
#&gt;     3     7     7     7     4 
#&gt; Time normalisation factors $f_time_norm:
#&gt; [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938
#&gt; Meta information $meta:
#&gt;                      study  usda_soil_type study_moisture_ref_type rel_moisture
#&gt; Calke        Unsworth 2014      Sandy loam                     pF2         1.00
#&gt; Borstel  Staudenmaier 2009            Sand                     pF1         0.50
#&gt; Elliot 1        Wendt 1997       Clay loam                   pF2.5         0.75
#&gt; Elliot 2        Wendt 1997       Clay loam                   pF2.5         0.75
#&gt; Flaach          König 1996 Sandy clay loam                     pF1         0.40
#&gt; BBA 2.2         König 1995      Loamy sand                     pF1         0.40
#&gt; BBA 2.3         König 1995      Sandy loam                     pF1         0.40
#&gt;          study_ref_moisture temperature
#&gt; Calke                    NA          20
#&gt; Borstel               23.00          20
#&gt; Elliot 1              33.37          23
#&gt; Elliot 2              33.37          23
#&gt; Flaach                   NA          20
#&gt; BBA 2.2                  NA          20
#&gt; BBA 2.3                  NA          20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>7</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='va'>ds_i</span> <span class='op'>&lt;-</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</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'>&lt;-</span>  <span class='st'>"DMTA"</span>
  <span class='va'>ds_i</span><span class='op'>$</span><span class='va'>time</span> <span class='op'>&lt;-</span> <span class='va'>ds_i</span><span class='op'>$</span><span class='va'>time</span> <span class='op'>*</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>f_time_norm</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span>
  <span class='va'>ds_i</span>
<span class='op'>}</span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>)</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>sapply</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>$</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'>&lt;-</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'>&lt;-</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'>&lt;-</span> <span class='cn'>NULL</span>
<span class='co'># \dontrun{</span>
<span class='va'>dfop_sfo3_plus</span> <span class='op'>&lt;-</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'>&lt;-</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'>#&gt; <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'>#&gt; function () 
#&gt; {
#&gt;     ini({
#&gt;         DMTA_0 = 98.7132391714013
#&gt;         eta.DMTA_0 ~ 2.32692496033921
#&gt;         log_k_M23 = -3.92162409637283
#&gt;         eta.log_k_M23 ~ 0.549278519419884
#&gt;         log_k_M27 = -4.33057580082049
#&gt;         eta.log_k_M27 ~ 0.855184233768426
#&gt;         log_k_M31 = -4.24415516780733
#&gt;         eta.log_k_M31 ~ 0.745746058085877
#&gt;         log_k1 = -2.23515804885306
#&gt;         eta.log_k1 ~ 0.901033446532357
#&gt;         log_k2 = -3.77581484944379
#&gt;         eta.log_k2 ~ 1.57682329638124
#&gt;         g_qlogis = 0.436302910942805
#&gt;         eta.g_qlogis ~ 3.10190528862808
#&gt;         f_DMTA_tffm0_1_qlogis = -2.0914852208395
#&gt;         eta.f_DMTA_tffm0_1_qlogis ~ 0.3
#&gt;         f_DMTA_tffm0_2_qlogis = -2.17879574608926
#&gt;         eta.f_DMTA_tffm0_2_qlogis ~ 0.3
#&gt;         f_DMTA_tffm0_3_qlogis = -2.14036526460782
#&gt;         eta.f_DMTA_tffm0_3_qlogis ~ 0.3
#&gt;         sigma_low_DMTA = 0.700117227383809
#&gt;         rsd_high_DMTA = 0.0257724286053519
#&gt;         sigma_low_M23 = 0.700117227383809
#&gt;         rsd_high_M23 = 0.0257724286053519
#&gt;         sigma_low_M27 = 0.700117227383809
#&gt;         rsd_high_M27 = 0.0257724286053519
#&gt;         sigma_low_M31 = 0.700117227383809
#&gt;         rsd_high_M31 = 0.0257724286053519
#&gt;     })
#&gt;     model({
#&gt;         DMTA_0_model = DMTA_0 + eta.DMTA_0
#&gt;         DMTA(0) = DMTA_0_model
#&gt;         k_M23 = exp(log_k_M23 + eta.log_k_M23)
#&gt;         k_M27 = exp(log_k_M27 + eta.log_k_M27)
#&gt;         k_M31 = exp(log_k_M31 + eta.log_k_M31)
#&gt;         k1 = exp(log_k1 + eta.log_k1)
#&gt;         k2 = exp(log_k2 + eta.log_k2)
#&gt;         g = expit(g_qlogis + eta.g_qlogis)
#&gt;         f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis)
#&gt;         f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis)
#&gt;         f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis)
#&gt;         f_DMTA_to_M23 = f_DMTA_tffm0_1
#&gt;         f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1)
#&gt;         f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * 
#&gt;             (1 - f_DMTA_tffm0_1)
#&gt;         d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - 
#&gt;             g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - 
#&gt;             g) * exp(-k2 * time))) * DMTA
#&gt;         d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + 
#&gt;             k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + 
#&gt;             (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23
#&gt;         d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + 
#&gt;             k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + 
#&gt;             (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + 
#&gt;             k_M31 * M31
#&gt;         d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + 
#&gt;             k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + 
#&gt;             (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31
#&gt;         DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA)
#&gt;         M23 ~ add(sigma_low_M23) + prop(rsd_high_M23)
#&gt;         M27 ~ add(sigma_low_M27) + prop(rsd_high_M27)
#&gt;         M31 ~ add(sigma_low_M31) + prop(rsd_high_M31)
#&gt;     })
#&gt; }
#&gt; &lt;environment: 0x555559e97ac0&gt;</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'>&lt;-</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'>#&gt; <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'>#&gt; <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'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <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'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <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'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:02 
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:04 
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:01 
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:08 
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:07 
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:07 
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; [1] "CMT"</div><div class='output co'>#&gt; <span class='message'>RxODE 1.1.1 using 8 threads (see ?getRxThreads)</span>
#&gt; <span class='message'>  no cache: create with `rxCreateCache()`</span></div><div class='output co'>#&gt; <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
#&gt; F: Forward difference gradient approximation
#&gt; C: Central difference gradient approximation
#&gt; M: Mixed forward and central difference gradient approximation
#&gt; Unscaled parameters for Omegas=chol(solve(omega));
#&gt; Diagonals are transformed, as specified by foceiControl(diagXform=)
#&gt; |-----+---------------+-----------+-----------+-----------+-----------|
#&gt; |    #| Objective Fun |    DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 |
#&gt; |.....................|    log_k1 |    log_k2 |  g_qlogis |f_DMTA_tffm0_1_qlogis |
#&gt; |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low |  rsd_high |
#&gt; |.....................|        o1 |        o2 |        o3 |        o4 |
#&gt; |.....................|        o5 |        o6 |        o7 |        o8 |
#&gt; <span style='text-decoration: underline;'>|.....................|        o9 |       o10 |...........|...........|</span>
#&gt; calculating covariance matrix
#&gt; done</div><div class='output co'>#&gt; <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#&gt; <span class='message'>done</span></div><div class='output co'>#&gt; <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#&gt; <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#&gt; <span class='warning'>Warning: S matrix non-positive definite</span></div><div class='output co'>#&gt; <span class='warning'>Warning: using R matrix to calculate covariance</span></div><div class='output co'>#&gt; <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='output co'>#&gt;    user  system elapsed 
#&gt; 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'>#&gt; nlmixr version used for fitting:    2.0.5 
#&gt; mkin version used for pre-fitting:  1.1.0 
#&gt; R version used for fitting:         4.1.1 
#&gt; Date of fit:     Thu Sep 16 14:06:55 2021 
#&gt; Date of summary: Thu Sep 16 14:06:55 2021 
#&gt; 
#&gt; Equations:
#&gt; d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
#&gt;            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
#&gt;            * DMTA
#&gt; d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
#&gt;            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
#&gt;            exp(-k2 * time))) * DMTA - k_M23 * M23
#&gt; d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
#&gt;            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
#&gt;            exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31
#&gt; d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
#&gt;            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
#&gt;            exp(-k2 * time))) * DMTA - k_M31 * M31
#&gt; 
#&gt; Data:
#&gt; 563 observations of 4 variable(s) grouped in 6 datasets
#&gt; 
#&gt; Degradation model predictions using RxODE
#&gt; 
#&gt; Fitted in 238.792 s
#&gt; 
#&gt; Variance model: Two-component variance function 
#&gt; 
#&gt; Mean of starting values for individual parameters:
#&gt;       DMTA_0    log_k_M23    log_k_M27    log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 
#&gt;      98.7132      -3.9216      -4.3306      -4.2442       0.1376       0.1388 
#&gt; f_DMTA_ilr_3       log_k1       log_k2     g_qlogis 
#&gt;      -1.7554      -2.2352      -3.7758       0.4363 
#&gt; 
#&gt; Mean of starting values for error model parameters:
#&gt; sigma_low  rsd_high 
#&gt;   0.70012   0.02577 
#&gt; 
#&gt; Fixed degradation parameter values:
#&gt; None
#&gt; 
#&gt; Results:
#&gt; 
#&gt; Likelihood calculated by focei  
#&gt;    AIC  BIC logLik
#&gt;   1918 2014 -937.2
#&gt; 
#&gt; Optimised parameters:
#&gt;                          est.   lower   upper
#&gt; DMTA_0                98.7132 98.6801 98.7464
#&gt; log_k_M23             -3.9216 -3.9235 -3.9198
#&gt; log_k_M27             -4.3306 -4.3326 -4.3286
#&gt; log_k_M31             -4.2442 -4.2461 -4.2422
#&gt; log_k1                -2.2352 -2.2364 -2.2340
#&gt; log_k2                -3.7758 -3.7776 -3.7740
#&gt; g_qlogis               0.4363  0.4358  0.4368
#&gt; f_DMTA_tffm0_1_qlogis -2.0915 -2.0926 -2.0903
#&gt; f_DMTA_tffm0_2_qlogis -2.1788 -2.1800 -2.1776
#&gt; f_DMTA_tffm0_3_qlogis -2.1404 -2.1415 -2.1392
#&gt; 
#&gt; Correlation: 
#&gt;                       DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs
#&gt; log_k_M23             0                                               
#&gt; log_k_M27             0      0                                        
#&gt; log_k_M31             0      0      0                                 
#&gt; log_k1                0      0      0      0                          
#&gt; log_k2                0      0      0      0      0                   
#&gt; g_qlogis              0      0      0      0      0      0            
#&gt; f_DMTA_tffm0_1_qlogis 0      0      0      0      0      0      0     
#&gt; f_DMTA_tffm0_2_qlogis 0      0      0      0      0      0      0     
#&gt; f_DMTA_tffm0_3_qlogis 0      0      0      0      0      0      0     
#&gt;                       f_DMTA_0_1 f_DMTA_0_2
#&gt; log_k_M23                                  
#&gt; log_k_M27                                  
#&gt; log_k_M31                                  
#&gt; log_k1                                     
#&gt; log_k2                                     
#&gt; g_qlogis                                   
#&gt; f_DMTA_tffm0_1_qlogis                      
#&gt; f_DMTA_tffm0_2_qlogis 0                    
#&gt; f_DMTA_tffm0_3_qlogis 0          0         
#&gt; 
#&gt; Random effects (omega):
#&gt;                           eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31
#&gt; eta.DMTA_0                     2.327        0.0000        0.0000        0.0000
#&gt; eta.log_k_M23                  0.000        0.5493        0.0000        0.0000
#&gt; eta.log_k_M27                  0.000        0.0000        0.8552        0.0000
#&gt; eta.log_k_M31                  0.000        0.0000        0.0000        0.7457
#&gt; eta.log_k1                     0.000        0.0000        0.0000        0.0000
#&gt; eta.log_k2                     0.000        0.0000        0.0000        0.0000
#&gt; eta.g_qlogis                   0.000        0.0000        0.0000        0.0000
#&gt; eta.f_DMTA_tffm0_1_qlogis      0.000        0.0000        0.0000        0.0000
#&gt; eta.f_DMTA_tffm0_2_qlogis      0.000        0.0000        0.0000        0.0000
#&gt; eta.f_DMTA_tffm0_3_qlogis      0.000        0.0000        0.0000        0.0000
#&gt;                           eta.log_k1 eta.log_k2 eta.g_qlogis
#&gt; eta.DMTA_0                     0.000      0.000        0.000
#&gt; eta.log_k_M23                  0.000      0.000        0.000
#&gt; eta.log_k_M27                  0.000      0.000        0.000
#&gt; eta.log_k_M31                  0.000      0.000        0.000
#&gt; eta.log_k1                     0.901      0.000        0.000
#&gt; eta.log_k2                     0.000      1.577        0.000
#&gt; eta.g_qlogis                   0.000      0.000        3.102
#&gt; eta.f_DMTA_tffm0_1_qlogis      0.000      0.000        0.000
#&gt; eta.f_DMTA_tffm0_2_qlogis      0.000      0.000        0.000
#&gt; eta.f_DMTA_tffm0_3_qlogis      0.000      0.000        0.000
#&gt;                           eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis
#&gt; eta.DMTA_0                                      0.0                       0.0
#&gt; eta.log_k_M23                                   0.0                       0.0
#&gt; eta.log_k_M27                                   0.0                       0.0
#&gt; eta.log_k_M31                                   0.0                       0.0
#&gt; eta.log_k1                                      0.0                       0.0
#&gt; eta.log_k2                                      0.0                       0.0
#&gt; eta.g_qlogis                                    0.0                       0.0
#&gt; eta.f_DMTA_tffm0_1_qlogis                       0.3                       0.0
#&gt; eta.f_DMTA_tffm0_2_qlogis                       0.0                       0.3
#&gt; eta.f_DMTA_tffm0_3_qlogis                       0.0                       0.0
#&gt;                           eta.f_DMTA_tffm0_3_qlogis
#&gt; eta.DMTA_0                                      0.0
#&gt; eta.log_k_M23                                   0.0
#&gt; eta.log_k_M27                                   0.0
#&gt; eta.log_k_M31                                   0.0
#&gt; eta.log_k1                                      0.0
#&gt; eta.log_k2                                      0.0
#&gt; eta.g_qlogis                                    0.0
#&gt; eta.f_DMTA_tffm0_1_qlogis                       0.0
#&gt; eta.f_DMTA_tffm0_2_qlogis                       0.0
#&gt; eta.f_DMTA_tffm0_3_qlogis                       0.3
#&gt; 
#&gt; Variance model:
#&gt; sigma_low  rsd_high 
#&gt;   0.70012   0.02577 
#&gt; 
#&gt; Backtransformed parameters:
#&gt;                   est.    lower    upper
#&gt; DMTA_0        98.71324 98.68012 98.74636
#&gt; k_M23          0.01981  0.01977  0.01985
#&gt; k_M27          0.01316  0.01313  0.01319
#&gt; k_M31          0.01435  0.01432  0.01438
#&gt; f_DMTA_to_M23  0.10993       NA       NA
#&gt; f_DMTA_to_M27  0.09049       NA       NA
#&gt; f_DMTA_to_M31  0.08414       NA       NA
#&gt; k1             0.10698  0.10685  0.10710
#&gt; k2             0.02292  0.02288  0.02296
#&gt; g              0.60738  0.60725  0.60751
#&gt; 
#&gt; Resulting formation fractions:
#&gt;                ff
#&gt; DMTA_M23  0.10993
#&gt; DMTA_M27  0.09049
#&gt; DMTA_M31  0.08414
#&gt; DMTA_sink 0.71543
#&gt; 
#&gt; Estimated disappearance times:
#&gt;       DT50  DT90 DT50back DT50_k1 DT50_k2
#&gt; DMTA 10.72  60.1    18.09    6.48   30.24
#&gt; M23  34.99 116.2       NA      NA      NA
#&gt; M27  52.67 175.0       NA      NA      NA
#&gt; 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'>&lt;-</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'>#&gt; Running main SAEM algorithm
#&gt; [1] "Thu Sep 16 14:06:56 2021"
#&gt; ....
#&gt;     Minimisation finished
#&gt; [1] "Thu Sep 16 14:25:28 2021"</div><div class='output co'>#&gt;     user   system  elapsed 
#&gt; 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'>&lt;-</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'>#&gt; <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'>#&gt; <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'>#&gt; <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'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; 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
#&gt; 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'>#&gt; <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <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'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; [1] "CMT"</div><div class='output co'>#&gt; <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#&gt; <span class='message'>done</span></div><div class='output co'>#&gt;    user  system elapsed 
#&gt; 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'>#&gt; <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'>#&gt; nlmixr version used for fitting:    2.0.5 
#&gt; mkin version used for pre-fitting:  1.1.0 
#&gt; R version used for fitting:         4.1.1 
#&gt; Date of fit:     Thu Sep 16 14:29:02 2021 
#&gt; Date of summary: Thu Sep 16 14:29:02 2021 
#&gt; 
#&gt; Equations:
#&gt; d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
#&gt;            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
#&gt;            * DMTA
#&gt; d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
#&gt;            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
#&gt;            exp(-k2 * time))) * DMTA - k_M23 * M23
#&gt; d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
#&gt;            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
#&gt;            exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31
#&gt; d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
#&gt;            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
#&gt;            exp(-k2 * time))) * DMTA - k_M31 * M31
#&gt; 
#&gt; Data:
#&gt; 563 observations of 4 variable(s) grouped in 6 datasets
#&gt; 
#&gt; Degradation model predictions using RxODE
#&gt; 
#&gt; Fitted in 149.421 s
#&gt; 
#&gt; Variance model: Two-component variance function 
#&gt; 
#&gt; Mean of starting values for individual parameters:
#&gt;       DMTA_0    log_k_M23    log_k_M27    log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 
#&gt;      98.7132      -3.9216      -4.3306      -4.2442       0.1376       0.1388 
#&gt; f_DMTA_ilr_3       log_k1       log_k2     g_qlogis 
#&gt;      -1.7554      -2.2352      -3.7758       0.4363 
#&gt; 
#&gt; Mean of starting values for error model parameters:
#&gt; sigma_low_DMTA  rsd_high_DMTA  sigma_low_M23   rsd_high_M23  sigma_low_M27 
#&gt;        0.70012        0.02577        0.70012        0.02577        0.70012 
#&gt;   rsd_high_M27  sigma_low_M31   rsd_high_M31 
#&gt;        0.02577        0.70012        0.02577 
#&gt; 
#&gt; Fixed degradation parameter values:
#&gt; None
#&gt; 
#&gt; Results:
#&gt; 
#&gt; Likelihood calculated by focei  
#&gt;    AIC  BIC logLik
#&gt;   1953 2074 -948.3
#&gt; 
#&gt; Optimised parameters:
#&gt;                         est.  lower  upper
#&gt; DMTA_0                97.821 95.862 99.780
#&gt; log_k_M23             -4.403 -5.376 -3.430
#&gt; log_k_M27             -4.087 -4.545 -3.629
#&gt; log_k_M31             -4.129 -4.639 -3.618
#&gt; log_k1                -2.828 -3.389 -2.266
#&gt; log_k2                -4.351 -5.472 -3.229
#&gt; g_qlogis               2.661  0.824  4.499
#&gt; f_DMTA_tffm0_1_qlogis -2.125 -2.449 -1.801
#&gt; f_DMTA_tffm0_2_qlogis -2.131 -2.468 -1.794
#&gt; f_DMTA_tffm0_3_qlogis -2.075 -2.540 -1.610
#&gt; 
#&gt; Correlation: 
#&gt;                       DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs
#&gt; log_k_M23             -0.019                                          
#&gt; log_k_M27             -0.028  0.004                                   
#&gt; log_k_M31             -0.019  0.003  0.075                            
#&gt; log_k1                 0.038 -0.004 -0.006 -0.003                     
#&gt; log_k2                 0.046  0.011  0.008  0.009  0.068              
#&gt; g_qlogis              -0.067  0.004  0.006  0.001 -0.076 -0.409       
#&gt; f_DMTA_tffm0_1_qlogis -0.062  0.055  0.006  0.004 -0.008 -0.004  0.012
#&gt; f_DMTA_tffm0_2_qlogis -0.062  0.010  0.058 -0.034 -0.008 -0.007  0.014
#&gt; f_DMTA_tffm0_3_qlogis -0.052  0.009  0.056  0.071 -0.006 -0.001  0.008
#&gt;                       f_DMTA_0_1 f_DMTA_0_2
#&gt; log_k_M23                                  
#&gt; log_k_M27                                  
#&gt; log_k_M31                                  
#&gt; log_k1                                     
#&gt; log_k2                                     
#&gt; g_qlogis                                   
#&gt; f_DMTA_tffm0_1_qlogis                      
#&gt; f_DMTA_tffm0_2_qlogis  0.017               
#&gt; f_DMTA_tffm0_3_qlogis  0.014     -0.005    
#&gt; 
#&gt; Random effects (omega):
#&gt;                           eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31
#&gt; eta.DMTA_0                     2.946         0.000        0.0000        0.0000
#&gt; eta.log_k_M23                  0.000         1.293        0.0000        0.0000
#&gt; eta.log_k_M27                  0.000         0.000        0.2802        0.0000
#&gt; eta.log_k_M31                  0.000         0.000        0.0000        0.3467
#&gt; eta.log_k1                     0.000         0.000        0.0000        0.0000
#&gt; eta.log_k2                     0.000         0.000        0.0000        0.0000
#&gt; eta.g_qlogis                   0.000         0.000        0.0000        0.0000
#&gt; eta.f_DMTA_tffm0_1_qlogis      0.000         0.000        0.0000        0.0000
#&gt; eta.f_DMTA_tffm0_2_qlogis      0.000         0.000        0.0000        0.0000
#&gt; eta.f_DMTA_tffm0_3_qlogis      0.000         0.000        0.0000        0.0000
#&gt;                           eta.log_k1 eta.log_k2 eta.g_qlogis
#&gt; eta.DMTA_0                    0.0000     0.0000        0.000
#&gt; eta.log_k_M23                 0.0000     0.0000        0.000
#&gt; eta.log_k_M27                 0.0000     0.0000        0.000
#&gt; eta.log_k_M31                 0.0000     0.0000        0.000
#&gt; eta.log_k1                    0.4814     0.0000        0.000
#&gt; eta.log_k2                    0.0000     0.7877        0.000
#&gt; eta.g_qlogis                  0.0000     0.0000        3.074
#&gt; eta.f_DMTA_tffm0_1_qlogis     0.0000     0.0000        0.000
#&gt; eta.f_DMTA_tffm0_2_qlogis     0.0000     0.0000        0.000
#&gt; eta.f_DMTA_tffm0_3_qlogis     0.0000     0.0000        0.000
#&gt;                           eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis
#&gt; eta.DMTA_0                                   0.0000                    0.0000
#&gt; eta.log_k_M23                                0.0000                    0.0000
#&gt; eta.log_k_M27                                0.0000                    0.0000
#&gt; eta.log_k_M31                                0.0000                    0.0000
#&gt; eta.log_k1                                   0.0000                    0.0000
#&gt; eta.log_k2                                   0.0000                    0.0000
#&gt; eta.g_qlogis                                 0.0000                    0.0000
#&gt; eta.f_DMTA_tffm0_1_qlogis                    0.1508                    0.0000
#&gt; eta.f_DMTA_tffm0_2_qlogis                    0.0000                    0.1523
#&gt; eta.f_DMTA_tffm0_3_qlogis                    0.0000                    0.0000
#&gt;                           eta.f_DMTA_tffm0_3_qlogis
#&gt; eta.DMTA_0                                   0.0000
#&gt; eta.log_k_M23                                0.0000
#&gt; eta.log_k_M27                                0.0000
#&gt; eta.log_k_M31                                0.0000
#&gt; eta.log_k1                                   0.0000
#&gt; eta.log_k2                                   0.0000
#&gt; eta.g_qlogis                                 0.0000
#&gt; eta.f_DMTA_tffm0_1_qlogis                    0.0000
#&gt; eta.f_DMTA_tffm0_2_qlogis                    0.0000
#&gt; eta.f_DMTA_tffm0_3_qlogis                    0.3155
#&gt; 
#&gt; Variance model:
#&gt; sigma_low_DMTA  rsd_high_DMTA  sigma_low_M23   rsd_high_M23  sigma_low_M27 
#&gt;        0.95572        0.03325        0.47871        0.10733        0.68264 
#&gt;   rsd_high_M27  sigma_low_M31   rsd_high_M31 
#&gt;        0.07072        0.78486        0.03557 
#&gt; 
#&gt; Backtransformed parameters:
#&gt;                   est.     lower    upper
#&gt; DMTA_0        97.82122 95.862233 99.78020
#&gt; k_M23          0.01224  0.004625  0.03239
#&gt; k_M27          0.01679  0.010615  0.02654
#&gt; k_M31          0.01610  0.009664  0.02683
#&gt; f_DMTA_to_M23  0.10668        NA       NA
#&gt; f_DMTA_to_M27  0.09481        NA       NA
#&gt; f_DMTA_to_M31  0.08908        NA       NA
#&gt; k1             0.05914  0.033731  0.10370
#&gt; k2             0.01290  0.004204  0.03958
#&gt; g              0.93471  0.695081  0.98900
#&gt; 
#&gt; Resulting formation fractions:
#&gt;                ff
#&gt; DMTA_M23  0.10668
#&gt; DMTA_M27  0.09481
#&gt; DMTA_M31  0.08908
#&gt; DMTA_sink 0.70943
#&gt; 
#&gt; Estimated disappearance times:
#&gt;       DT50   DT90 DT50back DT50_k1 DT50_k2
#&gt; DMTA 12.57  45.43    13.67   11.72   53.73
#&gt; M23  56.63 188.11       NA      NA      NA
#&gt; M27  41.29 137.18       NA      NA      NA
#&gt; 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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