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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'>#> <span class='warning'>Warning: number of items to replace is not a multiple of replacement length</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_to_M23 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis)
#> f_DMTA_to_M23 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis)
#> f_DMTA_to_M23 = 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: 0x555559d89920></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='warning'>Warning: number of items to replace is not a multiple of replacement length</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'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:09
#> </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'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:06
#> </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'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(DMTA);</span>
#> <span class='message'>cmt(M23);</span>
#> <span class='message'>cmt(M27);</span>
#> <span class='message'>cmt(M31);</span>
#> <span class='message'>rx_expr_14~ETA[1]+THETA[1];</span>
#> <span class='message'>DMTA(0)=rx_expr_14;</span>
#> <span class='message'>rx_expr_15~ETA[5]+THETA[5];</span>
#> <span class='message'>rx_expr_16~ETA[7]+THETA[7];</span>
#> <span class='message'>rx_expr_17~ETA[6]+THETA[6];</span>
#> <span class='message'>rx_expr_24~exp(rx_expr_15);</span>
#> <span class='message'>rx_expr_25~exp(rx_expr_17);</span>
#> <span class='message'>rx_expr_29~t*rx_expr_24;</span>
#> <span class='message'>rx_expr_30~t*rx_expr_25;</span>
#> <span class='message'>rx_expr_31~exp(-(rx_expr_16));</span>
#> <span class='message'>rx_expr_35~1+rx_expr_31;</span>
#> <span class='message'>rx_expr_40~1/(rx_expr_35);</span>
#> <span class='message'>rx_expr_42~(rx_expr_40);</span>
#> <span class='message'>rx_expr_43~1-rx_expr_42;</span>
#> <span class='message'>d/dt(DMTA)=-DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
#> <span class='message'>rx_expr_18~ETA[2]+THETA[2];</span>
#> <span class='message'>rx_expr_26~exp(rx_expr_18);</span>
#> <span class='message'>d/dt(M23)=-rx_expr_26*M23+DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_1/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
#> <span class='message'>rx_expr_19~ETA[3]+THETA[3];</span>
#> <span class='message'>rx_expr_20~ETA[4]+THETA[4];</span>
#> <span class='message'>rx_expr_21~1-f_DMTA_tffm0_1;</span>
#> <span class='message'>rx_expr_27~exp(rx_expr_19);</span>
#> <span class='message'>rx_expr_28~exp(rx_expr_20);</span>
#> <span class='message'>d/dt(M27)=-rx_expr_27*M27+rx_expr_28*M31+DMTA*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_2/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
#> <span class='message'>rx_expr_22~1-f_DMTA_tffm0_2;</span>
#> <span class='message'>d/dt(M31)=-rx_expr_28*M31+DMTA*(rx_expr_22)*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_3/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
#> <span class='message'>rx_expr_0~CMT==4;</span>
#> <span class='message'>rx_expr_1~CMT==2;</span>
#> <span class='message'>rx_expr_2~CMT==1;</span>
#> <span class='message'>rx_expr_3~CMT==3;</span>
#> <span class='message'>rx_expr_4~1-(rx_expr_0);</span>
#> <span class='message'>rx_expr_5~1-(rx_expr_1);</span>
#> <span class='message'>rx_expr_6~1-(rx_expr_3);</span>
#> <span class='message'>rx_yj_~(rx_expr_4)*((2*(rx_expr_5)*(rx_expr_2)+2*(rx_expr_1))*(rx_expr_6)+2*(rx_expr_3))+2*(rx_expr_0);</span>
#> <span class='message'>rx_expr_7~(rx_expr_1);</span>
#> <span class='message'>rx_expr_8~(rx_expr_3);</span>
#> <span class='message'>rx_expr_9~(rx_expr_0);</span>
#> <span class='message'>rx_expr_13~(rx_expr_5);</span>
#> <span class='message'>rx_expr_32~rx_expr_13*(rx_expr_2);</span>
#> <span class='message'>rx_lambda_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9;</span>
#> <span class='message'>rx_hi_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9;</span>
#> <span class='message'>rx_low_~0;</span>
#> <span class='message'>rx_expr_10~M31*(rx_expr_0);</span>
#> <span class='message'>rx_expr_11~M27*(rx_expr_3);</span>
#> <span class='message'>rx_expr_12~M23*(rx_expr_1);</span>
#> <span class='message'>rx_expr_23~DMTA*(rx_expr_5);</span>
#> <span class='message'>rx_expr_36~rx_expr_23*(rx_expr_2);</span>
#> <span class='message'>rx_pred_=(rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)));</span>
#> <span class='message'>rx_expr_33~Rx_pow_di(THETA[12],2);</span>
#> <span class='message'>rx_expr_34~Rx_pow_di(THETA[11],2);</span>
#> <span class='message'>rx_r_=(rx_expr_4)*((rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6)),2)+rx_expr_34)*(rx_expr_3)+((rx_expr_1)*(rx_expr_33*Rx_pow_di(((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)+(rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)*(rx_expr_5)*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_33*Rx_pow_di(((rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))),2)+rx_expr_34);</span>
#> <span class='message'>DMTA_0=THETA[1];</span>
#> <span class='message'>log_k_M23=THETA[2];</span>
#> <span class='message'>log_k_M27=THETA[3];</span>
#> <span class='message'>log_k_M31=THETA[4];</span>
#> <span class='message'>log_k1=THETA[5];</span>
#> <span class='message'>log_k2=THETA[6];</span>
#> <span class='message'>g_qlogis=THETA[7];</span>
#> <span class='message'>f_DMTA_tffm0_1_qlogis=THETA[8];</span>
#> <span class='message'>f_DMTA_tffm0_2_qlogis=THETA[9];</span>
#> <span class='message'>f_DMTA_tffm0_3_qlogis=THETA[10];</span>
#> <span class='message'>sigma_low=THETA[11];</span>
#> <span class='message'>rsd_high=THETA[12];</span>
#> <span class='message'>eta.DMTA_0=ETA[1];</span>
#> <span class='message'>eta.log_k_M23=ETA[2];</span>
#> <span class='message'>eta.log_k_M27=ETA[3];</span>
#> <span class='message'>eta.log_k_M31=ETA[4];</span>
#> <span class='message'>eta.log_k1=ETA[5];</span>
#> <span class='message'>eta.log_k2=ETA[6];</span>
#> <span class='message'>eta.g_qlogis=ETA[7];</span>
#> <span class='message'>eta.f_DMTA_tffm0_1_qlogis=ETA[8];</span>
#> <span class='message'>eta.f_DMTA_tffm0_2_qlogis=ETA[9];</span>
#> <span class='message'>eta.f_DMTA_tffm0_3_qlogis=ETA[10];</span>
#> <span class='message'>DMTA_0_model=rx_expr_14;</span>
#> <span class='message'>k_M23=rx_expr_26;</span>
#> <span class='message'>k_M27=rx_expr_27;</span>
#> <span class='message'>k_M31=rx_expr_28;</span>
#> <span class='message'>k1=rx_expr_24;</span>
#> <span class='message'>k2=rx_expr_25;</span>
#> <span class='message'>g=1/(rx_expr_35);</span>
#> <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[8]+THETA[8])));</span>
#> <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[9]+THETA[9])));</span>
#> <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[10]+THETA[10])));</span>
#> <span class='message'>f_DMTA_to_M23=f_DMTA_tffm0_1;</span>
#> <span class='message'>f_DMTA_to_M27=(rx_expr_21)*f_DMTA_tffm0_2;</span>
#> <span class='message'>f_DMTA_to_M31=(rx_expr_22)*(rx_expr_21)*f_DMTA_tffm0_3;</span>
#> <span class='message'>tad=tad();</span>
#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_DMTA_tffm0_1" "f_DMTA_tffm0_2" "f_DMTA_tffm0_3" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 121.4 8.294 129.7</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 121.5 8.294 129.9</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_focei</span><span class='op'>)</span>
</div><div class='output co'>#> <span class='error'>Error in summary(f_dmta_nlmixr_focei): object 'f_dmta_nlmixr_focei' not found</span></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='output co'>#> <span class='error'>Error in plot(f_dmta_nlmixr_focei): object 'f_dmta_nlmixr_focei' not found</span></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 Oct 5 16:58:50 2021"
#> ....
#> Minimisation finished
#> [1] "Tue Oct 5 17:17:24 2021"</div><div class='output co'>#> user system elapsed
#> 1181.365 0.031 1181.470 </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='warning'>Warning: number of items to replace is not a multiple of replacement length</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='error'>Error in eval(substitute(expr), data, enclos = parent.frame()): Cannot run SAEM since some of the parameters are not mu-referenced (eta.f_DMTA_tffm0_1_qlogis, eta.f_DMTA_tffm0_2_qlogis, eta.f_DMTA_tffm0_3_qlogis)</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.849 0.016 0.864</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.041 0.016 1.058</span></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'>#> <span class='error'>Error in summary(f_dmta_nlmixr_saem): object 'f_dmta_nlmixr_saem' not found</span></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='output co'>#> <span class='error'>Error in plot(f_dmta_nlmixr_saem): object 'f_dmta_nlmixr_saem' not found</span></div><div class='input'><span class='co'># }</span>
</div></pre>
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