diff options
Diffstat (limited to 'docs/dev/reference')
20 files changed, 544 insertions, 1028 deletions
diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png Binary files differindex 55aa7eec..92c7cc2d 100644 --- a/docs/dev/reference/Rplot005.png +++ b/docs/dev/reference/Rplot005.png diff --git a/docs/dev/reference/dimethenamid_2018-2.png b/docs/dev/reference/dimethenamid_2018-2.png Binary files differnew file mode 100644 index 00000000..a81b2aaf --- /dev/null +++ b/docs/dev/reference/dimethenamid_2018-2.png diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index e255765e..160dcaa3 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> @@ -168,7 +168,7 @@ constrained by data protection regulations.</p> <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour Rev. 2 - November 2017 -<a href='http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211'>http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211</a></p> +<a href='https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716'>https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a></p> <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> <p>The R code used to create this data object is installed with this package @@ -295,8 +295,11 @@ specific pieces of information in the comments.</p> #> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) #> }) #> } -#> <environment: 0x555559c2bd78></div><div class='input'><span class='va'>f_dmta_nlmixr_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, - control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/foceiControl.html'>foceiControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span><span class='op'>)</span><span class='op'>)</span> +#> <environment: 0x555559c00ce8></div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span> +<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span> + <span class='va'>f_dmta_nlmixr_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, + control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/foceiControl.html'>foceiControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span><span class='op'>)</span><span class='op'>)</span> +<span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:02 #> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:04 #> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 @@ -320,12 +323,13 @@ specific pieces of information in the comments.</p> #> |.....................| o5 | o6 | o7 | o8 | #> <span style='text-decoration: underline;'>|.....................| o9 | o10 |...........|...........|</span> #> calculating covariance matrix -#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: S matrix non-positive definite</span></div><div class='output co'>#> <span class='warning'>Warning: using R matrix to calculate covariance</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</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> +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: S matrix non-positive definite</span></div><div class='output co'>#> <span class='warning'>Warning: using R matrix to calculate covariance</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='output co'>#> user system elapsed +#> 227.879 9.742 237.728 </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'>#> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.0.5 +#> mkin version used for pre-fitting: 1.1.0 #> R version used for fitting: 4.1.0 -#> Date of fit: Thu Jun 17 14:04:58 2021 -#> Date of summary: Thu Jun 17 14:04:58 2021 +#> Date of fit: Tue Jul 27 16:02:33 2021 +#> Date of summary: Tue Jul 27 16:02:34 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -346,7 +350,7 @@ specific pieces of information in the comments.</p> #> #> Degradation model predictions using RxODE #> -#> Fitted in 242.937 s +#> Fitted in 237.547 s #> #> Variance model: Two-component variance function #> @@ -480,13 +484,194 @@ specific pieces of information in the comments.</p> #> M23 34.99 116.24 NA NA NA #> M27 53.05 176.23 NA NA NA #> 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> -</div><div class='img'><img src='dimethenamid_2018-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># saem has a problem with this model/data combination, maybe because of the</span> -<span class='co'># overparameterised error model, to be investigated</span> -<span class='co'>#f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem",</span> -<span class='co'># control = saemControl(print = 500))</span> -<span class='co'>#summary(f_dmta_nlmixr_saem)</span> -<span class='co'>#plot(f_dmta_nlmixr_saem)</span> -<span class='co'># }</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'><-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +<span class='op'>)</span> +</div><div class='output co'>#> Running main SAEM algorithm +#> [1] "Tue Jul 27 16:02:34 2021" +#> .... +#> Minimisation finished +#> [1] "Tue Jul 27 16:21:39 2021"</div><div class='output co'>#> user system elapsed +#> 1213.394 0.087 1213.578 </div><div class='input'> +<span class='co'># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span> +<span class='co'># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span> +<span class='co'># The likelihood calculated for the nlmixr fit is much lower than that found by saemix</span> +<span class='co'># Also, the trace plot and the plot of the individual predictions is not</span> +<span class='co'># convincing for the parent. It seems we are fitting an overparameterised</span> +<span class='co'># model, so the result we get strongly depends on starting parameters and control settings.</span> +<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span> + <span class='va'>f_dmta_nlmixr_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, + control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/saemControl.html'>saemControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span>, logLik <span class='op'>=</span> <span class='cn'>TRUE</span>, nmc <span class='op'>=</span> <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span> +<span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 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 +#> 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'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> user system elapsed +#> 818.782 3.808 154.926 </div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> +</div><div class='output co'>#> nlmixr version used for fitting: 2.0.4 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.0 +#> Date of fit: Tue Jul 27 16:25:23 2021 +#> Date of summary: Tue Jul 27 16:25:23 2021 +#> +#> Equations: +#> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) +#> * DMTA +#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 +#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M31 * M31 +#> +#> Data: +#> 568 observations of 4 variable(s) grouped in 6 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 154.632 s +#> +#> Variance model: Two-component variance function +#> +#> Mean of starting values for individual parameters: +#> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 +#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis +#> -1.7571 -2.2341 -3.7763 0.4502 +#> +#> Mean of starting values for error model parameters: +#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 +#> 0.69793 0.02577 0.69793 0.02577 0.69793 +#> rsd_high_M27 sigma_low_M31 rsd_high_M31 +#> 0.02577 0.69793 0.02577 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 2036 2157 -989.8 +#> +#> Optimised parameters: +#> est. lower upper +#> DMTA_0 97.828 96.121 99.535 +#> log_k_M23 -4.351 -5.300 -3.401 +#> log_k_M27 -4.032 -4.470 -3.594 +#> log_k_M31 -4.152 -4.689 -3.615 +#> log_k1 -3.055 -3.785 -2.325 +#> log_k2 -3.584 -4.517 -2.651 +#> g_qlogis 1.133 -2.165 4.430 +#> f_DMTA_tffm0_1_qlogis -2.087 -2.407 -1.768 +#> f_DMTA_tffm0_2_qlogis -2.042 -2.336 -1.748 +#> f_DMTA_tffm0_3_qlogis -2.075 -2.557 -1.593 +#> +#> Correlation: +#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs +#> log_k_M23 -0.031 +#> log_k_M27 -0.050 0.004 +#> log_k_M31 -0.032 0.003 0.078 +#> log_k1 0.014 -0.002 -0.002 -0.001 +#> log_k2 0.059 0.006 -0.001 0.002 -0.037 +#> g_qlogis -0.077 0.005 0.009 0.004 0.035 -0.201 +#> f_DMTA_tffm0_1_qlogis -0.104 0.066 0.009 0.006 0.000 -0.011 0.014 +#> f_DMTA_tffm0_2_qlogis -0.120 0.013 0.081 -0.033 -0.002 -0.013 0.017 +#> f_DMTA_tffm0_3_qlogis -0.086 0.010 0.060 0.078 -0.002 -0.005 0.010 +#> f_DMTA_0_1 f_DMTA_0_2 +#> log_k_M23 +#> log_k_M27 +#> log_k_M31 +#> log_k1 +#> log_k2 +#> g_qlogis +#> f_DMTA_tffm0_1_qlogis +#> f_DMTA_tffm0_2_qlogis 0.026 +#> f_DMTA_tffm0_3_qlogis 0.019 0.002 +#> +#> Random effects (omega): +#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 +#> eta.DMTA_0 0.296 0.000 0.0000 0.0000 +#> eta.log_k_M23 0.000 1.252 0.0000 0.0000 +#> eta.log_k_M27 0.000 0.000 0.2531 0.0000 +#> eta.log_k_M31 0.000 0.000 0.0000 0.3807 +#> eta.log_k1 0.000 0.000 0.0000 0.0000 +#> eta.log_k2 0.000 0.000 0.0000 0.0000 +#> eta.g_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000 +#> eta.log_k1 eta.log_k2 eta.g_qlogis +#> eta.DMTA_0 0.0000 0.0000 0.000 +#> eta.log_k_M23 0.0000 0.0000 0.000 +#> eta.log_k_M27 0.0000 0.0000 0.000 +#> eta.log_k_M31 0.0000 0.0000 0.000 +#> eta.log_k1 0.7928 0.0000 0.000 +#> eta.log_k2 0.0000 0.8863 0.000 +#> eta.g_qlogis 0.0000 0.0000 6.521 +#> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 0.000 +#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis +#> eta.DMTA_0 0.0000 0.0000 +#> eta.log_k_M23 0.0000 0.0000 +#> eta.log_k_M27 0.0000 0.0000 +#> eta.log_k_M31 0.0000 0.0000 +#> eta.log_k1 0.0000 0.0000 +#> eta.log_k2 0.0000 0.0000 +#> eta.g_qlogis 0.0000 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.1433 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1082 +#> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis +#> eta.DMTA_0 0.0000 +#> eta.log_k_M23 0.0000 +#> eta.log_k_M27 0.0000 +#> eta.log_k_M31 0.0000 +#> eta.log_k1 0.0000 +#> eta.log_k2 0.0000 +#> eta.g_qlogis 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.3353 +#> +#> Variance model: +#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 +#> 0.89603 0.04704 0.75015 0.04753 0.95265 +#> rsd_high_M27 sigma_low_M31 rsd_high_M31 +#> 0.02810 0.73212 0.05942 +#> +#> Backtransformed parameters: +#> est. lower upper +#> DMTA_0 97.82774 96.120503 99.53498 +#> k_M23 0.01290 0.004991 0.03334 +#> k_M27 0.01774 0.011451 0.02749 +#> k_M31 0.01573 0.009195 0.02692 +#> f_DMTA_to_M23 0.11033 NA NA +#> f_DMTA_to_M27 0.10218 NA NA +#> f_DMTA_to_M31 0.08784 NA NA +#> k1 0.04711 0.022707 0.09773 +#> k2 0.02775 0.010918 0.07056 +#> g 0.75632 0.102960 0.98823 +#> +#> Resulting formation fractions: +#> ff +#> DMTA_M23 0.11033 +#> DMTA_M27 0.10218 +#> DMTA_M31 0.08784 +#> DMTA_sink 0.69965 +#> +#> Estimated disappearance times: +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> DMTA 16.59 57.44 17.29 14.71 24.97 +#> M23 53.74 178.51 NA NA NA +#> M27 39.07 129.78 NA NA NA +#> 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> +</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> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> 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 413f0ae1..e57e7062 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -44,9 +44,7 @@ variable, specifying the corresponding submodel as well as outgoing pathways (see examples). Print mkinmod objects in a way that the user finds his way to get to its -components. -This is a convenience function to set up the lists used as arguments for -mkinmod." /> +components." /> <meta name="robots" content="noindex"> @@ -78,7 +76,7 @@ mkinmod." /> </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">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -155,8 +153,6 @@ variable, specifying the corresponding submodel as well as outgoing pathways (see examples).</p> <p>Print mkinmod objects in a way that the user finds his way to get to its components.</p> -<p>This is a convenience function to set up the lists used as arguments for -<code>mkinmod</code>.</p> </div> <pre class="usage"><span class='fu'>mkinmod</span><span class='op'>(</span> @@ -348,7 +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'>#> <span class='message'>Copied DLL from /tmp/Rtmp92fCb2/file133ad522561845.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'>#> <span class='message'>Copied DLL from /tmp/RtmpPWWdbW/fileccff46a6d9773.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> @@ -397,7 +393,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> #> }) #> return(predicted) #> } -#> <environment: 0x5555572517f8></div><div class='input'> +#> <environment: 0x5555645abab8></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'><-</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 Binary files differindex 365aaef0..f739089a 100644 --- a/docs/dev/reference/nlme-1.png +++ b/docs/dev/reference/nlme-1.png diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png Binary files differindex 40841404..d3b29bb0 100644 --- a/docs/dev/reference/nlme-2.png +++ b/docs/dev/reference/nlme-2.png diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index 55a94443..184585df 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 #> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) #> Data: grouped_data #> AIC BIC logLik -#> 300.6824 310.2426 -145.3412 +#> 278.1355 287.7946 -134.0677 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual -#> StdDev: 1.697361 0.6801209 3.666073 +#> StdDev: 3.406042 0.6437579 2.620833 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 #> Value Std.Error DF t-value p-value -#> parent_0 100.99378 1.3890416 46 72.70753 0 -#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 +#> parent_0 101.50173 2.123709 47 47.79457 0 +#> log_k_parent_sink -3.07597 0.379775 47 -8.09945 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.027 +#> log_k_parent_sink 0.01 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 +#> Min Q1 Med Q3 Max +#> -2.06889303 -0.50100169 -0.06268253 0.62557544 2.19922001 #> -#> Number of Observations: 50 +#> Number of Observations: 51 #> 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 2bbf4f80..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> @@ -194,10 +194,9 @@ mmkin model are used as fixed parameters</p></td> </tr> <tr> <th>random</th> - <td><p>If not specified, correlated random effects are set up -for all optimised degradation model parameters using the log-Cholesky -parameterization <a href='https://rdrr.io/pkg/nlme/man/pdLogChol.html'>nlme::pdLogChol</a> that is also the default of -the generic <a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a> method.</p></td> + <td><p>If not specified, no correlations between random effects are +set up for the optimised degradation model parameters. This is +achieved by using the <a href='https://rdrr.io/pkg/nlme/man/pdDiag.html'>nlme::pdDiag</a> method.</p></td> </tr> <tr> <th>groups</th> diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html index d09f2ad4..328bad43 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> @@ -4501,7 +4501,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>k_A1=rx_expr_11;</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[4]+THETA[4])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 5.607 0.474 6.078</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 5.548 0.415 5.961</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4550,7 +4550,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.853 0.393 7.242</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.895 0.416 7.309</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4607,10 +4607,10 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>g=1/(rx_expr_20);</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.18 0.414 15.6</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.03 0.478 15.51</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'><-</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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 1.22 0.089 1.31</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 1.294 0.134 1.427</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4659,8 +4659,8 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.784 0.418 7.2</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 1.357 0.096 1.452</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.584 0.393 6.976</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 1.302 0.142 1.443</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4717,7 +4717,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>g=1/(rx_expr_19);</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.17 0.353 15.52</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 14.58 0.482 15.06</span></div><div class='input'> <span class='co'># Identical two-component error for all variables is only possible with</span> <span class='co'># est = 'focei' in nlmixr</span> <span class='va'>f_nlmixr_fomc_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> @@ -4771,7 +4771,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.708 0.429 9.135</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.484 0.401 8.883</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4830,12 +4830,12 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>g=1/(rx_expr_21);</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.05 0.446 18.5</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.44 0.438 18.87</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'><-</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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 0.763 0.036 0.799</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 0.784 0.028 0.812</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> @@ -4887,9 +4887,9 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.196 0.388 8.584</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.157 0.51 8.664</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 0.843 0.028 0.871</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 0.81 0.045 0.854</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> @@ -4949,7 +4949,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>g=1/(rx_expr_19);</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 17.73 0.411 18.14</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 17.34 0.397 17.73</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span> <span class='va'>f_nlmixr_sfo_sfo_focei_const</span><span class='op'>$</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-3.png b/docs/dev/reference/plot.mixed.mmkin-3.png Binary files differindex a9b96726..7e2876b3 100644 --- a/docs/dev/reference/plot.mixed.mmkin-3.png +++ b/docs/dev/reference/plot.mixed.mmkin-3.png diff --git a/docs/dev/reference/plot.mixed.mmkin-4.png b/docs/dev/reference/plot.mixed.mmkin-4.png Binary files differindex 22219e5e..945c4d41 100644 --- a/docs/dev/reference/plot.mixed.mmkin-4.png +++ b/docs/dev/reference/plot.mixed.mmkin-4.png diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index a4222991..7f3faa90 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'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:56:37 2021" +#> [1] "Tue Jul 27 16:30:50 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:56:44 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> +#> [1] "Tue Jul 27 16:30:58 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'><-</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'><-</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-5.png b/docs/dev/reference/saem-5.png Binary files differindex 8212ec67..d22e7285 100644 --- a/docs/dev/reference/saem-5.png +++ b/docs/dev/reference/saem-5.png diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 98faad6f..15271c8a 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> @@ -158,7 +158,7 @@ Expectation Maximisation algorithm (SAEM).</p> <span class='va'>object</span>, transformations <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'>"mkin"</span>, <span class='st'>"saemix"</span><span class='op'>)</span>, degparms_start <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></span><span class='op'>(</span><span class='op'>)</span>, - test_log_parms <span class='op'>=</span> <span class='cn'>FALSE</span>, + test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span>, conf.level <span class='op'>=</span> <span class='fl'>0.6</span>, solution_type <span class='op'>=</span> <span class='st'>"auto"</span>, nbiter.saemix <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'>300</span>, <span class='fl'>100</span><span class='op'>)</span>, @@ -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'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:56:49 2021" +#> [1] "Tue Jul 27 16:31:02 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:56:51 2021"</div><div class='input'> +#> [1] "Tue Jul 27 16:31:04 2021"</div><div class='input'> <span class='va'>f_mmkin_parent</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/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'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:56:53 2021" +#> [1] "Tue Jul 27 16:31:06 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:56:54 2021"</div><div class='input'><span class='va'>f_saem_fomc</span> <span class='op'><-</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> +#> [1] "Tue Jul 27 16:31:07 2021"</div><div class='input'><span class='va'>f_saem_fomc</span> <span class='op'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:56:54 2021" +#> [1] "Tue Jul 27 16:31:07 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:56:57 2021"</div><div class='input'><span class='va'>f_saem_dfop</span> <span class='op'><-</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> +#> [1] "Tue Jul 27 16:31:09 2021"</div><div class='input'><span class='va'>f_saem_dfop</span> <span class='op'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:56:57 2021" +#> [1] "Tue Jul 27 16:31:10 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:00 2021"</div><div class='input'> +#> [1] "Tue Jul 27 16:31:12 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'><-</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'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:57:03 2021" +#> [1] "Tue Jul 27 16:31:16 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:09 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'>$</span><span class='va'>so</span><span class='op'>)</span> +#> [1] "Tue Jul 27 16:31:20 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</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'>#> <span class='message'>Likelihoods calculated by importance sampling</span></div><div class='output co'>#> AIC BIC #> 1 467.7096 464.9757 #> 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'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:57:12 2021" +#> [1] "Tue Jul 27 16:31:24 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:17 2021"</div><div class='input'><span class='va'>f_saem_dfop_sfo</span> <span class='op'><-</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> +#> [1] "Tue Jul 27 16:31:29 2021"</div><div class='input'><span class='va'>f_saem_dfop_sfo</span> <span class='op'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:57:17 2021" +#> [1] "Tue Jul 27 16:31:30 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:26 2021"</div><div class='input'><span class='co'># We can use print, plot and summary methods to check the results</span> +#> [1] "Tue Jul 27 16:31:38 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'>#> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -405,35 +405,35 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> #> Likelihood computed by importance sampling #> AIC BIC logLik -#> 841.6 836.5 -407.8 +#> 839.6 834.6 -406.8 #> #> Fitted parameters: #> estimate lower upper -#> parent_0 93.76647 91.15312 96.3798 -#> log_k_A1 -6.13235 -8.45788 -3.8068 -#> f_parent_qlogis -0.97364 -1.36940 -0.5779 -#> log_k1 -2.53176 -3.80372 -1.2598 -#> log_k2 -3.58667 -5.29524 -1.8781 -#> g_qlogis 0.01238 -1.07968 1.1044 -#> Var.parent_0 7.61106 -3.34955 18.5717 -#> Var.log_k_A1 4.64679 -2.73133 12.0249 -#> Var.f_parent_qlogis 0.19693 -0.05498 0.4488 -#> Var.log_k1 2.01717 -0.51980 4.5542 -#> Var.log_k2 3.63412 -0.92964 8.1979 -#> Var.g_qlogis 0.20045 -0.97425 1.3751 -#> a.1 1.88335 1.66636 2.1004 -#> SD.parent_0 2.75881 0.77234 4.7453 -#> SD.log_k_A1 2.15564 0.44429 3.8670 -#> SD.f_parent_qlogis 0.44377 0.15994 0.7276 -#> SD.log_k1 1.42027 0.52714 2.3134 -#> SD.log_k2 1.90634 0.70934 3.1033 -#> SD.g_qlogis 0.44771 -0.86417 1.7596</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> +#> parent_0 93.80521 91.22487 96.3856 +#> log_k_A1 -6.06244 -8.26517 -3.8597 +#> f_parent_qlogis -0.97319 -1.37024 -0.5761 +#> log_k1 -2.55394 -4.00815 -1.0997 +#> log_k2 -3.47160 -5.18763 -1.7556 +#> g_qlogis -0.09324 -1.42737 1.2409 +#> Var.parent_0 7.42157 -3.25683 18.1000 +#> Var.log_k_A1 4.22850 -2.46339 10.9204 +#> Var.f_parent_qlogis 0.19803 -0.05541 0.4515 +#> Var.log_k1 2.28644 -0.86079 5.4337 +#> Var.log_k2 3.35626 -1.14639 7.8589 +#> Var.g_qlogis 0.20084 -1.32516 1.7268 +#> a.1 1.88399 1.66794 2.1000 +#> SD.parent_0 2.72425 0.76438 4.6841 +#> SD.log_k_A1 2.05633 0.42919 3.6835 +#> SD.f_parent_qlogis 0.44501 0.16025 0.7298 +#> SD.log_k1 1.51210 0.47142 2.5528 +#> SD.log_k2 1.83201 0.60313 3.0609 +#> 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'>#> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.5 +#> mkin version used for pre-fitting: 1.1.0 #> R version used for fitting: 4.1.0 -#> Date of fit: Fri Jun 11 10:57:27 2021 -#> Date of summary: Fri Jun 11 10:57:27 2021 +#> Date of fit: Tue Jul 27 16:31:39 2021 +#> Date of summary: Tue Jul 27 16:31:39 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -448,13 +448,13 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> #> Model predictions using solution type analytical #> -#> Fitted in 9.712 s using 300, 100 iterations +#> Fitted in 9.479 s using 300, 100 iterations #> #> Variance model: Constant variance #> #> Mean of starting values for individual parameters: #> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 -#> 93.8102 -9.7647 -0.9711 -1.8799 -4.2708 +#> 93.8102 -5.3734 -0.9711 -1.8799 -4.2708 #> g_qlogis #> 0.1356 #> @@ -465,46 +465,46 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> #> Likelihood computed by importance sampling #> AIC BIC logLik -#> 841.6 836.5 -407.8 +#> 839.6 834.6 -406.8 #> #> Optimised parameters: #> est. lower upper -#> parent_0 93.76647 91.153 96.3798 -#> log_k_A1 -6.13235 -8.458 -3.8068 -#> f_parent_qlogis -0.97364 -1.369 -0.5779 -#> log_k1 -2.53176 -3.804 -1.2598 -#> log_k2 -3.58667 -5.295 -1.8781 -#> g_qlogis 0.01238 -1.080 1.1044 +#> parent_0 93.80521 91.225 96.3856 +#> log_k_A1 -6.06244 -8.265 -3.8597 +#> f_parent_qlogis -0.97319 -1.370 -0.5761 +#> log_k1 -2.55394 -4.008 -1.0997 +#> log_k2 -3.47160 -5.188 -1.7556 +#> g_qlogis -0.09324 -1.427 1.2409 #> #> Correlation: #> prnt_0 lg__A1 f_prn_ log_k1 log_k2 -#> log_k_A1 -0.013 -#> f_parent_qlogis -0.025 0.050 -#> log_k1 0.030 0.000 -0.005 -#> log_k2 0.010 0.005 -0.003 0.032 -#> g_qlogis -0.063 -0.015 0.010 -0.167 -0.177 +#> log_k_A1 -0.014 +#> f_parent_qlogis -0.025 0.054 +#> log_k1 0.027 -0.003 -0.005 +#> log_k2 0.011 0.005 -0.002 -0.070 +#> g_qlogis -0.067 -0.009 0.011 -0.189 -0.171 #> #> Random effects: #> est. lower upper -#> SD.parent_0 2.7588 0.7723 4.7453 -#> SD.log_k_A1 2.1556 0.4443 3.8670 -#> SD.f_parent_qlogis 0.4438 0.1599 0.7276 -#> SD.log_k1 1.4203 0.5271 2.3134 -#> SD.log_k2 1.9063 0.7093 3.1033 -#> SD.g_qlogis 0.4477 -0.8642 1.7596 +#> SD.parent_0 2.7243 0.7644 4.6841 +#> SD.log_k_A1 2.0563 0.4292 3.6835 +#> SD.f_parent_qlogis 0.4450 0.1602 0.7298 +#> SD.log_k1 1.5121 0.4714 2.5528 +#> SD.log_k2 1.8320 0.6031 3.0609 +#> SD.g_qlogis 0.4482 -1.2544 2.1507 #> #> Variance model: #> est. lower upper -#> a.1 1.883 1.666 2.1 +#> a.1 1.884 1.668 2.1 #> #> Backtransformed parameters: #> est. lower upper -#> parent_0 93.766473 9.115e+01 96.37983 -#> k_A1 0.002171 2.122e-04 0.02222 -#> f_parent_to_A1 0.274156 2.027e-01 0.35942 -#> k1 0.079519 2.229e-02 0.28371 -#> k2 0.027691 5.015e-03 0.15288 -#> g 0.503095 2.536e-01 0.75109 +#> parent_0 93.805214 9.122e+01 96.38556 +#> k_A1 0.002329 2.573e-04 0.02107 +#> f_parent_to_A1 0.274245 2.026e-01 0.35982 +#> k1 0.077775 1.817e-02 0.33296 +#> k2 0.031067 5.585e-03 0.17281 +#> g 0.476707 1.935e-01 0.77572 #> #> Resulting formation fractions: #> ff @@ -512,182 +512,182 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> parent_sink 0.7258 #> #> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 14.11 59.53 17.92 8.717 25.03 -#> A1 319.21 1060.38 NA NA NA +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> parent 13.96 55.4 16.68 8.912 22.31 +#> A1 297.65 988.8 NA NA NA #> #> Data: -#> ds name time observed predicted residual std standardized -#> Dataset 6 parent 0 97.2 95.79523 1.40477 1.883 0.745888 -#> Dataset 6 parent 0 96.4 95.79523 0.60477 1.883 0.321114 -#> Dataset 6 parent 3 71.1 71.32042 -0.22042 1.883 -0.117035 -#> Dataset 6 parent 3 69.2 71.32042 -2.12042 1.883 -1.125873 -#> Dataset 6 parent 6 58.1 56.45256 1.64744 1.883 0.874739 -#> Dataset 6 parent 6 56.6 56.45256 0.14744 1.883 0.078288 -#> Dataset 6 parent 10 44.4 44.48523 -0.08523 1.883 -0.045257 -#> Dataset 6 parent 10 43.4 44.48523 -1.08523 1.883 -0.576224 -#> Dataset 6 parent 20 33.3 29.75774 3.54226 1.883 1.880826 -#> Dataset 6 parent 20 29.2 29.75774 -0.55774 1.883 -0.296141 -#> Dataset 6 parent 34 17.6 19.35710 -1.75710 1.883 -0.932966 -#> Dataset 6 parent 34 18.0 19.35710 -1.35710 1.883 -0.720579 -#> Dataset 6 parent 55 10.5 10.48443 0.01557 1.883 0.008266 -#> Dataset 6 parent 55 9.3 10.48443 -1.18443 1.883 -0.628895 -#> Dataset 6 parent 90 4.5 3.78622 0.71378 1.883 0.378995 -#> Dataset 6 parent 90 4.7 3.78622 0.91378 1.883 0.485188 -#> Dataset 6 parent 112 3.0 1.99608 1.00392 1.883 0.533048 -#> Dataset 6 parent 112 3.4 1.99608 1.40392 1.883 0.745435 -#> Dataset 6 parent 132 2.3 1.11539 1.18461 1.883 0.628990 -#> Dataset 6 parent 132 2.7 1.11539 1.58461 1.883 0.841377 -#> Dataset 6 A1 3 4.3 4.66132 -0.36132 1.883 -0.191849 -#> Dataset 6 A1 3 4.6 4.66132 -0.06132 1.883 -0.032559 -#> Dataset 6 A1 6 7.0 7.41087 -0.41087 1.883 -0.218157 -#> Dataset 6 A1 6 7.2 7.41087 -0.21087 1.883 -0.111964 -#> Dataset 6 A1 10 8.2 9.50878 -1.30878 1.883 -0.694921 -#> Dataset 6 A1 10 8.0 9.50878 -1.50878 1.883 -0.801114 -#> Dataset 6 A1 20 11.0 11.69902 -0.69902 1.883 -0.371157 -#> Dataset 6 A1 20 13.7 11.69902 2.00098 1.883 1.062455 -#> Dataset 6 A1 34 11.5 12.67784 -1.17784 1.883 -0.625396 -#> Dataset 6 A1 34 12.7 12.67784 0.02216 1.883 0.011765 -#> Dataset 6 A1 55 14.9 12.78556 2.11444 1.883 1.122701 -#> Dataset 6 A1 55 14.5 12.78556 1.71444 1.883 0.910314 -#> Dataset 6 A1 90 12.1 11.52954 0.57046 1.883 0.302898 -#> Dataset 6 A1 90 12.3 11.52954 0.77046 1.883 0.409092 -#> Dataset 6 A1 112 9.9 10.43825 -0.53825 1.883 -0.285793 -#> Dataset 6 A1 112 10.2 10.43825 -0.23825 1.883 -0.126503 -#> Dataset 6 A1 132 8.8 9.42830 -0.62830 1.883 -0.333609 -#> Dataset 6 A1 132 7.8 9.42830 -1.62830 1.883 -0.864577 -#> Dataset 7 parent 0 93.6 90.91477 2.68523 1.883 1.425772 -#> Dataset 7 parent 0 92.3 90.91477 1.38523 1.883 0.735514 -#> Dataset 7 parent 3 87.0 84.76874 2.23126 1.883 1.184726 -#> Dataset 7 parent 3 82.2 84.76874 -2.56874 1.883 -1.363919 -#> Dataset 7 parent 7 74.0 77.62735 -3.62735 1.883 -1.926003 -#> Dataset 7 parent 7 73.9 77.62735 -3.72735 1.883 -1.979100 -#> Dataset 7 parent 14 64.2 67.52266 -3.32266 1.883 -1.764224 -#> Dataset 7 parent 14 69.5 67.52266 1.97734 1.883 1.049904 -#> Dataset 7 parent 30 54.0 52.41949 1.58051 1.883 0.839202 -#> Dataset 7 parent 30 54.6 52.41949 2.18051 1.883 1.157783 -#> Dataset 7 parent 60 41.1 39.36582 1.73418 1.883 0.920794 -#> Dataset 7 parent 60 38.4 39.36582 -0.96582 1.883 -0.512818 -#> Dataset 7 parent 90 32.5 33.75388 -1.25388 1.883 -0.665771 -#> Dataset 7 parent 90 35.5 33.75388 1.74612 1.883 0.927132 -#> Dataset 7 parent 120 28.1 30.41716 -2.31716 1.883 -1.230335 -#> Dataset 7 parent 120 29.0 30.41716 -1.41716 1.883 -0.752464 -#> Dataset 7 parent 180 26.5 25.66046 0.83954 1.883 0.445767 -#> Dataset 7 parent 180 27.6 25.66046 1.93954 1.883 1.029832 -#> Dataset 7 A1 3 3.9 2.69355 1.20645 1.883 0.640585 -#> Dataset 7 A1 3 3.1 2.69355 0.40645 1.883 0.215811 -#> Dataset 7 A1 7 6.9 5.81807 1.08193 1.883 0.574470 -#> Dataset 7 A1 7 6.6 5.81807 0.78193 1.883 0.415180 -#> Dataset 7 A1 14 10.4 10.22529 0.17471 1.883 0.092767 -#> Dataset 7 A1 14 8.3 10.22529 -1.92529 1.883 -1.022265 -#> Dataset 7 A1 30 14.4 16.75484 -2.35484 1.883 -1.250345 -#> Dataset 7 A1 30 13.7 16.75484 -3.05484 1.883 -1.622022 -#> Dataset 7 A1 60 22.1 22.22540 -0.12540 1.883 -0.066583 -#> Dataset 7 A1 60 22.3 22.22540 0.07460 1.883 0.039610 -#> Dataset 7 A1 90 27.5 24.38799 3.11201 1.883 1.652376 -#> Dataset 7 A1 90 25.4 24.38799 1.01201 1.883 0.537344 -#> Dataset 7 A1 120 28.0 25.53294 2.46706 1.883 1.309927 -#> Dataset 7 A1 120 26.6 25.53294 1.06706 1.883 0.566572 -#> Dataset 7 A1 180 25.8 26.94943 -1.14943 1.883 -0.610309 -#> Dataset 7 A1 180 25.3 26.94943 -1.64943 1.883 -0.875793 -#> Dataset 8 parent 0 91.9 91.53246 0.36754 1.883 0.195151 -#> Dataset 8 parent 0 90.8 91.53246 -0.73246 1.883 -0.388914 -#> Dataset 8 parent 1 64.9 67.73197 -2.83197 1.883 -1.503686 -#> Dataset 8 parent 1 66.2 67.73197 -1.53197 1.883 -0.813428 -#> Dataset 8 parent 3 43.5 41.58448 1.91552 1.883 1.017081 -#> Dataset 8 parent 3 44.1 41.58448 2.51552 1.883 1.335662 -#> Dataset 8 parent 8 18.3 19.62286 -1.32286 1.883 -0.702395 -#> Dataset 8 parent 8 18.1 19.62286 -1.52286 1.883 -0.808588 -#> Dataset 8 parent 14 10.2 10.77819 -0.57819 1.883 -0.306999 -#> Dataset 8 parent 14 10.8 10.77819 0.02181 1.883 0.011582 -#> Dataset 8 parent 27 4.9 3.26977 1.63023 1.883 0.865599 -#> Dataset 8 parent 27 3.3 3.26977 0.03023 1.883 0.016051 -#> Dataset 8 parent 48 1.6 0.48024 1.11976 1.883 0.594557 -#> Dataset 8 parent 48 1.5 0.48024 1.01976 1.883 0.541460 -#> Dataset 8 parent 70 1.1 0.06438 1.03562 1.883 0.549881 -#> Dataset 8 parent 70 0.9 0.06438 0.83562 1.883 0.443688 -#> Dataset 8 A1 1 9.6 7.61539 1.98461 1.883 1.053761 -#> Dataset 8 A1 1 7.7 7.61539 0.08461 1.883 0.044923 -#> Dataset 8 A1 3 15.0 15.47954 -0.47954 1.883 -0.254622 -#> Dataset 8 A1 3 15.1 15.47954 -0.37954 1.883 -0.201525 -#> Dataset 8 A1 8 21.2 20.22616 0.97384 1.883 0.517075 -#> Dataset 8 A1 8 21.1 20.22616 0.87384 1.883 0.463979 -#> Dataset 8 A1 14 19.7 20.00067 -0.30067 1.883 -0.159645 -#> Dataset 8 A1 14 18.9 20.00067 -1.10067 1.883 -0.584419 -#> Dataset 8 A1 27 17.5 16.38142 1.11858 1.883 0.593928 -#> Dataset 8 A1 27 15.9 16.38142 -0.48142 1.883 -0.255620 -#> Dataset 8 A1 48 9.5 10.25357 -0.75357 1.883 -0.400124 -#> Dataset 8 A1 48 9.8 10.25357 -0.45357 1.883 -0.240833 -#> Dataset 8 A1 70 6.2 5.95728 0.24272 1.883 0.128878 -#> Dataset 8 A1 70 6.1 5.95728 0.14272 1.883 0.075781 -#> Dataset 9 parent 0 99.8 97.47274 2.32726 1.883 1.235697 -#> Dataset 9 parent 0 98.3 97.47274 0.82726 1.883 0.439246 -#> Dataset 9 parent 1 77.1 79.72257 -2.62257 1.883 -1.392500 -#> Dataset 9 parent 1 77.2 79.72257 -2.52257 1.883 -1.339404 -#> Dataset 9 parent 3 59.0 56.26497 2.73503 1.883 1.452212 -#> Dataset 9 parent 3 58.1 56.26497 1.83503 1.883 0.974342 -#> Dataset 9 parent 8 27.4 31.66985 -4.26985 1.883 -2.267151 -#> Dataset 9 parent 8 29.2 31.66985 -2.46985 1.883 -1.311410 -#> Dataset 9 parent 14 19.1 22.39789 -3.29789 1.883 -1.751071 -#> Dataset 9 parent 14 29.6 22.39789 7.20211 1.883 3.824090 -#> Dataset 9 parent 27 10.1 14.21758 -4.11758 1.883 -2.186301 -#> Dataset 9 parent 27 18.2 14.21758 3.98242 1.883 2.114537 -#> Dataset 9 parent 48 4.5 7.27921 -2.77921 1.883 -1.475671 -#> Dataset 9 parent 48 9.1 7.27921 1.82079 1.883 0.966780 -#> Dataset 9 parent 70 2.3 3.61470 -1.31470 1.883 -0.698065 -#> Dataset 9 parent 70 2.9 3.61470 -0.71470 1.883 -0.379485 -#> Dataset 9 parent 91 2.0 1.85303 0.14697 1.883 0.078038 -#> Dataset 9 parent 91 1.8 1.85303 -0.05303 1.883 -0.028155 -#> Dataset 9 parent 120 2.0 0.73645 1.26355 1.883 0.670906 -#> Dataset 9 parent 120 2.2 0.73645 1.46355 1.883 0.777099 -#> Dataset 9 A1 1 4.2 3.87843 0.32157 1.883 0.170743 -#> Dataset 9 A1 1 3.9 3.87843 0.02157 1.883 0.011453 -#> Dataset 9 A1 3 7.4 8.90535 -1.50535 1.883 -0.799291 -#> Dataset 9 A1 3 7.9 8.90535 -1.00535 1.883 -0.533807 -#> Dataset 9 A1 8 14.5 13.75172 0.74828 1.883 0.397312 -#> Dataset 9 A1 8 13.7 13.75172 -0.05172 1.883 -0.027462 -#> Dataset 9 A1 14 14.2 14.97541 -0.77541 1.883 -0.411715 -#> Dataset 9 A1 14 12.2 14.97541 -2.77541 1.883 -1.473650 -#> Dataset 9 A1 27 13.7 14.94728 -1.24728 1.883 -0.662266 -#> Dataset 9 A1 27 13.2 14.94728 -1.74728 1.883 -0.927750 -#> Dataset 9 A1 48 13.6 13.66078 -0.06078 1.883 -0.032272 -#> Dataset 9 A1 48 15.4 13.66078 1.73922 1.883 0.923470 -#> Dataset 9 A1 70 10.4 11.84899 -1.44899 1.883 -0.769365 -#> Dataset 9 A1 70 11.6 11.84899 -0.24899 1.883 -0.132204 -#> Dataset 9 A1 91 10.0 10.09177 -0.09177 1.883 -0.048727 -#> Dataset 9 A1 91 9.5 10.09177 -0.59177 1.883 -0.314211 -#> Dataset 9 A1 120 9.1 7.91379 1.18621 1.883 0.629841 -#> Dataset 9 A1 120 9.0 7.91379 1.08621 1.883 0.576744 -#> Dataset 10 parent 0 96.1 93.65257 2.44743 1.883 1.299505 -#> Dataset 10 parent 0 94.3 93.65257 0.64743 1.883 0.343763 -#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 -#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 -#> Dataset 10 parent 14 69.4 70.17143 -0.77143 1.883 -0.409606 -#> Dataset 10 parent 14 73.1 70.17143 2.92857 1.883 1.554974 -#> Dataset 10 parent 21 65.6 63.99188 1.60812 1.883 0.853862 -#> Dataset 10 parent 21 65.3 63.99188 1.30812 1.883 0.694572 -#> Dataset 10 parent 41 55.9 54.64292 1.25708 1.883 0.667467 -#> Dataset 10 parent 41 54.4 54.64292 -0.24292 1.883 -0.128985 -#> Dataset 10 parent 63 47.0 49.61303 -2.61303 1.883 -1.387433 -#> Dataset 10 parent 63 49.3 49.61303 -0.31303 1.883 -0.166207 -#> Dataset 10 parent 91 44.7 45.17807 -0.47807 1.883 -0.253839 -#> Dataset 10 parent 91 46.7 45.17807 1.52193 1.883 0.808096 -#> Dataset 10 parent 120 42.1 41.27970 0.82030 1.883 0.435552 -#> Dataset 10 parent 120 41.3 41.27970 0.02030 1.883 0.010778 -#> Dataset 10 A1 8 3.3 3.99294 -0.69294 1.883 -0.367929 -#> Dataset 10 A1 8 3.4 3.99294 -0.59294 1.883 -0.314832 -#> Dataset 10 A1 14 3.9 5.92756 -2.02756 1.883 -1.076570 -#> Dataset 10 A1 14 2.9 5.92756 -3.02756 1.883 -1.607538 -#> Dataset 10 A1 21 6.4 7.47313 -1.07313 1.883 -0.569799 -#> Dataset 10 A1 21 7.2 7.47313 -0.27313 1.883 -0.145025 -#> Dataset 10 A1 41 9.1 9.76819 -0.66819 1.883 -0.354786 -#> Dataset 10 A1 41 8.5 9.76819 -1.26819 1.883 -0.673367 -#> Dataset 10 A1 63 11.7 10.94733 0.75267 1.883 0.399643 -#> Dataset 10 A1 63 12.0 10.94733 1.05267 1.883 0.558933 -#> Dataset 10 A1 91 13.3 11.93773 1.36227 1.883 0.723321 -#> Dataset 10 A1 91 13.2 11.93773 1.26227 1.883 0.670224 -#> Dataset 10 A1 120 14.3 12.77666 1.52334 1.883 0.808847 -#> Dataset 10 A1 120 12.1 12.77666 -0.67666 1.883 -0.359282</div><div class='input'> +#> ds name time observed predicted residual std standardized +#> Dataset 6 parent 0 97.2 95.75408 1.445920 1.884 0.767479 +#> Dataset 6 parent 0 96.4 95.75408 0.645920 1.884 0.342847 +#> Dataset 6 parent 3 71.1 71.22466 -0.124662 1.884 -0.066169 +#> Dataset 6 parent 3 69.2 71.22466 -2.024662 1.884 -1.074669 +#> Dataset 6 parent 6 58.1 56.42290 1.677100 1.884 0.890187 +#> Dataset 6 parent 6 56.6 56.42290 0.177100 1.884 0.094003 +#> Dataset 6 parent 10 44.4 44.55255 -0.152554 1.884 -0.080974 +#> Dataset 6 parent 10 43.4 44.55255 -1.152554 1.884 -0.611763 +#> Dataset 6 parent 20 33.3 29.88846 3.411537 1.884 1.810807 +#> Dataset 6 parent 20 29.2 29.88846 -0.688463 1.884 -0.365429 +#> Dataset 6 parent 34 17.6 19.40826 -1.808260 1.884 -0.959805 +#> Dataset 6 parent 34 18.0 19.40826 -1.408260 1.884 -0.747489 +#> Dataset 6 parent 55 10.5 10.45560 0.044398 1.884 0.023566 +#> Dataset 6 parent 55 9.3 10.45560 -1.155602 1.884 -0.613381 +#> Dataset 6 parent 90 4.5 3.74026 0.759744 1.884 0.403264 +#> Dataset 6 parent 90 4.7 3.74026 0.959744 1.884 0.509421 +#> Dataset 6 parent 112 3.0 1.96015 1.039853 1.884 0.551943 +#> Dataset 6 parent 112 3.4 1.96015 1.439853 1.884 0.764258 +#> Dataset 6 parent 132 2.3 1.08940 1.210603 1.884 0.642575 +#> Dataset 6 parent 132 2.7 1.08940 1.610603 1.884 0.854890 +#> Dataset 6 A1 3 4.3 4.75601 -0.456009 1.884 -0.242045 +#> Dataset 6 A1 3 4.6 4.75601 -0.156009 1.884 -0.082808 +#> Dataset 6 A1 6 7.0 7.53839 -0.538391 1.884 -0.285772 +#> Dataset 6 A1 6 7.2 7.53839 -0.338391 1.884 -0.179614 +#> Dataset 6 A1 10 8.2 9.64728 -1.447276 1.884 -0.768198 +#> Dataset 6 A1 10 8.0 9.64728 -1.647276 1.884 -0.874356 +#> Dataset 6 A1 20 11.0 11.83954 -0.839545 1.884 -0.445621 +#> Dataset 6 A1 20 13.7 11.83954 1.860455 1.884 0.987509 +#> Dataset 6 A1 34 11.5 12.81233 -1.312327 1.884 -0.696569 +#> Dataset 6 A1 34 12.7 12.81233 -0.112327 1.884 -0.059622 +#> Dataset 6 A1 55 14.9 12.87919 2.020809 1.884 1.072624 +#> Dataset 6 A1 55 14.5 12.87919 1.620809 1.884 0.860308 +#> Dataset 6 A1 90 12.1 11.52464 0.575364 1.884 0.305397 +#> Dataset 6 A1 90 12.3 11.52464 0.775364 1.884 0.411555 +#> Dataset 6 A1 112 9.9 10.37694 -0.476938 1.884 -0.253153 +#> Dataset 6 A1 112 10.2 10.37694 -0.176938 1.884 -0.093917 +#> Dataset 6 A1 132 8.8 9.32474 -0.524742 1.884 -0.278528 +#> Dataset 6 A1 132 7.8 9.32474 -1.524742 1.884 -0.809317 +#> Dataset 7 parent 0 93.6 90.16918 3.430816 1.884 1.821040 +#> Dataset 7 parent 0 92.3 90.16918 2.130816 1.884 1.131014 +#> Dataset 7 parent 3 87.0 84.05442 2.945583 1.884 1.563483 +#> Dataset 7 parent 3 82.2 84.05442 -1.854417 1.884 -0.984304 +#> Dataset 7 parent 7 74.0 77.00960 -3.009596 1.884 -1.597461 +#> Dataset 7 parent 7 73.9 77.00960 -3.109596 1.884 -1.650540 +#> Dataset 7 parent 14 64.2 67.15684 -2.956840 1.884 -1.569459 +#> Dataset 7 parent 14 69.5 67.15684 2.343160 1.884 1.243724 +#> Dataset 7 parent 30 54.0 52.66290 1.337101 1.884 0.709719 +#> Dataset 7 parent 30 54.6 52.66290 1.937101 1.884 1.028192 +#> Dataset 7 parent 60 41.1 40.04995 1.050050 1.884 0.557355 +#> Dataset 7 parent 60 38.4 40.04995 -1.649950 1.884 -0.875775 +#> Dataset 7 parent 90 32.5 34.09675 -1.596746 1.884 -0.847535 +#> Dataset 7 parent 90 35.5 34.09675 1.403254 1.884 0.744832 +#> Dataset 7 parent 120 28.1 30.12281 -2.022814 1.884 -1.073688 +#> Dataset 7 parent 120 29.0 30.12281 -1.122814 1.884 -0.595977 +#> Dataset 7 parent 180 26.5 24.10888 2.391123 1.884 1.269182 +#> Dataset 7 parent 180 27.6 24.10888 3.491123 1.884 1.853050 +#> Dataset 7 A1 3 3.9 2.77684 1.123161 1.884 0.596161 +#> Dataset 7 A1 3 3.1 2.77684 0.323161 1.884 0.171530 +#> Dataset 7 A1 7 6.9 5.96705 0.932950 1.884 0.495200 +#> Dataset 7 A1 7 6.6 5.96705 0.632950 1.884 0.335963 +#> Dataset 7 A1 14 10.4 10.40535 -0.005348 1.884 -0.002839 +#> Dataset 7 A1 14 8.3 10.40535 -2.105348 1.884 -1.117496 +#> Dataset 7 A1 30 14.4 16.83722 -2.437216 1.884 -1.293648 +#> Dataset 7 A1 30 13.7 16.83722 -3.137216 1.884 -1.665200 +#> Dataset 7 A1 60 22.1 22.15018 -0.050179 1.884 -0.026635 +#> Dataset 7 A1 60 22.3 22.15018 0.149821 1.884 0.079523 +#> Dataset 7 A1 90 27.5 24.36286 3.137143 1.884 1.665161 +#> Dataset 7 A1 90 25.4 24.36286 1.037143 1.884 0.550504 +#> Dataset 7 A1 120 28.0 25.64064 2.359361 1.884 1.252323 +#> Dataset 7 A1 120 26.6 25.64064 0.959361 1.884 0.509218 +#> Dataset 7 A1 180 25.8 27.25486 -1.454858 1.884 -0.772223 +#> Dataset 7 A1 180 25.3 27.25486 -1.954858 1.884 -1.037617 +#> Dataset 8 parent 0 91.9 91.72652 0.173479 1.884 0.092081 +#> Dataset 8 parent 0 90.8 91.72652 -0.926521 1.884 -0.491787 +#> Dataset 8 parent 1 64.9 67.22810 -2.328104 1.884 -1.235732 +#> Dataset 8 parent 1 66.2 67.22810 -1.028104 1.884 -0.545706 +#> Dataset 8 parent 3 43.5 41.46375 2.036251 1.884 1.080820 +#> Dataset 8 parent 3 44.1 41.46375 2.636251 1.884 1.399293 +#> Dataset 8 parent 8 18.3 19.83597 -1.535968 1.884 -0.815275 +#> Dataset 8 parent 8 18.1 19.83597 -1.735968 1.884 -0.921433 +#> Dataset 8 parent 14 10.2 10.34793 -0.147927 1.884 -0.078518 +#> Dataset 8 parent 14 10.8 10.34793 0.452073 1.884 0.239956 +#> Dataset 8 parent 27 4.9 2.67641 2.223595 1.884 1.180260 +#> Dataset 8 parent 27 3.3 2.67641 0.623595 1.884 0.330997 +#> Dataset 8 parent 48 1.6 0.30218 1.297822 1.884 0.688870 +#> Dataset 8 parent 48 1.5 0.30218 1.197822 1.884 0.635791 +#> Dataset 8 parent 70 1.1 0.03075 1.069248 1.884 0.567545 +#> Dataset 8 parent 70 0.9 0.03075 0.869248 1.884 0.461388 +#> Dataset 8 A1 1 9.6 7.74066 1.859342 1.884 0.986918 +#> Dataset 8 A1 1 7.7 7.74066 -0.040658 1.884 -0.021581 +#> Dataset 8 A1 3 15.0 15.37549 -0.375495 1.884 -0.199309 +#> Dataset 8 A1 3 15.1 15.37549 -0.275495 1.884 -0.146230 +#> Dataset 8 A1 8 21.2 19.95900 1.241003 1.884 0.658711 +#> Dataset 8 A1 8 21.1 19.95900 1.141003 1.884 0.605632 +#> Dataset 8 A1 14 19.7 19.92898 -0.228978 1.884 -0.121539 +#> Dataset 8 A1 14 18.9 19.92898 -1.028978 1.884 -0.546170 +#> Dataset 8 A1 27 17.5 16.34046 1.159536 1.884 0.615469 +#> Dataset 8 A1 27 15.9 16.34046 -0.440464 1.884 -0.233793 +#> Dataset 8 A1 48 9.5 10.12131 -0.621313 1.884 -0.329786 +#> Dataset 8 A1 48 9.8 10.12131 -0.321313 1.884 -0.170550 +#> Dataset 8 A1 70 6.2 5.84753 0.352469 1.884 0.187087 +#> Dataset 8 A1 70 6.1 5.84753 0.252469 1.884 0.134008 +#> Dataset 9 parent 0 99.8 98.23600 1.564002 1.884 0.830155 +#> Dataset 9 parent 0 98.3 98.23600 0.064002 1.884 0.033972 +#> Dataset 9 parent 1 77.1 79.68007 -2.580074 1.884 -1.369475 +#> Dataset 9 parent 1 77.2 79.68007 -2.480074 1.884 -1.316396 +#> Dataset 9 parent 3 59.0 55.81142 3.188584 1.884 1.692465 +#> Dataset 9 parent 3 58.1 55.81142 2.288584 1.884 1.214755 +#> Dataset 9 parent 8 27.4 31.81995 -4.419948 1.884 -2.346060 +#> Dataset 9 parent 8 29.2 31.81995 -2.619948 1.884 -1.390640 +#> Dataset 9 parent 14 19.1 22.78328 -3.683282 1.884 -1.955046 +#> Dataset 9 parent 14 29.6 22.78328 6.816718 1.884 3.618240 +#> Dataset 9 parent 27 10.1 14.15172 -4.051720 1.884 -2.150609 +#> Dataset 9 parent 27 18.2 14.15172 4.048280 1.884 2.148783 +#> Dataset 9 parent 48 4.5 6.86094 -2.360941 1.884 -1.253162 +#> Dataset 9 parent 48 9.1 6.86094 2.239059 1.884 1.188468 +#> Dataset 9 parent 70 2.3 3.21580 -0.915798 1.884 -0.486096 +#> Dataset 9 parent 70 2.9 3.21580 -0.315798 1.884 -0.167622 +#> Dataset 9 parent 91 2.0 1.56010 0.439897 1.884 0.233492 +#> Dataset 9 parent 91 1.8 1.56010 0.239897 1.884 0.127335 +#> Dataset 9 parent 120 2.0 0.57458 1.425424 1.884 0.756600 +#> Dataset 9 parent 120 2.2 0.57458 1.625424 1.884 0.862757 +#> Dataset 9 A1 1 4.2 4.01796 0.182037 1.884 0.096623 +#> Dataset 9 A1 1 3.9 4.01796 -0.117963 1.884 -0.062613 +#> Dataset 9 A1 3 7.4 9.08527 -1.685270 1.884 -0.894523 +#> Dataset 9 A1 3 7.9 9.08527 -1.185270 1.884 -0.629129 +#> Dataset 9 A1 8 14.5 13.75054 0.749457 1.884 0.397804 +#> Dataset 9 A1 8 13.7 13.75054 -0.050543 1.884 -0.026827 +#> Dataset 9 A1 14 14.2 14.91180 -0.711804 1.884 -0.377818 +#> Dataset 9 A1 14 12.2 14.91180 -2.711804 1.884 -1.439396 +#> Dataset 9 A1 27 13.7 14.97813 -1.278129 1.884 -0.678417 +#> Dataset 9 A1 27 13.2 14.97813 -1.778129 1.884 -0.943812 +#> Dataset 9 A1 48 13.6 13.75574 -0.155745 1.884 -0.082668 +#> Dataset 9 A1 48 15.4 13.75574 1.644255 1.884 0.872753 +#> Dataset 9 A1 70 10.4 11.92861 -1.528608 1.884 -0.811369 +#> Dataset 9 A1 70 11.6 11.92861 -0.328608 1.884 -0.174422 +#> Dataset 9 A1 91 10.0 10.14395 -0.143947 1.884 -0.076405 +#> Dataset 9 A1 91 9.5 10.14395 -0.643947 1.884 -0.341800 +#> Dataset 9 A1 120 9.1 7.93869 1.161307 1.884 0.616409 +#> Dataset 9 A1 120 9.0 7.93869 1.061307 1.884 0.563330 +#> Dataset 10 parent 0 96.1 93.65914 2.440862 1.884 1.295583 +#> Dataset 10 parent 0 94.3 93.65914 0.640862 1.884 0.340163 +#> Dataset 10 parent 8 73.9 77.83065 -3.930647 1.884 -2.086344 +#> Dataset 10 parent 8 73.9 77.83065 -3.930647 1.884 -2.086344 +#> Dataset 10 parent 14 69.4 70.15862 -0.758619 1.884 -0.402667 +#> Dataset 10 parent 14 73.1 70.15862 2.941381 1.884 1.561253 +#> Dataset 10 parent 21 65.6 64.00840 1.591600 1.884 0.844804 +#> Dataset 10 parent 21 65.3 64.00840 1.291600 1.884 0.685567 +#> Dataset 10 parent 41 55.9 54.71192 1.188076 1.884 0.630618 +#> Dataset 10 parent 41 54.4 54.71192 -0.311924 1.884 -0.165566 +#> Dataset 10 parent 63 47.0 49.66775 -2.667747 1.884 -1.416011 +#> Dataset 10 parent 63 49.3 49.66775 -0.367747 1.884 -0.195196 +#> Dataset 10 parent 91 44.7 45.17119 -0.471186 1.884 -0.250101 +#> Dataset 10 parent 91 46.7 45.17119 1.528814 1.884 0.811478 +#> Dataset 10 parent 120 42.1 41.20430 0.895699 1.884 0.475427 +#> Dataset 10 parent 120 41.3 41.20430 0.095699 1.884 0.050796 +#> Dataset 10 A1 8 3.3 4.00920 -0.709204 1.884 -0.376438 +#> Dataset 10 A1 8 3.4 4.00920 -0.609204 1.884 -0.323359 +#> Dataset 10 A1 14 3.9 5.94267 -2.042668 1.884 -1.084226 +#> Dataset 10 A1 14 2.9 5.94267 -3.042668 1.884 -1.615015 +#> Dataset 10 A1 21 6.4 7.48222 -1.082219 1.884 -0.574430 +#> Dataset 10 A1 21 7.2 7.48222 -0.282219 1.884 -0.149799 +#> Dataset 10 A1 41 9.1 9.76246 -0.662460 1.884 -0.351626 +#> Dataset 10 A1 41 8.5 9.76246 -1.262460 1.884 -0.670100 +#> Dataset 10 A1 63 11.7 10.93972 0.760278 1.884 0.403547 +#> Dataset 10 A1 63 12.0 10.93972 1.060278 1.884 0.562784 +#> Dataset 10 A1 91 13.3 11.93666 1.363337 1.884 0.723645 +#> Dataset 10 A1 91 13.2 11.93666 1.263337 1.884 0.670566 +#> Dataset 10 A1 120 14.3 12.78218 1.517817 1.884 0.805641 +#> Dataset 10 A1 120 12.1 12.78218 -0.682183 1.884 -0.362095</div><div class='input'> <span class='co'># The following takes about 6 minutes</span> <span class='co'>#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",</span> <span class='co'># control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))</span> diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html index 0fead0df..373ce75f 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> @@ -258,737 +258,73 @@ 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'><-</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'>#> Running main SAEM algorithm -#> [1] "Fri Jun 11 10:57:31 2021" +#> [1] "Tue Jul 27 16:31:43 2021" #> .... #> Minimisation finished -#> [1] "Fri Jun 11 10:57:43 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'><-</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> +#> [1] "Tue Jul 27 16:31:55 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'><-</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'>#> <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'>#> <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'><-</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'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 1.0127e+02 -3.8515e+00 -2.0719e+00 -3.7271e+00 -1.9335e+00 4.0311e-01 6.9594e+00 1.5021e-01 5.3947e-01 1.9686e-01 3.7429e-01 5.4209e-01 8.4121e+00 7.3391e-02 7.1185e+00 2.5869e-01 -#> 2: 1.0136e+02 -3.8005e+00 -2.3424e+00 -4.0759e+00 -1.6475e+00 1.1598e-01 6.6115e+00 1.4406e-01 5.1249e-01 1.8701e-01 3.5786e-01 5.1499e-01 4.9102e+00 6.2829e-02 4.7230e+00 7.8901e-02 -#> 3: 1.0126e+02 -4.0285e+00 -2.3629e+00 -4.1271e+00 -1.1733e+00 1.7634e-02 6.2809e+00 1.6892e-01 4.8687e-01 1.7766e-01 3.3997e-01 4.8924e-01 3.2256e+00 6.6693e-02 3.3261e+00 8.7190e-02 -#> 4: 1.0105e+02 -4.0894e+00 -2.5516e+00 -4.1037e+00 -1.0816e+00 4.5377e-02 5.9668e+00 1.6048e-01 4.6252e-01 1.6878e-01 3.2297e-01 4.6478e-01 2.4343e+00 7.0557e-02 2.2610e+00 9.2498e-02 -#> 5: 1.0101e+02 -4.1364e+00 -2.4605e+00 -4.0737e+00 -1.0920e+00 -4.7953e-03 5.9593e+00 1.5245e-01 4.3940e-01 1.8078e-01 3.0682e-01 5.4688e-01 1.7424e+00 7.4776e-02 1.5144e+00 1.0787e-01 -#> 6: 1.0042e+02 -4.0933e+00 -2.4472e+00 -4.1090e+00 -9.7996e-01 -9.0472e-02 6.0175e+00 1.4483e-01 4.1743e-01 1.8824e-01 2.9148e-01 5.3033e-01 1.5545e+00 6.8588e-02 1.3401e+00 9.8865e-02 -#> 7: 1.0078e+02 -4.0911e+00 -2.4335e+00 -4.0758e+00 -9.9422e-01 -7.8849e-02 6.6318e+00 1.3759e-01 3.9656e-01 1.7882e-01 2.7691e-01 5.0381e-01 1.3780e+00 6.9978e-02 1.1346e+00 9.6162e-02 -#> 8: 1.0077e+02 -4.0196e+00 -2.4345e+00 -4.0444e+00 -9.3483e-01 -1.1032e-01 6.3002e+00 1.3071e-01 3.7673e-01 1.6988e-01 2.6306e-01 4.8191e-01 1.1774e+00 7.4232e-02 1.0270e+00 9.5616e-02 -#> 9: 1.0118e+02 -4.0436e+00 -2.4649e+00 -4.0207e+00 -8.9829e-01 -1.7784e-01 5.9852e+00 1.2417e-01 3.5789e-01 1.6139e-01 2.4991e-01 5.5466e-01 1.1040e+00 7.1515e-02 1.0342e+00 9.3972e-02 -#> 10: 1.0143e+02 -4.0523e+00 -2.3737e+00 -4.0184e+00 -9.1167e-01 -2.3828e-01 5.8520e+00 1.1797e-01 3.4196e-01 1.5332e-01 2.3741e-01 5.2849e-01 1.0510e+00 7.5719e-02 1.0638e+00 9.3973e-02 -#> 11: 1.0119e+02 -4.0699e+00 -2.3680e+00 -4.0191e+00 -9.4858e-01 -1.7310e-01 6.9958e+00 1.1207e-01 3.6891e-01 1.4565e-01 2.2554e-01 5.0206e-01 1.0247e+00 7.5497e-02 1.0292e+00 9.3707e-02 -#> 12: 1.0121e+02 -4.0189e+00 -2.4198e+00 -4.0139e+00 -9.1693e-01 -2.0613e-01 6.6460e+00 1.0646e-01 3.5046e-01 1.3837e-01 2.1427e-01 5.7696e-01 1.1046e+00 7.6090e-02 9.3689e-01 9.4115e-02 -#> 13: 1.0083e+02 -4.0451e+00 -2.4395e+00 -4.0235e+00 -9.4535e-01 -1.4723e-01 6.3137e+00 1.0114e-01 3.3294e-01 1.3145e-01 2.0355e-01 5.4811e-01 1.0360e+00 7.3381e-02 9.7078e-01 9.1659e-02 -#> 14: 1.0056e+02 -4.0401e+00 -2.4045e+00 -4.0054e+00 -9.4191e-01 -1.3928e-01 5.9980e+00 9.6084e-02 3.4934e-01 1.2488e-01 1.9338e-01 5.2071e-01 1.0303e+00 7.7118e-02 8.8372e-01 9.0469e-02 -#> 15: 1.0070e+02 -4.0388e+00 -2.4210e+00 -4.0113e+00 -9.1136e-01 -1.2702e-01 5.6981e+00 9.1279e-02 3.3187e-01 1.1864e-01 1.8371e-01 4.9467e-01 1.0486e+00 7.2427e-02 7.8179e-01 9.1572e-02 -#> 16: 1.0078e+02 -4.0175e+00 -2.4766e+00 -4.0191e+00 -9.0733e-01 -1.1952e-01 5.4132e+00 8.6716e-02 3.1528e-01 1.1270e-01 1.7452e-01 4.8928e-01 9.7799e-01 8.1464e-02 8.2935e-01 8.6520e-02 -#> 17: 1.0069e+02 -4.0533e+00 -2.5110e+00 -4.0294e+00 -9.1841e-01 -6.8363e-03 5.1426e+00 8.2380e-02 2.9952e-01 1.0707e-01 1.6580e-01 4.6482e-01 9.1609e-01 8.1008e-02 8.1783e-01 8.8818e-02 -#> 18: 99.9647 -4.0672 -2.5327 -4.0416 -0.9273 0.0097 4.8854 0.0783 0.2970 0.1280 0.1941 0.5053 0.9306 0.0764 0.8097 0.0881 -#> 19: 1.0027e+02 -4.0667e+00 -2.4653e+00 -4.0579e+00 -9.2776e-01 3.0417e-02 4.6412e+00 7.4348e-02 3.3694e-01 1.2164e-01 1.8435e-01 5.1797e-01 9.7386e-01 7.4954e-02 7.9297e-01 8.9915e-02 -#> 20: 1.0006e+02 -4.0935e+00 -2.4804e+00 -4.0721e+00 -9.3737e-01 1.9496e-02 4.4091e+00 7.0630e-02 3.3728e-01 1.2544e-01 1.7513e-01 6.0925e-01 1.0232e+00 7.4618e-02 7.9988e-01 8.9642e-02 -#> 21: 1.0043e+02 -4.0542e+00 -2.5168e+00 -4.0623e+00 -9.1553e-01 3.9474e-02 4.1887e+00 6.7099e-02 3.4553e-01 1.1917e-01 1.6638e-01 6.0827e-01 1.0155e+00 8.0771e-02 7.8424e-01 8.6213e-02 -#> 22: 1.0049e+02 -4.0449e+00 -2.5082e+00 -4.0849e+00 -9.2553e-01 4.5424e-02 3.9792e+00 6.3744e-02 3.2825e-01 1.2365e-01 1.5806e-01 5.8922e-01 8.2860e-01 8.3384e-02 8.2525e-01 8.9218e-02 -#> 23: 1.0067e+02 -4.0411e+00 -2.5460e+00 -4.0736e+00 -9.2578e-01 5.2422e-02 3.7803e+00 6.0557e-02 3.1661e-01 1.2306e-01 1.5016e-01 5.8274e-01 9.3412e-01 8.0508e-02 8.1829e-01 8.6377e-02 -#> 24: 1.0091e+02 -4.0314e+00 -2.5298e+00 -4.0566e+00 -8.9743e-01 3.7634e-02 3.5913e+00 5.7529e-02 3.5267e-01 1.2194e-01 1.4265e-01 5.5360e-01 9.6271e-01 7.6960e-02 8.8466e-01 8.5693e-02 -#> 25: 1.0100e+02 -4.0442e+00 -2.5399e+00 -4.0568e+00 -8.9494e-01 1.7415e-02 3.4117e+00 5.4652e-02 3.3504e-01 1.2781e-01 1.3552e-01 5.2592e-01 9.6040e-01 7.7299e-02 8.9561e-01 8.6893e-02 -#> 26: 1.0111e+02 -4.0354e+00 -2.5182e+00 -4.0899e+00 -9.0799e-01 7.6464e-02 4.8614e+00 5.1920e-02 3.1829e-01 1.2142e-01 1.3110e-01 4.9963e-01 9.6997e-01 7.4932e-02 8.2521e-01 9.3659e-02 -#> 27: 1.0159e+02 -4.0653e+00 -2.4934e+00 -4.0803e+00 -9.5632e-01 2.8659e-03 4.6184e+00 4.9324e-02 3.0237e-01 1.1535e-01 1.4743e-01 4.7465e-01 9.4314e-01 7.7860e-02 8.9820e-01 8.8210e-02 -#> 28: 1.0154e+02 -4.0487e+00 -2.4844e+00 -4.0511e+00 -9.6473e-01 -4.7382e-02 4.3874e+00 4.6858e-02 3.2049e-01 1.0958e-01 1.5243e-01 4.5091e-01 9.8808e-01 7.4786e-02 8.6833e-01 8.8720e-02 -#> 29: 1.0144e+02 -4.0414e+00 -2.4105e+00 -4.0504e+00 -9.4039e-01 -3.6753e-02 4.1681e+00 4.4515e-02 3.2754e-01 1.0410e-01 1.4940e-01 4.2837e-01 9.5520e-01 7.8507e-02 8.2408e-01 8.5998e-02 -#> 30: 1.0137e+02 -4.0292e+00 -2.4174e+00 -4.0382e+00 -9.3180e-01 -7.1482e-02 5.4636e+00 4.2289e-02 3.2074e-01 9.8896e-02 1.6877e-01 4.0695e-01 8.8153e-01 7.5106e-02 8.5239e-01 8.8266e-02 -#> 31: 1.0105e+02 -4.0387e+00 -2.4368e+00 -4.0346e+00 -9.1098e-01 -5.4730e-02 5.1904e+00 4.0175e-02 3.0470e-01 9.3951e-02 1.6034e-01 3.8660e-01 8.7853e-01 8.0278e-02 8.7981e-01 8.6404e-02 -#> 32: 1.0147e+02 -4.0435e+00 -2.4530e+00 -4.0365e+00 -9.1241e-01 -7.1281e-02 4.9309e+00 3.8166e-02 2.8947e-01 9.4694e-02 1.7475e-01 3.6727e-01 8.7005e-01 8.1398e-02 8.7784e-01 8.8976e-02 -#> 33: 1.0144e+02 -4.0092e+00 -2.4279e+00 -4.0090e+00 -8.8656e-01 -1.4017e-01 5.2945e+00 3.6258e-02 2.9770e-01 1.0169e-01 1.6601e-01 3.4891e-01 9.2202e-01 7.8841e-02 8.7551e-01 8.4011e-02 -#> 34: 1.0157e+02 -3.9839e+00 -2.4469e+00 -4.0180e+00 -8.3877e-01 -1.4664e-01 6.3506e+00 3.4445e-02 2.8282e-01 1.0831e-01 1.6850e-01 3.3146e-01 8.4403e-01 7.9056e-02 8.4620e-01 8.6363e-02 -#> 35: 1.0149e+02 -3.9928e+00 -2.4771e+00 -4.0106e+00 -8.6974e-01 -1.4219e-01 6.2039e+00 3.2722e-02 2.8123e-01 1.1283e-01 1.6008e-01 3.1489e-01 9.1308e-01 7.8685e-02 7.8939e-01 8.7289e-02 -#> 36: 1.0162e+02 -4.0099e+00 -2.4822e+00 -3.9880e+00 -8.7959e-01 -1.3237e-01 5.8937e+00 3.1086e-02 3.2200e-01 1.0719e-01 1.6077e-01 2.9914e-01 9.0821e-01 8.4066e-02 7.5559e-01 8.4838e-02 -#> 37: 1.0102e+02 -3.9962e+00 -2.4852e+00 -3.9954e+00 -8.8307e-01 -9.2070e-02 5.5991e+00 2.9532e-02 3.3713e-01 1.0183e-01 1.5333e-01 2.8419e-01 8.3918e-01 8.5231e-02 7.6007e-01 8.9541e-02 -#> 38: 1.0102e+02 -3.9987e+00 -2.5129e+00 -3.9833e+00 -8.7454e-01 -1.6469e-01 5.3191e+00 2.8055e-02 3.2027e-01 1.0792e-01 1.4707e-01 2.6998e-01 9.1490e-01 8.4715e-02 7.6778e-01 8.9241e-02 -#> 39: 1.0054e+02 -3.9875e+00 -2.4301e+00 -3.9797e+00 -8.7222e-01 -1.9597e-01 7.3800e+00 2.6653e-02 3.0426e-01 1.0801e-01 1.4393e-01 2.5648e-01 9.5901e-01 7.8320e-02 8.1559e-01 9.2429e-02 -#> 40: 1.0077e+02 -4.0057e+00 -2.4630e+00 -3.9849e+00 -8.6788e-01 -1.9606e-01 7.0110e+00 2.5320e-02 3.0385e-01 1.3164e-01 1.4567e-01 3.0284e-01 9.7123e-01 7.6328e-02 8.3681e-01 8.9349e-02 -#> 41: 1.0069e+02 -4.0143e+00 -2.3805e+00 -3.9962e+00 -8.7503e-01 -1.8532e-01 6.6604e+00 2.4054e-02 3.0707e-01 1.4668e-01 1.5021e-01 3.0404e-01 1.0072e+00 7.3629e-02 9.4494e-01 8.4745e-02 -#> 42: 1.0073e+02 -3.9861e+00 -2.4464e+00 -3.9919e+00 -8.7912e-01 -1.8435e-01 6.3274e+00 2.2851e-02 2.9171e-01 1.3935e-01 1.5080e-01 2.8883e-01 9.6502e-01 7.7470e-02 9.4221e-01 8.2459e-02 -#> 43: 1.0104e+02 -3.9881e+00 -2.4156e+00 -3.9688e+00 -8.9448e-01 -2.3739e-01 6.0110e+00 2.1709e-02 2.7713e-01 1.3238e-01 1.5603e-01 2.7439e-01 9.7714e-01 7.1720e-02 8.5890e-01 8.6635e-02 -#> 44: 1.0084e+02 -4.0117e+00 -2.4455e+00 -3.9753e+00 -8.8716e-01 -2.0112e-01 5.7105e+00 2.0623e-02 2.6327e-01 1.2741e-01 1.5200e-01 2.6067e-01 9.3289e-01 8.0543e-02 8.5055e-01 8.2921e-02 -#> 45: 1.0071e+02 -3.9996e+00 -2.4359e+00 -3.9764e+00 -9.1082e-01 -2.4578e-01 5.4250e+00 1.9592e-02 2.5011e-01 1.3254e-01 1.6132e-01 2.8273e-01 9.5805e-01 7.7734e-02 7.8171e-01 8.4571e-02 -#> 46: 1.0018e+02 -4.0077e+00 -2.4835e+00 -3.9739e+00 -8.6079e-01 -1.6592e-01 5.1537e+00 1.8613e-02 2.3760e-01 1.3830e-01 1.5392e-01 3.0295e-01 1.0931e+00 7.3274e-02 8.9544e-01 8.8388e-02 -#> 47: 99.9834 -3.9991 -2.5292 -3.9863 -0.8820 -0.0796 4.8960 0.0177 0.2348 0.1376 0.1639 0.2878 0.9864 0.0837 0.9094 0.0832 -#> 48: 99.9155 -4.0224 -2.5422 -3.9854 -0.8719 -0.0750 4.6512 0.0184 0.2251 0.1307 0.1596 0.2734 0.9841 0.0835 0.8696 0.0843 -#> 49: 99.6136 -4.0397 -2.5172 -4.0115 -0.8774 -0.0922 5.2402 0.0175 0.2558 0.1242 0.1551 0.2597 0.9060 0.0816 0.8365 0.0869 -#> 50: 99.4747 -4.0542 -2.4192 -3.9834 -0.9041 -0.1798 4.9782 0.0219 0.2695 0.1234 0.1474 0.2468 0.9269 0.0783 0.8593 0.0854 -#> 51: 99.3401 -4.0386 -2.3951 -3.9661 -0.9181 -0.1887 4.7574 0.0213 0.2746 0.1522 0.1400 0.2344 0.9901 0.0781 0.8863 0.0928 -#> 52: 99.7109 -4.0509 -2.4227 -3.9770 -0.9247 -0.1431 4.9004 0.0203 0.2688 0.1446 0.1330 0.2227 0.8999 0.0791 1.0265 0.0890 -#> 53: 99.6496 -4.0397 -2.4398 -3.9752 -0.9193 -0.2119 5.1106 0.0193 0.2795 0.1527 0.1325 0.2116 0.8949 0.0788 0.9447 0.0872 -#> 54: 99.9071 -4.0211 -2.3887 -3.9812 -0.9233 -0.1946 5.0887 0.0183 0.2763 0.1450 0.1365 0.2010 0.8793 0.0875 0.8643 0.0903 -#> 55: 1.0012e+02 -4.0401e+00 -2.4203e+00 -3.9511e+00 -9.0712e-01 -2.5566e-01 5.7301e+00 1.7375e-02 2.7324e-01 1.3780e-01 1.6204e-01 1.9094e-01 9.7803e-01 7.6146e-02 9.0756e-01 8.7636e-02 -#> 56: 1.0032e+02 -4.0207e+00 -2.4263e+00 -3.9533e+00 -8.7574e-01 -2.3076e-01 6.5321e+00 1.6507e-02 3.0821e-01 1.3091e-01 1.5394e-01 1.8139e-01 8.8520e-01 7.6350e-02 9.2796e-01 8.5283e-02 -#> 57: 1.0028e+02 -4.0037e+00 -2.4301e+00 -3.9655e+00 -8.8472e-01 -1.8969e-01 9.8969e+00 1.5681e-02 2.9280e-01 1.2436e-01 1.4624e-01 1.7232e-01 9.2902e-01 7.4974e-02 8.9204e-01 8.4563e-02 -#> 58: 1.0048e+02 -3.9928e+00 -2.4961e+00 -3.9709e+00 -9.0263e-01 -1.4516e-01 9.4021e+00 1.6151e-02 2.7816e-01 1.1814e-01 1.4165e-01 1.6370e-01 9.5145e-01 8.0233e-02 8.2896e-01 8.3498e-02 -#> 59: 1.0060e+02 -4.0181e+00 -2.4963e+00 -3.9751e+00 -9.0684e-01 -1.1186e-01 8.9320e+00 1.9914e-02 3.0097e-01 1.1224e-01 1.4109e-01 1.5552e-01 9.9121e-01 7.3120e-02 8.6454e-01 8.2239e-02 -#> 60: 1.0047e+02 -3.9976e+00 -2.4797e+00 -3.9780e+00 -8.9328e-01 -1.0814e-01 8.4854e+00 1.8918e-02 3.2275e-01 1.1591e-01 1.3404e-01 1.4774e-01 9.6968e-01 7.4984e-02 8.9831e-01 8.1655e-02 -#> 61: 1.0040e+02 -4.0068e+00 -2.5217e+00 -3.9844e+00 -8.6447e-01 -1.0567e-01 8.0611e+00 1.7972e-02 3.1372e-01 1.1011e-01 1.2973e-01 1.4036e-01 9.1698e-01 7.8118e-02 9.1811e-01 8.4420e-02 -#> 62: 1.0076e+02 -4.0080e+00 -2.4931e+00 -3.9623e+00 -8.9789e-01 -8.3896e-02 7.6580e+00 1.7073e-02 3.0460e-01 1.1254e-01 1.2324e-01 1.3334e-01 9.9032e-01 7.7618e-02 8.3808e-01 8.5031e-02 -#> 63: 1.0064e+02 -4.0129e+00 -2.4731e+00 -3.9561e+00 -8.9103e-01 -8.8987e-02 7.2751e+00 1.6220e-02 2.8944e-01 1.1647e-01 1.4845e-01 1.2667e-01 1.0745e+00 7.6375e-02 8.4316e-01 8.6681e-02 -#> 64: 1.0098e+02 -4.0094e+00 -2.4541e+00 -3.9604e+00 -9.1524e-01 -9.3413e-02 6.9114e+00 1.5409e-02 2.7497e-01 1.2065e-01 1.7095e-01 1.2034e-01 1.0963e+00 7.8304e-02 8.7104e-01 8.5727e-02 -#> 65: 1.0070e+02 -4.0433e+00 -2.4793e+00 -3.9722e+00 -9.3012e-01 -6.5917e-02 6.5658e+00 1.4638e-02 2.7040e-01 1.1462e-01 1.9067e-01 1.1432e-01 9.7444e-01 8.4510e-02 8.7028e-01 8.6292e-02 -#> 66: 1.0049e+02 -4.0656e+00 -2.4659e+00 -3.9898e+00 -9.4278e-01 -7.5929e-02 6.2375e+00 1.3906e-02 2.9347e-01 1.1997e-01 1.8114e-01 1.0860e-01 9.9830e-01 8.0902e-02 9.3551e-01 8.5261e-02 -#> 67: 1.0046e+02 -4.0477e+00 -2.4685e+00 -3.9907e+00 -9.1503e-01 -9.8019e-02 5.9256e+00 1.3211e-02 3.2166e-01 1.1506e-01 1.7208e-01 1.0317e-01 8.6453e-01 9.0533e-02 8.3598e-01 8.6343e-02 -#> 68: 1.0077e+02 -4.0575e+00 -2.4709e+00 -3.9523e+00 -9.2903e-01 -8.1099e-02 5.6294e+00 1.2818e-02 3.1005e-01 1.3665e-01 1.6347e-01 9.8015e-02 9.0181e-01 8.7058e-02 8.4937e-01 8.3248e-02 -#> 69: 1.0086e+02 -4.0626e+00 -2.3922e+00 -3.9557e+00 -9.6741e-01 -3.5986e-02 5.3479e+00 1.2844e-02 3.3024e-01 1.2982e-01 1.5530e-01 9.3115e-02 9.8180e-01 8.3132e-02 8.6549e-01 8.8939e-02 -#> 70: 1.0082e+02 -4.0640e+00 -2.4449e+00 -3.9787e+00 -9.5159e-01 -3.2904e-02 5.0805e+00 1.4346e-02 3.1373e-01 1.2333e-01 1.4754e-01 8.8459e-02 1.0129e+00 7.4856e-02 8.6688e-01 8.4769e-02 -#> 71: 1.0072e+02 -4.0642e+00 -2.5069e+00 -3.9493e+00 -9.3453e-01 -4.4116e-02 4.8265e+00 1.3628e-02 3.0428e-01 1.2122e-01 1.4091e-01 8.4036e-02 1.0454e+00 7.7023e-02 8.9566e-01 8.1639e-02 -#> 72: 1.0049e+02 -4.0609e+00 -2.4472e+00 -3.9669e+00 -9.3972e-01 -7.7498e-02 4.5852e+00 1.4441e-02 3.2552e-01 1.3911e-01 1.4144e-01 8.1899e-02 1.0114e+00 7.7019e-02 8.2312e-01 8.2494e-02 -#> 73: 1.0022e+02 -4.0598e+00 -2.4410e+00 -3.9952e+00 -9.2810e-01 -1.1309e-01 4.3559e+00 1.3719e-02 3.3556e-01 1.3303e-01 1.4990e-01 1.1303e-01 9.6726e-01 7.6776e-02 8.6331e-01 8.3048e-02 -#> 74: 1.0024e+02 -4.0628e+00 -2.4358e+00 -3.9977e+00 -9.1347e-01 -9.1966e-02 4.1381e+00 1.3033e-02 3.4332e-01 1.3418e-01 1.8099e-01 1.0738e-01 1.0158e+00 7.4697e-02 8.6366e-01 8.4370e-02 -#> 75: 99.7847 -4.0500 -2.4401 -4.0018 -0.9252 -0.1013 4.4651 0.0124 0.3365 0.1399 0.1817 0.1020 1.0278 0.0779 0.9008 0.0841 -#> 76: 99.9526 -4.0482 -2.4819 -3.9947 -0.9049 -0.0557 4.2419 0.0126 0.3248 0.1494 0.1726 0.1135 1.0493 0.0778 0.9341 0.0804 -#> 77: 99.9982 -4.0184 -2.4951 -4.0043 -0.8927 -0.0688 5.2538 0.0120 0.3696 0.1419 0.1817 0.1078 1.0402 0.0839 0.9605 0.0848 -#> 78: 1.0007e+02 -4.0210e+00 -2.4725e+00 -4.0040e+00 -8.9827e-01 2.3164e-03 6.4464e+00 1.1395e-02 3.7410e-01 1.3481e-01 2.0294e-01 1.0879e-01 9.7822e-01 8.7445e-02 9.9990e-01 8.2845e-02 -#> 79: 99.3513 -4.0171 -2.5065 -4.0078 -0.8962 -0.0029 7.7527 0.0108 0.3554 0.1281 0.1928 0.1069 1.0455 0.0866 0.9982 0.0870 -#> 80: 98.9945 -4.0172 -2.5412 -4.0341 -0.8891 -0.0187 9.8218 0.0103 0.3376 0.1217 0.1831 0.1457 0.9733 0.0894 1.0164 0.0832 -#> 81: 99.0936 -4.0275 -2.5134 -4.0127 -0.8552 -0.0614 12.1567 0.0098 0.3494 0.1156 0.1740 0.1384 0.9509 0.0843 1.0171 0.0855 -#> 82: 99.2481 -3.9996 -2.4945 -4.0011 -0.8914 -0.0492 11.5489 0.0128 0.3792 0.1098 0.1653 0.1315 0.9915 0.0818 1.0405 0.0928 -#> 83: 99.6941 -3.9998 -2.4851 -3.9845 -0.8802 -0.0560 10.9714 0.0146 0.3602 0.1043 0.1570 0.1249 0.9934 0.0852 0.9707 0.0866 -#> 84: 99.2185 -3.9920 -2.4843 -4.0051 -0.8546 -0.0642 10.4228 0.0153 0.3422 0.0991 0.1492 0.1187 0.9923 0.0833 0.9799 0.0873 -#> 85: 98.8470 -3.9956 -2.4652 -4.0201 -0.8483 -0.0414 9.9017 0.0146 0.3251 0.0941 0.1417 0.1128 0.9732 0.0901 0.9035 0.0858 -#> 86: 98.5012 -3.9841 -2.5148 -4.0250 -0.8408 -0.0551 9.4066 0.0148 0.3088 0.0962 0.1346 0.1071 0.8570 0.0932 0.8532 0.0896 -#> 87: 99.0868 -4.0055 -2.5058 -4.0249 -0.8522 -0.0311 10.3528 0.0175 0.2934 0.1013 0.1411 0.1018 0.8802 0.0838 0.8849 0.0862 -#> 88: 99.5158 -4.0031 -2.4437 -3.9866 -0.8894 -0.0963 9.9832 0.0167 0.3049 0.1030 0.1447 0.0967 0.9955 0.0834 0.8861 0.0893 -#> 89: 99.5538 -4.0347 -2.4494 -4.0213 -0.8695 -0.0494 9.4841 0.0158 0.2897 0.0978 0.1543 0.0918 0.8597 0.0904 0.8959 0.0880 -#> 90: 99.4422 -4.0453 -2.4398 -4.0114 -0.9279 -0.0745 9.8221 0.0150 0.2842 0.0929 0.1466 0.0944 0.9009 0.0871 0.8696 0.0924 -#> 91: 98.8721 -4.0328 -2.4996 -4.0041 -0.8832 -0.0689 9.3310 0.0143 0.2700 0.0896 0.1444 0.1137 0.9567 0.0904 0.8680 0.0891 -#> 92: 99.8390 -4.0418 -2.4914 -4.0182 -0.9279 -0.0460 10.9801 0.0136 0.2585 0.0949 0.1461 0.1210 1.0043 0.0908 0.8310 0.0939 -#> 93: 1.0029e+02 -4.0313e+00 -2.4620e+00 -4.0187e+00 -8.9083e-01 -1.0908e-01 1.0431e+01 1.2890e-02 2.4559e-01 9.5757e-02 1.3878e-01 1.1565e-01 9.9174e-01 9.0056e-02 8.9538e-01 8.8925e-02 -#> 94: 99.3285 -4.0295 -2.4523 -4.0235 -0.8828 -0.1190 10.9003 0.0137 0.2333 0.0915 0.1318 0.1212 1.0729 0.0779 0.9543 0.0907 -#> 95: 99.4117 -4.0422 -2.3807 -4.0870 -0.8960 -0.0889 10.3553 0.0130 0.2216 0.0870 0.1253 0.1366 0.9127 0.0864 0.8901 0.0911 -#> 96: 99.3348 -4.0401 -2.4009 -4.0698 -0.8730 -0.0622 9.8375 0.0123 0.2106 0.0826 0.1241 0.1297 0.8504 0.0836 0.9140 0.0881 -#> 97: 99.4898 -4.0419 -2.4310 -4.0589 -0.8932 -0.0634 9.3456 0.0132 0.2000 0.0785 0.1224 0.1233 0.8770 0.0836 0.8715 0.0837 -#> 98: 99.3750 -4.0704 -2.4353 -4.0616 -0.9333 -0.0846 8.8783 0.0136 0.1900 0.0746 0.1245 0.1171 0.8907 0.0838 0.9066 0.0832 -#> 99: 99.6234 -4.0366 -2.3740 -4.0657 -0.9242 -0.0675 8.4344 0.0129 0.1805 0.0708 0.1182 0.1112 0.8814 0.0808 0.9511 0.0863 -#> 100: 1.0025e+02 -4.0420e+00 -2.3557e+00 -4.0579e+00 -9.5051e-01 -6.3418e-02 8.0319e+00 1.2286e-02 1.7150e-01 6.7291e-02 1.1232e-01 1.0568e-01 8.5851e-01 8.7881e-02 8.9363e-01 8.5897e-02 -#> 101: 1.0041e+02 -4.0461e+00 -2.3840e+00 -4.0384e+00 -9.3752e-01 -7.7594e-02 9.5649e+00 1.1672e-02 1.7509e-01 6.3926e-02 1.2760e-01 1.0039e-01 8.6733e-01 8.2748e-02 9.6277e-01 8.4274e-02 -#> 102: 1.0095e+02 -4.0372e+00 -2.3633e+00 -4.0286e+00 -9.1961e-01 -6.5350e-02 1.1428e+01 1.1088e-02 1.8557e-01 6.0730e-02 1.3211e-01 9.5374e-02 9.3928e-01 8.0161e-02 9.7913e-01 8.4081e-02 -#> 103: 1.0019e+02 -4.0236e+00 -2.4105e+00 -4.0337e+00 -9.1362e-01 -7.3859e-02 1.0856e+01 1.0534e-02 1.7629e-01 5.7693e-02 1.2695e-01 9.1362e-02 9.8491e-01 8.1430e-02 9.7682e-01 8.2250e-02 -#> 104: 99.7755 -4.0280 -2.4452 -4.0197 -0.9112 -0.0810 11.0317 0.0100 0.1796 0.0548 0.1301 0.0868 0.9418 0.0816 0.9170 0.0806 -#> 105: 1.0010e+02 -4.0418e+00 -2.4294e+00 -4.0225e+00 -9.1111e-01 -8.9920e-02 1.0480e+01 9.5070e-03 1.7060e-01 5.2068e-02 1.3987e-01 8.2454e-02 9.1944e-01 7.8110e-02 8.9266e-01 8.7228e-02 -#> 106: 1.0025e+02 -4.0507e+00 -2.4134e+00 -4.0343e+00 -9.0244e-01 -8.4683e-02 1.3506e+01 9.0316e-03 1.6207e-01 4.9465e-02 1.5337e-01 7.8331e-02 9.9609e-01 8.4473e-02 8.7046e-01 8.5479e-02 -#> 107: 1.0014e+02 -4.0468e+00 -2.3972e+00 -4.0196e+00 -9.3650e-01 -2.4087e-02 1.2830e+01 8.5801e-03 1.6027e-01 4.6992e-02 1.5429e-01 8.2493e-02 9.8959e-01 8.2626e-02 8.3427e-01 8.8197e-02 -#> 108: 1.0114e+02 -4.0338e+00 -2.4307e+00 -4.0724e+00 -9.1363e-01 1.1952e-02 1.2189e+01 8.1511e-03 1.5563e-01 4.4854e-02 1.7315e-01 7.8368e-02 9.8589e-01 7.8130e-02 9.0460e-01 8.2870e-02 -#> 109: 1.0066e+02 -4.0550e+00 -2.4094e+00 -4.0641e+00 -9.0945e-01 -1.5401e-03 1.3149e+01 7.7435e-03 1.4785e-01 4.2612e-02 1.7232e-01 7.4450e-02 1.0942e+00 7.4816e-02 9.1706e-01 8.5333e-02 -#> 110: 1.0111e+02 -4.0266e+00 -2.4047e+00 -4.0646e+00 -9.0541e-01 -1.7212e-02 1.2492e+01 7.3563e-03 1.4046e-01 4.0481e-02 1.8132e-01 7.0727e-02 1.0508e+00 7.9457e-02 9.8990e-01 8.2975e-02 -#> 111: 1.0155e+02 -4.0274e+00 -2.3645e+00 -4.0663e+00 -9.4902e-01 -1.8882e-02 1.1867e+01 8.7757e-03 1.4436e-01 3.8457e-02 1.7225e-01 6.7191e-02 1.0217e+00 7.7437e-02 9.9196e-01 8.1580e-02 -#> 112: 1.0209e+02 -4.0230e+00 -2.3938e+00 -4.0375e+00 -9.5447e-01 -5.0888e-02 1.4321e+01 8.3370e-03 1.4863e-01 3.6534e-02 1.6778e-01 8.2186e-02 9.3085e-01 8.3291e-02 9.8775e-01 7.9492e-02 -#> 113: 1.0188e+02 -4.0173e+00 -2.3804e+00 -4.0403e+00 -9.6152e-01 -7.7453e-02 1.3605e+01 7.9201e-03 1.5060e-01 3.4708e-02 1.7341e-01 8.4506e-02 9.0783e-01 8.7383e-02 9.4854e-01 8.2648e-02 -#> 114: 1.0239e+02 -4.0081e+00 -2.3724e+00 -4.0332e+00 -9.4315e-01 -7.4933e-02 1.2925e+01 7.5241e-03 1.4307e-01 3.2972e-02 1.6695e-01 8.0281e-02 9.2775e-01 8.4314e-02 9.6195e-01 7.9448e-02 -#> 115: 1.0199e+02 -4.0127e+00 -2.3773e+00 -4.0472e+00 -9.5157e-01 -2.0947e-02 1.2279e+01 7.4483e-03 1.3592e-01 3.1324e-02 1.6705e-01 7.6267e-02 9.4956e-01 7.6989e-02 1.0340e+00 8.5564e-02 -#> 116: 1.0122e+02 -4.0264e+00 -2.4014e+00 -4.0509e+00 -9.1462e-01 -2.3511e-02 1.1665e+01 7.0759e-03 1.2912e-01 2.9757e-02 1.5870e-01 7.2453e-02 9.3580e-01 8.2952e-02 9.3341e-01 8.3302e-02 -#> 117: 1.0112e+02 -4.0326e+00 -2.4093e+00 -4.0559e+00 -8.9743e-01 -2.0572e-02 1.1082e+01 6.7221e-03 1.2266e-01 2.8269e-02 1.5339e-01 6.8831e-02 9.0879e-01 8.4441e-02 9.1432e-01 8.0538e-02 -#> 118: 1.0123e+02 -4.0411e+00 -2.4077e+00 -4.0556e+00 -9.2971e-01 -2.1885e-02 1.0528e+01 6.3860e-03 1.1653e-01 3.3123e-02 1.6947e-01 6.5389e-02 9.7140e-01 8.6671e-02 8.9874e-01 8.1670e-02 -#> 119: 1.0098e+02 -4.0538e+00 -2.3515e+00 -4.0607e+00 -9.5433e-01 -7.5743e-02 1.0001e+01 6.0667e-03 1.1070e-01 3.1467e-02 1.8338e-01 6.2120e-02 9.1537e-01 8.4827e-02 9.2420e-01 8.2769e-02 -#> 120: 1.0076e+02 -4.0573e+00 -2.3627e+00 -4.0329e+00 -9.3251e-01 -6.7669e-02 9.5011e+00 5.7634e-03 1.0517e-01 3.2868e-02 1.7422e-01 6.6096e-02 9.5247e-01 8.5343e-02 9.4678e-01 8.5335e-02 -#> 121: 1.0085e+02 -4.0450e+00 -2.3478e+00 -4.0692e+00 -9.2333e-01 -9.8005e-03 9.0261e+00 5.4752e-03 9.9911e-02 3.1225e-02 1.6550e-01 7.1593e-02 8.5572e-01 8.8654e-02 1.0248e+00 8.0646e-02 -#> 122: 1.0164e+02 -4.0325e+00 -2.3562e+00 -4.0680e+00 -9.4287e-01 -1.2103e-02 8.5748e+00 5.3493e-03 9.4915e-02 2.9663e-02 1.6347e-01 6.8014e-02 8.4872e-01 8.6803e-02 1.0282e+00 8.0381e-02 -#> 123: 1.0184e+02 -4.0521e+00 -2.3504e+00 -4.0714e+00 -9.5966e-01 -9.1996e-05 8.1460e+00 5.0818e-03 9.8247e-02 3.0007e-02 1.7746e-01 6.4613e-02 9.7181e-01 8.0986e-02 9.8860e-01 8.0317e-02 -#> 124: 1.0235e+02 -4.0674e+00 -2.3315e+00 -4.0874e+00 -9.9802e-01 3.8818e-02 7.7387e+00 4.8277e-03 9.3335e-02 2.8506e-02 1.7611e-01 6.8940e-02 9.7376e-01 7.6658e-02 9.9156e-01 8.4407e-02 -#> 125: 1.0257e+02 -4.0718e+00 -2.3604e+00 -4.0627e+00 -1.0591e+00 2.4685e-02 7.3518e+00 4.5863e-03 8.8668e-02 3.0650e-02 1.8671e-01 6.5493e-02 1.0275e+00 8.2278e-02 1.0896e+00 8.0976e-02 -#> 126: 1.0287e+02 -4.0691e+00 -2.3103e+00 -4.0552e+00 -1.0174e+00 2.1863e-02 7.5644e+00 4.3570e-03 1.0937e-01 2.9117e-02 1.7738e-01 6.2218e-02 9.2668e-01 7.9560e-02 9.5409e-01 8.4671e-02 -#> 127: 1.0327e+02 -4.0528e+00 -2.3141e+00 -4.0522e+00 -1.0108e+00 4.4779e-03 7.1862e+00 4.1392e-03 1.2239e-01 2.7661e-02 1.6925e-01 5.9107e-02 9.1372e-01 7.9536e-02 9.9164e-01 8.2999e-02 -#> 128: 1.0352e+02 -4.0496e+00 -2.2880e+00 -4.0496e+00 -1.0063e+00 -1.3248e-02 7.6721e+00 3.9613e-03 1.1627e-01 2.6278e-02 1.7517e-01 8.0231e-02 8.4407e-01 8.5078e-02 9.4382e-01 8.7530e-02 -#> 129: 1.0345e+02 -4.0715e+00 -2.3090e+00 -4.0400e+00 -1.0276e+00 -1.8301e-02 8.2197e+00 3.7633e-03 1.1046e-01 2.7141e-02 1.9366e-01 7.6220e-02 9.3357e-01 8.2674e-02 9.7064e-01 8.6011e-02 -#> 130: 1.0245e+02 -4.0787e+00 -2.3263e+00 -4.0106e+00 -1.0200e+00 -8.5976e-02 7.8087e+00 4.0830e-03 1.3607e-01 2.6631e-02 2.2700e-01 7.2409e-02 9.8233e-01 7.9348e-02 9.6780e-01 8.2658e-02 -#> 131: 1.0217e+02 -4.0760e+00 -2.2525e+00 -4.0082e+00 -1.0099e+00 -1.6111e-01 7.4183e+00 3.8789e-03 1.3972e-01 2.5299e-02 2.2508e-01 6.8788e-02 1.0066e+00 7.8692e-02 9.4684e-01 8.4349e-02 -#> 132: 1.0185e+02 -4.0792e+00 -2.2309e+00 -3.9996e+00 -9.8302e-01 -2.2504e-01 7.0474e+00 4.0356e-03 1.3743e-01 2.4034e-02 2.1383e-01 7.7346e-02 9.4225e-01 7.9110e-02 9.5160e-01 8.4398e-02 -#> 133: 1.0135e+02 -4.0818e+00 -2.2219e+00 -4.0054e+00 -9.7264e-01 -1.8912e-01 7.1932e+00 3.8338e-03 1.3056e-01 2.2833e-02 2.0314e-01 7.6769e-02 1.0031e+00 8.5400e-02 1.0034e+00 8.4805e-02 -#> 134: 1.0148e+02 -4.0782e+00 -2.2492e+00 -3.9886e+00 -9.5184e-01 -1.5049e-01 6.8336e+00 3.6422e-03 1.2403e-01 2.3398e-02 1.9298e-01 7.2931e-02 9.3696e-01 8.3566e-02 9.4742e-01 8.9137e-02 -#> 135: 1.0145e+02 -4.0852e+00 -2.3062e+00 -4.0011e+00 -9.4444e-01 -1.6803e-01 6.4919e+00 3.4600e-03 1.1783e-01 2.2228e-02 1.8333e-01 6.9284e-02 9.4846e-01 8.3087e-02 9.7774e-01 8.2610e-02 -#> 136: 1.0177e+02 -4.0861e+00 -2.2785e+00 -3.9890e+00 -9.9625e-01 -1.8938e-01 6.1673e+00 3.2870e-03 1.1752e-01 2.1116e-02 1.8815e-01 6.5820e-02 9.3634e-01 8.5255e-02 1.1001e+00 8.5332e-02 -#> 137: 1.0200e+02 -4.0928e+00 -2.1946e+00 -3.9974e+00 -1.0098e+00 -1.8810e-01 5.8589e+00 3.1227e-03 1.2394e-01 2.1203e-02 1.7874e-01 7.2232e-02 1.0048e+00 7.3422e-02 1.0222e+00 8.3484e-02 -#> 138: 1.0214e+02 -4.0820e+00 -2.2052e+00 -3.9737e+00 -1.0420e+00 -2.0594e-01 5.5660e+00 3.8937e-03 1.9164e-01 2.0143e-02 1.6980e-01 6.8621e-02 1.0126e+00 7.6106e-02 1.0780e+00 8.2960e-02 -#> 139: 1.0249e+02 -4.0785e+00 -2.1649e+00 -3.9567e+00 -1.0095e+00 -2.8807e-01 5.2877e+00 3.6990e-03 1.8647e-01 1.9135e-02 1.6131e-01 6.5190e-02 1.0030e+00 7.9858e-02 1.0611e+00 8.4109e-02 -#> 140: 1.0184e+02 -4.0847e+00 -2.1800e+00 -3.9565e+00 -9.9415e-01 -2.8869e-01 5.0233e+00 4.0857e-03 1.9502e-01 1.8179e-02 1.6676e-01 6.2879e-02 9.5962e-01 7.8117e-02 9.9649e-01 8.4914e-02 -#> 141: 1.0195e+02 -4.1012e+00 -2.1831e+00 -3.9488e+00 -9.9515e-01 -3.1864e-01 4.7721e+00 3.8814e-03 1.8527e-01 1.7270e-02 1.6797e-01 6.1084e-02 9.0969e-01 8.2722e-02 1.0122e+00 8.2518e-02 -#> 142: 1.0233e+02 -4.1139e+00 -2.1692e+00 -3.9542e+00 -1.0023e+00 -3.3242e-01 4.5335e+00 3.6873e-03 2.0662e-01 1.6406e-02 1.5957e-01 5.8030e-02 9.4761e-01 8.4629e-02 1.0342e+00 8.3954e-02 -#> 143: 1.0217e+02 -4.1103e+00 -2.1380e+00 -3.9511e+00 -1.0300e+00 -2.5992e-01 5.2035e+00 4.7053e-03 1.9629e-01 1.5586e-02 1.5979e-01 5.5128e-02 8.9255e-01 7.9042e-02 1.0461e+00 8.6952e-02 -#> 144: 1.0185e+02 -4.1335e+00 -2.1911e+00 -3.9650e+00 -1.0440e+00 -2.4451e-01 5.0998e+00 4.4700e-03 1.8648e-01 1.9590e-02 1.5534e-01 5.2372e-02 9.7863e-01 8.3932e-02 1.0197e+00 8.7673e-02 -#> 145: 1.0242e+02 -4.1445e+00 -2.1203e+00 -3.9616e+00 -1.0426e+00 -2.7120e-01 4.8448e+00 4.2465e-03 1.7715e-01 1.8611e-02 1.4757e-01 4.9753e-02 1.0024e+00 8.4131e-02 1.0768e+00 8.5388e-02 -#> 146: 1.0236e+02 -4.1519e+00 -2.1958e+00 -3.9779e+00 -9.8615e-01 -2.5863e-01 4.6026e+00 4.0718e-03 1.6829e-01 1.7680e-02 1.6407e-01 4.7266e-02 1.0740e+00 8.2413e-02 1.0706e+00 8.3410e-02 -#> 147: 1.0251e+02 -4.1465e+00 -2.2042e+00 -3.9775e+00 -1.0317e+00 -2.2757e-01 4.3725e+00 3.8682e-03 1.5988e-01 1.6796e-02 1.7016e-01 4.4902e-02 9.7748e-01 8.3376e-02 1.0880e+00 8.1968e-02 -#> 148: 1.0244e+02 -4.1432e+00 -2.1786e+00 -3.9792e+00 -1.0442e+00 -2.2002e-01 4.9671e+00 3.6748e-03 1.5189e-01 1.5956e-02 2.2196e-01 4.2657e-02 1.0412e+00 7.8051e-02 1.1051e+00 8.1618e-02 -#> 149: 1.0219e+02 -4.1384e+00 -2.2318e+00 -3.9757e+00 -1.0438e+00 -2.4124e-01 4.7187e+00 3.4910e-03 1.4429e-01 1.6061e-02 2.1086e-01 4.0524e-02 1.0082e+00 8.0377e-02 1.1455e+00 8.0545e-02 -#> 150: 1.0264e+02 -4.1498e+00 -2.2352e+00 -3.9915e+00 -1.0669e+00 -2.1255e-01 4.4828e+00 3.3165e-03 1.3708e-01 1.7218e-02 2.0032e-01 3.8498e-02 9.5031e-01 8.7248e-02 9.8770e-01 8.3250e-02 -#> 151: 1.0250e+02 -4.1365e+00 -2.1876e+00 -3.9939e+00 -1.0568e+00 -1.8159e-01 4.2587e+00 3.1507e-03 1.3022e-01 1.7383e-02 1.9030e-01 3.6573e-02 9.6938e-01 8.0203e-02 1.0578e+00 8.3430e-02 -#> 152: 1.0256e+02 -4.1370e+00 -2.2238e+00 -4.0047e+00 -1.0406e+00 -1.8764e-01 1.9609e+00 1.4191e-03 1.1882e-01 1.7924e-02 1.6889e-01 4.1216e-02 9.1972e-01 7.8573e-02 1.0717e+00 8.0882e-02 -#> 153: 1.0219e+02 -4.1299e+00 -2.2139e+00 -3.9917e+00 -9.9964e-01 -2.0505e-01 1.8258e+00 1.0432e-03 8.4660e-02 2.1446e-02 1.7634e-01 3.5573e-02 9.3702e-01 8.4860e-02 1.0145e+00 8.3329e-02 -#> 154: 1.0199e+02 -4.1354e+00 -2.2231e+00 -3.9779e+00 -1.0155e+00 -2.2573e-01 2.6463e+00 5.8153e-04 8.8101e-02 2.3167e-02 1.6103e-01 3.3874e-02 9.5360e-01 8.6215e-02 9.5723e-01 8.4603e-02 -#> 155: 1.0234e+02 -4.1239e+00 -2.2137e+00 -3.9802e+00 -1.0070e+00 -2.3158e-01 2.9697e+00 6.6709e-04 1.1190e-01 2.0949e-02 1.8298e-01 3.1557e-02 9.2910e-01 8.2509e-02 9.8680e-01 8.5206e-02 -#> 156: 1.0253e+02 -4.1269e+00 -2.2370e+00 -3.9682e+00 -1.0420e+00 -2.1219e-01 2.7267e+00 6.8451e-04 8.9651e-02 2.4380e-02 1.6613e-01 3.4846e-02 9.3608e-01 8.7506e-02 9.0446e-01 8.1755e-02 -#> 157: 1.0265e+02 -4.1241e+00 -2.2179e+00 -3.9676e+00 -1.0308e+00 -2.2480e-01 2.1278e+00 4.9811e-04 6.7161e-02 1.9758e-02 1.5607e-01 4.4198e-02 9.4162e-01 8.7311e-02 9.9147e-01 7.9857e-02 -#> 158: 1.0239e+02 -4.1219e+00 -2.1615e+00 -3.9781e+00 -1.0384e+00 -2.6750e-01 2.5310e+00 4.8270e-04 6.5662e-02 1.8085e-02 1.7665e-01 4.4020e-02 8.8632e-01 8.6004e-02 1.0425e+00 8.2894e-02 -#> 159: 1.0270e+02 -4.1204e+00 -2.1837e+00 -3.9530e+00 -1.0587e+00 -2.5809e-01 3.4348e+00 5.6788e-04 6.5500e-02 1.9540e-02 1.8629e-01 4.0730e-02 9.5079e-01 8.2399e-02 9.9316e-01 8.3381e-02 -#> 160: 1.0282e+02 -4.1223e+00 -2.1325e+00 -3.9734e+00 -1.0068e+00 -2.8751e-01 3.9652e+00 7.6565e-04 8.5246e-02 1.7068e-02 1.7587e-01 3.0778e-02 9.1802e-01 8.0158e-02 9.9642e-01 8.1564e-02 -#> 161: 1.0330e+02 -4.1180e+00 -2.1879e+00 -3.9743e+00 -1.0268e+00 -2.8812e-01 4.9153e+00 5.8033e-04 8.0457e-02 1.8555e-02 1.7312e-01 3.3941e-02 8.6920e-01 8.2509e-02 9.5632e-01 8.1798e-02 -#> 162: 1.0335e+02 -4.1182e+00 -2.2089e+00 -3.9566e+00 -1.0409e+00 -2.7390e-01 3.6169e+00 2.8392e-04 1.0776e-01 1.9589e-02 1.6479e-01 2.8481e-02 8.8603e-01 8.7799e-02 9.5197e-01 7.9563e-02 -#> 163: 1.0294e+02 -4.1181e+00 -2.2025e+00 -3.9462e+00 -9.9783e-01 -3.0753e-01 3.7234e+00 1.6293e-04 9.6922e-02 2.4842e-02 1.9367e-01 3.1473e-02 9.0380e-01 9.1697e-02 9.4394e-01 8.2786e-02 -#> 164: 1.0246e+02 -4.1155e+00 -2.2157e+00 -3.9736e+00 -9.9866e-01 -2.9356e-01 3.9439e+00 1.9405e-04 1.0404e-01 2.8435e-02 1.9043e-01 3.1239e-02 8.9853e-01 8.9427e-02 9.2586e-01 8.3170e-02 -#> 165: 1.0204e+02 -4.1117e+00 -2.2133e+00 -3.9674e+00 -1.0079e+00 -2.6996e-01 3.0774e+00 1.6591e-04 7.0005e-02 2.8285e-02 2.0813e-01 2.4574e-02 8.9719e-01 9.1629e-02 9.8242e-01 8.3692e-02 -#> 166: 1.0207e+02 -4.1164e+00 -2.2192e+00 -3.9893e+00 -1.0354e+00 -2.7396e-01 1.8145e+00 8.4168e-05 9.0739e-02 2.7410e-02 2.1403e-01 2.4311e-02 8.9386e-01 9.2727e-02 9.4636e-01 8.4238e-02 -#> 167: 1.0187e+02 -4.1149e+00 -2.2185e+00 -3.9708e+00 -1.0036e+00 -2.5751e-01 1.5355e+00 4.0974e-05 9.9346e-02 2.2030e-02 2.1916e-01 2.6726e-02 9.1055e-01 8.1030e-02 1.0098e+00 7.9180e-02 -#> 168: 1.0172e+02 -4.1167e+00 -2.2673e+00 -3.9702e+00 -9.8388e-01 -2.1404e-01 1.4836e+00 2.7779e-05 7.7509e-02 2.9513e-02 1.9543e-01 3.4526e-02 1.0152e+00 8.1248e-02 9.7482e-01 8.0746e-02 -#> 169: 1.0175e+02 -4.1171e+00 -2.2634e+00 -3.9701e+00 -9.5962e-01 -2.4130e-01 1.4263e+00 4.7370e-05 5.0986e-02 2.8211e-02 2.2554e-01 3.9909e-02 9.8519e-01 7.8842e-02 1.0023e+00 8.5684e-02 -#> 170: 1.0177e+02 -4.1189e+00 -2.2417e+00 -3.9834e+00 -1.0059e+00 -2.6551e-01 9.9010e-01 3.7247e-05 4.2517e-02 2.9791e-02 1.8705e-01 4.2435e-02 9.6604e-01 8.8427e-02 9.6699e-01 8.3986e-02 -#> 171: 1.0182e+02 -4.1187e+00 -2.2464e+00 -3.9953e+00 -9.8154e-01 -2.5146e-01 7.4179e-01 3.2420e-05 5.0690e-02 3.0483e-02 1.7888e-01 6.3177e-02 9.2784e-01 8.4814e-02 1.0018e+00 8.4070e-02 -#> 172: 1.0184e+02 -4.1178e+00 -2.2483e+00 -4.0009e+00 -1.0096e+00 -2.2636e-01 9.6710e-01 2.6981e-05 3.1321e-02 2.7772e-02 1.9767e-01 7.4969e-02 9.9720e-01 8.1434e-02 9.5483e-01 8.3419e-02 -#> 173: 1.0160e+02 -4.1183e+00 -2.2513e+00 -3.9920e+00 -9.8456e-01 -2.0144e-01 4.9964e-01 2.1222e-05 4.1909e-02 2.8101e-02 2.1163e-01 1.2811e-01 9.6384e-01 8.0352e-02 9.2496e-01 8.2328e-02 -#> 174: 1.0159e+02 -4.1179e+00 -2.2334e+00 -4.0068e+00 -1.0316e+00 -2.0656e-01 4.6608e-01 1.8044e-05 4.4647e-02 2.8273e-02 2.0083e-01 1.2780e-01 9.4612e-01 8.3630e-02 8.9385e-01 8.3930e-02 -#> 175: 1.0159e+02 -4.1182e+00 -2.2567e+00 -3.9972e+00 -1.0299e+00 -1.6534e-01 4.5228e-01 2.0060e-05 8.5751e-02 2.5343e-02 1.7864e-01 8.6977e-02 9.5795e-01 7.8867e-02 8.9213e-01 8.4362e-02 -#> 176: 1.0159e+02 -4.1183e+00 -2.2109e+00 -3.9983e+00 -1.0210e+00 -2.0879e-01 5.3694e-01 2.0264e-05 1.2835e-01 2.5563e-02 1.9469e-01 6.0808e-02 9.1537e-01 7.8520e-02 9.3355e-01 8.3608e-02 -#> 177: 1.0155e+02 -4.1193e+00 -2.2587e+00 -3.9825e+00 -1.0180e+00 -1.6859e-01 4.4935e-01 3.0321e-05 1.3509e-01 2.4979e-02 2.0113e-01 6.3617e-02 9.7277e-01 7.8515e-02 9.2667e-01 8.5309e-02 -#> 178: 1.0158e+02 -4.1196e+00 -2.2679e+00 -4.0231e+00 -1.0143e+00 -1.6084e-01 6.7629e-01 3.2855e-05 6.8816e-02 2.7808e-02 1.8944e-01 8.1814e-02 8.8319e-01 8.0114e-02 9.5183e-01 8.2195e-02 -#> 179: 1.0166e+02 -4.1190e+00 -2.2764e+00 -3.9875e+00 -1.0061e+00 -1.8260e-01 7.1129e-01 3.8250e-05 7.5489e-02 2.4148e-02 1.8082e-01 7.1172e-02 9.1387e-01 8.0813e-02 9.6660e-01 8.2457e-02 -#> 180: 1.0179e+02 -4.1202e+00 -2.2848e+00 -3.9974e+00 -9.9825e-01 -2.0277e-01 5.5755e-01 2.8041e-05 8.6779e-02 2.7193e-02 1.8826e-01 6.5133e-02 8.8812e-01 8.2655e-02 9.2100e-01 7.9919e-02 -#> 181: 1.0176e+02 -4.1200e+00 -2.2704e+00 -3.9954e+00 -1.0194e+00 -1.6896e-01 4.3842e-01 2.2428e-05 7.4093e-02 3.0526e-02 2.3473e-01 1.0537e-01 9.2303e-01 8.2141e-02 9.2941e-01 8.4699e-02 -#> 182: 1.0182e+02 -4.1211e+00 -2.3159e+00 -4.0259e+00 -1.0162e+00 -1.2876e-01 3.4993e-01 1.5716e-05 5.9887e-02 2.6422e-02 2.1757e-01 1.0488e-01 9.1725e-01 9.4143e-02 9.7674e-01 8.8668e-02 -#> 183: 1.0184e+02 -4.1216e+00 -2.2985e+00 -4.0278e+00 -1.0136e+00 -1.3154e-01 2.6456e-01 1.2552e-05 5.7149e-02 3.2712e-02 2.0632e-01 1.5501e-01 9.2464e-01 8.5394e-02 8.8699e-01 8.4279e-02 -#> 184: 1.0172e+02 -4.1212e+00 -2.2726e+00 -4.0189e+00 -1.0280e+00 -1.2967e-01 3.0582e-01 7.5239e-06 8.2812e-02 2.9556e-02 1.9725e-01 1.3753e-01 9.0862e-01 8.1319e-02 9.0031e-01 8.3491e-02 -#> 185: 1.0178e+02 -4.1208e+00 -2.2858e+00 -4.0272e+00 -1.0063e+00 -1.6155e-01 3.0856e-01 4.5894e-06 8.8870e-02 2.5817e-02 1.9251e-01 1.0670e-01 9.1157e-01 7.7834e-02 9.6258e-01 7.8990e-02 -#> 186: 1.0198e+02 -4.1208e+00 -2.2682e+00 -4.0401e+00 -9.8523e-01 -1.1556e-01 2.4761e-01 3.2640e-06 7.5614e-02 2.1067e-02 1.9085e-01 9.0045e-02 8.5090e-01 8.6621e-02 1.0145e+00 8.1864e-02 -#> 187: 1.0197e+02 -4.1208e+00 -2.2788e+00 -4.0281e+00 -1.0066e+00 -1.0149e-01 2.0460e-01 4.5073e-06 7.8797e-02 2.3861e-02 2.0725e-01 7.9771e-02 9.6253e-01 8.2363e-02 9.3855e-01 8.3939e-02 -#> 188: 1.0196e+02 -4.1207e+00 -2.3105e+00 -4.0149e+00 -1.0217e+00 -9.0603e-02 2.2178e-01 3.6903e-06 8.9793e-02 2.1775e-02 1.9248e-01 8.2415e-02 9.4078e-01 8.1247e-02 9.1756e-01 8.2786e-02 -#> 189: 1.0202e+02 -4.1204e+00 -2.2702e+00 -4.0430e+00 -1.0032e+00 -1.1308e-01 2.2944e-01 3.5141e-06 7.8575e-02 2.4885e-02 2.0968e-01 8.2380e-02 9.5115e-01 8.1619e-02 9.2134e-01 8.9958e-02 -#> 190: 1.0195e+02 -4.1207e+00 -2.3126e+00 -4.0312e+00 -1.0154e+00 -6.3842e-02 2.5129e-01 2.6517e-06 4.2267e-02 2.2084e-02 1.9361e-01 7.0492e-02 9.3985e-01 8.5817e-02 9.3893e-01 8.7011e-02 -#> 191: 1.0203e+02 -4.1206e+00 -2.2758e+00 -4.0290e+00 -1.0102e+00 -3.1042e-02 1.7935e-01 3.4489e-06 5.7444e-02 2.3544e-02 1.9651e-01 7.9509e-02 9.5213e-01 8.2030e-02 1.0054e+00 8.7523e-02 -#> 192: 1.0199e+02 -4.1205e+00 -2.2969e+00 -4.0329e+00 -1.0364e+00 -8.3705e-02 1.5785e-01 3.5081e-06 7.4305e-02 2.2992e-02 1.9662e-01 7.7684e-02 9.2601e-01 8.3027e-02 9.8642e-01 8.3428e-02 -#> 193: 1.0196e+02 -4.1205e+00 -2.2661e+00 -4.0513e+00 -9.9271e-01 -4.6516e-02 1.2084e-01 2.6911e-06 6.8360e-02 3.5444e-02 1.9649e-01 7.5188e-02 9.1949e-01 7.9194e-02 1.0046e+00 8.5964e-02 -#> 194: 1.0198e+02 -4.1207e+00 -2.2817e+00 -4.0520e+00 -9.9852e-01 -8.4466e-02 1.3596e-01 1.5511e-06 6.5142e-02 4.1562e-02 1.9137e-01 9.6992e-02 9.6709e-01 7.6757e-02 9.7566e-01 8.3784e-02 -#> 195: 1.0200e+02 -4.1207e+00 -2.3076e+00 -4.0637e+00 -1.0028e+00 -7.2489e-02 1.0942e-01 1.6451e-06 6.1364e-02 4.6242e-02 1.9470e-01 9.3546e-02 9.9614e-01 8.1292e-02 9.7814e-01 8.1909e-02 -#> 196: 1.0194e+02 -4.1205e+00 -2.2970e+00 -4.0482e+00 -9.8816e-01 -6.8493e-02 1.1918e-01 1.2629e-06 4.2775e-02 3.6925e-02 2.3565e-01 7.7784e-02 8.9524e-01 9.2250e-02 9.8003e-01 8.2408e-02 -#> 197: 1.0199e+02 -4.1205e+00 -2.3075e+00 -4.0418e+00 -1.0196e+00 -6.8458e-02 1.7674e-01 7.5205e-07 5.2125e-02 2.9288e-02 2.1892e-01 8.4416e-02 8.9857e-01 9.1154e-02 1.0377e+00 8.3604e-02 -#> 198: 1.0197e+02 -4.1206e+00 -2.3051e+00 -4.0367e+00 -1.0252e+00 -6.9200e-02 9.1625e-02 6.6068e-07 4.7665e-02 2.8907e-02 1.8679e-01 7.5787e-02 9.0272e-01 8.8077e-02 9.2929e-01 8.0385e-02 -#> 199: 1.0192e+02 -4.1204e+00 -2.3163e+00 -4.0506e+00 -1.0152e+00 -5.3872e-02 6.8196e-02 5.5789e-07 6.0471e-02 3.1730e-02 2.0053e-01 6.8557e-02 9.0478e-01 8.5910e-02 9.3814e-01 8.2211e-02 -#> 200: 1.0195e+02 -4.1205e+00 -2.3141e+00 -4.0728e+00 -1.0010e+00 -2.5675e-03 6.5235e-02 6.9762e-07 5.8458e-02 2.8504e-02 2.0377e-01 4.9513e-02 8.5640e-01 8.6640e-02 9.5731e-01 8.4390e-02 -#> 201: 1.0195e+02 -4.1205e+00 -2.3106e+00 -4.0774e+00 -9.9012e-01 5.1724e-03 5.1225e-02 5.4222e-07 6.0577e-02 3.3554e-02 2.0505e-01 4.4738e-02 8.8073e-01 8.5488e-02 9.6928e-01 8.4895e-02 -#> 202: 1.0194e+02 -4.1205e+00 -2.3078e+00 -4.0767e+00 -9.9283e-01 3.9328e-03 4.5461e-02 4.8520e-07 6.7405e-02 3.4599e-02 2.1312e-01 4.6664e-02 9.0528e-01 8.4189e-02 9.8043e-01 8.5266e-02 -#> 203: 1.0193e+02 -4.1205e+00 -2.3029e+00 -4.0790e+00 -9.8990e-01 -9.1380e-03 4.7128e-02 5.0468e-07 6.8524e-02 3.6050e-02 2.1378e-01 5.1774e-02 9.0923e-01 8.4899e-02 9.8928e-01 8.4613e-02 -#> 204: 1.0192e+02 -4.1205e+00 -2.3080e+00 -4.0760e+00 -9.8833e-01 -1.2434e-02 4.8184e-02 4.9472e-07 6.4152e-02 3.5604e-02 2.0954e-01 5.1354e-02 9.1294e-01 8.5219e-02 9.8301e-01 8.4374e-02 -#> 205: 1.0192e+02 -4.1205e+00 -2.3100e+00 -4.0712e+00 -9.9253e-01 -2.3365e-02 4.5888e-02 5.0564e-07 5.9894e-02 3.5053e-02 2.0322e-01 5.4423e-02 9.0925e-01 8.5899e-02 9.8418e-01 8.3421e-02 -#> 206: 1.0192e+02 -4.1205e+00 -2.3095e+00 -4.0715e+00 -9.9721e-01 -2.6262e-02 4.3985e-02 5.1954e-07 5.8681e-02 3.4539e-02 2.0202e-01 5.8248e-02 9.1301e-01 8.5459e-02 9.8621e-01 8.3465e-02 -#> 207: 1.0192e+02 -4.1205e+00 -2.3179e+00 -4.0731e+00 -9.9906e-01 -2.3191e-02 4.3649e-02 5.3824e-07 5.7537e-02 3.4790e-02 2.0220e-01 6.0242e-02 9.1783e-01 8.5307e-02 9.8436e-01 8.3111e-02 -#> 208: 1.0191e+02 -4.1205e+00 -2.3238e+00 -4.0734e+00 -9.9920e-01 -1.9434e-02 4.3223e-02 5.3831e-07 5.7908e-02 3.4909e-02 2.0126e-01 6.0353e-02 9.2010e-01 8.5244e-02 9.8002e-01 8.2975e-02 -#> 209: 1.0191e+02 -4.1205e+00 -2.3279e+00 -4.0726e+00 -1.0053e+00 -1.5390e-02 4.1064e-02 5.3171e-07 5.8749e-02 3.4510e-02 1.9942e-01 6.3063e-02 9.3192e-01 8.4436e-02 9.8298e-01 8.3187e-02 -#> 210: 1.0191e+02 -4.1205e+00 -2.3310e+00 -4.0705e+00 -1.0061e+00 -1.3507e-02 3.8265e-02 5.2762e-07 5.9344e-02 3.3374e-02 1.9612e-01 6.7006e-02 9.3199e-01 8.4573e-02 9.8382e-01 8.3227e-02 -#> 211: 1.0191e+02 -4.1205e+00 -2.3383e+00 -4.0683e+00 -1.0043e+00 -1.3973e-02 3.6076e-02 5.2584e-07 6.1568e-02 3.2369e-02 1.9504e-01 6.9982e-02 9.4179e-01 8.4625e-02 9.9145e-01 8.3067e-02 -#> 212: 1.0192e+02 -4.1204e+00 -2.3396e+00 -4.0662e+00 -1.0055e+00 -1.8011e-02 3.4746e-02 5.4375e-07 6.2747e-02 3.1588e-02 1.9405e-01 7.2360e-02 9.4525e-01 8.4466e-02 9.9581e-01 8.2952e-02 -#> 213: 1.0192e+02 -4.1204e+00 -2.3407e+00 -4.0649e+00 -1.0066e+00 -2.1077e-02 3.4708e-02 5.5843e-07 6.1940e-02 3.0715e-02 1.9382e-01 7.4602e-02 9.4611e-01 8.4322e-02 9.9397e-01 8.2717e-02 -#> 214: 1.0192e+02 -4.1204e+00 -2.3392e+00 -4.0648e+00 -1.0076e+00 -2.3417e-02 3.4282e-02 5.8157e-07 6.1893e-02 3.0158e-02 1.9322e-01 7.8942e-02 9.5250e-01 8.3922e-02 9.9723e-01 8.2793e-02 -#> 215: 1.0192e+02 -4.1204e+00 -2.3410e+00 -4.0645e+00 -1.0087e+00 -2.1950e-02 3.5820e-02 6.0691e-07 6.2032e-02 2.9890e-02 1.9172e-01 8.3774e-02 9.5617e-01 8.3280e-02 1.0003e+00 8.2881e-02 -#> 216: 1.0192e+02 -4.1203e+00 -2.3425e+00 -4.0628e+00 -1.0069e+00 -2.4268e-02 3.7597e-02 6.4187e-07 6.1733e-02 2.9353e-02 1.9092e-01 8.8150e-02 9.5834e-01 8.3091e-02 1.0027e+00 8.2753e-02 -#> 217: 1.0192e+02 -4.1203e+00 -2.3439e+00 -4.0613e+00 -1.0064e+00 -2.4197e-02 3.9291e-02 6.5775e-07 6.2318e-02 2.8903e-02 1.8958e-01 9.0470e-02 9.5766e-01 8.3234e-02 1.0020e+00 8.2707e-02 -#> 218: 1.0191e+02 -4.1203e+00 -2.3441e+00 -4.0619e+00 -1.0065e+00 -2.2460e-02 4.0043e-02 6.4921e-07 6.2280e-02 2.8349e-02 1.8800e-01 9.4476e-02 9.5499e-01 8.3416e-02 1.0036e+00 8.2628e-02 -#> 219: 1.0191e+02 -4.1203e+00 -2.3437e+00 -4.0624e+00 -1.0066e+00 -1.9698e-02 3.9735e-02 6.3365e-07 6.2264e-02 2.7720e-02 1.8768e-01 9.7275e-02 9.4994e-01 8.3350e-02 1.0047e+00 8.2572e-02 -#> 220: 1.0191e+02 -4.1203e+00 -2.3447e+00 -4.0630e+00 -1.0070e+00 -1.5871e-02 4.0198e-02 6.3507e-07 6.1981e-02 2.7259e-02 1.8786e-01 9.9168e-02 9.4781e-01 8.3355e-02 1.0046e+00 8.2752e-02 -#> 221: 1.0191e+02 -4.1203e+00 -2.3459e+00 -4.0638e+00 -1.0069e+00 -1.4298e-02 4.0161e-02 6.2865e-07 6.2113e-02 2.7201e-02 1.8810e-01 1.0163e-01 9.4863e-01 8.3154e-02 1.0042e+00 8.2670e-02 -#> 222: 1.0191e+02 -4.1203e+00 -2.3472e+00 -4.0646e+00 -1.0064e+00 -1.0921e-02 4.0310e-02 6.2450e-07 6.2436e-02 2.6979e-02 1.8736e-01 1.0306e-01 9.4914e-01 8.3081e-02 1.0050e+00 8.2757e-02 -#> 223: 1.0191e+02 -4.1203e+00 -2.3478e+00 -4.0650e+00 -1.0063e+00 -1.1053e-02 3.9741e-02 6.1973e-07 6.2918e-02 2.6636e-02 1.8806e-01 1.0506e-01 9.4996e-01 8.2927e-02 1.0054e+00 8.2579e-02 -#> 224: 1.0191e+02 -4.1203e+00 -2.3478e+00 -4.0653e+00 -1.0061e+00 -1.0324e-02 3.9480e-02 6.1421e-07 6.4180e-02 2.6403e-02 1.8833e-01 1.0733e-01 9.4750e-01 8.2697e-02 1.0033e+00 8.2517e-02 -#> 225: 1.0191e+02 -4.1203e+00 -2.3479e+00 -4.0654e+00 -1.0060e+00 -1.0650e-02 3.9188e-02 6.0959e-07 6.3862e-02 2.6122e-02 1.8815e-01 1.0786e-01 9.4504e-01 8.2833e-02 1.0002e+00 8.2398e-02 -#> 226: 1.0192e+02 -4.1204e+00 -2.3469e+00 -4.0657e+00 -1.0052e+00 -1.0205e-02 3.9129e-02 6.0577e-07 6.4045e-02 2.5875e-02 1.8762e-01 1.0921e-01 9.4663e-01 8.2599e-02 9.9857e-01 8.2472e-02 -#> 227: 1.0192e+02 -4.1203e+00 -2.3467e+00 -4.0658e+00 -1.0053e+00 -1.0189e-02 3.8797e-02 6.0837e-07 6.5125e-02 2.5679e-02 1.8721e-01 1.1060e-01 9.4729e-01 8.2470e-02 9.9802e-01 8.2753e-02 -#> 228: 1.0192e+02 -4.1204e+00 -2.3469e+00 -4.0657e+00 -1.0054e+00 -1.0575e-02 3.8741e-02 6.0738e-07 6.5467e-02 2.5448e-02 1.8548e-01 1.1134e-01 9.4840e-01 8.2580e-02 9.9829e-01 8.2888e-02 -#> 229: 1.0192e+02 -4.1204e+00 -2.3479e+00 -4.0650e+00 -1.0056e+00 -1.1215e-02 3.9360e-02 6.0182e-07 6.4817e-02 2.5237e-02 1.8448e-01 1.1090e-01 9.5039e-01 8.2625e-02 9.9900e-01 8.2896e-02 -#> 230: 1.0192e+02 -4.1204e+00 -2.3482e+00 -4.0652e+00 -1.0060e+00 -9.9775e-03 3.9501e-02 5.9385e-07 6.4132e-02 2.5093e-02 1.8510e-01 1.1122e-01 9.4938e-01 8.2763e-02 9.9961e-01 8.2886e-02 -#> 231: 1.0192e+02 -4.1204e+00 -2.3479e+00 -4.0654e+00 -1.0070e+00 -8.9509e-03 3.9907e-02 5.9290e-07 6.3744e-02 2.4829e-02 1.8560e-01 1.1062e-01 9.4790e-01 8.2872e-02 1.0022e+00 8.2955e-02 -#> 232: 1.0192e+02 -4.1204e+00 -2.3484e+00 -4.0657e+00 -1.0081e+00 -6.9066e-03 4.0738e-02 5.7862e-07 6.3242e-02 2.4729e-02 1.8626e-01 1.0975e-01 9.4866e-01 8.2846e-02 1.0036e+00 8.3065e-02 -#> 233: 1.0191e+02 -4.1204e+00 -2.3487e+00 -4.0660e+00 -1.0080e+00 -5.1163e-03 4.0708e-02 5.7326e-07 6.2392e-02 2.4475e-02 1.8701e-01 1.0932e-01 9.4816e-01 8.2933e-02 1.0059e+00 8.3155e-02 -#> 234: 1.0191e+02 -4.1204e+00 -2.3500e+00 -4.0660e+00 -1.0077e+00 -4.0637e-03 4.1065e-02 5.6885e-07 6.1938e-02 2.4418e-02 1.8673e-01 1.0923e-01 9.5001e-01 8.3005e-02 1.0080e+00 8.3207e-02 -#> 235: 1.0191e+02 -4.1204e+00 -2.3526e+00 -4.0653e+00 -1.0074e+00 -3.6541e-03 4.1151e-02 5.6498e-07 6.2228e-02 2.4447e-02 1.8667e-01 1.0995e-01 9.5059e-01 8.3101e-02 1.0055e+00 8.3101e-02 -#> 236: 1.0191e+02 -4.1204e+00 -2.3540e+00 -4.0648e+00 -1.0078e+00 -4.0127e-03 4.0966e-02 5.7047e-07 6.1779e-02 2.4457e-02 1.8777e-01 1.0971e-01 9.4919e-01 8.3203e-02 1.0044e+00 8.3078e-02 -#> 237: 1.0191e+02 -4.1204e+00 -2.3528e+00 -4.0645e+00 -1.0078e+00 -4.4251e-03 4.0491e-02 5.6811e-07 6.1507e-02 2.4421e-02 1.8827e-01 1.1047e-01 9.4870e-01 8.3149e-02 1.0031e+00 8.3008e-02 -#> 238: 1.0190e+02 -4.1204e+00 -2.3517e+00 -4.0647e+00 -1.0076e+00 -5.2540e-03 3.9988e-02 5.6832e-07 6.1612e-02 2.4262e-02 1.8801e-01 1.1019e-01 9.4737e-01 8.3172e-02 1.0037e+00 8.2959e-02 -#> 239: 1.0190e+02 -4.1204e+00 -2.3509e+00 -4.0650e+00 -1.0089e+00 -5.1598e-03 3.9373e-02 5.6396e-07 6.1635e-02 2.4040e-02 1.8812e-01 1.1055e-01 9.4885e-01 8.3140e-02 1.0053e+00 8.2956e-02 -#> 240: 1.0190e+02 -4.1204e+00 -2.3515e+00 -4.0643e+00 -1.0095e+00 -4.5817e-03 3.9031e-02 5.5929e-07 6.2233e-02 2.3840e-02 1.8766e-01 1.1014e-01 9.5018e-01 8.3172e-02 1.0066e+00 8.2937e-02 -#> 241: 1.0190e+02 -4.1204e+00 -2.3524e+00 -4.0642e+00 -1.0097e+00 -3.9061e-03 3.8686e-02 5.5496e-07 6.3349e-02 2.3663e-02 1.8759e-01 1.1046e-01 9.4922e-01 8.3162e-02 1.0064e+00 8.2940e-02 -#> 242: 1.0190e+02 -4.1204e+00 -2.3535e+00 -4.0642e+00 -1.0092e+00 -2.9411e-03 3.8674e-02 5.5359e-07 6.3930e-02 2.3604e-02 1.8742e-01 1.1027e-01 9.4748e-01 8.3177e-02 1.0052e+00 8.2955e-02 -#> 243: 1.0190e+02 -4.1204e+00 -2.3551e+00 -4.0642e+00 -1.0089e+00 -1.6071e-03 3.8635e-02 5.4669e-07 6.4141e-02 2.3570e-02 1.8666e-01 1.1022e-01 9.4770e-01 8.3208e-02 1.0048e+00 8.2962e-02 -#> 244: 1.0190e+02 -4.1204e+00 -2.3566e+00 -4.0645e+00 -1.0093e+00 -7.0474e-04 3.8502e-02 5.4194e-07 6.4399e-02 2.3591e-02 1.8627e-01 1.0938e-01 9.4615e-01 8.3402e-02 1.0043e+00 8.2891e-02 -#> 245: 1.0189e+02 -4.1204e+00 -2.3575e+00 -4.0649e+00 -1.0093e+00 1.3351e-03 3.8372e-02 5.4266e-07 6.4935e-02 2.3511e-02 1.8609e-01 1.0840e-01 9.4586e-01 8.3393e-02 1.0041e+00 8.2835e-02 -#> 246: 1.0189e+02 -4.1204e+00 -2.3595e+00 -4.0655e+00 -1.0085e+00 4.2316e-03 3.8487e-02 5.4393e-07 6.5284e-02 2.3457e-02 1.8581e-01 1.0811e-01 9.4656e-01 8.3372e-02 1.0036e+00 8.2746e-02 -#> 247: 1.0189e+02 -4.1204e+00 -2.3608e+00 -4.0659e+00 -1.0081e+00 6.1314e-03 3.8249e-02 5.4752e-07 6.5440e-02 2.3455e-02 1.8584e-01 1.0706e-01 9.4795e-01 8.3330e-02 1.0025e+00 8.2710e-02 -#> 248: 1.0189e+02 -4.1204e+00 -2.3617e+00 -4.0662e+00 -1.0084e+00 8.1978e-03 3.8017e-02 5.4713e-07 6.5853e-02 2.3439e-02 1.8637e-01 1.0634e-01 9.4748e-01 8.3377e-02 1.0016e+00 8.2677e-02 -#> 249: 1.0189e+02 -4.1204e+00 -2.3633e+00 -4.0667e+00 -1.0085e+00 9.8011e-03 3.7934e-02 5.5069e-07 6.6442e-02 2.3533e-02 1.8652e-01 1.0606e-01 9.4761e-01 8.3449e-02 1.0009e+00 8.2712e-02 -#> 250: 1.0189e+02 -4.1204e+00 -2.3644e+00 -4.0668e+00 -1.0087e+00 1.0992e-02 3.8199e-02 5.5486e-07 6.6746e-02 2.3638e-02 1.8739e-01 1.0611e-01 9.4838e-01 8.3442e-02 9.9958e-01 8.2692e-02 -#> 251: 1.0189e+02 -4.1204e+00 -2.3644e+00 -4.0671e+00 -1.0097e+00 1.2215e-02 3.8648e-02 5.5448e-07 6.6916e-02 2.3592e-02 1.8753e-01 1.0607e-01 9.4773e-01 8.3511e-02 9.9919e-01 8.2701e-02 -#> 252: 1.0189e+02 -4.1204e+00 -2.3645e+00 -4.0671e+00 -1.0100e+00 1.2881e-02 3.8792e-02 5.5615e-07 6.7323e-02 2.3559e-02 1.8811e-01 1.0641e-01 9.4665e-01 8.3575e-02 9.9809e-01 8.2743e-02 -#> 253: 1.0189e+02 -4.1204e+00 -2.3646e+00 -4.0675e+00 -1.0100e+00 1.3605e-02 3.9013e-02 5.5568e-07 6.7625e-02 2.3432e-02 1.8825e-01 1.0688e-01 9.4424e-01 8.3598e-02 9.9825e-01 8.2702e-02 -#> 254: 1.0189e+02 -4.1204e+00 -2.3642e+00 -4.0677e+00 -1.0101e+00 1.3119e-02 3.8838e-02 5.5231e-07 6.7802e-02 2.3429e-02 1.8849e-01 1.0680e-01 9.4281e-01 8.3706e-02 9.9829e-01 8.2631e-02 -#> 255: 1.0189e+02 -4.1204e+00 -2.3627e+00 -4.0679e+00 -1.0104e+00 1.2490e-02 3.8574e-02 5.4955e-07 6.8395e-02 2.3368e-02 1.8890e-01 1.0661e-01 9.4101e-01 8.3756e-02 9.9798e-01 8.2674e-02 -#> 256: 1.0189e+02 -4.1204e+00 -2.3615e+00 -4.0677e+00 -1.0102e+00 1.1525e-02 3.8502e-02 5.4764e-07 6.8824e-02 2.3405e-02 1.8912e-01 1.0649e-01 9.4109e-01 8.3709e-02 9.9811e-01 8.2698e-02 -#> 257: 1.0189e+02 -4.1204e+00 -2.3604e+00 -4.0673e+00 -1.0104e+00 1.0381e-02 3.8286e-02 5.4694e-07 6.9020e-02 2.3338e-02 1.8925e-01 1.0614e-01 9.4075e-01 8.3695e-02 9.9738e-01 8.2689e-02 -#> 258: 1.0189e+02 -4.1204e+00 -2.3591e+00 -4.0670e+00 -1.0103e+00 8.9559e-03 3.7972e-02 5.4665e-07 6.9077e-02 2.3267e-02 1.8919e-01 1.0590e-01 9.4089e-01 8.3618e-02 9.9742e-01 8.2681e-02 -#> 259: 1.0189e+02 -4.1204e+00 -2.3585e+00 -4.0669e+00 -1.0099e+00 8.6011e-03 3.7874e-02 5.4788e-07 6.9455e-02 2.3264e-02 1.8885e-01 1.0519e-01 9.3952e-01 8.3583e-02 9.9610e-01 8.2650e-02 -#> 260: 1.0189e+02 -4.1204e+00 -2.3584e+00 -4.0666e+00 -1.0098e+00 8.0471e-03 3.7771e-02 5.5294e-07 7.0269e-02 2.3292e-02 1.8877e-01 1.0442e-01 9.3898e-01 8.3519e-02 9.9504e-01 8.2641e-02 -#> 261: 1.0189e+02 -4.1204e+00 -2.3583e+00 -4.0664e+00 -1.0100e+00 7.9344e-03 3.7597e-02 5.5650e-07 7.1087e-02 2.3370e-02 1.8867e-01 1.0399e-01 9.3810e-01 8.3488e-02 9.9419e-01 8.2673e-02 -#> 262: 1.0189e+02 -4.1204e+00 -2.3575e+00 -4.0662e+00 -1.0106e+00 7.2123e-03 3.7203e-02 5.5375e-07 7.1794e-02 2.3393e-02 1.8855e-01 1.0356e-01 9.3773e-01 8.3458e-02 9.9406e-01 8.2739e-02 -#> 263: 1.0189e+02 -4.1204e+00 -2.3564e+00 -4.0659e+00 -1.0112e+00 6.6044e-03 3.6977e-02 5.5306e-07 7.2290e-02 2.3475e-02 1.8847e-01 1.0316e-01 9.3744e-01 8.3383e-02 9.9341e-01 8.2818e-02 -#> 264: 1.0189e+02 -4.1204e+00 -2.3549e+00 -4.0657e+00 -1.0118e+00 6.0119e-03 3.6749e-02 5.5152e-07 7.2896e-02 2.3530e-02 1.8849e-01 1.0277e-01 9.3658e-01 8.3443e-02 9.9248e-01 8.2877e-02 -#> 265: 1.0189e+02 -4.1204e+00 -2.3545e+00 -4.0655e+00 -1.0121e+00 5.6547e-03 3.6562e-02 5.4816e-07 7.3238e-02 2.3560e-02 1.8863e-01 1.0269e-01 9.3597e-01 8.3434e-02 9.9139e-01 8.2879e-02 -#> 266: 1.0189e+02 -4.1204e+00 -2.3545e+00 -4.0651e+00 -1.0121e+00 5.0995e-03 3.6357e-02 5.4458e-07 7.3522e-02 2.3561e-02 1.8883e-01 1.0270e-01 9.3607e-01 8.3407e-02 9.9133e-01 8.2857e-02 -#> 267: 1.0189e+02 -4.1204e+00 -2.3541e+00 -4.0648e+00 -1.0122e+00 4.0105e-03 3.6306e-02 5.4160e-07 7.3833e-02 2.3499e-02 1.8889e-01 1.0317e-01 9.3624e-01 8.3359e-02 9.9151e-01 8.2865e-02 -#> 268: 1.0189e+02 -4.1204e+00 -2.3530e+00 -4.0646e+00 -1.0122e+00 3.0925e-03 3.6248e-02 5.3845e-07 7.4663e-02 2.3413e-02 1.8895e-01 1.0371e-01 9.3624e-01 8.3277e-02 9.9210e-01 8.2909e-02 -#> 269: 1.0189e+02 -4.1204e+00 -2.3518e+00 -4.0643e+00 -1.0123e+00 2.0507e-03 3.6181e-02 5.3602e-07 7.5442e-02 2.3291e-02 1.8886e-01 1.0397e-01 9.3581e-01 8.3260e-02 9.9238e-01 8.2898e-02 -#> 270: 1.0189e+02 -4.1204e+00 -2.3513e+00 -4.0640e+00 -1.0127e+00 1.3309e-03 3.5900e-02 5.3234e-07 7.6677e-02 2.3220e-02 1.8860e-01 1.0367e-01 9.3573e-01 8.3250e-02 9.9169e-01 8.2904e-02 -#> 271: 1.0189e+02 -4.1204e+00 -2.3514e+00 -4.0637e+00 -1.0129e+00 1.1237e-03 3.5608e-02 5.3092e-07 7.7065e-02 2.3102e-02 1.8826e-01 1.0384e-01 9.3645e-01 8.3228e-02 9.9173e-01 8.2896e-02 -#> 272: 1.0189e+02 -4.1204e+00 -2.3510e+00 -4.0639e+00 -1.0134e+00 9.7855e-04 3.5328e-02 5.3100e-07 7.7173e-02 2.3014e-02 1.8817e-01 1.0367e-01 9.3538e-01 8.3266e-02 9.9139e-01 8.2943e-02 -#> 273: 1.0189e+02 -4.1204e+00 -2.3501e+00 -4.0643e+00 -1.0133e+00 1.1275e-03 3.5187e-02 5.3298e-07 7.7467e-02 2.2923e-02 1.8793e-01 1.0344e-01 9.3474e-01 8.3194e-02 9.9249e-01 8.2973e-02 -#> 274: 1.0189e+02 -4.1204e+00 -2.3498e+00 -4.0643e+00 -1.0134e+00 1.4524e-03 3.4996e-02 5.3407e-07 7.7929e-02 2.2819e-02 1.8837e-01 1.0316e-01 9.3399e-01 8.3168e-02 9.9307e-01 8.2981e-02 -#> 275: 1.0189e+02 -4.1204e+00 -2.3500e+00 -4.0641e+00 -1.0136e+00 1.3605e-03 3.4786e-02 5.3269e-07 7.8177e-02 2.2747e-02 1.8855e-01 1.0305e-01 9.3319e-01 8.3205e-02 9.9277e-01 8.2938e-02 -#> 276: 1.0189e+02 -4.1204e+00 -2.3504e+00 -4.0641e+00 -1.0136e+00 1.5273e-03 3.4581e-02 5.3172e-07 7.8495e-02 2.2764e-02 1.8824e-01 1.0297e-01 9.3267e-01 8.3223e-02 9.9204e-01 8.2884e-02 -#> 277: 1.0189e+02 -4.1204e+00 -2.3506e+00 -4.0643e+00 -1.0133e+00 1.2961e-03 3.4373e-02 5.2917e-07 7.8721e-02 2.2791e-02 1.8801e-01 1.0288e-01 9.3253e-01 8.3185e-02 9.9192e-01 8.2854e-02 -#> 278: 1.0189e+02 -4.1204e+00 -2.3508e+00 -4.0643e+00 -1.0129e+00 1.1750e-03 3.4396e-02 5.2693e-07 7.8999e-02 2.2787e-02 1.8793e-01 1.0278e-01 9.3279e-01 8.3113e-02 9.9144e-01 8.2856e-02 -#> 279: 1.0189e+02 -4.1204e+00 -2.3507e+00 -4.0642e+00 -1.0126e+00 1.2755e-03 3.4381e-02 5.2405e-07 7.9351e-02 2.2804e-02 1.8779e-01 1.0255e-01 9.3319e-01 8.3049e-02 9.9099e-01 8.2875e-02 -#> 280: 1.0189e+02 -4.1204e+00 -2.3507e+00 -4.0641e+00 -1.0127e+00 6.3408e-04 3.4519e-02 5.2180e-07 7.9825e-02 2.2801e-02 1.8775e-01 1.0292e-01 9.3349e-01 8.2970e-02 9.9076e-01 8.2918e-02 -#> 281: 1.0189e+02 -4.1204e+00 -2.3508e+00 -4.0639e+00 -1.0124e+00 6.2438e-04 3.4782e-02 5.1859e-07 8.0328e-02 2.2816e-02 1.8757e-01 1.0299e-01 9.3299e-01 8.3025e-02 9.9050e-01 8.2897e-02 -#> 282: 1.0189e+02 -4.1205e+00 -2.3511e+00 -4.0641e+00 -1.0122e+00 1.1770e-03 3.4754e-02 5.1798e-07 8.0649e-02 2.2836e-02 1.8766e-01 1.0297e-01 9.3351e-01 8.2989e-02 9.9171e-01 8.2893e-02 -#> 283: 1.0189e+02 -4.1205e+00 -2.3519e+00 -4.0644e+00 -1.0120e+00 2.1716e-03 3.4711e-02 5.1567e-07 8.0910e-02 2.2836e-02 1.8774e-01 1.0270e-01 9.3288e-01 8.3029e-02 9.9246e-01 8.2853e-02 -#> 284: 1.0189e+02 -4.1205e+00 -2.3524e+00 -4.0647e+00 -1.0115e+00 2.6623e-03 3.4646e-02 5.1350e-07 8.1153e-02 2.2950e-02 1.8775e-01 1.0277e-01 9.3238e-01 8.2990e-02 9.9212e-01 8.2836e-02 -#> 285: 1.0189e+02 -4.1205e+00 -2.3531e+00 -4.0649e+00 -1.0116e+00 3.7830e-03 3.4626e-02 5.1216e-07 8.1058e-02 2.3007e-02 1.8782e-01 1.0270e-01 9.3232e-01 8.3017e-02 9.9094e-01 8.2829e-02 -#> 286: 1.0189e+02 -4.1205e+00 -2.3539e+00 -4.0651e+00 -1.0111e+00 5.1752e-03 3.4599e-02 5.0989e-07 8.0970e-02 2.3004e-02 1.8757e-01 1.0254e-01 9.3280e-01 8.3006e-02 9.9130e-01 8.2818e-02 -#> 287: 1.0189e+02 -4.1205e+00 -2.3541e+00 -4.0654e+00 -1.0112e+00 6.3747e-03 3.4592e-02 5.0930e-07 8.1117e-02 2.2959e-02 1.8756e-01 1.0222e-01 9.3212e-01 8.3146e-02 9.9183e-01 8.2863e-02 -#> 288: 1.0189e+02 -4.1205e+00 -2.3540e+00 -4.0656e+00 -1.0115e+00 6.5668e-03 3.4598e-02 5.0976e-07 8.1125e-02 2.2895e-02 1.8782e-01 1.0183e-01 9.3310e-01 8.3169e-02 9.9404e-01 8.2836e-02 -#> 289: 1.0189e+02 -4.1205e+00 -2.3539e+00 -4.0658e+00 -1.0119e+00 7.3521e-03 3.4525e-02 5.1097e-07 8.1097e-02 2.2869e-02 1.8753e-01 1.0126e-01 9.3336e-01 8.3244e-02 9.9435e-01 8.2833e-02 -#> 290: 1.0189e+02 -4.1205e+00 -2.3539e+00 -4.0659e+00 -1.0122e+00 7.5226e-03 3.4377e-02 5.0846e-07 8.1212e-02 2.2831e-02 1.8724e-01 1.0073e-01 9.3292e-01 8.3261e-02 9.9415e-01 8.2837e-02 -#> 291: 1.0189e+02 -4.1205e+00 -2.3536e+00 -4.0659e+00 -1.0122e+00 7.2889e-03 3.4263e-02 5.0823e-07 8.1182e-02 2.2801e-02 1.8711e-01 1.0056e-01 9.3309e-01 8.3300e-02 9.9427e-01 8.2805e-02 -#> 292: 1.0189e+02 -4.1205e+00 -2.3531e+00 -4.0659e+00 -1.0123e+00 7.1827e-03 3.4146e-02 5.0825e-07 8.1696e-02 2.2760e-02 1.8703e-01 1.0039e-01 9.3324e-01 8.3306e-02 9.9379e-01 8.2784e-02 -#> 293: 1.0189e+02 -4.1205e+00 -2.3528e+00 -4.0660e+00 -1.0125e+00 7.7142e-03 3.4126e-02 5.0971e-07 8.2026e-02 2.2705e-02 1.8721e-01 1.0036e-01 9.3316e-01 8.3316e-02 9.9357e-01 8.2756e-02 -#> 294: 1.0188e+02 -4.1204e+00 -2.3529e+00 -4.0663e+00 -1.0126e+00 8.5146e-03 3.4314e-02 5.0823e-07 8.2197e-02 2.2608e-02 1.8743e-01 1.0009e-01 9.3356e-01 8.3308e-02 9.9367e-01 8.2719e-02 -#> 295: 1.0188e+02 -4.1204e+00 -2.3532e+00 -4.0666e+00 -1.0123e+00 9.2199e-03 3.4472e-02 5.0839e-07 8.2550e-02 2.2529e-02 1.8745e-01 9.9731e-02 9.3393e-01 8.3255e-02 9.9373e-01 8.2686e-02 -#> 296: 1.0188e+02 -4.1204e+00 -2.3537e+00 -4.0667e+00 -1.0121e+00 9.7869e-03 3.4678e-02 5.0983e-07 8.3059e-02 2.2497e-02 1.8729e-01 9.9260e-02 9.3395e-01 8.3198e-02 9.9300e-01 8.2681e-02 -#> 297: 1.0188e+02 -4.1204e+00 -2.3540e+00 -4.0670e+00 -1.0118e+00 1.0166e-02 3.4957e-02 5.1049e-07 8.3080e-02 2.2448e-02 1.8710e-01 9.8969e-02 9.3321e-01 8.3178e-02 9.9255e-01 8.2663e-02 -#> 298: 1.0188e+02 -4.1204e+00 -2.3544e+00 -4.0673e+00 -1.0117e+00 1.0649e-02 3.5259e-02 5.1103e-07 8.3179e-02 2.2383e-02 1.8704e-01 9.8442e-02 9.3227e-01 8.3199e-02 9.9266e-01 8.2646e-02 -#> 299: 1.0188e+02 -4.1204e+00 -2.3542e+00 -4.0676e+00 -1.0117e+00 1.0927e-02 3.5438e-02 5.1128e-07 8.3068e-02 2.2378e-02 1.8699e-01 9.8203e-02 9.3263e-01 8.3138e-02 9.9353e-01 8.2671e-02 -#> 300: 1.0188e+02 -4.1204e+00 -2.3544e+00 -4.0678e+00 -1.0116e+00 1.1083e-02 3.5694e-02 5.1107e-07 8.2896e-02 2.2344e-02 1.8733e-01 9.7775e-02 9.3179e-01 8.3124e-02 9.9379e-01 8.2657e-02 -#> 301: 1.0188e+02 -4.1204e+00 -2.3542e+00 -4.0680e+00 -1.0115e+00 1.0992e-02 3.5896e-02 5.1262e-07 8.2816e-02 2.2349e-02 1.8753e-01 9.7431e-02 9.3209e-01 8.3086e-02 9.9388e-01 8.2674e-02 -#> 302: 1.0188e+02 -4.1204e+00 -2.3540e+00 -4.0681e+00 -1.0113e+00 1.0410e-02 3.6050e-02 5.1256e-07 8.2817e-02 2.2308e-02 1.8734e-01 9.7153e-02 9.3221e-01 8.3073e-02 9.9402e-01 8.2670e-02 -#> 303: 1.0188e+02 -4.1204e+00 -2.3540e+00 -4.0681e+00 -1.0112e+00 1.0301e-02 3.6150e-02 5.1127e-07 8.2826e-02 2.2325e-02 1.8730e-01 9.6656e-02 9.3192e-01 8.3040e-02 9.9393e-01 8.2665e-02 -#> 304: 1.0188e+02 -4.1204e+00 -2.3536e+00 -4.0681e+00 -1.0113e+00 1.0235e-02 3.6393e-02 5.1176e-07 8.2606e-02 2.2353e-02 1.8724e-01 9.6171e-02 9.3161e-01 8.3068e-02 9.9361e-01 8.2698e-02 -#> 305: 1.0188e+02 -4.1204e+00 -2.3533e+00 -4.0683e+00 -1.0112e+00 9.9655e-03 3.6369e-02 5.1442e-07 8.2520e-02 2.2378e-02 1.8707e-01 9.5656e-02 9.3113e-01 8.3109e-02 9.9338e-01 8.2731e-02 -#> 306: 1.0188e+02 -4.1204e+00 -2.3531e+00 -4.0684e+00 -1.0110e+00 9.9701e-03 3.6346e-02 5.1546e-07 8.2789e-02 2.2360e-02 1.8702e-01 9.5116e-02 9.3102e-01 8.3065e-02 9.9405e-01 8.2761e-02 -#> 307: 1.0188e+02 -4.1204e+00 -2.3530e+00 -4.0684e+00 -1.0112e+00 1.0194e-02 3.6300e-02 5.1196e-07 8.3035e-02 2.2381e-02 1.8704e-01 9.4760e-02 9.3082e-01 8.3003e-02 9.9410e-01 8.2779e-02 -#> 308: 1.0189e+02 -4.1204e+00 -2.3530e+00 -4.0685e+00 -1.0109e+00 9.9531e-03 3.6400e-02 5.1140e-07 8.3511e-02 2.2334e-02 1.8726e-01 9.4494e-02 9.3151e-01 8.2910e-02 9.9484e-01 8.2760e-02 -#> 309: 1.0188e+02 -4.1204e+00 -2.3530e+00 -4.0685e+00 -1.0107e+00 1.0089e-02 3.6382e-02 5.1081e-07 8.3917e-02 2.2276e-02 1.8728e-01 9.4285e-02 9.3133e-01 8.2875e-02 9.9545e-01 8.2757e-02 -#> 310: 1.0188e+02 -4.1204e+00 -2.3533e+00 -4.0685e+00 -1.0105e+00 1.0805e-02 3.6375e-02 5.1041e-07 8.4245e-02 2.2246e-02 1.8753e-01 9.3894e-02 9.3052e-01 8.2899e-02 9.9500e-01 8.2743e-02 -#> 311: 1.0188e+02 -4.1204e+00 -2.3534e+00 -4.0685e+00 -1.0103e+00 1.1449e-02 3.6311e-02 5.0884e-07 8.4434e-02 2.2231e-02 1.8783e-01 9.3542e-02 9.3039e-01 8.2864e-02 9.9458e-01 8.2733e-02 -#> 312: 1.0188e+02 -4.1204e+00 -2.3535e+00 -4.0685e+00 -1.0102e+00 1.2173e-02 3.6373e-02 5.0821e-07 8.4730e-02 2.2176e-02 1.8769e-01 9.3317e-02 9.2982e-01 8.2916e-02 9.9438e-01 8.2740e-02 -#> 313: 1.0188e+02 -4.1204e+00 -2.3533e+00 -4.0688e+00 -1.0103e+00 1.2812e-02 3.6558e-02 5.0751e-07 8.5211e-02 2.2131e-02 1.8754e-01 9.3387e-02 9.2962e-01 8.2892e-02 9.9458e-01 8.2741e-02 -#> 314: 1.0188e+02 -4.1204e+00 -2.3534e+00 -4.0690e+00 -1.0103e+00 1.3241e-02 3.6680e-02 5.0887e-07 8.5667e-02 2.2079e-02 1.8772e-01 9.3442e-02 9.2941e-01 8.2947e-02 9.9511e-01 8.2743e-02 -#> 315: 1.0188e+02 -4.1204e+00 -2.3534e+00 -4.0691e+00 -1.0104e+00 1.3699e-02 3.6924e-02 5.0965e-07 8.5865e-02 2.2028e-02 1.8766e-01 9.3264e-02 9.2904e-01 8.2986e-02 9.9543e-01 8.2763e-02 -#> 316: 1.0188e+02 -4.1204e+00 -2.3533e+00 -4.0693e+00 -1.0103e+00 1.4121e-02 3.7218e-02 5.1041e-07 8.6216e-02 2.2076e-02 1.8773e-01 9.3035e-02 9.2917e-01 8.3013e-02 9.9486e-01 8.2782e-02 -#> 317: 1.0188e+02 -4.1204e+00 -2.3533e+00 -4.0694e+00 -1.0102e+00 1.4588e-02 3.7304e-02 5.0994e-07 8.6513e-02 2.2128e-02 1.8773e-01 9.2766e-02 9.2943e-01 8.3025e-02 9.9441e-01 8.2779e-02 -#> 318: 1.0188e+02 -4.1204e+00 -2.3534e+00 -4.0693e+00 -1.0101e+00 1.4714e-02 3.7538e-02 5.0773e-07 8.6801e-02 2.2128e-02 1.8767e-01 9.2698e-02 9.2907e-01 8.3052e-02 9.9378e-01 8.2780e-02 -#> 319: 1.0187e+02 -4.1204e+00 -2.3533e+00 -4.0692e+00 -1.0099e+00 1.4582e-02 3.7563e-02 5.0550e-07 8.6669e-02 2.2135e-02 1.8775e-01 9.2604e-02 9.2925e-01 8.3042e-02 9.9356e-01 8.2773e-02 -#> 320: 1.0187e+02 -4.1204e+00 -2.3535e+00 -4.0690e+00 -1.0102e+00 1.4511e-02 3.7580e-02 5.0281e-07 8.6617e-02 2.2121e-02 1.8780e-01 9.2508e-02 9.3001e-01 8.3032e-02 9.9322e-01 8.2780e-02 -#> 321: 1.0187e+02 -4.1204e+00 -2.3534e+00 -4.0688e+00 -1.0100e+00 1.4288e-02 3.7624e-02 5.0172e-07 8.6311e-02 2.2115e-02 1.8783e-01 9.2445e-02 9.3011e-01 8.3054e-02 9.9288e-01 8.2772e-02 -#> 322: 1.0187e+02 -4.1204e+00 -2.3532e+00 -4.0687e+00 -1.0098e+00 1.3834e-02 3.7497e-02 5.0086e-07 8.6187e-02 2.2111e-02 1.8791e-01 9.2699e-02 9.3037e-01 8.3069e-02 9.9284e-01 8.2773e-02 -#> 323: 1.0187e+02 -4.1204e+00 -2.3524e+00 -4.0683e+00 -1.0097e+00 1.2977e-02 3.7420e-02 4.9925e-07 8.6082e-02 2.2084e-02 1.8818e-01 9.3123e-02 9.3036e-01 8.3012e-02 9.9265e-01 8.2813e-02 -#> 324: 1.0187e+02 -4.1204e+00 -2.3523e+00 -4.0682e+00 -1.0096e+00 1.2679e-02 3.7420e-02 4.9836e-07 8.5721e-02 2.2071e-02 1.8829e-01 9.3535e-02 9.3062e-01 8.3011e-02 9.9241e-01 8.2827e-02 -#> 325: 1.0187e+02 -4.1204e+00 -2.3520e+00 -4.0680e+00 -1.0094e+00 1.2196e-02 3.7298e-02 4.9735e-07 8.5411e-02 2.2028e-02 1.8848e-01 9.3706e-02 9.3043e-01 8.3020e-02 9.9256e-01 8.2826e-02 -#> 326: 1.0187e+02 -4.1204e+00 -2.3517e+00 -4.0678e+00 -1.0091e+00 1.1924e-02 3.7185e-02 4.9661e-07 8.5453e-02 2.1983e-02 1.8830e-01 9.3688e-02 9.3050e-01 8.2996e-02 9.9284e-01 8.2806e-02 -#> 327: 1.0187e+02 -4.1204e+00 -2.3516e+00 -4.0677e+00 -1.0090e+00 1.1449e-02 3.7155e-02 4.9755e-07 8.5761e-02 2.1967e-02 1.8819e-01 9.3936e-02 9.3052e-01 8.2912e-02 9.9245e-01 8.2806e-02 -#> 328: 1.0187e+02 -4.1204e+00 -2.3514e+00 -4.0675e+00 -1.0089e+00 1.0758e-02 3.7146e-02 4.9892e-07 8.6019e-02 2.1971e-02 1.8806e-01 9.4361e-02 9.3070e-01 8.2833e-02 9.9182e-01 8.2840e-02 -#> 329: 1.0187e+02 -4.1204e+00 -2.3515e+00 -4.0672e+00 -1.0087e+00 1.0256e-02 3.7342e-02 5.0019e-07 8.5965e-02 2.1989e-02 1.8796e-01 9.4614e-02 9.3067e-01 8.2818e-02 9.9159e-01 8.2858e-02 -#> 330: 1.0187e+02 -4.1204e+00 -2.3520e+00 -4.0670e+00 -1.0086e+00 1.0021e-02 3.7376e-02 4.9911e-07 8.6124e-02 2.1978e-02 1.8796e-01 9.4836e-02 9.3036e-01 8.2819e-02 9.9148e-01 8.2866e-02 -#> 331: 1.0187e+02 -4.1204e+00 -2.3521e+00 -4.0668e+00 -1.0086e+00 9.5790e-03 3.7296e-02 4.9753e-07 8.6122e-02 2.1951e-02 1.8782e-01 9.5042e-02 9.3064e-01 8.2783e-02 9.9196e-01 8.2863e-02 -#> 332: 1.0187e+02 -4.1204e+00 -2.3523e+00 -4.0667e+00 -1.0085e+00 9.2971e-03 3.7221e-02 4.9729e-07 8.6215e-02 2.1952e-02 1.8787e-01 9.5082e-02 9.3103e-01 8.2782e-02 9.9224e-01 8.2861e-02 -#> 333: 1.0187e+02 -4.1204e+00 -2.3524e+00 -4.0667e+00 -1.0084e+00 9.2591e-03 3.7097e-02 4.9556e-07 8.6302e-02 2.1922e-02 1.8792e-01 9.5155e-02 9.3058e-01 8.2798e-02 9.9202e-01 8.2831e-02 -#> 334: 1.0187e+02 -4.1204e+00 -2.3528e+00 -4.0667e+00 -1.0082e+00 9.5799e-03 3.6997e-02 4.9398e-07 8.6409e-02 2.1911e-02 1.8792e-01 9.5231e-02 9.3035e-01 8.2810e-02 9.9157e-01 8.2803e-02 -#> 335: 1.0187e+02 -4.1204e+00 -2.3529e+00 -4.0667e+00 -1.0080e+00 9.5724e-03 3.6912e-02 4.9206e-07 8.6310e-02 2.1923e-02 1.8791e-01 9.5379e-02 9.3054e-01 8.2759e-02 9.9143e-01 8.2776e-02 -#> 336: 1.0187e+02 -4.1204e+00 -2.3525e+00 -4.0667e+00 -1.0082e+00 9.5794e-03 3.6983e-02 4.9255e-07 8.6282e-02 2.1882e-02 1.8789e-01 9.5422e-02 9.3064e-01 8.2705e-02 9.9138e-01 8.2778e-02 -#> 337: 1.0187e+02 -4.1204e+00 -2.3525e+00 -4.0669e+00 -1.0083e+00 1.0008e-02 3.6943e-02 4.9278e-07 8.6483e-02 2.1844e-02 1.8794e-01 9.5240e-02 9.2981e-01 8.2753e-02 9.9100e-01 8.2774e-02 -#> 338: 1.0187e+02 -4.1204e+00 -2.3525e+00 -4.0669e+00 -1.0084e+00 1.0297e-02 3.6869e-02 4.9309e-07 8.6547e-02 2.1803e-02 1.8808e-01 9.5094e-02 9.2978e-01 8.2764e-02 9.9113e-01 8.2759e-02 -#> 339: 1.0187e+02 -4.1204e+00 -2.3528e+00 -4.0669e+00 -1.0083e+00 1.0465e-02 3.6822e-02 4.9257e-07 8.6779e-02 2.1769e-02 1.8813e-01 9.5062e-02 9.3020e-01 8.2702e-02 9.9135e-01 8.2750e-02 -#> 340: 1.0187e+02 -4.1204e+00 -2.3531e+00 -4.0668e+00 -1.0083e+00 1.0321e-02 3.6733e-02 4.9228e-07 8.7033e-02 2.1721e-02 1.8827e-01 9.4862e-02 9.3062e-01 8.2698e-02 9.9195e-01 8.2729e-02 -#> 341: 1.0187e+02 -4.1204e+00 -2.3531e+00 -4.0670e+00 -1.0085e+00 1.0501e-02 3.6671e-02 4.9236e-07 8.7297e-02 2.1733e-02 1.8820e-01 9.4558e-02 9.3121e-01 8.2677e-02 9.9238e-01 8.2713e-02 -#> 342: 1.0187e+02 -4.1204e+00 -2.3534e+00 -4.0670e+00 -1.0085e+00 1.0818e-02 3.6715e-02 4.9084e-07 8.7450e-02 2.1726e-02 1.8801e-01 9.4252e-02 9.3160e-01 8.2657e-02 9.9232e-01 8.2708e-02 -#> 343: 1.0187e+02 -4.1204e+00 -2.3535e+00 -4.0670e+00 -1.0087e+00 1.1046e-02 3.6784e-02 4.8872e-07 8.7718e-02 2.1718e-02 1.8799e-01 9.3887e-02 9.3187e-01 8.2645e-02 9.9225e-01 8.2722e-02 -#> 344: 1.0187e+02 -4.1204e+00 -2.3535e+00 -4.0670e+00 -1.0087e+00 1.0652e-02 3.6736e-02 4.8852e-07 8.7725e-02 2.1730e-02 1.8792e-01 9.3731e-02 9.3202e-01 8.2618e-02 9.9218e-01 8.2712e-02 -#> 345: 1.0187e+02 -4.1204e+00 -2.3536e+00 -4.0669e+00 -1.0090e+00 1.0445e-02 3.6714e-02 4.8690e-07 8.7782e-02 2.1751e-02 1.8798e-01 9.3450e-02 9.3223e-01 8.2582e-02 9.9239e-01 8.2706e-02 -#> 346: 1.0187e+02 -4.1204e+00 -2.3536e+00 -4.0668e+00 -1.0089e+00 1.0173e-02 3.6743e-02 4.8656e-07 8.7733e-02 2.1810e-02 1.8814e-01 9.3151e-02 9.3257e-01 8.2575e-02 9.9181e-01 8.2708e-02 -#> 347: 1.0187e+02 -4.1205e+00 -2.3532e+00 -4.0668e+00 -1.0089e+00 9.9457e-03 3.6808e-02 4.8756e-07 8.7948e-02 2.1819e-02 1.8813e-01 9.3040e-02 9.3255e-01 8.2599e-02 9.9124e-01 8.2727e-02 -#> 348: 1.0187e+02 -4.1205e+00 -2.3534e+00 -4.0667e+00 -1.0090e+00 9.8498e-03 3.6967e-02 4.8883e-07 8.7998e-02 2.1841e-02 1.8820e-01 9.3180e-02 9.3259e-01 8.2612e-02 9.9148e-01 8.2750e-02 -#> 349: 1.0187e+02 -4.1205e+00 -2.3535e+00 -4.0668e+00 -1.0091e+00 9.6211e-03 3.6891e-02 4.8867e-07 8.8006e-02 2.1930e-02 1.8819e-01 9.3390e-02 9.3232e-01 8.2612e-02 9.9073e-01 8.2738e-02 -#> 350: 1.0187e+02 -4.1205e+00 -2.3534e+00 -4.0669e+00 -1.0090e+00 9.7176e-03 3.6813e-02 4.8925e-07 8.7923e-02 2.1964e-02 1.8820e-01 9.3434e-02 9.3224e-01 8.2600e-02 9.9031e-01 8.2734e-02 -#> 351: 1.0187e+02 -4.1204e+00 -2.3535e+00 -4.0669e+00 -1.0090e+00 9.6652e-03 3.6769e-02 4.8873e-07 8.7985e-02 2.2046e-02 1.8814e-01 9.3529e-02 9.3220e-01 8.2558e-02 9.8978e-01 8.2728e-02 -#> 352: 1.0187e+02 -4.1204e+00 -2.3536e+00 -4.0669e+00 -1.0089e+00 9.8745e-03 3.6732e-02 4.8969e-07 8.8016e-02 2.2094e-02 1.8799e-01 9.3644e-02 9.3168e-01 8.2577e-02 9.8913e-01 8.2722e-02 -#> 353: 1.0187e+02 -4.1204e+00 -2.3537e+00 -4.0669e+00 -1.0088e+00 9.7530e-03 3.6700e-02 4.9008e-07 8.7949e-02 2.2116e-02 1.8798e-01 9.3769e-02 9.3165e-01 8.2559e-02 9.8871e-01 8.2711e-02 -#> 354: 1.0187e+02 -4.1204e+00 -2.3538e+00 -4.0667e+00 -1.0089e+00 9.4103e-03 3.6653e-02 4.9045e-07 8.7894e-02 2.2118e-02 1.8793e-01 9.3872e-02 9.3188e-01 8.2551e-02 9.8887e-01 8.2692e-02 -#> 355: 1.0187e+02 -4.1204e+00 -2.3540e+00 -4.0666e+00 -1.0088e+00 9.1684e-03 3.6536e-02 4.9125e-07 8.7920e-02 2.2107e-02 1.8812e-01 9.4123e-02 9.3223e-01 8.2517e-02 9.8893e-01 8.2687e-02 -#> 356: 1.0187e+02 -4.1204e+00 -2.3542e+00 -4.0664e+00 -1.0086e+00 8.9025e-03 3.6431e-02 4.9325e-07 8.7949e-02 2.2110e-02 1.8827e-01 9.4135e-02 9.3252e-01 8.2503e-02 9.8907e-01 8.2649e-02 -#> 357: 1.0187e+02 -4.1204e+00 -2.3542e+00 -4.0663e+00 -1.0085e+00 8.6757e-03 3.6417e-02 4.9505e-07 8.8052e-02 2.2096e-02 1.8848e-01 9.4192e-02 9.3281e-01 8.2490e-02 9.8957e-01 8.2624e-02 -#> 358: 1.0187e+02 -4.1204e+00 -2.3542e+00 -4.0661e+00 -1.0084e+00 8.1812e-03 3.6349e-02 4.9610e-07 8.8344e-02 2.2104e-02 1.8844e-01 9.4129e-02 9.3294e-01 8.2493e-02 9.8977e-01 8.2614e-02 -#> 359: 1.0187e+02 -4.1204e+00 -2.3541e+00 -4.0659e+00 -1.0083e+00 8.0905e-03 3.6475e-02 4.9675e-07 8.8647e-02 2.2116e-02 1.8831e-01 9.4146e-02 9.3322e-01 8.2473e-02 9.8978e-01 8.2622e-02 -#> 360: 1.0187e+02 -4.1204e+00 -2.3542e+00 -4.0657e+00 -1.0082e+00 7.8390e-03 3.6468e-02 4.9649e-07 8.8981e-02 2.2120e-02 1.8815e-01 9.4249e-02 9.3361e-01 8.2430e-02 9.8997e-01 8.2616e-02 -#> 361: 1.0187e+02 -4.1204e+00 -2.3545e+00 -4.0656e+00 -1.0083e+00 7.9104e-03 3.6434e-02 4.9737e-07 8.9447e-02 2.2133e-02 1.8808e-01 9.4085e-02 9.3387e-01 8.2426e-02 9.9025e-01 8.2616e-02 -#> 362: 1.0187e+02 -4.1204e+00 -2.3547e+00 -4.0655e+00 -1.0087e+00 7.6341e-03 3.6428e-02 4.9748e-07 8.9872e-02 2.2148e-02 1.8805e-01 9.4025e-02 9.3407e-01 8.2456e-02 9.9070e-01 8.2609e-02 -#> 363: 1.0187e+02 -4.1204e+00 -2.3546e+00 -4.0653e+00 -1.0087e+00 7.2351e-03 3.6392e-02 4.9842e-07 9.0125e-02 2.2179e-02 1.8818e-01 9.4157e-02 9.3437e-01 8.2439e-02 9.9051e-01 8.2626e-02 -#> 364: 1.0187e+02 -4.1204e+00 -2.3543e+00 -4.0651e+00 -1.0089e+00 6.7851e-03 3.6303e-02 4.9890e-07 9.0448e-02 2.2189e-02 1.8831e-01 9.4432e-02 9.3513e-01 8.2433e-02 9.9051e-01 8.2655e-02 -#> 365: 1.0187e+02 -4.1204e+00 -2.3538e+00 -4.0650e+00 -1.0089e+00 6.2935e-03 3.6267e-02 4.9829e-07 9.0718e-02 2.2204e-02 1.8818e-01 9.4507e-02 9.3580e-01 8.2387e-02 9.9049e-01 8.2678e-02 -#> 366: 1.0187e+02 -4.1204e+00 -2.3535e+00 -4.0649e+00 -1.0088e+00 5.8910e-03 3.6339e-02 4.9911e-07 9.0727e-02 2.2231e-02 1.8801e-01 9.4683e-02 9.3567e-01 8.2359e-02 9.8997e-01 8.2681e-02 -#> 367: 1.0187e+02 -4.1204e+00 -2.3533e+00 -4.0649e+00 -1.0088e+00 5.8610e-03 3.6366e-02 5.0123e-07 9.0732e-02 2.2245e-02 1.8793e-01 9.4666e-02 9.3556e-01 8.2339e-02 9.8945e-01 8.2691e-02 -#> 368: 1.0187e+02 -4.1204e+00 -2.3531e+00 -4.0650e+00 -1.0088e+00 6.1043e-03 3.6424e-02 5.0107e-07 9.0729e-02 2.2248e-02 1.8780e-01 9.4599e-02 9.3554e-01 8.2315e-02 9.8903e-01 8.2705e-02 -#> 369: 1.0187e+02 -4.1204e+00 -2.3530e+00 -4.0650e+00 -1.0088e+00 6.1767e-03 3.6436e-02 5.0046e-07 9.0617e-02 2.2226e-02 1.8787e-01 9.4410e-02 9.3504e-01 8.2361e-02 9.8843e-01 8.2694e-02 -#> 370: 1.0187e+02 -4.1204e+00 -2.3528e+00 -4.0651e+00 -1.0088e+00 6.2532e-03 3.6467e-02 5.0024e-07 9.0741e-02 2.2223e-02 1.8794e-01 9.4288e-02 9.3472e-01 8.2374e-02 9.8781e-01 8.2703e-02 -#> 371: 1.0186e+02 -4.1204e+00 -2.3525e+00 -4.0652e+00 -1.0088e+00 6.2117e-03 3.6465e-02 4.9964e-07 9.0904e-02 2.2220e-02 1.8788e-01 9.4310e-02 9.3470e-01 8.2367e-02 9.8730e-01 8.2731e-02 -#> 372: 1.0186e+02 -4.1204e+00 -2.3524e+00 -4.0651e+00 -1.0089e+00 6.1363e-03 3.6367e-02 5.0037e-07 9.1177e-02 2.2230e-02 1.8783e-01 9.4288e-02 9.3496e-01 8.2365e-02 9.8699e-01 8.2729e-02 -#> 373: 1.0186e+02 -4.1204e+00 -2.3523e+00 -4.0650e+00 -1.0089e+00 6.0384e-03 3.6353e-02 5.0195e-07 9.1402e-02 2.2219e-02 1.8764e-01 9.4343e-02 9.3478e-01 8.2430e-02 9.8641e-01 8.2747e-02 -#> 374: 1.0186e+02 -4.1204e+00 -2.3523e+00 -4.0650e+00 -1.0091e+00 5.9821e-03 3.6377e-02 5.0424e-07 9.1532e-02 2.2243e-02 1.8761e-01 9.4219e-02 9.3466e-01 8.2418e-02 9.8614e-01 8.2735e-02 -#> 375: 1.0186e+02 -4.1204e+00 -2.3524e+00 -4.0649e+00 -1.0091e+00 5.8843e-03 3.6358e-02 5.0568e-07 9.1556e-02 2.2250e-02 1.8768e-01 9.4173e-02 9.3432e-01 8.2413e-02 9.8592e-01 8.2728e-02 -#> 376: 1.0186e+02 -4.1204e+00 -2.3526e+00 -4.0649e+00 -1.0090e+00 5.7256e-03 3.6406e-02 5.0673e-07 9.1590e-02 2.2260e-02 1.8765e-01 9.4159e-02 9.3417e-01 8.2400e-02 9.8565e-01 8.2701e-02 -#> 377: 1.0186e+02 -4.1204e+00 -2.3527e+00 -4.0647e+00 -1.0091e+00 5.2782e-03 3.6397e-02 5.0740e-07 9.1564e-02 2.2263e-02 1.8765e-01 9.4084e-02 9.3434e-01 8.2395e-02 9.8563e-01 8.2680e-02 -#> 378: 1.0186e+02 -4.1204e+00 -2.3524e+00 -4.0646e+00 -1.0091e+00 4.8184e-03 3.6478e-02 5.0759e-07 9.1590e-02 2.2213e-02 1.8766e-01 9.4162e-02 9.3432e-01 8.2353e-02 9.8595e-01 8.2681e-02 -#> 379: 1.0186e+02 -4.1204e+00 -2.3521e+00 -4.0646e+00 -1.0089e+00 4.4861e-03 3.6557e-02 5.0710e-07 9.1595e-02 2.2159e-02 1.8767e-01 9.3894e-02 9.3395e-01 8.2341e-02 9.8636e-01 8.2671e-02 -#> 380: 1.0186e+02 -4.1204e+00 -2.3517e+00 -4.0644e+00 -1.0089e+00 3.9799e-03 3.6543e-02 5.0682e-07 9.1532e-02 2.2143e-02 1.8768e-01 9.3854e-02 9.3372e-01 8.2331e-02 9.8640e-01 8.2678e-02 -#> 381: 1.0186e+02 -4.1204e+00 -2.3515e+00 -4.0643e+00 -1.0089e+00 3.6269e-03 3.6531e-02 5.0770e-07 9.1364e-02 2.2157e-02 1.8768e-01 9.3897e-02 9.3383e-01 8.2326e-02 9.8630e-01 8.2675e-02 -#> 382: 1.0186e+02 -4.1204e+00 -2.3513e+00 -4.0643e+00 -1.0090e+00 3.1691e-03 3.6469e-02 5.0860e-07 9.1318e-02 2.2188e-02 1.8767e-01 9.3787e-02 9.3433e-01 8.2306e-02 9.8643e-01 8.2670e-02 -#> 383: 1.0186e+02 -4.1204e+00 -2.3508e+00 -4.0642e+00 -1.0090e+00 2.6209e-03 3.6416e-02 5.0893e-07 9.1374e-02 2.2165e-02 1.8759e-01 9.3654e-02 9.3443e-01 8.2289e-02 9.8663e-01 8.2672e-02 -#> 384: 1.0186e+02 -4.1204e+00 -2.3505e+00 -4.0640e+00 -1.0090e+00 2.1556e-03 3.6403e-02 5.0834e-07 9.1550e-02 2.2148e-02 1.8750e-01 9.3422e-02 9.3444e-01 8.2277e-02 9.8639e-01 8.2670e-02 -#> 385: 1.0186e+02 -4.1204e+00 -2.3505e+00 -4.0638e+00 -1.0089e+00 1.7048e-03 3.6391e-02 5.0788e-07 9.1717e-02 2.2160e-02 1.8746e-01 9.3178e-02 9.3457e-01 8.2261e-02 9.8616e-01 8.2636e-02 -#> 386: 1.0186e+02 -4.1204e+00 -2.3504e+00 -4.0637e+00 -1.0089e+00 1.4309e-03 3.6372e-02 5.0847e-07 9.1895e-02 2.2157e-02 1.8754e-01 9.2918e-02 9.3439e-01 8.2246e-02 9.8601e-01 8.2617e-02 -#> 387: 1.0186e+02 -4.1204e+00 -2.3505e+00 -4.0636e+00 -1.0089e+00 1.3524e-03 3.6446e-02 5.0896e-07 9.2022e-02 2.2182e-02 1.8768e-01 9.2684e-02 9.3470e-01 8.2216e-02 9.8593e-01 8.2620e-02 -#> 388: 1.0186e+02 -4.1204e+00 -2.3506e+00 -4.0635e+00 -1.0089e+00 1.2887e-03 3.6478e-02 5.0904e-07 9.2117e-02 2.2174e-02 1.8761e-01 9.2506e-02 9.3463e-01 8.2221e-02 9.8563e-01 8.2609e-02 -#> 389: 1.0186e+02 -4.1204e+00 -2.3505e+00 -4.0635e+00 -1.0089e+00 1.2044e-03 3.6479e-02 5.0969e-07 9.2068e-02 2.2180e-02 1.8751e-01 9.2308e-02 9.3438e-01 8.2241e-02 9.8516e-01 8.2592e-02 -#> 390: 1.0186e+02 -4.1204e+00 -2.3506e+00 -4.0635e+00 -1.0087e+00 1.1442e-03 3.6497e-02 5.0878e-07 9.1995e-02 2.2156e-02 1.8744e-01 9.2169e-02 9.3410e-01 8.2257e-02 9.8511e-01 8.2581e-02 -#> 391: 1.0186e+02 -4.1204e+00 -2.3508e+00 -4.0635e+00 -1.0089e+00 1.0925e-03 3.6454e-02 5.0876e-07 9.1945e-02 2.2177e-02 1.8739e-01 9.1989e-02 9.3439e-01 8.2254e-02 9.8472e-01 8.2579e-02 -#> 392: 1.0186e+02 -4.1204e+00 -2.3506e+00 -4.0633e+00 -1.0091e+00 7.9940e-04 3.6417e-02 5.0874e-07 9.1956e-02 2.2185e-02 1.8730e-01 9.1977e-02 9.3422e-01 8.2244e-02 9.8463e-01 8.2589e-02 -#> 393: 1.0186e+02 -4.1204e+00 -2.3504e+00 -4.0632e+00 -1.0093e+00 4.2112e-04 3.6433e-02 5.0843e-07 9.1868e-02 2.2211e-02 1.8739e-01 9.2106e-02 9.3464e-01 8.2217e-02 9.8458e-01 8.2594e-02 -#> 394: 1.0186e+02 -4.1204e+00 -2.3502e+00 -4.0631e+00 -1.0093e+00 1.4926e-04 3.6534e-02 5.0862e-07 9.1713e-02 2.2244e-02 1.8735e-01 9.2105e-02 9.3454e-01 8.2239e-02 9.8410e-01 8.2601e-02 -#> 395: 1.0186e+02 -4.1204e+00 -2.3499e+00 -4.0630e+00 -1.0095e+00 5.2506e-05 3.6667e-02 5.0955e-07 9.1548e-02 2.2269e-02 1.8733e-01 9.2151e-02 9.3450e-01 8.2256e-02 9.8389e-01 8.2612e-02 -#> 396: 1.0186e+02 -4.1204e+00 -2.3497e+00 -4.0630e+00 -1.0097e+00 1.6581e-05 3.6789e-02 5.1002e-07 9.1431e-02 2.2299e-02 1.8742e-01 9.2120e-02 9.3450e-01 8.2252e-02 9.8367e-01 8.2620e-02 -#> 397: 1.0186e+02 -4.1205e+00 -2.3495e+00 -4.0629e+00 -1.0098e+00 -5.0310e-05 3.6860e-02 5.0949e-07 9.1311e-02 2.2323e-02 1.8738e-01 9.2130e-02 9.3467e-01 8.2250e-02 9.8388e-01 8.2628e-02 -#> 398: 1.0186e+02 -4.1205e+00 -2.3494e+00 -4.0629e+00 -1.0097e+00 -1.4918e-04 3.6902e-02 5.0935e-07 9.1211e-02 2.2330e-02 1.8747e-01 9.2144e-02 9.3478e-01 8.2260e-02 9.8420e-01 8.2632e-02 -#> 399: 1.0186e+02 -4.1205e+00 -2.3497e+00 -4.0628e+00 -1.0097e+00 -2.2152e-04 3.6932e-02 5.0927e-07 9.1209e-02 2.2377e-02 1.8750e-01 9.2136e-02 9.3481e-01 8.2286e-02 9.8431e-01 8.2622e-02 -#> 400: 1.0186e+02 -4.1205e+00 -2.3499e+00 -4.0629e+00 -1.0097e+00 3.2878e-05 3.6943e-02 5.0892e-07 9.1092e-02 2.2388e-02 1.8752e-01 9.2072e-02 9.3534e-01 8.2276e-02 9.8472e-01 8.2615e-02 -#> 401: 1.0186e+02 -4.1205e+00 -2.3501e+00 -4.0630e+00 -1.0097e+00 2.6776e-04 3.6950e-02 5.0860e-07 9.1038e-02 2.2395e-02 1.8740e-01 9.1911e-02 9.3515e-01 8.2331e-02 9.8459e-01 8.2615e-02 -#> 402: 1.0186e+02 -4.1205e+00 -2.3502e+00 -4.0632e+00 -1.0097e+00 3.9988e-04 3.6912e-02 5.0849e-07 9.0944e-02 2.2401e-02 1.8737e-01 9.1701e-02 9.3494e-01 8.2353e-02 9.8479e-01 8.2609e-02 -#> 403: 1.0186e+02 -4.1205e+00 -2.3503e+00 -4.0633e+00 -1.0098e+00 4.9714e-04 3.6935e-02 5.0805e-07 9.0895e-02 2.2404e-02 1.8741e-01 9.1609e-02 9.3444e-01 8.2372e-02 9.8505e-01 8.2638e-02 -#> 404: 1.0186e+02 -4.1205e+00 -2.3504e+00 -4.0633e+00 -1.0100e+00 5.8465e-04 3.6978e-02 5.0889e-07 9.0862e-02 2.2453e-02 1.8746e-01 9.1650e-02 9.3491e-01 8.2364e-02 9.8484e-01 8.2653e-02 -#> 405: 1.0186e+02 -4.1205e+00 -2.3505e+00 -4.0634e+00 -1.0099e+00 5.5970e-04 3.6999e-02 5.0964e-07 9.0930e-02 2.2480e-02 1.8742e-01 9.1823e-02 9.3458e-01 8.2371e-02 9.8465e-01 8.2670e-02 -#> 406: 1.0186e+02 -4.1205e+00 -2.3507e+00 -4.0634e+00 -1.0098e+00 5.4464e-04 3.7123e-02 5.1046e-07 9.1008e-02 2.2478e-02 1.8749e-01 9.1930e-02 9.3449e-01 8.2361e-02 9.8440e-01 8.2666e-02 -#> 407: 1.0186e+02 -4.1205e+00 -2.3506e+00 -4.0634e+00 -1.0097e+00 3.5564e-04 3.7226e-02 5.0978e-07 9.0891e-02 2.2469e-02 1.8751e-01 9.2130e-02 9.3444e-01 8.2380e-02 9.8462e-01 8.2660e-02 -#> 408: 1.0186e+02 -4.1205e+00 -2.3506e+00 -4.0635e+00 -1.0097e+00 3.8362e-04 3.7354e-02 5.0967e-07 9.0892e-02 2.2461e-02 1.8747e-01 9.2230e-02 9.3453e-01 8.2363e-02 9.8466e-01 8.2661e-02 -#> 409: 1.0186e+02 -4.1205e+00 -2.3505e+00 -4.0635e+00 -1.0097e+00 2.6671e-04 3.7473e-02 5.0928e-07 9.0894e-02 2.2519e-02 1.8751e-01 9.2243e-02 9.3447e-01 8.2347e-02 9.8449e-01 8.2667e-02 -#> 410: 1.0186e+02 -4.1205e+00 -2.3506e+00 -4.0634e+00 -1.0098e+00 1.7963e-04 3.7438e-02 5.0981e-07 9.0898e-02 2.2600e-02 1.8764e-01 9.2237e-02 9.3502e-01 8.2320e-02 9.8430e-01 8.2663e-02 -#> 411: 1.0186e+02 -4.1205e+00 -2.3506e+00 -4.0634e+00 -1.0098e+00 1.0085e-04 3.7381e-02 5.0970e-07 9.0820e-02 2.2618e-02 1.8767e-01 9.2103e-02 9.3480e-01 8.2324e-02 9.8427e-01 8.2652e-02 -#> 412: 1.0186e+02 -4.1205e+00 -2.3508e+00 -4.0633e+00 -1.0097e+00 1.9452e-04 3.7315e-02 5.0984e-07 9.0784e-02 2.2605e-02 1.8772e-01 9.2118e-02 9.3504e-01 8.2314e-02 9.8431e-01 8.2636e-02 -#> 413: 1.0186e+02 -4.1205e+00 -2.3508e+00 -4.0632e+00 -1.0097e+00 1.8432e-04 3.7243e-02 5.0946e-07 9.0798e-02 2.2604e-02 1.8765e-01 9.2206e-02 9.3499e-01 8.2299e-02 9.8426e-01 8.2636e-02 -#> 414: 1.0186e+02 -4.1205e+00 -2.3505e+00 -4.0632e+00 -1.0097e+00 2.1744e-04 3.7203e-02 5.0880e-07 9.0769e-02 2.2604e-02 1.8757e-01 9.2403e-02 9.3516e-01 8.2279e-02 9.8414e-01 8.2659e-02 -#> 415: 1.0186e+02 -4.1205e+00 -2.3505e+00 -4.0633e+00 -1.0097e+00 1.9330e-04 3.7197e-02 5.0896e-07 9.0657e-02 2.2618e-02 1.8764e-01 9.2565e-02 9.3514e-01 8.2264e-02 9.8435e-01 8.2655e-02 -#> 416: 1.0186e+02 -4.1205e+00 -2.3501e+00 -4.0634e+00 -1.0097e+00 2.1450e-04 3.7144e-02 5.0882e-07 9.0762e-02 2.2645e-02 1.8761e-01 9.2614e-02 9.3511e-01 8.2277e-02 9.8415e-01 8.2669e-02 -#> 417: 1.0186e+02 -4.1205e+00 -2.3498e+00 -4.0634e+00 -1.0099e+00 1.0737e-04 3.7092e-02 5.0932e-07 9.0804e-02 2.2631e-02 1.8754e-01 9.2581e-02 9.3509e-01 8.2284e-02 9.8430e-01 8.2667e-02 -#> 418: 1.0186e+02 -4.1205e+00 -2.3495e+00 -4.0633e+00 -1.0099e+00 2.4734e-05 3.7061e-02 5.0972e-07 9.0913e-02 2.2624e-02 1.8736e-01 9.2572e-02 9.3482e-01 8.2275e-02 9.8413e-01 8.2682e-02 -#> 419: 1.0186e+02 -4.1205e+00 -2.3495e+00 -4.0633e+00 -1.0099e+00 -3.9197e-05 3.7070e-02 5.1000e-07 9.1084e-02 2.2644e-02 1.8727e-01 9.2636e-02 9.3494e-01 8.2259e-02 9.8382e-01 8.2673e-02 -#> 420: 1.0186e+02 -4.1205e+00 -2.3494e+00 -4.0633e+00 -1.0098e+00 -1.2434e-04 3.7103e-02 5.1037e-07 9.1152e-02 2.2631e-02 1.8733e-01 9.2862e-02 9.3515e-01 8.2244e-02 9.8388e-01 8.2656e-02 -#> 421: 1.0186e+02 -4.1205e+00 -2.3494e+00 -4.0632e+00 -1.0097e+00 -1.5440e-04 3.7123e-02 5.1205e-07 9.1233e-02 2.2626e-02 1.8744e-01 9.2935e-02 9.3523e-01 8.2241e-02 9.8360e-01 8.2652e-02 -#> 422: 1.0186e+02 -4.1205e+00 -2.3495e+00 -4.0633e+00 -1.0095e+00 -8.9184e-05 3.7182e-02 5.1296e-07 9.1123e-02 2.2617e-02 1.8749e-01 9.2915e-02 9.3509e-01 8.2276e-02 9.8367e-01 8.2637e-02 -#> 423: 1.0186e+02 -4.1205e+00 -2.3497e+00 -4.0634e+00 -1.0095e+00 6.7469e-05 3.7194e-02 5.1323e-07 9.1083e-02 2.2642e-02 1.8739e-01 9.3097e-02 9.3529e-01 8.2270e-02 9.8367e-01 8.2642e-02 -#> 424: 1.0186e+02 -4.1205e+00 -2.3498e+00 -4.0635e+00 -1.0094e+00 1.5970e-04 3.7258e-02 5.1292e-07 9.0998e-02 2.2667e-02 1.8730e-01 9.3311e-02 9.3525e-01 8.2262e-02 9.8362e-01 8.2648e-02 -#> 425: 1.0186e+02 -4.1205e+00 -2.3499e+00 -4.0636e+00 -1.0095e+00 2.7004e-04 3.7298e-02 5.1307e-07 9.0839e-02 2.2665e-02 1.8744e-01 9.3429e-02 9.3497e-01 8.2282e-02 9.8395e-01 8.2657e-02 -#> 426: 1.0186e+02 -4.1205e+00 -2.3499e+00 -4.0636e+00 -1.0094e+00 3.9201e-04 3.7303e-02 5.1305e-07 9.0647e-02 2.2675e-02 1.8743e-01 9.3523e-02 9.3477e-01 8.2314e-02 9.8371e-01 8.2655e-02 -#> 427: 1.0186e+02 -4.1205e+00 -2.3496e+00 -4.0636e+00 -1.0093e+00 2.9359e-04 3.7366e-02 5.1245e-07 9.0630e-02 2.2673e-02 1.8738e-01 9.3813e-02 9.3495e-01 8.2291e-02 9.8368e-01 8.2653e-02 -#> 428: 1.0186e+02 -4.1204e+00 -2.3496e+00 -4.0635e+00 -1.0094e+00 2.5099e-04 3.7411e-02 5.1144e-07 9.0647e-02 2.2674e-02 1.8732e-01 9.3993e-02 9.3493e-01 8.2273e-02 9.8373e-01 8.2652e-02 -#> 429: 1.0186e+02 -4.1204e+00 -2.3495e+00 -4.0635e+00 -1.0095e+00 2.4723e-04 3.7543e-02 5.1084e-07 9.0600e-02 2.2677e-02 1.8723e-01 9.4269e-02 9.3518e-01 8.2286e-02 9.8396e-01 8.2659e-02 -#> 430: 1.0186e+02 -4.1204e+00 -2.3494e+00 -4.0635e+00 -1.0096e+00 2.7711e-04 3.7579e-02 5.1022e-07 9.0496e-02 2.2679e-02 1.8708e-01 9.4484e-02 9.3525e-01 8.2309e-02 9.8433e-01 8.2672e-02 -#> 431: 1.0186e+02 -4.1204e+00 -2.3494e+00 -4.0634e+00 -1.0095e+00 1.3934e-05 3.7631e-02 5.0908e-07 9.0378e-02 2.2671e-02 1.8708e-01 9.4770e-02 9.3528e-01 8.2302e-02 9.8470e-01 8.2682e-02 -#> 432: 1.0186e+02 -4.1204e+00 -2.3495e+00 -4.0633e+00 -1.0096e+00 -8.9401e-05 3.7677e-02 5.0861e-07 9.0278e-02 2.2654e-02 1.8702e-01 9.4882e-02 9.3518e-01 8.2318e-02 9.8488e-01 8.2667e-02 -#> 433: 1.0186e+02 -4.1205e+00 -2.3495e+00 -4.0633e+00 -1.0096e+00 -3.6841e-04 3.7706e-02 5.0854e-07 9.0108e-02 2.2652e-02 1.8703e-01 9.5039e-02 9.3487e-01 8.2331e-02 9.8494e-01 8.2669e-02 -#> 434: 1.0186e+02 -4.1205e+00 -2.3493e+00 -4.0632e+00 -1.0096e+00 -4.3399e-04 3.7671e-02 5.0796e-07 9.0036e-02 2.2661e-02 1.8701e-01 9.5122e-02 9.3474e-01 8.2331e-02 9.8474e-01 8.2675e-02 -#> 435: 1.0186e+02 -4.1205e+00 -2.3491e+00 -4.0632e+00 -1.0096e+00 -6.1398e-04 3.7654e-02 5.0727e-07 8.9940e-02 2.2664e-02 1.8691e-01 9.5242e-02 9.3451e-01 8.2346e-02 9.8466e-01 8.2677e-02 -#> 436: 1.0186e+02 -4.1205e+00 -2.3487e+00 -4.0632e+00 -1.0094e+00 -7.2148e-04 3.7647e-02 5.0649e-07 8.9838e-02 2.2661e-02 1.8694e-01 9.5465e-02 9.3429e-01 8.2365e-02 9.8475e-01 8.2683e-02 -#> 437: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0632e+00 -1.0093e+00 -1.1480e-03 3.7613e-02 5.0662e-07 8.9719e-02 2.2674e-02 1.8698e-01 9.5631e-02 9.3419e-01 8.2380e-02 9.8490e-01 8.2684e-02 -#> 438: 1.0186e+02 -4.1204e+00 -2.3482e+00 -4.0631e+00 -1.0092e+00 -1.5547e-03 3.7583e-02 5.0753e-07 8.9612e-02 2.2680e-02 1.8703e-01 9.5913e-02 9.3413e-01 8.2394e-02 9.8523e-01 8.2678e-02 -#> 439: 1.0186e+02 -4.1205e+00 -2.3483e+00 -4.0630e+00 -1.0093e+00 -1.9392e-03 3.7463e-02 5.0769e-07 8.9410e-02 2.2706e-02 1.8706e-01 9.6149e-02 9.3392e-01 8.2425e-02 9.8512e-01 8.2670e-02 -#> 440: 1.0186e+02 -4.1205e+00 -2.3482e+00 -4.0629e+00 -1.0094e+00 -2.1940e-03 3.7360e-02 5.0743e-07 8.9245e-02 2.2742e-02 1.8710e-01 9.6213e-02 9.3400e-01 8.2445e-02 9.8490e-01 8.2676e-02 -#> 441: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0629e+00 -1.0095e+00 -2.3414e-03 3.7297e-02 5.0838e-07 8.9137e-02 2.2806e-02 1.8721e-01 9.6155e-02 9.3405e-01 8.2450e-02 9.8470e-01 8.2684e-02 -#> 442: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0628e+00 -1.0095e+00 -2.6378e-03 3.7241e-02 5.0923e-07 8.9066e-02 2.2846e-02 1.8727e-01 9.6135e-02 9.3389e-01 8.2465e-02 9.8454e-01 8.2686e-02 -#> 443: 1.0186e+02 -4.1205e+00 -2.3482e+00 -4.0627e+00 -1.0094e+00 -2.8716e-03 3.7214e-02 5.1026e-07 8.9128e-02 2.2901e-02 1.8740e-01 9.6077e-02 9.3386e-01 8.2444e-02 9.8421e-01 8.2692e-02 -#> 444: 1.0186e+02 -4.1205e+00 -2.3483e+00 -4.0627e+00 -1.0092e+00 -2.9147e-03 3.7196e-02 5.1104e-07 8.9190e-02 2.2985e-02 1.8744e-01 9.5999e-02 9.3390e-01 8.2424e-02 9.8381e-01 8.2696e-02 -#> 445: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0626e+00 -1.0090e+00 -2.9638e-03 3.7251e-02 5.1283e-07 8.9335e-02 2.3004e-02 1.8756e-01 9.5788e-02 9.3382e-01 8.2416e-02 9.8347e-01 8.2683e-02 -#> 446: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0627e+00 -1.0090e+00 -2.8796e-03 3.7331e-02 5.1479e-07 8.9470e-02 2.3017e-02 1.8762e-01 9.5656e-02 9.3368e-01 8.2405e-02 9.8325e-01 8.2680e-02 -#> 447: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0628e+00 -1.0091e+00 -2.7695e-03 3.7473e-02 5.1656e-07 8.9568e-02 2.3030e-02 1.8757e-01 9.5575e-02 9.3379e-01 8.2386e-02 9.8306e-01 8.2690e-02 -#> 448: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0629e+00 -1.0091e+00 -2.6293e-03 3.7498e-02 5.1814e-07 8.9776e-02 2.3052e-02 1.8762e-01 9.5422e-02 9.3373e-01 8.2372e-02 9.8274e-01 8.2685e-02 -#> 449: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0628e+00 -1.0092e+00 -2.5640e-03 3.7542e-02 5.1867e-07 8.9888e-02 2.3056e-02 1.8763e-01 9.5364e-02 9.3400e-01 8.2365e-02 9.8239e-01 8.2691e-02 -#> 450: 1.0186e+02 -4.1205e+00 -2.3483e+00 -4.0628e+00 -1.0093e+00 -2.5816e-03 3.7622e-02 5.1849e-07 9.0050e-02 2.3061e-02 1.8765e-01 9.5274e-02 9.3435e-01 8.2341e-02 9.8235e-01 8.2699e-02 -#> 451: 1.0186e+02 -4.1205e+00 -2.3483e+00 -4.0627e+00 -1.0094e+00 -2.4837e-03 3.7631e-02 5.1931e-07 9.0177e-02 2.3053e-02 1.8766e-01 9.5103e-02 9.3459e-01 8.2322e-02 9.8226e-01 8.2715e-02 -#> 452: 1.0186e+02 -4.1205e+00 -2.3482e+00 -4.0627e+00 -1.0094e+00 -2.4156e-03 3.7606e-02 5.1901e-07 9.0333e-02 2.3047e-02 1.8763e-01 9.4959e-02 9.3485e-01 8.2289e-02 9.8210e-01 8.2713e-02 -#> 453: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0627e+00 -1.0093e+00 -2.4619e-03 3.7552e-02 5.1874e-07 9.0495e-02 2.3066e-02 1.8761e-01 9.4960e-02 9.3485e-01 8.2293e-02 9.8178e-01 8.2703e-02 -#> 454: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0627e+00 -1.0092e+00 -2.4816e-03 3.7514e-02 5.1835e-07 9.0606e-02 2.3073e-02 1.8754e-01 9.4896e-02 9.3491e-01 8.2277e-02 9.8154e-01 8.2696e-02 -#> 455: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0627e+00 -1.0092e+00 -2.3708e-03 3.7457e-02 5.1742e-07 9.0715e-02 2.3099e-02 1.8756e-01 9.4804e-02 9.3481e-01 8.2272e-02 9.8122e-01 8.2688e-02 -#> 456: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0627e+00 -1.0093e+00 -2.2313e-03 3.7409e-02 5.1680e-07 9.0906e-02 2.3131e-02 1.8743e-01 9.4814e-02 9.3476e-01 8.2261e-02 9.8108e-01 8.2694e-02 -#> 457: 1.0186e+02 -4.1205e+00 -2.3483e+00 -4.0628e+00 -1.0095e+00 -2.1182e-03 3.7342e-02 5.1630e-07 9.0986e-02 2.3158e-02 1.8733e-01 9.4843e-02 9.3488e-01 8.2244e-02 9.8094e-01 8.2700e-02 -#> 458: 1.0186e+02 -4.1205e+00 -2.3483e+00 -4.0628e+00 -1.0095e+00 -1.9242e-03 3.7244e-02 5.1605e-07 9.1085e-02 2.3168e-02 1.8720e-01 9.4820e-02 9.3509e-01 8.2228e-02 9.8093e-01 8.2703e-02 -#> 459: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0629e+00 -1.0095e+00 -1.7643e-03 3.7203e-02 5.1566e-07 9.1179e-02 2.3175e-02 1.8715e-01 9.4809e-02 9.3516e-01 8.2216e-02 9.8087e-01 8.2690e-02 -#> 460: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0629e+00 -1.0094e+00 -1.5479e-03 3.7151e-02 5.1547e-07 9.1211e-02 2.3155e-02 1.8712e-01 9.4703e-02 9.3539e-01 8.2201e-02 9.8100e-01 8.2683e-02 -#> 461: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0630e+00 -1.0095e+00 -1.4993e-03 3.7111e-02 5.1446e-07 9.1225e-02 2.3159e-02 1.8705e-01 9.4569e-02 9.3555e-01 8.2183e-02 9.8078e-01 8.2680e-02 -#> 462: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0629e+00 -1.0094e+00 -1.4890e-03 3.7056e-02 5.1361e-07 9.1446e-02 2.3158e-02 1.8694e-01 9.4494e-02 9.3557e-01 8.2171e-02 9.8058e-01 8.2688e-02 -#> 463: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0629e+00 -1.0094e+00 -1.3999e-03 3.6996e-02 5.1319e-07 9.1659e-02 2.3176e-02 1.8695e-01 9.4436e-02 9.3570e-01 8.2153e-02 9.8053e-01 8.2686e-02 -#> 464: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0630e+00 -1.0095e+00 -1.1544e-03 3.6949e-02 5.1300e-07 9.1885e-02 2.3162e-02 1.8688e-01 9.4378e-02 9.3599e-01 8.2134e-02 9.8051e-01 8.2694e-02 -#> 465: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0630e+00 -1.0097e+00 -9.7372e-04 3.6943e-02 5.1235e-07 9.2014e-02 2.3136e-02 1.8692e-01 9.4288e-02 9.3605e-01 8.2141e-02 9.8053e-01 8.2693e-02 -#> 466: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0630e+00 -1.0097e+00 -9.2442e-04 3.6916e-02 5.1246e-07 9.2074e-02 2.3132e-02 1.8688e-01 9.4254e-02 9.3590e-01 8.2131e-02 9.8016e-01 8.2691e-02 -#> 467: 1.0186e+02 -4.1205e+00 -2.3485e+00 -4.0631e+00 -1.0098e+00 -8.2540e-04 3.6928e-02 5.1340e-07 9.2164e-02 2.3141e-02 1.8690e-01 9.4382e-02 9.3620e-01 8.2106e-02 9.7996e-01 8.2705e-02 -#> 468: 1.0186e+02 -4.1205e+00 -2.3484e+00 -4.0631e+00 -1.0097e+00 -7.3368e-04 3.6925e-02 5.1395e-07 9.2218e-02 2.3136e-02 1.8695e-01 9.4504e-02 9.3629e-01 8.2094e-02 9.7985e-01 8.2716e-02 -#> 469: 1.0186e+02 -4.1205e+00 -2.3483e+00 -4.0630e+00 -1.0096e+00 -7.4343e-04 3.6891e-02 5.1401e-07 9.2204e-02 2.3114e-02 1.8700e-01 9.4639e-02 9.3643e-01 8.2078e-02 9.7996e-01 8.2709e-02 -#> 470: 1.0186e+02 -4.1205e+00 -2.3482e+00 -4.0630e+00 -1.0096e+00 -7.8250e-04 3.6874e-02 5.1370e-07 9.2209e-02 2.3083e-02 1.8702e-01 9.4728e-02 9.3646e-01 8.2073e-02 9.7989e-01 8.2703e-02 -#> 471: 1.0186e+02 -4.1205e+00 -2.3480e+00 -4.0629e+00 -1.0095e+00 -1.0440e-03 3.6843e-02 5.1358e-07 9.2194e-02 2.3062e-02 1.8710e-01 9.4741e-02 9.3649e-01 8.2082e-02 9.8003e-01 8.2701e-02 -#> 472: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0629e+00 -1.0096e+00 -9.5438e-04 3.6869e-02 5.1330e-07 9.2176e-02 2.3050e-02 1.8712e-01 9.4766e-02 9.3666e-01 8.2080e-02 9.7996e-01 8.2691e-02 -#> 473: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0096e+00 -8.2178e-04 3.6877e-02 5.1283e-07 9.2191e-02 2.3021e-02 1.8703e-01 9.4747e-02 9.3670e-01 8.2072e-02 9.8007e-01 8.2693e-02 -#> 474: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0096e+00 -7.0189e-04 3.6927e-02 5.1196e-07 9.2195e-02 2.2989e-02 1.8702e-01 9.4746e-02 9.3669e-01 8.2054e-02 9.8029e-01 8.2689e-02 -#> 475: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0095e+00 -7.1989e-04 3.6993e-02 5.1125e-07 9.2159e-02 2.2963e-02 1.8700e-01 9.4813e-02 9.3681e-01 8.2051e-02 9.8027e-01 8.2680e-02 -#> 476: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0629e+00 -1.0096e+00 -7.1806e-04 3.7018e-02 5.1105e-07 9.2091e-02 2.2933e-02 1.8696e-01 9.4837e-02 9.3713e-01 8.2067e-02 9.8033e-01 8.2674e-02 -#> 477: 1.0186e+02 -4.1205e+00 -2.3479e+00 -4.0630e+00 -1.0097e+00 -7.3438e-04 3.6986e-02 5.1121e-07 9.2046e-02 2.2909e-02 1.8698e-01 9.4809e-02 9.3743e-01 8.2059e-02 9.8045e-01 8.2693e-02 -#> 478: 1.0186e+02 -4.1205e+00 -2.3478e+00 -4.0630e+00 -1.0096e+00 -7.9338e-04 3.6912e-02 5.1224e-07 9.2056e-02 2.2881e-02 1.8698e-01 9.4791e-02 9.3757e-01 8.2042e-02 9.8072e-01 8.2682e-02 -#> 479: 1.0186e+02 -4.1205e+00 -2.3476e+00 -4.0629e+00 -1.0096e+00 -8.6158e-04 3.6882e-02 5.1284e-07 9.2159e-02 2.2867e-02 1.8694e-01 9.4774e-02 9.3749e-01 8.2051e-02 9.8088e-01 8.2679e-02 -#> 480: 1.0186e+02 -4.1205e+00 -2.3474e+00 -4.0629e+00 -1.0096e+00 -1.1334e-03 3.6851e-02 5.1423e-07 9.2253e-02 2.2869e-02 1.8696e-01 9.4820e-02 9.3751e-01 8.2063e-02 9.8097e-01 8.2693e-02 -#> 481: 1.0186e+02 -4.1205e+00 -2.3470e+00 -4.0629e+00 -1.0096e+00 -1.2444e-03 3.6785e-02 5.1490e-07 9.2397e-02 2.2853e-02 1.8694e-01 9.4838e-02 9.3770e-01 8.2031e-02 9.8124e-01 8.2707e-02 -#> 482: 1.0186e+02 -4.1205e+00 -2.3467e+00 -4.0629e+00 -1.0095e+00 -1.3612e-03 3.6750e-02 5.1658e-07 9.2440e-02 2.2842e-02 1.8683e-01 9.4800e-02 9.3786e-01 8.2041e-02 9.8107e-01 8.2719e-02 -#> 483: 1.0186e+02 -4.1205e+00 -2.3467e+00 -4.0628e+00 -1.0096e+00 -1.5168e-03 3.6783e-02 5.1708e-07 9.2590e-02 2.2804e-02 1.8674e-01 9.4804e-02 9.3790e-01 8.2042e-02 9.8116e-01 8.2719e-02 -#> 484: 1.0186e+02 -4.1205e+00 -2.3466e+00 -4.0628e+00 -1.0097e+00 -1.5218e-03 3.6848e-02 5.1669e-07 9.2717e-02 2.2775e-02 1.8670e-01 9.4940e-02 9.3798e-01 8.2028e-02 9.8106e-01 8.2719e-02 -#> 485: 1.0186e+02 -4.1205e+00 -2.3464e+00 -4.0628e+00 -1.0097e+00 -1.4177e-03 3.6867e-02 5.1615e-07 9.2806e-02 2.2765e-02 1.8669e-01 9.5018e-02 9.3816e-01 8.2020e-02 9.8090e-01 8.2721e-02 -#> 486: 1.0186e+02 -4.1205e+00 -2.3462e+00 -4.0628e+00 -1.0098e+00 -1.5257e-03 3.6968e-02 5.1513e-07 9.3019e-02 2.2762e-02 1.8663e-01 9.5111e-02 9.3816e-01 8.2013e-02 9.8071e-01 8.2732e-02 -#> 487: 1.0186e+02 -4.1205e+00 -2.3460e+00 -4.0628e+00 -1.0097e+00 -1.7055e-03 3.7021e-02 5.1446e-07 9.3161e-02 2.2732e-02 1.8652e-01 9.5373e-02 9.3832e-01 8.1997e-02 9.8078e-01 8.2737e-02 -#> 488: 1.0186e+02 -4.1205e+00 -2.3459e+00 -4.0628e+00 -1.0097e+00 -1.8502e-03 3.7069e-02 5.1391e-07 9.3282e-02 2.2741e-02 1.8641e-01 9.5414e-02 9.3818e-01 8.2001e-02 9.8064e-01 8.2738e-02 -#> 489: 1.0186e+02 -4.1205e+00 -2.3458e+00 -4.0628e+00 -1.0097e+00 -1.9091e-03 3.7017e-02 5.1291e-07 9.3286e-02 2.2738e-02 1.8639e-01 9.5453e-02 9.3808e-01 8.1991e-02 9.8047e-01 8.2728e-02 -#> 490: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0628e+00 -1.0097e+00 -1.8766e-03 3.6969e-02 5.1220e-07 9.3297e-02 2.2728e-02 1.8635e-01 9.5468e-02 9.3793e-01 8.1988e-02 9.8034e-01 8.2726e-02 -#> 491: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0627e+00 -1.0097e+00 -1.7736e-03 3.6915e-02 5.1153e-07 9.3298e-02 2.2716e-02 1.8634e-01 9.5548e-02 9.3772e-01 8.2005e-02 9.8025e-01 8.2722e-02 -#> 492: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0627e+00 -1.0097e+00 -1.7747e-03 3.6877e-02 5.1077e-07 9.3336e-02 2.2697e-02 1.8635e-01 9.5593e-02 9.3778e-01 8.2001e-02 9.8013e-01 8.2725e-02 -#> 493: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0628e+00 -1.0094e+00 -1.6324e-03 3.6857e-02 5.1020e-07 9.3348e-02 2.2668e-02 1.8636e-01 9.5735e-02 9.3764e-01 8.1984e-02 9.8019e-01 8.2723e-02 -#> 494: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0629e+00 -1.0094e+00 -1.5393e-03 3.6842e-02 5.1022e-07 9.3359e-02 2.2649e-02 1.8637e-01 9.5812e-02 9.3739e-01 8.1982e-02 9.8033e-01 8.2708e-02 -#> 495: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0629e+00 -1.0094e+00 -1.5166e-03 3.6841e-02 5.1004e-07 9.3321e-02 2.2642e-02 1.8640e-01 9.5849e-02 9.3716e-01 8.1979e-02 9.8016e-01 8.2700e-02 -#> 496: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0630e+00 -1.0095e+00 -1.4947e-03 3.6841e-02 5.0969e-07 9.3236e-02 2.2646e-02 1.8640e-01 9.5916e-02 9.3719e-01 8.1963e-02 9.8028e-01 8.2702e-02 -#> 497: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0629e+00 -1.0094e+00 -1.4507e-03 3.6827e-02 5.0937e-07 9.3185e-02 2.2663e-02 1.8638e-01 9.5991e-02 9.3707e-01 8.1954e-02 9.8047e-01 8.2718e-02 -#> 498: 1.0186e+02 -4.1205e+00 -2.3459e+00 -4.0630e+00 -1.0094e+00 -1.2569e-03 3.6805e-02 5.0854e-07 9.3089e-02 2.2677e-02 1.8634e-01 9.5931e-02 9.3719e-01 8.1952e-02 9.8051e-01 8.2718e-02 -#> 499: 1.0186e+02 -4.1205e+00 -2.3460e+00 -4.0630e+00 -1.0093e+00 -1.0466e-03 3.6769e-02 5.0789e-07 9.3029e-02 2.2690e-02 1.8631e-01 9.5862e-02 9.3729e-01 8.1956e-02 9.8046e-01 8.2731e-02 -#> 500: 1.0186e+02 -4.1205e+00 -2.3464e+00 -4.0630e+00 -1.0093e+00 -7.3346e-04 3.6766e-02 5.0769e-07 9.3093e-02 2.2701e-02 1.8633e-01 9.5687e-02 9.3739e-01 8.1977e-02 9.8039e-01 8.2728e-02</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 1.281 0.142 1.422</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'><-</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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT" -#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | parent_0 | log_k_m1 |f_parent_qlogis | log_k1 | -#> |.....................| log_k2 | g_qlogis | sigma_low | rsd_high | -#> |.....................| o1 | o2 | o3 | o4 | -#> <span style='text-decoration: underline;'>|.....................| o5 | o6 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 1</span>| 496.98032 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 496.98032 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 496.98032</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | G| Gill Diff. | 57.10 | -0.1453 | -0.1275 | 0.2854 | -#> |.....................| -0.6156 | 0.007043 | -23.49 | -32.87 | -#> |.....................| 3.669 | -17.46 | -13.05 | -13.08 | -#> <span style='text-decoration: underline;'>|.....................| -16.16 | -9.766 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 2</span>| 3094.8373 | 0.2572 | -0.9978 | -0.9392 | -0.9714 | -#> |.....................| -0.9920 | -0.9233 | -0.6037 | -0.4942 | -#> |.....................| -0.9579 | -0.6658 | -0.7293 | -0.7310 | -#> <span style='text-decoration: underline;'>|.....................| -0.6848 | -0.7742 |...........|...........|</span> -#> | U| 3094.8373 | 26.15 | -4.052 | -0.9415 | -2.363 | -#> |.....................| -4.062 | -0.01133 | 0.8386 | 0.08074 | -#> |.....................| 0.6445 | 1.946 | 1.477 | 1.348 | -#> <span style='text-decoration: underline;'>|.....................| 1.794 | 1.297 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 3094.8373</span> | 26.15 | 0.01739 | 0.2806 | 0.09412 | -#> |.....................| 0.01721 | 0.4972 | 0.8386 | 0.08074 | -#> |.....................| 0.6445 | 1.946 | 1.477 | 1.348 | -#> <span style='text-decoration: underline;'>|.....................| 1.794 | 1.297 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 3</span>| 557.60681 | 0.9257 | -0.9995 | -0.9407 | -0.9680 | -#> |.....................| -0.9992 | -0.9232 | -0.8787 | -0.8790 | -#> |.....................| -0.9150 | -0.8703 | -0.8821 | -0.8842 | -#> <span style='text-decoration: underline;'>|.....................| -0.8739 | -0.8885 |...........|...........|</span> -#> | U| 557.60681 | 94.11 | -4.053 | -0.9430 | -2.360 | -#> |.....................| -4.069 | -0.01133 | 0.7386 | 0.06794 | -#> |.....................| 0.6735 | 1.622 | 1.284 | 1.172 | -#> <span style='text-decoration: underline;'>|.....................| 1.513 | 1.165 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 557.60681</span> | 94.11 | 0.01736 | 0.2803 | 0.09444 | -#> |.....................| 0.01709 | 0.4972 | 0.7386 | 0.06794 | -#> |.....................| 0.6735 | 1.622 | 1.284 | 1.172 | -#> <span style='text-decoration: underline;'>|.....................| 1.513 | 1.165 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 4</span>| 543.47785 | 0.9926 | -0.9997 | -0.9408 | -0.9677 | -#> |.....................| -0.9999 | -0.9232 | -0.9062 | -0.9175 | -#> |.....................| -0.9107 | -0.8907 | -0.8974 | -0.8995 | -#> <span style='text-decoration: underline;'>|.....................| -0.8929 | -0.9000 |...........|...........|</span> -#> | U| 543.47785 | 100.9 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7286 | 0.06666 | -#> |.....................| 0.6764 | 1.589 | 1.264 | 1.154 | -#> <span style='text-decoration: underline;'>|.....................| 1.485 | 1.152 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 543.47785</span> | 100.9 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7286 | 0.06666 | -#> |.....................| 0.6764 | 1.589 | 1.264 | 1.154 | -#> <span style='text-decoration: underline;'>|.....................| 1.485 | 1.152 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 5</span>| 544.09017 | 0.9993 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9089 | -0.9213 | -#> |.....................| -0.9103 | -0.8928 | -0.8990 | -0.9010 | -#> <span style='text-decoration: underline;'>|.....................| -0.8948 | -0.9011 |...........|...........|</span> -#> | U| 544.09017 | 101.6 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7276 | 0.06654 | -#> |.....................| 0.6767 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.09017</span> | 101.6 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7276 | 0.06654 | -#> |.....................| 0.6767 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 6</span>| 544.17109 | 0.9999 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8949 | -0.9012 |...........|...........|</span> -#> | U| 544.17109 | 101.6 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.17109</span> | 101.6 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 7</span>| 544.17937 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.17937 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.17937</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 8</span>| 544.18025 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18025 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18025</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 9</span>| 544.18033 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18033 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18033</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 10</span>| 544.18034 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18034 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18034</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 11</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 12</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 13</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 14</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 15</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 16</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> |<span style='font-weight: bold;'> 17</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | -#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | -#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | -#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> -#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | -#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | -#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | -#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | -#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> -#> calculating covariance matrix -#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> df AIC -#> f_nlmixr_dfop_sfo_saem$nm 16 Inf -#> f_nlmixr_dfop_sfo_focei$nm 14 886.4573</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'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(m1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[6]+THETA[6];</span> +#> <span class='message'>rx_expr_9~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_12~exp(rx_expr_7);</span> +#> <span class='message'>rx_expr_13~exp(rx_expr_9);</span> +#> <span class='message'>rx_expr_15~t*rx_expr_12;</span> +#> <span class='message'>rx_expr_16~t*rx_expr_13;</span> +#> <span class='message'>rx_expr_19~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_21~1+rx_expr_19;</span> +#> <span class='message'>rx_expr_26~1/(rx_expr_21);</span> +#> <span class='message'>rx_expr_28~(rx_expr_26);</span> +#> <span class='message'>rx_expr_29~1-rx_expr_28;</span> +#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));</span> +#> <span class='message'>rx_expr_10~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_14~exp(rx_expr_10);</span> +#> <span class='message'>d/dt(m1)=-rx_expr_14*m1+parent*f_parent_to_m1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_20~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_20+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_20+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~m1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_24~rx_expr_11*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_expr_17~Rx_pow_di(THETA[8],2);</span> +#> <span class='message'>rx_expr_18~Rx_pow_di(THETA[7],2);</span> +#> <span class='message'>rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_0)+(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_m1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_k1=THETA[4];</span> +#> <span class='message'>log_k2=THETA[5];</span> +#> <span class='message'>g_qlogis=THETA[6];</span> +#> <span class='message'>sigma_low=THETA[7];</span> +#> <span class='message'>rsd_high=THETA[8];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_m1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_k1=ETA[4];</span> +#> <span class='message'>eta.log_k2=ETA[5];</span> +#> <span class='message'>eta.g_qlogis=ETA[6];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_m1=rx_expr_14;</span> +#> <span class='message'>k1=rx_expr_12;</span> +#> <span class='message'>k2=rx_expr_13;</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>g=1/(rx_expr_21);</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_m1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 19.01 0.403 19.42</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in AIC(f_nlmixr_dfop_sfo_saem$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'>#> <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> </div></pre> 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> |
