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-rw-r--r--docs/dev/reference/dimethenamid_2018-1.pngbin264364 -> 263830 bytes
-rw-r--r--docs/dev/reference/dimethenamid_2018-2.pngbin245108 -> 246327 bytes
-rw-r--r--docs/dev/reference/dimethenamid_2018.html320
-rw-r--r--docs/dev/reference/endpoints.html2
-rw-r--r--docs/dev/reference/index.html2
-rw-r--r--docs/dev/reference/mean_degparms.html2
-rw-r--r--docs/dev/reference/mkinmod.html6
-rw-r--r--docs/dev/reference/nlme-1.pngbin68943 -> 68789 bytes
-rw-r--r--docs/dev/reference/nlme-2.pngbin94409 -> 92904 bytes
-rw-r--r--docs/dev/reference/nlme.html18
-rw-r--r--docs/dev/reference/nlme.mmkin.html2
-rw-r--r--docs/dev/reference/nlmixr.mmkin.html31
-rw-r--r--docs/dev/reference/plot.mixed.mmkin.html6
-rw-r--r--docs/dev/reference/reexports.html2
-rw-r--r--docs/dev/reference/saem.html40
-rw-r--r--docs/dev/reference/summary.nlmixr.mmkin.html10
-rw-r--r--docs/dev/reference/tffm0.html2
17 files changed, 219 insertions, 224 deletions
diff --git a/docs/dev/reference/dimethenamid_2018-1.png b/docs/dev/reference/dimethenamid_2018-1.png
index 52b8a2be..b8c5355f 100644
--- a/docs/dev/reference/dimethenamid_2018-1.png
+++ b/docs/dev/reference/dimethenamid_2018-1.png
Binary files differ
diff --git a/docs/dev/reference/dimethenamid_2018-2.png b/docs/dev/reference/dimethenamid_2018-2.png
index a81b2aaf..3b8a123b 100644
--- a/docs/dev/reference/dimethenamid_2018-2.png
+++ b/docs/dev/reference/dimethenamid_2018-2.png
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diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html
index a77cf0f4..919e9363 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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
@@ -162,7 +162,7 @@ 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)
@@ -177,42 +177,36 @@ specific pieces of information in the comments.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; &lt;mkindsg&gt; holding 8 mkinds objects
+</div><div class='output co'>#&gt; &lt;mkindsg&gt; holding 7 mkinds objects
#&gt; Title $title: Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018
#&gt; Occurrence of observed compounds $observed_n:
#&gt; DMTAP M23 M27 M31 DMTA
-#&gt; 4 7 7 7 4
+#&gt; 3 7 7 7 4
#&gt; Time normalisation factors $f_time_norm:
-#&gt; [1] 1.0000000 0.9706477 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922
-#&gt; [8] 0.6733938
+#&gt; [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938
#&gt; Meta information $meta:
-#&gt; study usda_soil_type study_moisture_ref_type
-#&gt; Calke Unsworth 2014 Sandy loam pF2
-#&gt; Borstel 1 Staudenmaier 2013 Sand pF1
-#&gt; Borstel 2 Staudenmaier 2009 Sand pF1
-#&gt; Elliot 1 Wendt 1997 Clay loam pF2.5
-#&gt; Elliot 2 Wendt 1997 Clay loam pF2.5
-#&gt; Flaach König 1996 Sandy clay loam pF1
-#&gt; BBA 2.2 König 1995 Loamy sand pF1
-#&gt; BBA 2.3 König 1995 Sandy loam pF1
-#&gt; rel_moisture study_ref_moisture temperature
-#&gt; Calke 1.00 NA 20
-#&gt; Borstel 1 0.50 23.00 20
-#&gt; Borstel 2 0.50 23.00 20
-#&gt; Elliot 1 0.75 33.37 23
-#&gt; Elliot 2 0.75 33.37 23
-#&gt; Flaach 0.40 NA 20
-#&gt; BBA 2.2 0.40 NA 20
-#&gt; BBA 2.3 0.40 NA 20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span>
+#&gt; study usda_soil_type study_moisture_ref_type rel_moisture
+#&gt; Calke Unsworth 2014 Sandy loam pF2 1.00
+#&gt; Borstel Staudenmaier 2009 Sand pF1 0.50
+#&gt; Elliot 1 Wendt 1997 Clay loam pF2.5 0.75
+#&gt; Elliot 2 Wendt 1997 Clay loam pF2.5 0.75
+#&gt; Flaach König 1996 Sandy clay loam pF1 0.40
+#&gt; BBA 2.2 König 1995 Loamy sand pF1 0.40
+#&gt; BBA 2.3 König 1995 Sandy loam pF1 0.40
+#&gt; study_ref_moisture temperature
+#&gt; Calke NA 20
+#&gt; Borstel 23.00 20
+#&gt; Elliot 1 33.37 23
+#&gt; Elliot 2 33.37 23
+#&gt; Flaach NA 20
+#&gt; BBA 2.2 NA 20
+#&gt; BBA 2.3 NA 20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>7</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span>
<span class='va'>ds_i</span> <span class='op'>&lt;-</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>ds</span><span class='op'>[[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span>
<span class='va'>ds_i</span><span class='op'>[</span><span class='va'>ds_i</span><span class='op'>$</span><span class='va'>name</span> <span class='op'>==</span> <span class='st'>"DMTAP"</span>, <span class='st'>"name"</span><span class='op'>]</span> <span class='op'>&lt;-</span> <span class='st'>"DMTA"</span>
<span class='va'>ds_i</span><span class='op'>$</span><span class='va'>time</span> <span class='op'>&lt;-</span> <span class='va'>ds_i</span><span class='op'>$</span><span class='va'>time</span> <span class='op'>*</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>f_time_norm</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span>
<span class='va'>ds_i</span>
<span class='op'>}</span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>)</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>sapply</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>ds</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>ds</span><span class='op'>)</span> <span class='va'>ds</span><span class='op'>$</span><span class='va'>title</span><span class='op'>)</span>
-<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 1"</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 2"</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span>
-<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 1"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'>&lt;-</span> <span class='cn'>NULL</span>
-<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 2"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'>&lt;-</span> <span class='cn'>NULL</span>
<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 1"</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 2"</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span>
<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 1"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'>&lt;-</span> <span class='cn'>NULL</span>
<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 2"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'>&lt;-</span> <span class='cn'>NULL</span>
@@ -231,33 +225,33 @@ specific pieces of information in the comments.</p>
</div><div class='output co'>#&gt; <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#&gt; function ()
#&gt; {
#&gt; ini({
-#&gt; DMTA_0 = 98.7697627680706
-#&gt; eta.DMTA_0 ~ 2.35171765917765
+#&gt; DMTA_0 = 98.7132391714013
+#&gt; eta.DMTA_0 ~ 2.32692496033921
#&gt; log_k_M23 = -3.92162409637283
#&gt; eta.log_k_M23 ~ 0.549278519419884
-#&gt; log_k_M27 = -4.33774620773911
-#&gt; eta.log_k_M27 ~ 0.864474956685295
-#&gt; log_k_M31 = -4.24767627688461
-#&gt; eta.log_k_M31 ~ 0.750297149164171
-#&gt; log_k1 = -2.2341008812259
-#&gt; eta.log_k1 ~ 0.902976221565793
-#&gt; log_k2 = -3.7762779983269
-#&gt; eta.log_k2 ~ 1.57684519529298
-#&gt; g_qlogis = 0.450175725479389
-#&gt; eta.g_qlogis ~ 3.0851335687675
-#&gt; f_DMTA_tffm0_1_qlogis = -2.09240906629456
+#&gt; log_k_M27 = -4.33057580082049
+#&gt; eta.log_k_M27 ~ 0.855184233768426
+#&gt; log_k_M31 = -4.24415516780733
+#&gt; eta.log_k_M31 ~ 0.745746058085877
+#&gt; log_k1 = -2.23515804885306
+#&gt; eta.log_k1 ~ 0.901033446532357
+#&gt; log_k2 = -3.77581484944379
+#&gt; eta.log_k2 ~ 1.57682329638124
+#&gt; g_qlogis = 0.436302910942805
+#&gt; eta.g_qlogis ~ 3.10190528862808
+#&gt; f_DMTA_tffm0_1_qlogis = -2.0914852208395
#&gt; eta.f_DMTA_tffm0_1_qlogis ~ 0.3
-#&gt; f_DMTA_tffm0_2_qlogis = -2.18057573598794
+#&gt; f_DMTA_tffm0_2_qlogis = -2.17879574608926
#&gt; eta.f_DMTA_tffm0_2_qlogis ~ 0.3
-#&gt; f_DMTA_tffm0_3_qlogis = -2.14267187609763
+#&gt; f_DMTA_tffm0_3_qlogis = -2.14036526460782
#&gt; eta.f_DMTA_tffm0_3_qlogis ~ 0.3
-#&gt; sigma_low_DMTA = 0.697933852349996
+#&gt; sigma_low_DMTA = 0.700117227383809
#&gt; rsd_high_DMTA = 0.0257724286053519
-#&gt; sigma_low_M23 = 0.697933852349996
+#&gt; sigma_low_M23 = 0.700117227383809
#&gt; rsd_high_M23 = 0.0257724286053519
-#&gt; sigma_low_M27 = 0.697933852349996
+#&gt; sigma_low_M27 = 0.700117227383809
#&gt; rsd_high_M27 = 0.0257724286053519
-#&gt; sigma_low_M31 = 0.697933852349996
+#&gt; sigma_low_M31 = 0.700117227383809
#&gt; rsd_high_M31 = 0.0257724286053519
#&gt; })
#&gt; model({
@@ -295,7 +289,7 @@ specific pieces of information in the comments.</p>
#&gt; M31 ~ add(sigma_low_M31) + prop(rsd_high_M31)
#&gt; })
#&gt; }
-#&gt; &lt;environment: 0x555559ac3820&gt;</div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span>
+#&gt; &lt;environment: 0x555559e97ac0&gt;</div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span>
<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span>
<span class='va'>f_dmta_nlmixr_focei</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>,
control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/foceiControl.html'>foceiControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span><span class='op'>)</span><span class='op'>)</span>
@@ -308,7 +302,7 @@ specific pieces of information in the comments.</p>
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:07
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:00
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:00
-#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; [1] "CMT"</div><div class='output co'>#&gt; <span class='message'>RxODE 1.1.0 using 8 threads (see ?getRxThreads)</span>
+#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; [1] "CMT"</div><div class='output co'>#&gt; <span class='message'>RxODE 1.1.1 using 8 threads (see ?getRxThreads)</span>
#&gt; <span class='message'> no cache: create with `rxCreateCache()`</span></div><div class='output co'>#&gt; <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
#&gt; F: Forward difference gradient approximation
#&gt; C: Central difference gradient approximation
@@ -324,12 +318,12 @@ specific pieces of information in the comments.</p>
#&gt; <span style='text-decoration: underline;'>|.....................| o9 | o10 |...........|...........|</span>
#&gt; calculating covariance matrix
#&gt; done</div><div class='output co'>#&gt; <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#&gt; <span class='message'>done</span></div><div class='output co'>#&gt; <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#&gt; <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#&gt; <span class='warning'>Warning: S matrix non-positive definite</span></div><div class='output co'>#&gt; <span class='warning'>Warning: using R matrix to calculate covariance</span></div><div class='output co'>#&gt; <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='output co'>#&gt; user system elapsed
-#&gt; 232.621 14.126 246.850 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; nlmixr version used for fitting: 2.0.4
-#&gt; mkin version used for pre-fitting: 1.0.5
-#&gt; R version used for fitting: 4.1.0
-#&gt; Date of fit: Wed Aug 4 15:53:54 2021
-#&gt; Date of summary: Wed Aug 4 15:53:54 2021
+#&gt; 230.015 8.962 238.957 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span>
+</div><div class='output co'>#&gt; nlmixr version used for fitting: 2.0.5
+#&gt; mkin version used for pre-fitting: 1.1.0
+#&gt; R version used for fitting: 4.1.1
+#&gt; Date of fit: Thu Sep 16 14:06:55 2021
+#&gt; Date of summary: Thu Sep 16 14:06:55 2021
#&gt;
#&gt; Equations:
#&gt; d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -346,23 +340,23 @@ specific pieces of information in the comments.</p>
#&gt; exp(-k2 * time))) * DMTA - k_M31 * M31
#&gt;
#&gt; Data:
-#&gt; 568 observations of 4 variable(s) grouped in 6 datasets
+#&gt; 563 observations of 4 variable(s) grouped in 6 datasets
#&gt;
#&gt; Degradation model predictions using RxODE
#&gt;
-#&gt; Fitted in 246.669 s
+#&gt; Fitted in 238.792 s
#&gt;
#&gt; Variance model: Two-component variance function
#&gt;
#&gt; Mean of starting values for individual parameters:
#&gt; DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2
-#&gt; 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393
+#&gt; 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388
#&gt; f_DMTA_ilr_3 log_k1 log_k2 g_qlogis
-#&gt; -1.7571 -2.2341 -3.7763 0.4502
+#&gt; -1.7554 -2.2352 -3.7758 0.4363
#&gt;
#&gt; Mean of starting values for error model parameters:
#&gt; sigma_low rsd_high
-#&gt; 0.69793 0.02577
+#&gt; 0.70012 0.02577
#&gt;
#&gt; Fixed degradation parameter values:
#&gt; None
@@ -371,20 +365,20 @@ specific pieces of information in the comments.</p>
#&gt;
#&gt; Likelihood calculated by focei
#&gt; AIC BIC logLik
-#&gt; 1936 2031 -945.9
+#&gt; 1918 2014 -937.2
#&gt;
#&gt; Optimised parameters:
#&gt; est. lower upper
-#&gt; DMTA_0 98.7698 98.7356 98.8039
-#&gt; log_k_M23 -3.9216 -3.9235 -3.9197
-#&gt; log_k_M27 -4.3377 -4.3398 -4.3357
-#&gt; log_k_M31 -4.2477 -4.2497 -4.2457
-#&gt; log_k1 -2.2341 -2.2353 -2.2329
-#&gt; log_k2 -3.7763 -3.7781 -3.7744
-#&gt; g_qlogis 0.4502 0.4496 0.4507
-#&gt; f_DMTA_tffm0_1_qlogis -2.0924 -2.0936 -2.0912
-#&gt; f_DMTA_tffm0_2_qlogis -2.1806 -2.1818 -2.1794
-#&gt; f_DMTA_tffm0_3_qlogis -2.1427 -2.1439 -2.1415
+#&gt; DMTA_0 98.7132 98.6801 98.7464
+#&gt; log_k_M23 -3.9216 -3.9235 -3.9198
+#&gt; log_k_M27 -4.3306 -4.3326 -4.3286
+#&gt; log_k_M31 -4.2442 -4.2461 -4.2422
+#&gt; log_k1 -2.2352 -2.2364 -2.2340
+#&gt; log_k2 -3.7758 -3.7776 -3.7740
+#&gt; g_qlogis 0.4363 0.4358 0.4368
+#&gt; f_DMTA_tffm0_1_qlogis -2.0915 -2.0926 -2.0903
+#&gt; f_DMTA_tffm0_2_qlogis -2.1788 -2.1800 -2.1776
+#&gt; f_DMTA_tffm0_3_qlogis -2.1404 -2.1415 -2.1392
#&gt;
#&gt; Correlation:
#&gt; DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs
@@ -410,10 +404,10 @@ specific pieces of information in the comments.</p>
#&gt;
#&gt; Random effects (omega):
#&gt; eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31
-#&gt; eta.DMTA_0 2.352 0.0000 0.0000 0.0000
+#&gt; eta.DMTA_0 2.327 0.0000 0.0000 0.0000
#&gt; eta.log_k_M23 0.000 0.5493 0.0000 0.0000
-#&gt; eta.log_k_M27 0.000 0.0000 0.8645 0.0000
-#&gt; eta.log_k_M31 0.000 0.0000 0.0000 0.7503
+#&gt; eta.log_k_M27 0.000 0.0000 0.8552 0.0000
+#&gt; eta.log_k_M31 0.000 0.0000 0.0000 0.7457
#&gt; eta.log_k1 0.000 0.0000 0.0000 0.0000
#&gt; eta.log_k2 0.000 0.0000 0.0000 0.0000
#&gt; eta.g_qlogis 0.000 0.0000 0.0000 0.0000
@@ -425,9 +419,9 @@ specific pieces of information in the comments.</p>
#&gt; eta.log_k_M23 0.000 0.000 0.000
#&gt; eta.log_k_M27 0.000 0.000 0.000
#&gt; eta.log_k_M31 0.000 0.000 0.000
-#&gt; eta.log_k1 0.903 0.000 0.000
+#&gt; eta.log_k1 0.901 0.000 0.000
#&gt; eta.log_k2 0.000 1.577 0.000
-#&gt; eta.g_qlogis 0.000 0.000 3.085
+#&gt; eta.g_qlogis 0.000 0.000 3.102
#&gt; eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.000
#&gt; eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.000
#&gt; eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.000
@@ -456,44 +450,44 @@ specific pieces of information in the comments.</p>
#&gt;
#&gt; Variance model:
#&gt; sigma_low rsd_high
-#&gt; 0.69793 0.02577
+#&gt; 0.70012 0.02577
#&gt;
#&gt; Backtransformed parameters:
#&gt; est. lower upper
-#&gt; DMTA_0 98.76976 98.73563 98.80390
+#&gt; DMTA_0 98.71324 98.68012 98.74636
#&gt; k_M23 0.01981 0.01977 0.01985
-#&gt; k_M27 0.01307 0.01304 0.01309
-#&gt; k_M31 0.01430 0.01427 0.01433
-#&gt; f_DMTA_to_M23 0.10984 NA NA
-#&gt; f_DMTA_to_M27 0.09036 NA NA
-#&gt; f_DMTA_to_M31 0.08399 NA NA
-#&gt; k1 0.10709 0.10696 0.10722
-#&gt; k2 0.02291 0.02287 0.02295
-#&gt; g 0.61068 0.61055 0.61081
+#&gt; k_M27 0.01316 0.01313 0.01319
+#&gt; k_M31 0.01435 0.01432 0.01438
+#&gt; f_DMTA_to_M23 0.10993 NA NA
+#&gt; f_DMTA_to_M27 0.09049 NA NA
+#&gt; f_DMTA_to_M31 0.08414 NA NA
+#&gt; k1 0.10698 0.10685 0.10710
+#&gt; k2 0.02292 0.02288 0.02296
+#&gt; g 0.60738 0.60725 0.60751
#&gt;
#&gt; Resulting formation fractions:
#&gt; ff
-#&gt; DMTA_M23 0.10984
-#&gt; DMTA_M27 0.09036
-#&gt; DMTA_M31 0.08399
-#&gt; DMTA_sink 0.71581
+#&gt; DMTA_M23 0.10993
+#&gt; DMTA_M27 0.09049
+#&gt; DMTA_M31 0.08414
+#&gt; DMTA_sink 0.71543
#&gt;
#&gt; Estimated disappearance times:
-#&gt; DT50 DT90 DT50back DT50_k1 DT50_k2
-#&gt; DMTA 10.66 59.78 18 6.473 30.26
-#&gt; M23 34.99 116.24 NA NA NA
-#&gt; M27 53.05 176.23 NA NA NA
-#&gt; M31 48.48 161.05 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span>
+#&gt; DT50 DT90 DT50back DT50_k1 DT50_k2
+#&gt; DMTA 10.72 60.1 18.09 6.48 30.24
+#&gt; M23 34.99 116.2 NA NA NA
+#&gt; M27 52.67 175.0 NA NA NA
+#&gt; M31 48.31 160.5 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span>
</div><div class='img'><img src='dimethenamid_2018-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># Using saemix takes about 18 minutes</span>
<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span>
<span class='va'>f_dmta_saemix</span> <span class='op'>&lt;-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
<span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 15:53:55 2021"
+#&gt; [1] "Thu Sep 16 14:06:56 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:12:40 2021"</div><div class='output co'>#&gt; user system elapsed
-#&gt; 1192.021 0.064 1192.182 </div><div class='input'>
+#&gt; [1] "Thu Sep 16 14:25:28 2021"</div><div class='output co'>#&gt; user system elapsed
+#&gt; 1176.278 0.021 1176.388 </div><div class='input'>
<span class='co'># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span>
<span class='co'># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span>
<span class='co'># The likelihood calculated for the nlmixr fit is much lower than that found by saemix</span>
@@ -504,15 +498,15 @@ specific pieces of information in the comments.</p>
<span class='va'>f_dmta_nlmixr_saem</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>,
control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/saemControl.html'>saemControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span>, logLik <span class='op'>=</span> <span class='cn'>TRUE</span>, nmc <span class='op'>=</span> <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span>
<span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; 1: 98.3427 -3.5148 -3.3187 -3.7728 -2.1163 -2.8457 0.9482 -2.8064 -2.7412 -2.8745 2.7912 0.6805 0.8213 0.8055 0.8578 1.4980 2.9309 0.2850 0.2854 0.2850 4.0990 0.3821 3.5349 0.6537 5.4143 0.0002 4.5093 0.1905
-#&gt; 500: 97.8277 -4.3506 -4.0318 -4.1520 -3.0553 -3.5843 1.1326 -2.0873 -2.0421 -2.0751 0.2960 1.2515 0.2531 0.3807 0.7928 0.8863 6.5211 0.1433 0.1082 0.3353 0.8960 0.0470 0.7501 0.0475 0.9527 0.0281 0.7321 0.0594</div><div class='output co'>#&gt; <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; [1] "CMT"</div><div class='output co'>#&gt; <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#&gt; <span class='message'>done</span></div><div class='output co'>#&gt; user system elapsed
-#&gt; 813.299 3.736 151.935 </div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span>
+</div><div class='output co'>#&gt; <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; 1: 98.3400 -3.5096 -3.3392 -3.7596 -2.2055 -2.7755 1.0281 -2.7872 -2.7223 -2.8341 2.6422 0.7027 0.8124 0.7085 0.8560 1.4980 3.2777 0.3063 0.2850 0.2850 4.1120 0.3716 4.4582 0.3994 4.4820 0.4025 3.7803 0.5780
+#&gt; 500: 97.8212 -4.4030 -4.0872 -4.1289 -2.8278 -4.3505 2.6614 -2.1252 -2.1308 -2.0749 2.9463 1.2933 0.2802 0.3467 0.4814 0.7877 3.0743 0.1508 0.1523 0.3155 0.9557 0.0333 0.4787 0.1073 0.6826 0.0707 0.7849 0.0356</div><div class='output co'>#&gt; <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; [1] "CMT"</div><div class='output co'>#&gt; <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#&gt; <span class='message'>done</span></div><div class='output co'>#&gt; user system elapsed
+#&gt; 800.784 3.715 149.687 </div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='error'>Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; nlmixr version used for fitting: 2.0.4
-#&gt; mkin version used for pre-fitting: 1.0.5
-#&gt; R version used for fitting: 4.1.0
-#&gt; Date of fit: Wed Aug 4 16:16:18 2021
-#&gt; Date of summary: Wed Aug 4 16:16:18 2021
+</div><div class='output co'>#&gt; nlmixr version used for fitting: 2.0.5
+#&gt; mkin version used for pre-fitting: 1.1.0
+#&gt; R version used for fitting: 4.1.1
+#&gt; Date of fit: Thu Sep 16 14:29:02 2021
+#&gt; Date of summary: Thu Sep 16 14:29:02 2021
#&gt;
#&gt; Equations:
#&gt; d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -529,25 +523,25 @@ specific pieces of information in the comments.</p>
#&gt; exp(-k2 * time))) * DMTA - k_M31 * M31
#&gt;
#&gt; Data:
-#&gt; 568 observations of 4 variable(s) grouped in 6 datasets
+#&gt; 563 observations of 4 variable(s) grouped in 6 datasets
#&gt;
#&gt; Degradation model predictions using RxODE
#&gt;
-#&gt; Fitted in 151.67 s
+#&gt; Fitted in 149.421 s
#&gt;
#&gt; Variance model: Two-component variance function
#&gt;
#&gt; Mean of starting values for individual parameters:
#&gt; DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2
-#&gt; 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393
+#&gt; 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388
#&gt; f_DMTA_ilr_3 log_k1 log_k2 g_qlogis
-#&gt; -1.7571 -2.2341 -3.7763 0.4502
+#&gt; -1.7554 -2.2352 -3.7758 0.4363
#&gt;
#&gt; Mean of starting values for error model parameters:
#&gt; sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27
-#&gt; 0.69793 0.02577 0.69793 0.02577 0.69793
+#&gt; 0.70012 0.02577 0.70012 0.02577 0.70012
#&gt; rsd_high_M27 sigma_low_M31 rsd_high_M31
-#&gt; 0.02577 0.69793 0.02577
+#&gt; 0.02577 0.70012 0.02577
#&gt;
#&gt; Fixed degradation parameter values:
#&gt; None
@@ -556,32 +550,32 @@ specific pieces of information in the comments.</p>
#&gt;
#&gt; Likelihood calculated by focei
#&gt; AIC BIC logLik
-#&gt; 2036 2157 -989.8
+#&gt; 1953 2074 -948.3
#&gt;
#&gt; Optimised parameters:
#&gt; est. lower upper
-#&gt; DMTA_0 97.828 96.121 99.535
-#&gt; log_k_M23 -4.351 -5.300 -3.401
-#&gt; log_k_M27 -4.032 -4.470 -3.594
-#&gt; log_k_M31 -4.152 -4.689 -3.615
-#&gt; log_k1 -3.055 -3.785 -2.325
-#&gt; log_k2 -3.584 -4.517 -2.651
-#&gt; g_qlogis 1.133 -2.165 4.430
-#&gt; f_DMTA_tffm0_1_qlogis -2.087 -2.407 -1.768
-#&gt; f_DMTA_tffm0_2_qlogis -2.042 -2.336 -1.748
-#&gt; f_DMTA_tffm0_3_qlogis -2.075 -2.557 -1.593
+#&gt; DMTA_0 97.821 95.862 99.780
+#&gt; log_k_M23 -4.403 -5.376 -3.430
+#&gt; log_k_M27 -4.087 -4.545 -3.629
+#&gt; log_k_M31 -4.129 -4.639 -3.618
+#&gt; log_k1 -2.828 -3.389 -2.266
+#&gt; log_k2 -4.351 -5.472 -3.229
+#&gt; g_qlogis 2.661 0.824 4.499
+#&gt; f_DMTA_tffm0_1_qlogis -2.125 -2.449 -1.801
+#&gt; f_DMTA_tffm0_2_qlogis -2.131 -2.468 -1.794
+#&gt; f_DMTA_tffm0_3_qlogis -2.075 -2.540 -1.610
#&gt;
#&gt; Correlation:
#&gt; DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs
-#&gt; log_k_M23 -0.031
-#&gt; log_k_M27 -0.050 0.004
-#&gt; log_k_M31 -0.032 0.003 0.078
-#&gt; log_k1 0.014 -0.002 -0.002 -0.001
-#&gt; log_k2 0.059 0.006 -0.001 0.002 -0.037
-#&gt; g_qlogis -0.077 0.005 0.009 0.004 0.035 -0.201
-#&gt; f_DMTA_tffm0_1_qlogis -0.104 0.066 0.009 0.006 0.000 -0.011 0.014
-#&gt; f_DMTA_tffm0_2_qlogis -0.120 0.013 0.081 -0.033 -0.002 -0.013 0.017
-#&gt; f_DMTA_tffm0_3_qlogis -0.086 0.010 0.060 0.078 -0.002 -0.005 0.010
+#&gt; log_k_M23 -0.019
+#&gt; log_k_M27 -0.028 0.004
+#&gt; log_k_M31 -0.019 0.003 0.075
+#&gt; log_k1 0.038 -0.004 -0.006 -0.003
+#&gt; log_k2 0.046 0.011 0.008 0.009 0.068
+#&gt; g_qlogis -0.067 0.004 0.006 0.001 -0.076 -0.409
+#&gt; f_DMTA_tffm0_1_qlogis -0.062 0.055 0.006 0.004 -0.008 -0.004 0.012
+#&gt; f_DMTA_tffm0_2_qlogis -0.062 0.010 0.058 -0.034 -0.008 -0.007 0.014
+#&gt; f_DMTA_tffm0_3_qlogis -0.052 0.009 0.056 0.071 -0.006 -0.001 0.008
#&gt; f_DMTA_0_1 f_DMTA_0_2
#&gt; log_k_M23
#&gt; log_k_M27
@@ -590,15 +584,15 @@ specific pieces of information in the comments.</p>
#&gt; log_k2
#&gt; g_qlogis
#&gt; f_DMTA_tffm0_1_qlogis
-#&gt; f_DMTA_tffm0_2_qlogis 0.026
-#&gt; f_DMTA_tffm0_3_qlogis 0.019 0.002
+#&gt; f_DMTA_tffm0_2_qlogis 0.017
+#&gt; f_DMTA_tffm0_3_qlogis 0.014 -0.005
#&gt;
#&gt; Random effects (omega):
#&gt; eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31
-#&gt; eta.DMTA_0 0.296 0.000 0.0000 0.0000
-#&gt; eta.log_k_M23 0.000 1.252 0.0000 0.0000
-#&gt; eta.log_k_M27 0.000 0.000 0.2531 0.0000
-#&gt; eta.log_k_M31 0.000 0.000 0.0000 0.3807
+#&gt; eta.DMTA_0 2.946 0.000 0.0000 0.0000
+#&gt; eta.log_k_M23 0.000 1.293 0.0000 0.0000
+#&gt; eta.log_k_M27 0.000 0.000 0.2802 0.0000
+#&gt; eta.log_k_M31 0.000 0.000 0.0000 0.3467
#&gt; eta.log_k1 0.000 0.000 0.0000 0.0000
#&gt; eta.log_k2 0.000 0.000 0.0000 0.0000
#&gt; eta.g_qlogis 0.000 0.000 0.0000 0.0000
@@ -610,9 +604,9 @@ specific pieces of information in the comments.</p>
#&gt; eta.log_k_M23 0.0000 0.0000 0.000
#&gt; eta.log_k_M27 0.0000 0.0000 0.000
#&gt; eta.log_k_M31 0.0000 0.0000 0.000
-#&gt; eta.log_k1 0.7928 0.0000 0.000
-#&gt; eta.log_k2 0.0000 0.8863 0.000
-#&gt; eta.g_qlogis 0.0000 0.0000 6.521
+#&gt; eta.log_k1 0.4814 0.0000 0.000
+#&gt; eta.log_k2 0.0000 0.7877 0.000
+#&gt; eta.g_qlogis 0.0000 0.0000 3.074
#&gt; eta.f_DMTA_tffm0_1_qlogis 0.0000 0.0000 0.000
#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0000 0.0000 0.000
#&gt; eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 0.000
@@ -624,8 +618,8 @@ specific pieces of information in the comments.</p>
#&gt; eta.log_k1 0.0000 0.0000
#&gt; eta.log_k2 0.0000 0.0000
#&gt; eta.g_qlogis 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.1433 0.0000
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1082
+#&gt; eta.f_DMTA_tffm0_1_qlogis 0.1508 0.0000
+#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1523
#&gt; eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000
#&gt; eta.f_DMTA_tffm0_3_qlogis
#&gt; eta.DMTA_0 0.0000
@@ -637,40 +631,40 @@ specific pieces of information in the comments.</p>
#&gt; eta.g_qlogis 0.0000
#&gt; eta.f_DMTA_tffm0_1_qlogis 0.0000
#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0000
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.3353
+#&gt; eta.f_DMTA_tffm0_3_qlogis 0.3155
#&gt;
#&gt; Variance model:
#&gt; sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27
-#&gt; 0.89603 0.04704 0.75015 0.04753 0.95265
+#&gt; 0.95572 0.03325 0.47871 0.10733 0.68264
#&gt; rsd_high_M27 sigma_low_M31 rsd_high_M31
-#&gt; 0.02810 0.73212 0.05942
+#&gt; 0.07072 0.78486 0.03557
#&gt;
#&gt; Backtransformed parameters:
#&gt; est. lower upper
-#&gt; DMTA_0 97.82774 96.120503 99.53498
-#&gt; k_M23 0.01290 0.004991 0.03334
-#&gt; k_M27 0.01774 0.011451 0.02749
-#&gt; k_M31 0.01573 0.009195 0.02692
-#&gt; f_DMTA_to_M23 0.11033 NA NA
-#&gt; f_DMTA_to_M27 0.10218 NA NA
-#&gt; f_DMTA_to_M31 0.08784 NA NA
-#&gt; k1 0.04711 0.022707 0.09773
-#&gt; k2 0.02775 0.010918 0.07056
-#&gt; g 0.75632 0.102960 0.98823
+#&gt; DMTA_0 97.82122 95.862233 99.78020
+#&gt; k_M23 0.01224 0.004625 0.03239
+#&gt; k_M27 0.01679 0.010615 0.02654
+#&gt; k_M31 0.01610 0.009664 0.02683
+#&gt; f_DMTA_to_M23 0.10668 NA NA
+#&gt; f_DMTA_to_M27 0.09481 NA NA
+#&gt; f_DMTA_to_M31 0.08908 NA NA
+#&gt; k1 0.05914 0.033731 0.10370
+#&gt; k2 0.01290 0.004204 0.03958
+#&gt; g 0.93471 0.695081 0.98900
#&gt;
#&gt; Resulting formation fractions:
#&gt; ff
-#&gt; DMTA_M23 0.11033
-#&gt; DMTA_M27 0.10218
-#&gt; DMTA_M31 0.08784
-#&gt; DMTA_sink 0.69965
+#&gt; DMTA_M23 0.10668
+#&gt; DMTA_M27 0.09481
+#&gt; DMTA_M31 0.08908
+#&gt; DMTA_sink 0.70943
#&gt;
#&gt; Estimated disappearance times:
#&gt; DT50 DT90 DT50back DT50_k1 DT50_k2
-#&gt; DMTA 16.59 57.44 17.29 14.71 24.97
-#&gt; M23 53.74 178.51 NA NA NA
-#&gt; M27 39.07 129.78 NA NA NA
-#&gt; M31 44.06 146.36 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span>
+#&gt; DMTA 12.57 45.43 13.67 11.72 53.73
+#&gt; M23 56.63 188.11 NA NA NA
+#&gt; M27 41.29 137.18 NA NA NA
+#&gt; M31 43.05 143.01 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span>
</div><div class='img'><img src='dimethenamid_2018-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span>
</div></pre>
</div>
diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html
index dc1d1f17..aa5bd773 100644
--- a/docs/dev/reference/endpoints.html
+++ b/docs/dev/reference/endpoints.html
@@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." />
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html
index bb030605..d5ec387a 100644
--- a/docs/dev/reference/index.html
+++ b/docs/dev/reference/index.html
@@ -71,7 +71,7 @@
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html
index f63dbc31..5981c625 100644
--- a/docs/dev/reference/mean_degparms.html
+++ b/docs/dev/reference/mean_degparms.html
@@ -72,7 +72,7 @@
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html
index ac7c2daa..6478cda4 100644
--- a/docs/dev/reference/mkinmod.html
+++ b/docs/dev/reference/mkinmod.html
@@ -76,7 +76,7 @@ components." />
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
@@ -344,7 +344,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
parent <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, full_name <span class='op'>=</span> <span class='st'>"Test compound"</span><span class='op'>)</span>,
m1 <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, full_name <span class='op'>=</span> <span class='st'>"Metabolite M1"</span><span class='op'>)</span>,
name <span class='op'>=</span> <span class='st'>"SFO_SFO"</span>, dll_dir <span class='op'>=</span> <span class='va'>DLL_dir</span>, unload <span class='op'>=</span> <span class='cn'>TRUE</span>, overwrite <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'>Copied DLL from /tmp/RtmpKZJMFk/file179ba717d15c81.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span>
+</div><div class='output co'>#&gt; <span class='message'>Copied DLL from /tmp/RtmpTzKqT5/file7993c266a7f90.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span>
<span class='fu'><a href='https://rdrr.io/r/base/readRDS.html'>saveRDS</a></span><span class='op'>(</span><span class='va'>SFO_SFO.2</span>, file <span class='op'>=</span> <span class='st'>"~/SFO_SFO.rds"</span><span class='op'>)</span>
<span class='co'># Terminate the R session here if you would like to check, and then do</span>
<span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://pkgdown.jrwb.de/mkin/'>mkin</a></span><span class='op'>)</span>
@@ -393,7 +393,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p>
#&gt; })
#&gt; return(predicted)
#&gt; }
-#&gt; &lt;environment: 0x555559c54f78&gt;</div><div class='input'>
+#&gt; &lt;environment: 0x555559365690&gt;</div><div class='input'>
<span class='co'># If we have several parallel metabolites</span>
<span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span>
<span class='va'>m_synth_DFOP_par</span> <span class='op'>&lt;-</span> <span class='fu'>mkinmod</span><span class='op'>(</span>
diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png
index 365aaef0..e4bc2fde 100644
--- a/docs/dev/reference/nlme-1.png
+++ b/docs/dev/reference/nlme-1.png
Binary files differ
diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png
index 40841404..31910ce4 100644
--- a/docs/dev/reference/nlme-2.png
+++ b/docs/dev/reference/nlme-2.png
Binary files differ
diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html
index 55a94443..500ac391 100644
--- a/docs/dev/reference/nlme.html
+++ b/docs/dev/reference/nlme.html
@@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." />
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
@@ -216,28 +216,28 @@ datasets. They are used internally by the <code><a href='nlme.mmkin.html'>nlme.m
#&gt; Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink)
#&gt; Data: grouped_data
#&gt; AIC BIC logLik
-#&gt; 300.6824 310.2426 -145.3412
+#&gt; 289.8295 299.4886 -139.9148
#&gt;
#&gt; Random effects:
#&gt; Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
#&gt; Level: ds
#&gt; Structure: Diagonal
#&gt; parent_0 log_k_parent_sink Residual
-#&gt; StdDev: 1.697361 0.6801209 3.666073
+#&gt; StdDev: 1.839278 0.6988919 3.059894
#&gt;
#&gt; Fixed effects: parent_0 + log_k_parent_sink ~ 1
#&gt; Value Std.Error DF t-value p-value
-#&gt; parent_0 100.99378 1.3890416 46 72.70753 0
-#&gt; log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0
+#&gt; parent_0 100.52780 1.3507449 47 74.42397 0
+#&gt; log_k_parent_sink -3.08477 0.4124053 47 -7.47995 0
#&gt; Correlation:
#&gt; prnt_0
-#&gt; log_k_parent_sink 0.027
+#&gt; log_k_parent_sink 0.019
#&gt;
#&gt; Standardized Within-Group Residuals:
-#&gt; Min Q1 Med Q3 Max
-#&gt; -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781
+#&gt; Min Q1 Med Q3 Max
+#&gt; -2.22350411 -0.51546184 0.04803417 0.55987705 3.49178405
#&gt;
-#&gt; Number of Observations: 50
+#&gt; Number of Observations: 51
#&gt; Number of Groups: 3 </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='fu'><a href='https://rdrr.io/pkg/nlme/man/augPred.html'>augPred</a></span><span class='op'>(</span><span class='va'>m_nlme</span>, level <span class='op'>=</span> <span class='fl'>0</span><span class='op'>:</span><span class='fl'>1</span><span class='op'>)</span>, layout <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span>
</div><div class='img'><img src='nlme-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># augPred does not work on fits with more than one state</span>
<span class='co'># variable</span>
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html
index db863392..866091ca 100644
--- a/docs/dev/reference/nlme.mmkin.html
+++ b/docs/dev/reference/nlme.mmkin.html
@@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." />
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html
index 99a7ad14..db114483 100644
--- a/docs/dev/reference/nlmixr.mmkin.html
+++ b/docs/dev/reference/nlmixr.mmkin.html
@@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." />
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
@@ -4416,7 +4416,8 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; |.....................| 5.960e-07 | 0.6941 | 1.222 | 1.493 |
#&gt; | X|<span style='font-weight: bold;'> 288.66432</span> | 94.40 | 0.8038 | 7.793 | 2.283 |
#&gt; |.....................| 5.960e-07 | 0.6941 | 1.222 | 1.493 |
-#&gt; done</div><div class='output co'>#&gt; <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#&gt; <span class='message'>done</span></div><div class='output co'>#&gt; <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#&gt; <span class='warning'>Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span></div><div class='output co'>#&gt; <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#&gt; <span class='warning'>Warning: parameter estimate near boundary; covariance not calculated</span>
+#&gt; done</div><div class='output co'>#&gt; <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#&gt; <span class='message'>done</span></div><div class='output co'>#&gt; <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#&gt; <span class='warning'>Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span></div><div class='output co'>#&gt; <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#&gt; <span class='warning'>Warning: parameter estimate near boundary; covariance not calculated:</span>
+#&gt; <span class='warning'> "rsd_high" </span>
#&gt; <span class='warning'> use 'getVarCov' to calculate anyway</span></div><div class='output co'>#&gt; <span class='warning'>Warning: gradient problems with initial estimate; see $scaleInfo</span></div><div class='input'>
<span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span>
<span class='va'>f_nlmixr_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span>,
@@ -4501,7 +4502,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>k_A1=rx_expr_11;</span>
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[4]+THETA[4])));</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 5.723 0.414 6.136</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_const</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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'>#&gt; <span class='message'>Timing stopped at: 5.579 0.328 5.909</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_const</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(A1);</span>
#&gt; <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span>
@@ -4550,7 +4551,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>beta=exp(rx_expr_8);</span>
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 6.874 0.399 7.27</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_const</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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'>#&gt; <span class='message'>Timing stopped at: 7.125 0.34 7.462</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_const</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(A1);</span>
#&gt; <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span>
@@ -4607,10 +4608,10 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>g=1/(rx_expr_20);</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 14.83 0.382 15.21</span></div><div class='input'>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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'>#&gt; <span class='message'>Timing stopped at: 15.03 0.407 15.43</span></div><div class='input'>
<span class='co'># Variance by variable is supported by 'saem' and 'focei'</span>
<span class='va'>f_nlmixr_fomc_sfo_saem_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.22 0.146 1.365</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
+</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.253 0.121 1.373</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(A1);</span>
#&gt; <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span>
@@ -4659,8 +4660,8 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>beta=exp(rx_expr_8);</span>
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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'>#&gt; <span class='message'>Timing stopped at: 6.6 0.416 7.016</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.551 0.126 1.673</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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'>#&gt; <span class='message'>Timing stopped at: 6.487 0.387 6.872</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span>
+</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.385 0.1 1.485</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(A1);</span>
#&gt; <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span>
@@ -4717,7 +4718,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>g=1/(rx_expr_19);</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 15.24 0.429 15.67</span></div><div class='input'>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 15.06 0.434 15.49</span></div><div class='input'>
<span class='co'># Identical two-component error for all variables is only possible with</span>
<span class='co'># est = 'focei' in nlmixr</span>
<span class='va'>f_nlmixr_fomc_sfo_focei_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
@@ -4771,7 +4772,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>beta=exp(rx_expr_8);</span>
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 8.644 0.416 9.058</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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'>#&gt; <span class='message'>Timing stopped at: 8.599 0.38 8.975</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(A1);</span>
#&gt; <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span>
@@ -4830,12 +4831,12 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>g=1/(rx_expr_21);</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 18.76 0.426 19.18</span></div><div class='input'>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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'>#&gt; <span class='message'>Timing stopped at: 17.64 0.453 18.09</span></div><div class='input'>
<span class='co'># Two-component error by variable is possible with both estimation methods</span>
<span class='co'># Variance by variable is supported by 'saem' and 'focei'</span>
<span class='va'>f_nlmixr_fomc_sfo_saem_obs_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>,
error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0.757 0.072 0.829</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>,
+</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0.772 0.04 0.813</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>,
error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(A1);</span>
@@ -4887,9 +4888,9 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>beta=exp(rx_expr_8);</span>
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 8.417 0.388 8.803</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>,
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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'>#&gt; <span class='message'>Timing stopped at: 8.33 0.419 8.745</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>,
error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0.82 0.035 0.857</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>,
+</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0.832 0.032 0.866</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>,
error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(A1);</span>
@@ -4949,7 +4950,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit.
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>g=1/(rx_expr_19);</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 17.61 0.452 18.06</span></div><div class='input'>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 18.15 0.42 18.57</span></div><div class='input'>
<span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span>
<span class='va'>f_nlmixr_sfo_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>,
<span class='va'>f_nlmixr_fomc_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>,
diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html
index 746a8640..9f0eb965 100644
--- a/docs/dev/reference/plot.mixed.mmkin.html
+++ b/docs/dev/reference/plot.mixed.mmkin.html
@@ -72,7 +72,7 @@
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
@@ -296,10 +296,10 @@ corresponding model prediction lines for the different datasets.</p></td>
</div><div class='img'><img src='plot.mixed.mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='va'>f_saem</span> <span class='op'>&lt;-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f</span>, transformations <span class='op'>=</span> <span class='st'>"saemix"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:21:52 2021"
+#&gt; [1] "Thu Sep 16 14:34:31 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:00 2021"</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_saem</span><span class='op'>)</span>
+#&gt; [1] "Thu Sep 16 14:34:38 2021"</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_saem</span><span class='op'>)</span>
</div><div class='img'><img src='plot.mixed.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='va'>f_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>dfop_sfo</span><span class='op'>)</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"obs"</span><span class='op'>)</span>
<span class='va'>f_nlmix</span> <span class='op'>&lt;-</span> <span class='fu'>nlmix</span><span class='op'>(</span><span class='va'>f_obs</span><span class='op'>)</span>
diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html
index f5ace044..ac4fa4d9 100644
--- a/docs/dev/reference/reexports.html
+++ b/docs/dev/reference/reexports.html
@@ -81,7 +81,7 @@ below to see their documentation.
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html
index 620173b2..8d986126 100644
--- a/docs/dev/reference/saem.html
+++ b/docs/dev/reference/saem.html
@@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." />
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
@@ -288,27 +288,27 @@ using <a href='mmkin.html'>mmkin</a>.</p>
state.ini <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span><span class='op'>)</span>, fixed_initials <span class='op'>=</span> <span class='st'>"parent"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
<span class='va'>f_saem_p0_fixed</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent_p0_fixed</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:05 2021"
+#&gt; [1] "Thu Sep 16 14:34:42 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:06 2021"</div><div class='input'>
+#&gt; [1] "Thu Sep 16 14:34:43 2021"</div><div class='input'>
<span class='va'>f_mmkin_parent</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span><span class='op'>)</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
<span class='va'>f_saem_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:08 2021"
+#&gt; [1] "Thu Sep 16 14:34:45 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:10 2021"</div><div class='input'><span class='va'>f_saem_fomc</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span>
+#&gt; [1] "Thu Sep 16 14:34:47 2021"</div><div class='input'><span class='va'>f_saem_fomc</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:10 2021"
+#&gt; [1] "Thu Sep 16 14:34:47 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:12 2021"</div><div class='input'><span class='va'>f_saem_dfop</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"DFOP"</span>, <span class='op'>]</span><span class='op'>)</span>
+#&gt; [1] "Thu Sep 16 14:34:49 2021"</div><div class='input'><span class='va'>f_saem_dfop</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"DFOP"</span>, <span class='op'>]</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:12 2021"
+#&gt; [1] "Thu Sep 16 14:34:49 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:16 2021"</div><div class='input'>
+#&gt; [1] "Thu Sep 16 14:34:52 2021"</div><div class='input'>
<span class='co'># The returned saem.mmkin object contains an SaemixObject, therefore we can use</span>
<span class='co'># functions from saemix</span>
<span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'>saemix</span><span class='op'>)</span>
@@ -357,10 +357,10 @@ using <a href='mmkin.html'>mmkin</a>.</p>
<span class='va'>f_mmkin_parent_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_mmkin_parent</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span>
<span class='va'>f_saem_fomc_tc</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent_tc</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:19 2021"
+#&gt; [1] "Thu Sep 16 14:34:55 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:24 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, <span class='va'>f_saem_fomc_tc</span><span class='op'>$</span><span class='va'>so</span><span class='op'>)</span>
+#&gt; [1] "Thu Sep 16 14:35:00 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, <span class='va'>f_saem_fomc_tc</span><span class='op'>$</span><span class='va'>so</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'>Likelihoods calculated by importance sampling</span></div><div class='output co'>#&gt; AIC BIC
#&gt; 1 467.7096 464.9757
#&gt; 2 469.6831 466.5586</div><div class='input'>
@@ -381,15 +381,15 @@ using <a href='mmkin.html'>mmkin</a>.</p>
<span class='co'># four minutes</span>
<span class='va'>f_saem_sfo_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"SFO-SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:27 2021"
+#&gt; [1] "Thu Sep 16 14:35:03 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:32 2021"</div><div class='input'><span class='va'>f_saem_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
+#&gt; [1] "Thu Sep 16 14:35:08 2021"</div><div class='input'><span class='va'>f_saem_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:33 2021"
+#&gt; [1] "Thu Sep 16 14:35:08 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:42 2021"</div><div class='input'><span class='co'># We can use print, plot and summary methods to check the results</span>
+#&gt; [1] "Thu Sep 16 14:35:17 2021"</div><div class='input'><span class='co'># We can use print, plot and summary methods to check the results</span>
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_saem_dfop_sfo</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Kinetic nonlinear mixed-effects model fit by SAEM
#&gt; Structural model:
@@ -430,10 +430,10 @@ using <a href='mmkin.html'>mmkin</a>.</p>
#&gt; SD.g_qlogis 0.44816 -1.25437 2.1507</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem_dfop_sfo</span><span class='op'>)</span>
</div><div class='img'><img src='saem-5.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_saem_dfop_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
</div><div class='output co'>#&gt; saemix version used for fitting: 3.1.9000
-#&gt; mkin version used for pre-fitting: 1.0.5
-#&gt; R version used for fitting: 4.1.0
-#&gt; Date of fit: Wed Aug 4 16:22:43 2021
-#&gt; Date of summary: Wed Aug 4 16:22:43 2021
+#&gt; mkin version used for pre-fitting: 1.1.0
+#&gt; R version used for fitting: 4.1.1
+#&gt; Date of fit: Thu Sep 16 14:35:18 2021
+#&gt; Date of summary: Thu Sep 16 14:35:18 2021
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -448,7 +448,7 @@ using <a href='mmkin.html'>mmkin</a>.</p>
#&gt;
#&gt; Model predictions using solution type analytical
#&gt;
-#&gt; Fitted in 10.143 s using 300, 100 iterations
+#&gt; Fitted in 9.349 s using 300, 100 iterations
#&gt;
#&gt; Variance model: Constant variance
#&gt;
diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html
index 70a71683..4831bbdf 100644
--- a/docs/dev/reference/summary.nlmixr.mmkin.html
+++ b/docs/dev/reference/summary.nlmixr.mmkin.html
@@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>
@@ -258,12 +258,12 @@ nlmixr authors for the parts inherited from nlmixr.</p>
quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, cores <span class='op'>=</span> <span class='fl'>5</span><span class='op'>)</span>
<span class='va'>f_saemix_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Wed Aug 4 16:22:46 2021"
+#&gt; [1] "Thu Sep 16 14:35:21 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Wed Aug 4 16:22:59 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span>
+#&gt; [1] "Thu Sep 16 14:35:33 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.383 0.12 1.503</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span>
+</div><div class='output co'>#&gt; <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'>→ generate SAEM model</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_m1</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.412 0.101 1.525</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span>
<span class='va'>f_nlmixr_dfop_sfo_focei</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Model:</span></div><div class='output co'>#&gt; <span class='message'>cmt(parent);</span>
#&gt; <span class='message'>cmt(m1);</span>
@@ -323,7 +323,7 @@ nlmixr authors for the parts inherited from nlmixr.</p>
#&gt; <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span>
#&gt; <span class='message'>g=1/(rx_expr_21);</span>
#&gt; <span class='message'>tad=tad();</span>
-#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_m1" "CMT" </span></div><div class='output co'>#&gt; <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 &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 18.43 0.422 18.87</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span>
+#&gt; <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; <span class='message'>[1] "f_parent_to_m1" "CMT" </span></div><div class='output co'>#&gt; <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt &lt;- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control &lt;- do.call(foceiControl, control) } if (is.null(env)) { .ret &lt;- new.env(parent = emptyenv()) } else { .ret &lt;- env } .ret$origData &lt;- data .ret$etaNames &lt;- etaNames .ret$thetaFixed &lt;- fixed .ret$control &lt;- control .ret$control$focei.mu.ref &lt;- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel &lt;- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel &lt;- TRUE model &lt;- RxODE::rxGetLin(PKpars) pred &lt;- eval(parse(text = "function(){return(Central);}")) } .square &lt;- function(x) x * x .ret$diagXformInv &lt;- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err &lt;- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames &lt;- .parNames &lt;- c() .ret$adjLik &lt;- control$adjLik .mixed &lt;- !is.null(inits$OMGA) &amp;&amp; length(inits$OMGA) &gt; 0 if (!exists("noLik", envir = .ret)) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol &lt;- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol &lt;- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- 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$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol .ssAtol &lt;- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol &lt;- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol &lt;- .ssAtol .ret$control$rxControl$ssRtol &lt;- .ssRtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars &lt;- .ret$model$extra.pars } else { if (.ret$noLik) { .atol &lt;- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol &lt;- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model &lt;- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol &lt;- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol &lt;- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol &lt;- .atol .ret$control$rxControl$rtol &lt;- .rtol } .covNames &lt;- .parNames &lt;- RxODE::rxParams(.ret$model$pred.only) .covNames &lt;- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) &lt;- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs &lt;- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) &gt; 0) { .covNames &lt;- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) &gt; 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 &lt;- .ret$model$extra.pars } else { .extraPars &lt;- NULL } } .ret$skipCov &lt;- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp &lt;- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) &lt; length(inits$THTA)) { .tmp &lt;- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp &lt;- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr &lt;- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr &lt;- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp &lt;- (.tmp | .uifErr) } .ret$skipCov &lt;- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref &lt;- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms &lt;- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) &amp;&amp; (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms &lt;- thetaNames } .ret$thetaNames &lt;- .nms .thetaReset$thetaNames &lt;- .nms if (length(lower) == 1) { lower &lt;- rep(lower, length(inits$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 &lt;- 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 &lt;- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) &gt; 0) { inits$THTA &lt;- c(inits$THTA, .ret$model$extra.pars) .lowerErr &lt;- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr &lt;- rep(Inf, length(.ret$model$extra.pars)) lower &lt;- c(lower, .lowerErr) upper &lt;- 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 &lt;- 0 if (is.null(data$AMT)) data$AMT &lt;- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] &lt;- as.double(data[[.v]]) } .ret$dataSav &lt;- data .ds &lt;- data[data$EVID != 0 &amp; data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w &lt;- which(tolower(names(data)) == "limit") .limitName &lt;- NULL if (length(.w) == 1L) { .limitName &lt;- names(data)[.w] } .censName &lt;- NULL .w &lt;- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName &lt;- names(data[.w]) } data &lt;- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w &lt;- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] &lt;- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh &lt;- .parseOM(inits$OMGA) .nlh &lt;- sapply(.lh, length) .osplt &lt;- rep(1:length(.lh), .nlh) .lini &lt;- list(inits$THTA, unlist(.lh)) .nlini &lt;- sapply(.lini, length) .nsplt &lt;- rep(1:length(.lini), .nlini) .om0 &lt;- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames &lt;- .ret$etaNames } else { .ret$etaNames &lt;- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv &lt;- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType &lt;- .ret$rxInv$xType .om0a &lt;- .om0 .om0a &lt;- .om0a/control$diagOmegaBoundLower .om0b &lt;- .om0 .om0b &lt;- .om0b * control$diagOmegaBoundUpper .om0a &lt;- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b &lt;- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf &lt;- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower &lt;- with(.omdf, ifelse(a &gt; b, b, a)) .omdf$lower &lt;- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower &lt;- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper &lt;- with(.omdf, ifelse(a &lt; b, b, a)) .omdf$upper &lt;- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper &lt;- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega &lt;- length(.omdf$lower) .ret$control$neta &lt;- sum(.omdf$diag) .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) lower &lt;- c(lower, .omdf$lower) upper &lt;- c(upper, .omdf$upper) } else { .ret$control$nomega &lt;- 0 .ret$control$neta &lt;- 0 .ret$xType &lt;- -1 .ret$control$ntheta &lt;- length(lower) .ret$control$nfixed &lt;- sum(fixed) } .ret$lower &lt;- lower .ret$upper &lt;- upper .ret$thetaIni &lt;- inits$THTA .scaleC &lt;- double(length(lower)) if (is.null(control$scaleC)) { .scaleC &lt;- rep(NA_real_, length(lower)) } else { .scaleC &lt;- as.double(control$scaleC) if (length(lower) &gt; length(.scaleC)) { .scaleC &lt;- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) &lt; length(.scaleC)) { .scaleC &lt;- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC &lt;- .scaleC if (exists("uif", envir = .ret)) { .ini &lt;- as.data.frame(.ret$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]] &lt;- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] &lt;- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- 1 } .ini &lt;- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] &lt;- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b &lt;- .ret$logitThetasLow[.i] .c &lt;- .ret$logitThetasHi[.i] .a &lt;- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] &lt;- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) &lt;- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) &amp; !is.null(control$etaMat)) { .ret$etaMat &lt;- control$etaMat } else { .ret$etaMat &lt;- etaMat } .ret$setupTime &lt;- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp &lt;- .ret$uif$logThetasList .ret$logThetas &lt;- .tmp[[1]] .ret$logThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasList .ret$logitThetas &lt;- .tmp[[1]] .ret$logitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListLow .ret$logitThetasLow &lt;- .tmp[[1]] .ret$logitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$logitThetasListHi .ret$logitThetasHi &lt;- .tmp[[1]] .ret$logitThetasHiF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasList .ret$probitThetas &lt;- .tmp[[1]] .ret$probitThetasF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListLow .ret$probitThetasLow &lt;- .tmp[[1]] .ret$probitThetasLowF &lt;- .tmp[[2]] .tmp &lt;- .ret$uif$probitThetasListHi .ret$probitThetasHi &lt;- .tmp[[1]] .ret$probitThetasHiF &lt;- .tmp[[2]] } else { .ret$logThetasF &lt;- integer(0) .ret$logitThetasF &lt;- integer(0) .ret$logitThetasHiF &lt;- numeric(0) .ret$logitThetasLowF &lt;- numeric(0) .ret$logitThetas &lt;- integer(0) .ret$logitThetasHi &lt;- numeric(0) .ret$logitThetasLow &lt;- numeric(0) .ret$probitThetasF &lt;- integer(0) .ret$probitThetasHiF &lt;- numeric(0) .ret$probitThetasLowF &lt;- numeric(0) .ret$probitThetas &lt;- integer(0) .ret$probitThetasHi &lt;- numeric(0) .ret$probitThetasLow &lt;- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params &lt;- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan &lt;- length(.ret$thetaIni) .ret$nobs &lt;- sum(data$EVID == 0) } } .ret$control$printTop &lt;- TRUE .ret$control$nF &lt;- 0 .est0 &lt;- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq &lt;- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq &lt;- 0L } .fitFun &lt;- function(.ret) { this.env &lt;- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 &lt;- 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$err == "theta reset") { .nm &lt;- names(.ret$thetaIni) .ret$thetaIni &lt;- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta &lt;- .thetaReset$omegaTheta .ret$control$printTop &lt;- FALSE .ret$etaMat &lt;- .thetaReset$etaMat .ret$control$etaMat &lt;- .thetaReset$etaMat .ret$control$maxInnerIterations &lt;- .thetaReset$maxInnerIterations .ret$control$nF &lt;- .thetaReset$nF .ret$control$gillRetC &lt;- .thetaReset$gillRetC .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillRet &lt;- .thetaReset$gillRet .ret$control$gillDf &lt;- .thetaReset$gillDf .ret$control$gillDf2 &lt;- .thetaReset$gillDf2 .ret$control$gillErr &lt;- .thetaReset$gillErr .ret$control$rEps &lt;- .thetaReset$rEps .ret$control$aEps &lt;- .thetaReset$aEps .ret$control$rEpsC &lt;- .thetaReset$rEpsC .ret$control$aEpsC &lt;- .thetaReset$aEpsC .ret$control$c1 &lt;- .thetaReset$c1 .ret$control$c2 &lt;- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations &lt;- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun &lt;- .bobyqa .ret$control$outerOpt &lt;- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- 1 while (inherits(.ret0, "try-error") &amp;&amp; control$maxOuterIterations != 0 &amp;&amp; .n &lt;= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF &lt;- 0 .estNew &lt;- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew &lt;- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] &lt; lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] &gt; upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni &lt;- .estNew .ret0 &lt;- try(.fitFun(.ret)) .n &lt;- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret &lt;- .ret0 if (exists("parHistData", .ret)) { .tmp &lt;- .ret$parHistData .tmp &lt;- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter &lt;- .tmp$iter .tmp &lt;- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked &lt;- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) &lt;- c("val", "par", "iter") .ret$parHist &lt;- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas &lt;- .ret$ranef .thetas &lt;- .ret$fixef .pars &lt;- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink &lt;- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table &lt;- tableControl() } if (control$calcTables) { .ret &lt;- 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 18.28 0.455 18.73</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='error'>Error in AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm): object 'f_nlmixr_dfop_sfo_saem' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found</span></div><div class='input'><span class='co'># }</span>
diff --git a/docs/dev/reference/tffm0.html b/docs/dev/reference/tffm0.html
index d993e8ff..67f26b85 100644
--- a/docs/dev/reference/tffm0.html
+++ b/docs/dev/reference/tffm0.html
@@ -81,7 +81,7 @@ from RxODE." />
</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">1.0.5</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span>
</span>
</div>

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