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authorJohannes Ranke <jranke@uni-bremen.de>2021-10-05 17:33:52 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2021-10-05 17:33:52 +0200
commitff83d8b2ba623513d92ac90fac4a1b0ec98c2cb5 (patch)
tree7811a1e083951d84bd54bfce9bb62af640da329e /docs/dev/reference/dimethenamid_2018.html
parent121e38654eea03c91aa117da58fc342feff01756 (diff)
Update docs
Diffstat (limited to 'docs/dev/reference/dimethenamid_2018.html')
-rw-r--r--docs/dev/reference/dimethenamid_2018.html463
1 files changed, 108 insertions, 355 deletions
diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html
index 919e9363..60c15ade 100644
--- a/docs/dev/reference/dimethenamid_2018.html
+++ b/docs/dev/reference/dimethenamid_2018.html
@@ -222,7 +222,7 @@ specific pieces of information in the comments.</p>
<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"DFOP-SFO3+"</span> <span class='op'>=</span> <span class='va'>dfop_sfo3_plus</span><span class='op'>)</span>,
<span class='va'>dmta_ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span>
<span class='fu'><a href='nlmixr.mmkin.html'>nlmixr_model</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#&gt; function ()
+</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='warning'>Warning: number of items to replace is not a multiple of replacement length</span></div><div class='output co'>#&gt; function ()
#&gt; {
#&gt; ini({
#&gt; DMTA_0 = 98.7132391714013
@@ -263,9 +263,9 @@ specific pieces of information in the comments.</p>
#&gt; k1 = exp(log_k1 + eta.log_k1)
#&gt; k2 = exp(log_k2 + eta.log_k2)
#&gt; g = expit(g_qlogis + eta.g_qlogis)
-#&gt; f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis)
-#&gt; f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis)
-#&gt; f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis)
+#&gt; f_DMTA_to_M23 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis)
+#&gt; f_DMTA_to_M23 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis)
+#&gt; f_DMTA_to_M23 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis)
#&gt; f_DMTA_to_M23 = f_DMTA_tffm0_1
#&gt; f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1)
#&gt; f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) *
@@ -289,205 +289,122 @@ specific pieces of information in the comments.</p>
#&gt; M31 ~ add(sigma_low_M31) + prop(rsd_high_M31)
#&gt; })
#&gt; }
-#&gt; &lt;environment: 0x555559e97ac0&gt;</div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span>
+#&gt; &lt;environment: 0x555559d89920&gt;</div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span>
<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span>
<span class='va'>f_dmta_nlmixr_focei</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>,
control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/foceiControl.html'>foceiControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span><span class='op'>)</span><span class='op'>)</span>
<span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:02
+</div><div class='output co'>#&gt; <span class='warning'>Warning: number of items to replace is not a multiple of replacement length</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ creating full model...</span></div><div class='output co'>#&gt; <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ calculate jacobian</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:02
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:04
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:01
-#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:08
+#&gt; </div><div class='output co'>#&gt; <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:09
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:07
-#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:07
+#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:06
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:00
#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#&gt; [====|====|====|====|====|====|====|====|====|====] 0:00:00
-#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling inner model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#&gt; </div><div class='output co'>#&gt; <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#&gt; <span class='message'> </span></div><div class='output co'>#&gt; <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#&gt; <span class='message'>Needed Covariates:</span></div><div class='output co'>#&gt; [1] "CMT"</div><div class='output co'>#&gt; <span class='message'>RxODE 1.1.1 using 8 threads (see ?getRxThreads)</span>
-#&gt; <span class='message'> no cache: create with `rxCreateCache()`</span></div><div class='output co'>#&gt; <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation
-#&gt; F: Forward difference gradient approximation
-#&gt; C: Central difference gradient approximation
-#&gt; M: Mixed forward and central difference gradient approximation
-#&gt; Unscaled parameters for Omegas=chol(solve(omega));
-#&gt; Diagonals are transformed, as specified by foceiControl(diagXform=)
-#&gt; |-----+---------------+-----------+-----------+-----------+-----------|
-#&gt; | #| Objective Fun | DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 |
-#&gt; |.....................| log_k1 | log_k2 | g_qlogis |f_DMTA_tffm0_1_qlogis |
-#&gt; |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high |
-#&gt; |.....................| o1 | o2 | o3 | o4 |
-#&gt; |.....................| o5 | o6 | o7 | o8 |
-#&gt; <span style='text-decoration: underline;'>|.....................| o9 | o10 |...........|...........|</span>
-#&gt; calculating covariance matrix
-#&gt; done</div><div class='output co'>#&gt; <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#&gt; <span class='message'>done</span></div><div class='output co'>#&gt; <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#&gt; <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#&gt; <span class='warning'>Warning: S matrix non-positive definite</span></div><div class='output co'>#&gt; <span class='warning'>Warning: using R matrix to calculate covariance</span></div><div class='output co'>#&gt; <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='output co'>#&gt; user system elapsed
-#&gt; 230.015 8.962 238.957 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; nlmixr version used for fitting: 2.0.5
-#&gt; mkin version used for pre-fitting: 1.1.0
-#&gt; R version used for fitting: 4.1.1
-#&gt; Date of fit: Thu Sep 16 14:06:55 2021
-#&gt; Date of summary: Thu Sep 16 14:06:55 2021
-#&gt;
-#&gt; Equations:
-#&gt; d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-#&gt; time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-#&gt; * DMTA
-#&gt; d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
-#&gt; * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-#&gt; exp(-k2 * time))) * DMTA - k_M23 * M23
-#&gt; d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
-#&gt; * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-#&gt; exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31
-#&gt; d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
-#&gt; * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-#&gt; exp(-k2 * time))) * DMTA - k_M31 * M31
-#&gt;
-#&gt; Data:
-#&gt; 563 observations of 4 variable(s) grouped in 6 datasets
-#&gt;
-#&gt; Degradation model predictions using RxODE
-#&gt;
-#&gt; Fitted in 238.792 s
-#&gt;
-#&gt; Variance model: Two-component variance function
-#&gt;
-#&gt; Mean of starting values for individual parameters:
-#&gt; DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2
-#&gt; 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388
-#&gt; f_DMTA_ilr_3 log_k1 log_k2 g_qlogis
-#&gt; -1.7554 -2.2352 -3.7758 0.4363
-#&gt;
-#&gt; Mean of starting values for error model parameters:
-#&gt; sigma_low rsd_high
-#&gt; 0.70012 0.02577
-#&gt;
-#&gt; Fixed degradation parameter values:
-#&gt; None
-#&gt;
-#&gt; Results:
-#&gt;
-#&gt; Likelihood calculated by focei
-#&gt; AIC BIC logLik
-#&gt; 1918 2014 -937.2
-#&gt;
-#&gt; Optimised parameters:
-#&gt; est. lower upper
-#&gt; DMTA_0 98.7132 98.6801 98.7464
-#&gt; log_k_M23 -3.9216 -3.9235 -3.9198
-#&gt; log_k_M27 -4.3306 -4.3326 -4.3286
-#&gt; log_k_M31 -4.2442 -4.2461 -4.2422
-#&gt; log_k1 -2.2352 -2.2364 -2.2340
-#&gt; log_k2 -3.7758 -3.7776 -3.7740
-#&gt; g_qlogis 0.4363 0.4358 0.4368
-#&gt; f_DMTA_tffm0_1_qlogis -2.0915 -2.0926 -2.0903
-#&gt; f_DMTA_tffm0_2_qlogis -2.1788 -2.1800 -2.1776
-#&gt; f_DMTA_tffm0_3_qlogis -2.1404 -2.1415 -2.1392
-#&gt;
-#&gt; Correlation:
-#&gt; DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs
-#&gt; log_k_M23 0
-#&gt; log_k_M27 0 0
-#&gt; log_k_M31 0 0 0
-#&gt; log_k1 0 0 0 0
-#&gt; log_k2 0 0 0 0 0
-#&gt; g_qlogis 0 0 0 0 0 0
-#&gt; f_DMTA_tffm0_1_qlogis 0 0 0 0 0 0 0
-#&gt; f_DMTA_tffm0_2_qlogis 0 0 0 0 0 0 0
-#&gt; f_DMTA_tffm0_3_qlogis 0 0 0 0 0 0 0
-#&gt; f_DMTA_0_1 f_DMTA_0_2
-#&gt; log_k_M23
-#&gt; log_k_M27
-#&gt; log_k_M31
-#&gt; log_k1
-#&gt; log_k2
-#&gt; g_qlogis
-#&gt; f_DMTA_tffm0_1_qlogis
-#&gt; f_DMTA_tffm0_2_qlogis 0
-#&gt; f_DMTA_tffm0_3_qlogis 0 0
-#&gt;
-#&gt; Random effects (omega):
-#&gt; eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31
-#&gt; eta.DMTA_0 2.327 0.0000 0.0000 0.0000
-#&gt; eta.log_k_M23 0.000 0.5493 0.0000 0.0000
-#&gt; eta.log_k_M27 0.000 0.0000 0.8552 0.0000
-#&gt; eta.log_k_M31 0.000 0.0000 0.0000 0.7457
-#&gt; eta.log_k1 0.000 0.0000 0.0000 0.0000
-#&gt; eta.log_k2 0.000 0.0000 0.0000 0.0000
-#&gt; eta.g_qlogis 0.000 0.0000 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.000 0.0000 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.000 0.0000 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.000 0.0000 0.0000 0.0000
-#&gt; eta.log_k1 eta.log_k2 eta.g_qlogis
-#&gt; eta.DMTA_0 0.000 0.000 0.000
-#&gt; eta.log_k_M23 0.000 0.000 0.000
-#&gt; eta.log_k_M27 0.000 0.000 0.000
-#&gt; eta.log_k_M31 0.000 0.000 0.000
-#&gt; eta.log_k1 0.901 0.000 0.000
-#&gt; eta.log_k2 0.000 1.577 0.000
-#&gt; eta.g_qlogis 0.000 0.000 3.102
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.000
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.000
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.000
-#&gt; eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis
-#&gt; eta.DMTA_0 0.0 0.0
-#&gt; eta.log_k_M23 0.0 0.0
-#&gt; eta.log_k_M27 0.0 0.0
-#&gt; eta.log_k_M31 0.0 0.0
-#&gt; eta.log_k1 0.0 0.0
-#&gt; eta.log_k2 0.0 0.0
-#&gt; eta.g_qlogis 0.0 0.0
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.3 0.0
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0 0.3
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.0 0.0
-#&gt; eta.f_DMTA_tffm0_3_qlogis
-#&gt; eta.DMTA_0 0.0
-#&gt; eta.log_k_M23 0.0
-#&gt; eta.log_k_M27 0.0
-#&gt; eta.log_k_M31 0.0
-#&gt; eta.log_k1 0.0
-#&gt; eta.log_k2 0.0
-#&gt; eta.g_qlogis 0.0
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.0
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.3
-#&gt;
-#&gt; Variance model:
-#&gt; sigma_low rsd_high
-#&gt; 0.70012 0.02577
-#&gt;
-#&gt; Backtransformed parameters:
-#&gt; est. lower upper
-#&gt; DMTA_0 98.71324 98.68012 98.74636
-#&gt; k_M23 0.01981 0.01977 0.01985
-#&gt; k_M27 0.01316 0.01313 0.01319
-#&gt; k_M31 0.01435 0.01432 0.01438
-#&gt; f_DMTA_to_M23 0.10993 NA NA
-#&gt; f_DMTA_to_M27 0.09049 NA NA
-#&gt; f_DMTA_to_M31 0.08414 NA NA
-#&gt; k1 0.10698 0.10685 0.10710
-#&gt; k2 0.02292 0.02288 0.02296
-#&gt; g 0.60738 0.60725 0.60751
-#&gt;
-#&gt; Resulting formation fractions:
-#&gt; ff
-#&gt; DMTA_M23 0.10993
-#&gt; DMTA_M27 0.09049
-#&gt; DMTA_M31 0.08414
-#&gt; DMTA_sink 0.71543
-#&gt;
-#&gt; Estimated disappearance times:
-#&gt; DT50 DT90 DT50back DT50_k1 DT50_k2
-#&gt; DMTA 10.72 60.1 18.09 6.48 30.24
-#&gt; M23 34.99 116.2 NA NA NA
-#&gt; M27 52.67 175.0 NA NA NA
-#&gt; M31 48.31 160.5 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span>
-</div><div class='img'><img src='dimethenamid_2018-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># Using saemix takes about 18 minutes</span>
+#&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(DMTA);</span>
+#&gt; <span class='message'>cmt(M23);</span>
+#&gt; <span class='message'>cmt(M27);</span>
+#&gt; <span class='message'>cmt(M31);</span>
+#&gt; <span class='message'>rx_expr_14~ETA[1]+THETA[1];</span>
+#&gt; <span class='message'>DMTA(0)=rx_expr_14;</span>
+#&gt; <span class='message'>rx_expr_15~ETA[5]+THETA[5];</span>
+#&gt; <span class='message'>rx_expr_16~ETA[7]+THETA[7];</span>
+#&gt; <span class='message'>rx_expr_17~ETA[6]+THETA[6];</span>
+#&gt; <span class='message'>rx_expr_24~exp(rx_expr_15);</span>
+#&gt; <span class='message'>rx_expr_25~exp(rx_expr_17);</span>
+#&gt; <span class='message'>rx_expr_29~t*rx_expr_24;</span>
+#&gt; <span class='message'>rx_expr_30~t*rx_expr_25;</span>
+#&gt; <span class='message'>rx_expr_31~exp(-(rx_expr_16));</span>
+#&gt; <span class='message'>rx_expr_35~1+rx_expr_31;</span>
+#&gt; <span class='message'>rx_expr_40~1/(rx_expr_35);</span>
+#&gt; <span class='message'>rx_expr_42~(rx_expr_40);</span>
+#&gt; <span class='message'>rx_expr_43~1-rx_expr_42;</span>
+#&gt; <span class='message'>d/dt(DMTA)=-DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
+#&gt; <span class='message'>rx_expr_18~ETA[2]+THETA[2];</span>
+#&gt; <span class='message'>rx_expr_26~exp(rx_expr_18);</span>
+#&gt; <span class='message'>d/dt(M23)=-rx_expr_26*M23+DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_1/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
+#&gt; <span class='message'>rx_expr_19~ETA[3]+THETA[3];</span>
+#&gt; <span class='message'>rx_expr_20~ETA[4]+THETA[4];</span>
+#&gt; <span class='message'>rx_expr_21~1-f_DMTA_tffm0_1;</span>
+#&gt; <span class='message'>rx_expr_27~exp(rx_expr_19);</span>
+#&gt; <span class='message'>rx_expr_28~exp(rx_expr_20);</span>
+#&gt; <span class='message'>d/dt(M27)=-rx_expr_27*M27+rx_expr_28*M31+DMTA*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_2/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
+#&gt; <span class='message'>rx_expr_22~1-f_DMTA_tffm0_2;</span>
+#&gt; <span class='message'>d/dt(M31)=-rx_expr_28*M31+DMTA*(rx_expr_22)*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_3/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
+#&gt; <span class='message'>rx_expr_0~CMT==4;</span>
+#&gt; <span class='message'>rx_expr_1~CMT==2;</span>
+#&gt; <span class='message'>rx_expr_2~CMT==1;</span>
+#&gt; <span class='message'>rx_expr_3~CMT==3;</span>
+#&gt; <span class='message'>rx_expr_4~1-(rx_expr_0);</span>
+#&gt; <span class='message'>rx_expr_5~1-(rx_expr_1);</span>
+#&gt; <span class='message'>rx_expr_6~1-(rx_expr_3);</span>
+#&gt; <span class='message'>rx_yj_~(rx_expr_4)*((2*(rx_expr_5)*(rx_expr_2)+2*(rx_expr_1))*(rx_expr_6)+2*(rx_expr_3))+2*(rx_expr_0);</span>
+#&gt; <span class='message'>rx_expr_7~(rx_expr_1);</span>
+#&gt; <span class='message'>rx_expr_8~(rx_expr_3);</span>
+#&gt; <span class='message'>rx_expr_9~(rx_expr_0);</span>
+#&gt; <span class='message'>rx_expr_13~(rx_expr_5);</span>
+#&gt; <span class='message'>rx_expr_32~rx_expr_13*(rx_expr_2);</span>
+#&gt; <span class='message'>rx_lambda_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9;</span>
+#&gt; <span class='message'>rx_hi_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9;</span>
+#&gt; <span class='message'>rx_low_~0;</span>
+#&gt; <span class='message'>rx_expr_10~M31*(rx_expr_0);</span>
+#&gt; <span class='message'>rx_expr_11~M27*(rx_expr_3);</span>
+#&gt; <span class='message'>rx_expr_12~M23*(rx_expr_1);</span>
+#&gt; <span class='message'>rx_expr_23~DMTA*(rx_expr_5);</span>
+#&gt; <span class='message'>rx_expr_36~rx_expr_23*(rx_expr_2);</span>
+#&gt; <span class='message'>rx_pred_=(rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)));</span>
+#&gt; <span class='message'>rx_expr_33~Rx_pow_di(THETA[12],2);</span>
+#&gt; <span class='message'>rx_expr_34~Rx_pow_di(THETA[11],2);</span>
+#&gt; <span class='message'>rx_r_=(rx_expr_4)*((rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6)),2)+rx_expr_34)*(rx_expr_3)+((rx_expr_1)*(rx_expr_33*Rx_pow_di(((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)+(rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)*(rx_expr_5)*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_33*Rx_pow_di(((rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))),2)+rx_expr_34);</span>
+#&gt; <span class='message'>DMTA_0=THETA[1];</span>
+#&gt; <span class='message'>log_k_M23=THETA[2];</span>
+#&gt; <span class='message'>log_k_M27=THETA[3];</span>
+#&gt; <span class='message'>log_k_M31=THETA[4];</span>
+#&gt; <span class='message'>log_k1=THETA[5];</span>
+#&gt; <span class='message'>log_k2=THETA[6];</span>
+#&gt; <span class='message'>g_qlogis=THETA[7];</span>
+#&gt; <span class='message'>f_DMTA_tffm0_1_qlogis=THETA[8];</span>
+#&gt; <span class='message'>f_DMTA_tffm0_2_qlogis=THETA[9];</span>
+#&gt; <span class='message'>f_DMTA_tffm0_3_qlogis=THETA[10];</span>
+#&gt; <span class='message'>sigma_low=THETA[11];</span>
+#&gt; <span class='message'>rsd_high=THETA[12];</span>
+#&gt; <span class='message'>eta.DMTA_0=ETA[1];</span>
+#&gt; <span class='message'>eta.log_k_M23=ETA[2];</span>
+#&gt; <span class='message'>eta.log_k_M27=ETA[3];</span>
+#&gt; <span class='message'>eta.log_k_M31=ETA[4];</span>
+#&gt; <span class='message'>eta.log_k1=ETA[5];</span>
+#&gt; <span class='message'>eta.log_k2=ETA[6];</span>
+#&gt; <span class='message'>eta.g_qlogis=ETA[7];</span>
+#&gt; <span class='message'>eta.f_DMTA_tffm0_1_qlogis=ETA[8];</span>
+#&gt; <span class='message'>eta.f_DMTA_tffm0_2_qlogis=ETA[9];</span>
+#&gt; <span class='message'>eta.f_DMTA_tffm0_3_qlogis=ETA[10];</span>
+#&gt; <span class='message'>DMTA_0_model=rx_expr_14;</span>
+#&gt; <span class='message'>k_M23=rx_expr_26;</span>
+#&gt; <span class='message'>k_M27=rx_expr_27;</span>
+#&gt; <span class='message'>k_M31=rx_expr_28;</span>
+#&gt; <span class='message'>k1=rx_expr_24;</span>
+#&gt; <span class='message'>k2=rx_expr_25;</span>
+#&gt; <span class='message'>g=1/(rx_expr_35);</span>
+#&gt; <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[8]+THETA[8])));</span>
+#&gt; <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[9]+THETA[9])));</span>
+#&gt; <span class='message'>f_DMTA_to_M23=1/(1+exp(-(ETA[10]+THETA[10])));</span>
+#&gt; <span class='message'>f_DMTA_to_M23=f_DMTA_tffm0_1;</span>
+#&gt; <span class='message'>f_DMTA_to_M27=(rx_expr_21)*f_DMTA_tffm0_2;</span>
+#&gt; <span class='message'>f_DMTA_to_M31=(rx_expr_22)*(rx_expr_21)*f_DMTA_tffm0_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_DMTA_tffm0_1" "f_DMTA_tffm0_2" "f_DMTA_tffm0_3" "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: 121.4 8.294 129.7</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 121.5 8.294 129.9</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span>
+</div><div class='output co'>#&gt; <span class='error'>Error in summary(f_dmta_nlmixr_focei): object 'f_dmta_nlmixr_focei' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span>
+</div><div class='output co'>#&gt; <span class='error'>Error in plot(f_dmta_nlmixr_focei): object 'f_dmta_nlmixr_focei' not found</span></div><div class='input'><span class='co'># Using saemix takes about 18 minutes</span>
<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span>
<span class='va'>f_dmta_saemix</span> <span class='op'>&lt;-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
<span class='op'>)</span>
</div><div class='output co'>#&gt; Running main SAEM algorithm
-#&gt; [1] "Thu Sep 16 14:06:56 2021"
+#&gt; [1] "Tue Oct 5 16:58:50 2021"
#&gt; ....
#&gt; Minimisation finished
-#&gt; [1] "Thu Sep 16 14:25:28 2021"</div><div class='output co'>#&gt; user system elapsed
-#&gt; 1176.278 0.021 1176.388 </div><div class='input'>
+#&gt; [1] "Tue Oct 5 17:17:24 2021"</div><div class='output co'>#&gt; user system elapsed
+#&gt; 1181.365 0.031 1181.470 </div><div class='input'>
<span class='co'># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span>
<span class='co'># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span>
<span class='co'># The likelihood calculated for the nlmixr fit is much lower than that found by saemix</span>
@@ -498,174 +415,10 @@ 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: 98.3400 -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='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='warning'>Warning: number of items to replace is not a multiple of replacement length</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='error'>Error in eval(substitute(expr), data, enclos = parent.frame()): Cannot run SAEM since some of the parameters are not mu-referenced (eta.f_DMTA_tffm0_1_qlogis, eta.f_DMTA_tffm0_2_qlogis, eta.f_DMTA_tffm0_3_qlogis)</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0.849 0.016 0.864</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 1.041 0.016 1.058</span></div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='error'>Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; nlmixr version used for fitting: 2.0.5
-#&gt; mkin version used for pre-fitting: 1.1.0
-#&gt; R version used for fitting: 4.1.1
-#&gt; Date of fit: Thu Sep 16 14:29:02 2021
-#&gt; Date of summary: Thu Sep 16 14:29:02 2021
-#&gt;
-#&gt; Equations:
-#&gt; d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
-#&gt; time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
-#&gt; * DMTA
-#&gt; d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
-#&gt; * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-#&gt; exp(-k2 * time))) * DMTA - k_M23 * M23
-#&gt; d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
-#&gt; * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-#&gt; exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31
-#&gt; d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
-#&gt; * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
-#&gt; exp(-k2 * time))) * DMTA - k_M31 * M31
-#&gt;
-#&gt; Data:
-#&gt; 563 observations of 4 variable(s) grouped in 6 datasets
-#&gt;
-#&gt; Degradation model predictions using RxODE
-#&gt;
-#&gt; Fitted in 149.421 s
-#&gt;
-#&gt; Variance model: Two-component variance function
-#&gt;
-#&gt; Mean of starting values for individual parameters:
-#&gt; DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2
-#&gt; 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388
-#&gt; f_DMTA_ilr_3 log_k1 log_k2 g_qlogis
-#&gt; -1.7554 -2.2352 -3.7758 0.4363
-#&gt;
-#&gt; Mean of starting values for error model parameters:
-#&gt; sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27
-#&gt; 0.70012 0.02577 0.70012 0.02577 0.70012
-#&gt; rsd_high_M27 sigma_low_M31 rsd_high_M31
-#&gt; 0.02577 0.70012 0.02577
-#&gt;
-#&gt; Fixed degradation parameter values:
-#&gt; None
-#&gt;
-#&gt; Results:
-#&gt;
-#&gt; Likelihood calculated by focei
-#&gt; AIC BIC logLik
-#&gt; 1953 2074 -948.3
-#&gt;
-#&gt; Optimised parameters:
-#&gt; est. lower upper
-#&gt; DMTA_0 97.821 95.862 99.780
-#&gt; log_k_M23 -4.403 -5.376 -3.430
-#&gt; log_k_M27 -4.087 -4.545 -3.629
-#&gt; log_k_M31 -4.129 -4.639 -3.618
-#&gt; log_k1 -2.828 -3.389 -2.266
-#&gt; log_k2 -4.351 -5.472 -3.229
-#&gt; g_qlogis 2.661 0.824 4.499
-#&gt; f_DMTA_tffm0_1_qlogis -2.125 -2.449 -1.801
-#&gt; f_DMTA_tffm0_2_qlogis -2.131 -2.468 -1.794
-#&gt; f_DMTA_tffm0_3_qlogis -2.075 -2.540 -1.610
-#&gt;
-#&gt; Correlation:
-#&gt; DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs
-#&gt; log_k_M23 -0.019
-#&gt; log_k_M27 -0.028 0.004
-#&gt; log_k_M31 -0.019 0.003 0.075
-#&gt; log_k1 0.038 -0.004 -0.006 -0.003
-#&gt; log_k2 0.046 0.011 0.008 0.009 0.068
-#&gt; g_qlogis -0.067 0.004 0.006 0.001 -0.076 -0.409
-#&gt; f_DMTA_tffm0_1_qlogis -0.062 0.055 0.006 0.004 -0.008 -0.004 0.012
-#&gt; f_DMTA_tffm0_2_qlogis -0.062 0.010 0.058 -0.034 -0.008 -0.007 0.014
-#&gt; f_DMTA_tffm0_3_qlogis -0.052 0.009 0.056 0.071 -0.006 -0.001 0.008
-#&gt; f_DMTA_0_1 f_DMTA_0_2
-#&gt; log_k_M23
-#&gt; log_k_M27
-#&gt; log_k_M31
-#&gt; log_k1
-#&gt; log_k2
-#&gt; g_qlogis
-#&gt; f_DMTA_tffm0_1_qlogis
-#&gt; f_DMTA_tffm0_2_qlogis 0.017
-#&gt; f_DMTA_tffm0_3_qlogis 0.014 -0.005
-#&gt;
-#&gt; Random effects (omega):
-#&gt; eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31
-#&gt; eta.DMTA_0 2.946 0.000 0.0000 0.0000
-#&gt; eta.log_k_M23 0.000 1.293 0.0000 0.0000
-#&gt; eta.log_k_M27 0.000 0.000 0.2802 0.0000
-#&gt; eta.log_k_M31 0.000 0.000 0.0000 0.3467
-#&gt; eta.log_k1 0.000 0.000 0.0000 0.0000
-#&gt; eta.log_k2 0.000 0.000 0.0000 0.0000
-#&gt; eta.g_qlogis 0.000 0.000 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000
-#&gt; eta.log_k1 eta.log_k2 eta.g_qlogis
-#&gt; eta.DMTA_0 0.0000 0.0000 0.000
-#&gt; eta.log_k_M23 0.0000 0.0000 0.000
-#&gt; eta.log_k_M27 0.0000 0.0000 0.000
-#&gt; eta.log_k_M31 0.0000 0.0000 0.000
-#&gt; eta.log_k1 0.4814 0.0000 0.000
-#&gt; eta.log_k2 0.0000 0.7877 0.000
-#&gt; eta.g_qlogis 0.0000 0.0000 3.074
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.0000 0.0000 0.000
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0000 0.0000 0.000
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 0.000
-#&gt; eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis
-#&gt; eta.DMTA_0 0.0000 0.0000
-#&gt; eta.log_k_M23 0.0000 0.0000
-#&gt; eta.log_k_M27 0.0000 0.0000
-#&gt; eta.log_k_M31 0.0000 0.0000
-#&gt; eta.log_k1 0.0000 0.0000
-#&gt; eta.log_k2 0.0000 0.0000
-#&gt; eta.g_qlogis 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.1508 0.0000
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1523
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000
-#&gt; eta.f_DMTA_tffm0_3_qlogis
-#&gt; eta.DMTA_0 0.0000
-#&gt; eta.log_k_M23 0.0000
-#&gt; eta.log_k_M27 0.0000
-#&gt; eta.log_k_M31 0.0000
-#&gt; eta.log_k1 0.0000
-#&gt; eta.log_k2 0.0000
-#&gt; eta.g_qlogis 0.0000
-#&gt; eta.f_DMTA_tffm0_1_qlogis 0.0000
-#&gt; eta.f_DMTA_tffm0_2_qlogis 0.0000
-#&gt; eta.f_DMTA_tffm0_3_qlogis 0.3155
-#&gt;
-#&gt; Variance model:
-#&gt; sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27
-#&gt; 0.95572 0.03325 0.47871 0.10733 0.68264
-#&gt; rsd_high_M27 sigma_low_M31 rsd_high_M31
-#&gt; 0.07072 0.78486 0.03557
-#&gt;
-#&gt; Backtransformed parameters:
-#&gt; est. lower upper
-#&gt; DMTA_0 97.82122 95.862233 99.78020
-#&gt; k_M23 0.01224 0.004625 0.03239
-#&gt; k_M27 0.01679 0.010615 0.02654
-#&gt; k_M31 0.01610 0.009664 0.02683
-#&gt; f_DMTA_to_M23 0.10668 NA NA
-#&gt; f_DMTA_to_M27 0.09481 NA NA
-#&gt; f_DMTA_to_M31 0.08908 NA NA
-#&gt; k1 0.05914 0.033731 0.10370
-#&gt; k2 0.01290 0.004204 0.03958
-#&gt; g 0.93471 0.695081 0.98900
-#&gt;
-#&gt; Resulting formation fractions:
-#&gt; ff
-#&gt; DMTA_M23 0.10668
-#&gt; DMTA_M27 0.09481
-#&gt; DMTA_M31 0.08908
-#&gt; DMTA_sink 0.70943
-#&gt;
-#&gt; Estimated disappearance times:
-#&gt; DT50 DT90 DT50back DT50_k1 DT50_k2
-#&gt; DMTA 12.57 45.43 13.67 11.72 53.73
-#&gt; M23 56.63 188.11 NA NA NA
-#&gt; M27 41.29 137.18 NA NA NA
-#&gt; M31 43.05 143.01 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span>
-</div><div class='img'><img src='dimethenamid_2018-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span>
+</div><div class='output co'>#&gt; <span class='error'>Error in summary(f_dmta_nlmixr_saem): object 'f_dmta_nlmixr_saem' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span>
+</div><div class='output co'>#&gt; <span class='error'>Error in plot(f_dmta_nlmixr_saem): object 'f_dmta_nlmixr_saem' not found</span></div><div class='input'><span class='co'># }</span>
</div></pre>
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