diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2022-02-08 17:17:29 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2022-02-08 17:17:29 +0100 |
| commit | 0fa8a770812775d697717ad723f7f61fb04b7fef (patch) | |
| tree | 17473ddf787541745d47dab063bc643ec59a9557 /docs/dev/reference/dimethenamid_2018.html | |
| parent | d081384ddcb75a9f92fad33e4e3f6d6796f98e67 (diff) | |
| parent | c0638c84568d475b3b059e2c6e593e6f03b846bc (diff) | |
Merge branch 'nlmixr'
Diffstat (limited to 'docs/dev/reference/dimethenamid_2018.html')
| -rw-r--r-- | docs/dev/reference/dimethenamid_2018.html | 266 |
1 files changed, 241 insertions, 25 deletions
diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index 21dea623..60c15ade 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -162,13 +162,13 @@ constrained by data protection regulations.</p> <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> - <p>An <a href='mkindsg.html'>mkindsg</a> object grouping eight datasets with some meta information</p> + <p>An <a href='mkindsg.html'>mkindsg</a> object grouping seven datasets with some meta information</p> <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour Rev. 2 - November 2017 -<a href='http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211'>http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211</a></p> +<a href='https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716'>https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a></p> <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> <p>The R code used to create this data object is installed with this package @@ -177,33 +177,249 @@ specific pieces of information in the comments.</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>)</span> -</div><div class='output co'>#> <mkindsg> holding 8 mkinds objects +</div><div class='output co'>#> <mkindsg> holding 7 mkinds objects #> Title $title: Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 #> Occurrence of observed compounds $observed_n: #> DMTAP M23 M27 M31 DMTA -#> 4 7 7 7 4 +#> 3 7 7 7 4 #> Time normalisation factors $f_time_norm: -#> [1] 1.0000000 0.9706477 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 -#> [8] 0.6733938 +#> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938 #> Meta information $meta: -#> study usda_soil_type study_moisture_ref_type -#> Calke Unsworth 2014 Sandy loam pF2 -#> Borstel 1 Staudenmaier 2013 Sand pF1 -#> Borstel 2 Staudenmaier 2009 Sand pF1 -#> Elliot 1 Wendt 1997 Clay loam pF2.5 -#> Elliot 2 Wendt 1997 Clay loam pF2.5 -#> Flaach König 1996 Sandy clay loam pF1 -#> BBA 2.2 König 1995 Loamy sand pF1 -#> BBA 2.3 König 1995 Sandy loam pF1 -#> rel_moisture study_ref_moisture temperature -#> Calke 1.00 NA 20 -#> Borstel 1 0.50 23.00 20 -#> Borstel 2 0.50 23.00 20 -#> Elliot 1 0.75 33.37 23 -#> Elliot 2 0.75 33.37 23 -#> Flaach 0.40 NA 20 -#> BBA 2.2 0.40 NA 20 -#> BBA 2.3 0.40 NA 20</div></pre> +#> study usda_soil_type study_moisture_ref_type rel_moisture +#> Calke Unsworth 2014 Sandy loam pF2 1.00 +#> Borstel Staudenmaier 2009 Sand pF1 0.50 +#> Elliot 1 Wendt 1997 Clay loam pF2.5 0.75 +#> Elliot 2 Wendt 1997 Clay loam pF2.5 0.75 +#> Flaach König 1996 Sandy clay loam pF1 0.40 +#> BBA 2.2 König 1995 Loamy sand pF1 0.40 +#> BBA 2.3 König 1995 Sandy loam pF1 0.40 +#> study_ref_moisture temperature +#> Calke NA 20 +#> Borstel 23.00 20 +#> Elliot 1 33.37 23 +#> Elliot 2 33.37 23 +#> Flaach NA 20 +#> BBA 2.2 NA 20 +#> BBA 2.3 NA 20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>7</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span> + <span class='va'>ds_i</span> <span class='op'><-</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</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> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"> |